ORCID Profile
0000-0003-4943-3969
Current Organisation
The University of Newcastle
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Biological Psychology (Neuropsychology, Psychopharmacology, | Neurosciences | Dynamical Systems | Applied Mathematics | Neurosciences Not Elsewhere Classified | Biological psychology | Neurogenetics | Biological Mathematics | Artificial Intelligence and Image Processing | Cognitive neuroscience | Neurosciences not elsewhere classified | Medical Physics | Complex Physical Systems | Biological Physics | Biophysics | Central Nervous System | Other Physical Sciences | Psychology | Sensory systems | Sensory Systems | Animal Physiology—Systems | Sensory Processes, Perception and Performance | Intelligent Robotics | Biomedical imaging
ANZSRC Socio-Economic Objective (SEO)
Nervous system and disorders | Expanding Knowledge in Psychology and Cognitive Sciences | Cardiovascular system and diseases | Biological sciences | Diagnostic methods | Integrated circuits and devices | Expanding Knowledge in the Medical and Health Sciences | Physical sciences | Behavioural and cognitive sciences | Expanding Knowledge in the Physical Sciences | Expanding Knowledge in the Biological Sciences | Machinery and equipment not elsewhere classified |
Related Links
Publications
Time-resolved smoothness of distributed brain activity tracks conscious states and unifies emergent neural phenomena
Publisher: Research Square Platform LLC
Date: 29-12-2021
DOI: 10.21203/RS.3.RS-1195514/V1
Abstract: Much of systems neuroscience posits that emergent neural phenomena underpin important aspects of brain function. Studies in the field variously emphasize the importance of distinct emergent phenomena, including weakly stable dynamics, arrhythmic 1/f activity, long-range temporal correlations, and scale-free avalanche statistics. Few studies, however, have sought to reconcile these often abstract phenomena with interpretable properties of neural activity. Here, we developed a method to efficiently and unbiasedly generate model data constrained by interpretable empirical features in long neurophysiological recordings. We used this method to ground several major emergent neural phenomena to time-resolved smoothness, the correlation of distributed brain activity between adjacent timepoints. We first found that in electrocorticography recordings, time-resolved smoothness closely tracked transitions between conscious and anesthetized states. We then showed that a minimal model constrained by time-resolved smoothness, variance, and mean, captured dynamical and statistical emergent neural phenomena across modalities and species. Our results thus decouple major emergent neural phenomena from network mechanisms of brain function, and instead couple these phenomena to spatially nonspecific, time-resolved changes of brain activity. These results anchor several theoretical frameworks to a single interpretable property of the neurophysiological signal and, in this way, ultimately help bridge abstract theories of brain function with observed properties of brain activity.
Time-resolved smoothness of distributed brain activity tracks conscious states and unifies emergent neural phenomena
Publisher: Research Square Platform LLC
Date: 07-02-2022
DOI: 10.21203/RS.3.RS-1195514/V2
Abstract: Much of systems neuroscience posits that emergent neural phenomena underpin important aspects of brain function. Studies in the field variously emphasize the importance of distinct emergent phenomena, including weakly stable dynamics, arrhythmic 1/f activity, long-range temporal correlations, and scale-free avalanche statistics. Few studies, however, have sought to reconcile these often abstract phenomena with interpretable properties of neural activity. Here, we developed a method to efficiently and unbiasedly generate model data constrained by interpretable empirical features in long multiregional neurophysiological recordings. We used this method to ground several major emergent neural phenomena to time-resolved smoothness, the correlation of distributed brain activity between adjacent timepoints. We first found that in electrocorticography recordings, time-resolved smoothness closely tracked transitions between conscious and anesthetized states. We then showed that a minimal model constrained by time-resolved smoothness, variance, and mean, captured dynamical and statistical emergent neural phenomena across modalities and species. Our results thus decouple major emergent neural phenomena from network mechanisms of brain function, and instead couple these phenomena to spatially nonspecific, time-resolved changes of brain activity. These results anchor several theoretical frameworks to a single interpretable property of the neurophysiological signal and, in this way, ultimately help bridge abstract theories of brain function with observed properties of brain activity.
Time-resolved correlation of distributed brain activity tracks E-I balance and accounts for diverse scale-free phenomena
Publisher: Research Square Platform LLC
Date: 24-03-2023
DOI: 10.21203/RS.3.RS-1195514/V3
Abstract: Much of systems neuroscience posits the functional importance of brain activity patterns that lack natural scales of sizes, durations, or frequencies. The field has developed prominent, and sometimes competing, explanations for the nature of this scale-free activity. Here, we reconcile these explanations across species and modalities. First, we link estimates of excitation-inhibition (E-I) balance with time-resolved correlation of distributed brain activity. Second, we develop an unbiased method for s ling timeseries constrained by this time-resolved correlation. Third, we use this method to show that estimates of E-I balance account for erse scale-free phenomena without need to attribute additional function or importance to these phenomena. Collectively, our results simplify existing explanations of scale-free brain activity, and provide stringent tests on future theories that seek to transcend these explanations.
Synchronous Gamma activity: A review and contribution to an integrative neuroscience model of schizophrenia
Publisher: Elsevier BV
Date: 2003
DOI: 10.1016/S0165-0173(02)00220-5
Abstract: Synchronous high frequency (Gamma band) activity has been proposed as a candidate mechanism for the integration or 'binding' of distributed brain activities. Since the first descriptions of schizophrenia, attempts to characterize this disorder have focused on disturbances in such integrative processing. Here, we review both micro- and macroscopic neuroscience research into Gamma synchrony, and its application to understanding schizophrenia. The review encompasses evidence from both animal and human studies for the functional significance of Gamma activity, the association between Gamma dysfunction and information processing disturbances, and the relevance of specific Gamma dysfunctions to the integration and extension of previous disconnection models of schizophrenia. Attention is given to the relationship between Gamma activity and the heterogeneous symptoms of schizophrenia. Existing studies show that measures of Gamma activity have the potential to explain far more of the variance in schizophrenia performance than previous neurophysiological measures. It is concluded that measures of Gamma synchrony offer a valuable window into the core integrative disturbance in schizophrenia cognition.
Intracranial-EEG evidence for medial temporal pole driving amygdala activity induced by multi-modal emotional stimuli
Publisher: Elsevier BV
Date: 09-2020
Quantification of free-living activity patterns using accelerometry in adults with mental illness
Publisher: Springer Science and Business Media LLC
Date: 07-03-2017
DOI: 10.1038/SREP43174
Abstract: Physical activity is disrupted in many psychiatric disorders. Advances in everyday technologies – such as accelerometers in smart phones – opens exciting possibilities for non-intrusive acquisition of activity data. Successful exploitation of this opportunity requires the validation of analytical methods that can capture the full movement spectrum. The study aim was to demonstrate an analytical approach to characterise accelerometer-derived activity patterns. Here, we use statistical methods to characterize accelerometer-derived activity patterns from a heterogeneous s le of 99 community-based adults with mental illnesses. Diagnoses were screened using the Mini International Neuropsychiatric Interview, and participants wore accelerometers for one week. We studied the relative ability of simple (exponential), complex (heavy-tailed), and composite models to explain patterns of activity and inactivity. Activity during wakefulness was a composite of brief random (exponential) movements and complex (heavy-tailed) processes, whereas movement during sleep lacked the heavy-tailed component. In contrast, inactivity followed a heavy-tailed process, lacking the random component. Activity patterns differed in nature between those with a diagnosis of bipolar disorder and a primary psychotic disorder. These results show the potential of complex models to quantify the rich nature of human movement captured by accelerometry during wake and sleep, and the interaction with diagnosis and health.
Dynamic mechanisms of multistability in human cortical rhythms
Publisher: Frontiers Media SA
Date: 2011
Modulation of cortical activity by transcranial direct current stimulation in patients with affective disorder
Publisher: Public Library of Science (PLoS)
Date: 10-06-2014
Effects of transcranial magnetic stimulation of the rostromedial prefrontal cortex in obsessive–compulsive disorder: a randomized clinical trial
Publisher: Springer Science and Business Media LLC
Date: 24-07-2023
On the nature of time-varying functional connectivity in resting fMRI
Publisher: Center for Open Science
Date: 24-12-2018
Abstract: The brain is a complex dynamical system composed of many interacting sub-regions. Knowledge of how these interactions reconfigure over time is critical to a full understanding of the brain’s functional architecture, the neural basis of flexible cognition and behavior, and how neural systems are disrupted in psychiatric and neurological illness. The idea that we might be able to study neural and cognitive dynamics through analysis of neuroimaging data has catalyzed substantial interest in methods which seek to estimate moment-to-moment fluctuations in functional connectivity (often referred to as “dynamic” or time-varying connectivity TVC). At the same time, debates have emerged regarding the application of TVC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive relevance of resting TVC. These and other unresolved issues complicate the interpretation of resting TVC findings and limit the insights which can be gained from this otherwise promising research area. This article reviews the current resting TVC literature in light of these issues. We introduce core concepts, define key terms, summarize current controversies and open questions, and present a forward-looking perspective on how resting TVC analyses can be rigorously applied to investigate a wide range of questions in cognitive and systems neuroscience.
Consistency-based thresholding of the human connectome
Publisher: Elsevier BV
Date: 2017
DOI: 10.1016/J.NEUROIMAGE.2016.09.053
Abstract: Densely seeded probabilistic tractography yields weighted networks that are nearly fully connected, hence containing many spurious fibers. It is thus necessary to prune spurious connections from probabilistically-derived networks to obtain a more reliable overall estimate of the connectivity. A standard method is to threshold by weight, keeping only the strongest edges. Here, by measuring the consistency of edge weights across subjects, we propose a new thresholding method that aims to reduce the rate of false-positives in group-averaged connectivity matrices. Close inspection of the relationship between consistency, weight, and distance suggests that the most consistent edges are in fact those that are strong for their length, rather than simply strong overall. Hence retaining the most consistent edges preserves more long-distance connections than traditional weight-based thresholding, which penalizes long connections for being weak regardless of anatomy. By comparing our thresholded networks to mouse and macaque tracer data, we also show that consistency-based thresholding exhibits the species-invariant exponential decay of connection weights with distance, while weight-based thresholding does not. We also show that consistency-based thresholding can be used to identify highly consistent and highly inconsistent subnetworks across subjects, enabling more nuanced analyses of group-level connectivity than just the mean connectivity.
Heritability of the network architecture of intrinsic brain functional connectivity
Publisher: Elsevier BV
Date: 11-2015
Biophysical Mechanisms of Multistability in Resting-State Cortical Rhythms
Publisher: Society for Neuroscience
Date: 27-04-2011
DOI: 10.1523/JNEUROSCI.6693-10.2011
Abstract: The human alpha (8–12 Hz) rhythm is one of the most prominent, robust, and widely studied attributes of ongoing cortical activity. Contrary to the prevalent notion that it simply “waxes and wanes,” spontaneous alpha activity bursts erratically between two distinct modes of activity. We now establish a mechanism for this multistable phenomenon in resting-state cortical recordings by characterizing the complex dynamics of a biophysical model of macroscopic corticothalamic activity. This is achieved by studying the predicted activity of cortical and thalamic neuronal populations in this model as a function of its dynamic stability and the role of nonspecific synaptic noise. We hence find that fluctuating noisy inputs into thalamic neurons elicit spontaneous bursts between low- and high- litude alpha oscillations when the system is near a particular type of dynamical instability, namely a subcritical Hopf bifurcation. When the postsynaptic potentials associated with these noisy inputs are modulated by cortical feedback, the SD of power within each of these modes scale in proportion to their mean, showing remarkable concordance with empirical data. Our state-dependent corticothalamic model hence exhibits multistability and scale-invariant fluctuations—key features of resting-state cortical activity and indeed, of human perception, cognition, and behavior—thus providing a unified account of these apparently ergent phenomena.
Anxiety, stress and perfectionism in bipolar disorder
Publisher: Elsevier BV
Date: 12-2013
DOI: 10.1016/J.JAD.2013.08.029
Abstract: Previous reports have highlighted perfectionism and related cognitive styles as a psychological risk factor for stress and anxiety symptoms as well as for the development of bipolar disorder symptoms. The anxiety disorders are highly comorbid with bipolar disorder but the mechanisms that underpin this comorbidity are yet to be determined. Measures of depressive, (hypo)manic, anxiety and stress symptoms and perfectionistic cognitive style were completed by a s le of 142 patients with bipolar disorder. Mediation models were used to explore the hypotheses that anxiety and stress symptoms would mediate relationships between perfectionistic cognitive styles, and bipolar disorder symptoms. Stress and anxiety both significantly mediated the relationship between both self-critical perfectionism and goal attainment values and bipolar depressive symptoms. Goal attainment values were not significantly related to hypomanic symptoms. Stress and anxiety symptoms did not significantly mediate the relationship between self-critical perfectionism and (hypo)manic symptoms. 1. These data are cross-sectional hence the causality implied in the mediation models can only be inferred. 2. The clinic patients were less likely to present with (hypo)manic symptoms and therefore the reduced variability in the data may have contributed to the null findings for the mediation models with (hypo) manic symptoms. 3. Those patients who were experiencing current (hypo)manic symptoms may have answered the cognitive styles questionnaires differently than when euthymic. These findings highlight a plausible mechanism to understand the relationship between bipolar disorder and the anxiety disorders. Targeting self-critical perfectionism in the psychological treatment of bipolar disorder when there is anxiety comorbidity may result in more parsimonious treatments.
Brain-behavior patterns define a dimensional biotype in medication-naïve adults with attention-deficit hyperactivity disorder
Publisher: Cold Spring Harbor Laboratory
Date: 19-09-2017
DOI: 10.1101/190660
Abstract: Childhood-onset attention-deficit hyperactivity disorder (ADHD) in adults is clinically heterogeneous and commonly presents with different patterns of cognitive deficits. It is unclear if this clinical heterogeneity expresses a dimensional or categorical difference in ADHD. We first studied differences in functional connectivity in multi-echo resting-state functional magnetic resonance imaging (rs-fMRI) acquired from 80 medication-naïve adults with ADHD and 123 matched healthy controls. We then used canonical correlation analysis (CCA) to identify latent relationships between symptoms and patterns of altered functional connectivity (dimensional biotype) in patients. Clustering methods were implemented to test if the in idual associations between resting-state brain connectivity and symptoms reflected a non-overlapping categorical biotype. Adults with ADHD showed stronger functional connectivity compared to healthy controls, predominantly between the default-mode, cingulo-opercular and subcortical networks. CCA identified a single mode of brain-symptom co-variation, corresponding to an ADHD dimensional biotype. This dimensional biotype is characterized by a unique combination of altered connectivity correlating with symptoms of hyperactivity-impulsivity, inattention, and intelligence. Clustering analyses did not support the existence of distinct categorical biotypes of adult ADHD. Overall, our data advance a novel finding that the reduced functional segregation between default-mode and cognitive control networks supports a clinically important dimensional biotype of childhood-onset adult ADHD. Despite the heterogeneity of its presentation, our work suggests that childhood-onset adult ADHD is a single disorder characterized by dimensional brain-symptom mediators.
Symmetries and itineracy in nonlinear systems with many degrees of freedom
Publisher: Cambridge University Press (CUP)
Date: 10-2001
DOI: 10.1017/S0140525X01250092
Abstract: Tsuda examines the potential contribution of nonlinear dynamical systems, with many degrees of freedom, to understanding brain function. We offer suggestions concerning symmetry and transients to strengthen the physiological motivation and theoretical consistency of this novel research direction: Symmetry plays a fundamental role, theoretically and in relation to real brains. We also highlight a distinction between chaotic “transience” and “itineracy.”
Reconfiguration of functional brain networks and metabolic cost converge during task performance
Publisher: eLife Sciences Publications, Ltd
Date: 21-04-2020
DOI: 10.7554/ELIFE.52443
Abstract: The ability to solve cognitive tasks depends upon adaptive changes in the organization of whole-brain functional networks. However, the link between task-induced network reconfigurations and their underlying energy demands is poorly understood. We address this by multimodal network analyses integrating functional and molecular neuroimaging acquired concurrently during a complex cognitive task. Task engagement elicited a marked increase in the association between glucose consumption and functional brain network reorganization. This convergence between metabolic and neural processes was specific to feedforward connections linking the visual and dorsal attention networks, in accordance with task requirements of visuo-spatial reasoning. Further increases in cognitive load above initial task engagement did not affect the relationship between metabolism and network reorganization but only modulated existing interactions. Our findings show how the upregulation of key computational mechanisms to support cognitive performance unveils the complex, interdependent changes in neural metabolism and neuro-vascular responses.
A disturbance of nonlinear interdependence in scalp EEG of subjects with first episode schizophrenia
Publisher: Elsevier BV
Date: 09-2003
DOI: 10.1016/S1053-8119(03)00332-X
Abstract: It has been proposed that schizophrenia arises through a disturbance of coupling between large-scale cortical systems. This "disconnection hypothesis" is tested by applying a measure of dynamical interdependence to scalp EEG data. EEG data were collected from 40 subjects with a first episode of schizophrenia and 40 matched healthy controls. An algorithm for the detection of dynamical interdependence was applied to six pairs of bipolar electrodes in each subject. The topographic organization of the interdependence was calculated and served as the principle measure of cortical integration. The rate of occurrence of dynamical interdependence did not statistically differ between subject groups at any of the sites. However, the topography across the scalp was significantly different between the two groups. Specifically, nonlinear interdependence tended to occur in larger concurrent "clusters" across the scalp in schizophrenia than in the healthy subjects. This disturbance was reflected most strongly in left intrahemispheric coupling and did not differ significantly according to symptomatology. Medication dose and subject arousal were not observed to be confounding factors. The study of dynamical interdependence in scalp EEG data does not support a straightforward interpretation of the disconnection hypothesis-that there is a decrease in the strength of functional coupling between adjacent cortical regions. Rather, it suggests a dysregulation in the organization of dynamical interactions across supraregional brain systems.
Insula cortex gates the interplay of action observation and preparation for controlled imitation
Publisher: Elsevier BV
Date: 10-2021
Maximized directed information transfer in critical neuronal networks
Publisher: Springer Science and Business Media LLC
Date: 18-07-2011
Small‐world properties of nonlinear brain activity in schizophrenia
Publisher: Wiley
Date: 19-01-2009
DOI: 10.1002/HBM.20517
Critical role for resource constraints in neural models
Publisher: Frontiers Media SA
Date: 22-08-2014
Fronto-limbic dysconnectivity leads to impaired brain network controllability in young people with bipolar disorder and those at high genetic risk
Publisher: Cold Spring Harbor Laboratory
Date: 20-11-2017
DOI: 10.1101/222216
Abstract: Recent investigations have used diffusion-weighted imaging to reveal disturbances in the neurocircuitry that underlie cognitive-emotional control in bipolar disorder (BD) and in unaffected siblings or children at high genetic risk (HR). It has been difficult to quantify the mechanism by which structural changes disrupt the superimposed brain dynamics, leading to the emotional lability that is characteristic of BD. Average controllability is a concept from network control theory that extends structural connectivity data to estimate the manner in which local neuronal fluctuations spread from a node or subnetwork to alter the state of the rest of the brain. We used this theory to ask whether structural connectivity deficits previously observed in HR ( n =84, mean age 22.4) in iduals, patients with BD ( n =38, mean age 23.9), and age- and gender-matched controls ( n =96, mean age 22.6) translate to differences in the ability of brain systems to be manipulated between states. Localized impairments in network controllability were seen in the left parahippoc al, left middle occipital, left superior frontal, right inferior frontal, and right precentral gyri in BD and HR groups. Subjects with BD had distributed deficits in a subnetwork containing the left superior and inferior frontal gyri, postcentral gyrus, and insula ( p =0.004). HR participants had controllability deficits in a right-lateralized subnetwork involving connections between the dorsomedial and ventrolateral prefrontal cortex, the superior temporal pole, putamen, and caudate nucleus ( p =0.008). Between-group controllability differences were attenuated after removal of topological factors by network randomization. Some previously reported differences in network connectivity were not associated with controllability-differences, likely reflecting the contribution of more complex brain network properties. These analyses highlight the potential functional consequences of altered brain networks in BD, and may guide future clinical interventions. Control theory estimates how neuronal fluctuations spread from local networks. We compare brain controllability in bipolar disorder and their high-risk relatives. These groups have impaired controllability in networks supporting cognitive and emotional control. Weaker connectivity as well as topological alterations contribute to these changes.
Cross-cultural detection of depression from nonverbal behaviour
Publisher: IEEE
Date: 05-2015
Clinical predictors of conversion to bipolar disorder in a prospective longitudinal familial high-risk sample: focus on depressive features
Publisher: Cambridge University Press (CUP)
Date: 07-11-2017
DOI: 10.1017/S0033291717003233
Abstract: Identifying clinical features that predict conversion to bipolar disorder (BD) in those at high familial risk (HR) would assist in identifying a more focused population for early intervention. In total 287 participants aged 12–30 (163 HR with a first-degree relative with BD and 124 controls (CONs)) were followed annually for a median of 5 years. We used the baseline presence of DSM-IV depressive, anxiety, behavioural and substance use disorders, as well as a constellation of specific depressive symptoms (as identified by the Probabilistic Approach to Bipolar Depression) to predict the subsequent development of hypo/manic episodes. At baseline, HR participants were significantly more likely to report ⩾4 Probabilistic features (40.4%) when depressed than CONs (6.7% p .05). Nineteen HR subjects later developed either threshold ( n = 8 4.9%) or subthreshold ( n = 11 6.7%) hypo/mania. The presence of ⩾4 Probabilistic features was associated with a seven-fold increase in the risk of ‘conversion’ to threshold BD (hazard ratio = 6.9, p .05) above and beyond the fourteen-fold increase in risk related to major depressive episodes (MDEs) per se (hazard ratio = 13.9, p .05). In idual depressive features predicting conversion were psychomotor retardation and ⩾5 MDEs. Behavioural disorders only predicted conversion to subthreshold BD (hazard ratio = 5.23, p .01), while anxiety and substance disorders did not predict either threshold or subthreshold hypo/mania. This study suggests that specific depressive characteristics substantially increase the risk of young people at familial risk of BD going on to develop future hypo/manic episodes and may identify a more targeted HR population for the development of early intervention programs.
The influence of prior experience and expected timing on vibrotactile discrimination
Publisher: Frontiers Media SA
Date: 2013
Fronto-limbic dysconnectivity leads to impaired brain network controllability in young people with bipolar disorder and those at high genetic risk
Publisher: Elsevier BV
Date: 2018
Can Body Expressions Contribute to Automatic Depression Analysis?
Publisher: IEEE
Date: 04-2013
Wavelets and functional magnetic resonance imaging of the human brain
Publisher: Elsevier BV
Date: 2004
DOI: 10.1016/J.NEUROIMAGE.2004.07.012
Abstract: The discrete wavelet transform (DWT) is widely used for multiresolution analysis and decorrelation or "whitening" of nonstationary time series and spatial processes. Wavelets are naturally appropriate for analysis of biological data, such as functional magnetic resonance images of the human brain, which often demonstrate scale invariant or fractal properties. We provide a brief formal introduction to key properties of the DWT and review the growing literature on its application to fMRI. We focus on three applications in particular: (i) wavelet coefficient res ling or "wavestrapping" of 1-D time series, 2- to 3-D spatial maps and 4-D spatiotemporal processes (ii) wavelet-based estimators for signal and noise parameters of time series regression models assuming the errors are fractional Gaussian noise (fGn) and (iii) wavelet shrinkage in frequentist and Bayesian frameworks to support multiresolution hypothesis testing on spatially extended statistic maps. We conclude that the wavelet domain is a rich source of new concepts and techniques to enhance the power of statistical analysis of human fMRI data.
The multiscale character of evoked cortical activity
Publisher: Elsevier BV
Date: 05-2006
DOI: 10.1016/J.NEUROIMAGE.2005.10.041
Abstract: Both the architecture and the dynamics of the brain have characteristic features at different spatial scales. However, the existence, nature and function of dynamical interdependencies between such scales have not been investigated. We studied the multiscale properties of functional magnetic resonance imaging (fMRI) data acquired while human subjects viewed a visual image. Traditional "region of interest" analysis of this data set revealed evoked activity in primary and extrastriate visual cortex. Wavelet transform in the spatial domain provides a multiscale representation of this evoked brain activity. Studying the correlation structure of this representation revealed strong and novel interdependencies in these data within and between different spatial scales. We found that such correlations are stronger than those evident in the original data and comparable in magnitude to those obtained after Gaussian smoothing. However, analysis of the data in the wavelet domain revealed additional structure such as positive correlations, strong anti-correlations and phase-lagged interdependencies. Statistical significance of these effects was inferred through nonparametric bootstrap techniques. We conclude that the spatial analysis of functional neuroimaging data in the wavelet domain provides novel information which may reflect complex spatiotemporal neuronal activity and information encoding. It also affords a quantitative means of testing hierarchical and multiscale models of cortical activity.
The Nonlinear Theory of Schizophrenia
Publisher: SAGE Publications
Date: 2006
DOI: 10.1080/J.1440-1614.2006.01737.X
Abstract: Objective: Nonlinear properties exist within the brain across a hierarchy of scales and within a variety of critical neural processes. Only a few studies of brain activity in schizophrenia, however, have used nonlinear methods. This review paper evaluates the contribution of the nonlinear sciences towards understanding schizophrenia. Method: Applications of nonlinear methods to the study of schizophrenia symptoms and to healthy and schizophrenia functional neuroscience data are reviewed. The main flaws of nonlinear algorithms and recent methods to correct these are also appraised. Results: Initial research methods utilized in the study of nonlinearity in schizophrenia have fundamental methodological limitations. In the last decade, many of these problems have been addressed, facilitating future progress. Research incorporating these improvements has been applied to normal electroencephalogram (EEG) data and to the symptoms of schizophrenia, but not systematically to brain imaging data collected from patients with schizophrenia. Conclusion: There is strong statistical evidence for weak nonlinearity in normal EEG and in the fluctuations of the symptoms of schizophrenia. However, the contribution of nonlinear processes to brain dysfunction in schizophrenia is yet to be properly established or accurately quantified. Despite this, recent methodological advances suggest that a ‘nonlinear theory’ of schizophrenia may be helpful in understanding this disorder.
Preprocessing strategy influences graph‐based exploration of altered functional networks in major depression
Publisher: Wiley
Date: 17-02-2016
DOI: 10.1002/HBM.23111
Correction: Connectomics of bipolar disorder: a critical review, and evidence for dynamic instabilities within interoceptive networks
Publisher: Springer Science and Business Media LLC
Date: 04-01-2019
DOI: 10.1038/S41380-018-0327-7
Abstract: This Article was originally published under Nature Research's License to Publish, but has now been made available under a [CC BY 4.0] license. The PDF and HTML versions of the Article have been modified accordingly.
Adrian IV [real name Nicholas Breakspear] (d. 1159), pope
Publisher: Oxford University Press
Date: 23-09-2004
DOI: 10.1093/REF:ODNB/173
Dynamic connectivity in neural systems: Theoretical and empirical considerations
Publisher: Springer Science and Business Media LLC
Date: 2004
DOI: 10.1385/NI:2:2:205
Criticality in the brain: A synthesis of neurobiology, models and cognition
Publisher: Elsevier BV
Date: 11-2017
DOI: 10.1016/J.PNEUROBIO.2017.07.002
Abstract: Cognitive function requires the coordination of neural activity across many scales, from neurons and circuits to large-scale networks. As such, it is unlikely that an explanatory framework focused upon any single scale will yield a comprehensive theory of brain activity and cognitive function. Modelling and analysis methods for neuroscience should aim to accommodate multiscale phenomena. Emerging research now suggests that multi-scale processes in the brain arise from so-called critical phenomena that occur very broadly in the natural world. Criticality arises in complex systems perched between order and disorder, and is marked by fluctuations that do not have any privileged spatial or temporal scale. We review the core nature of criticality, the evidence supporting its role in neural systems and its explanatory potential in brain health and disease.
Clinical Applications of Stochastic Dynamic Models of the Brain, Part II: A Review
Publisher: Elsevier BV
Date: 04-2017
DOI: 10.1016/J.BPSC.2016.12.009
Abstract: Brain activity derives from intrinsic dynamics (due to neurophysiology and anatomical connectivity) in concert with stochastic effects that arise from sensory fluctuations, brainstem discharges, and random microscopic states such as thermal noise. The dynamic evolution of systems composed of both dynamic and random fluctuations can be studied with stochastic dynamic models (SDMs). This article, Part II of a two-part series, reviews applications of SDMs to large-scale neural systems in health and disease. Stochastic models have already elucidated a number of pathophysiological phenomena, such as epilepsy and hypoxic ischemic encephalopathy, although their use in biological psychiatry remains rather nascent. Emerging research in this field includes phenomenological models of mood fluctuations in bipolar disorder and biophysical models of functional imaging data in psychotic and affective disorders. Together with deeper theoretical considerations, this work suggests that SDMs will play a unique and influential role in computational psychiatry, unifying empirical observations with models of perception and behavior.
Dynamics of a neural system with a multiscale architecture
Publisher: The Royal Society
Date: 29-05-2005
Abstract: The architecture of the brain is characterized by a modular organization repeated across a hierarchy of spatial scales—neurons, minicolumns, cortical columns, functional brain regions, and so on. It is important to consider that the processes governing neural dynamics at any given scale are not only determined by the behaviour of other neural structures at that scale, but also by the emergent behaviour of smaller scales, and the constraining influence of activity at larger scales. In this paper, we introduce a theoretical framework for neural systems in which the dynamics are nested within a multiscale architecture. In essence, the dynamics at each scale are determined by a coupled ensemble of nonlinear oscillators, which embody the principle scale-specific neurobiological processes. The dynamics at larger scales are ‘slaved’ to the emergent behaviour of smaller scales through a coupling function that depends on a multiscale wavelet decomposition. The approach is first explicated mathematically. Numerical ex les are then given to illustrate phenomena such as between-scale bifurcations, and how synchronization in small-scale structures influences the dynamics in larger structures in an intuitive manner that cannot be captured by existing modelling approaches. A framework for relating the dynamical behaviour of the system to measured observables is presented and further extensions to capture wave phenomena and mode coupling are suggested.
Cortical Representations of Cognitive Control and Working Memory Are Dependent Yet Non-Interacting
Publisher: Oxford University Press (OUP)
Date: 23-09-2016
Abstract: Cognitive control (CC) and working memory (WM) are concurrently necessary for adaptive human behavior. These processes are thought to rely on similar neural mechanisms, yet little is known of the potential competitive or cooperative brain dynamics that support their concurrent engagement during complex behavioral tasks. Here, statistical interactions (synergy/competition) and dependencies (correlations) in brain function related to CC and WM were measured using functional magnetic resonance imaging. Twenty-five healthy adults performed a novel factorial cognitive paradigm, in which a 2-back verbal WM task was combined with the multisource interference task. Overlapping main effects in neural activation were evident in all regions of the "cognitive control network," together with robust behavioral main effects. However, no significant behavioral or cortical interaction effects were apparent. Conversely, robust positive correlations between the 2 main effects were evident within many components of the network. The results offer robust evidence that the neural representations of WM and CC are statistically dependent, but do not compete. These findings support the notion that CC and WM demands may be dynamically and flexibly encoded within a common brain network to support the efficient production of adaptive behavior across erse task contexts.
Scroll-Wave Dynamics in Human Cardiac Tissue: Lessons from a Mathematical Model with Inhomogeneities and Fiber Architecture
Publisher: Public Library of Science (PLoS)
Date: 05-04-2011
Comparing the phenomenology of depressive episodes in bipolar I and II disorder and major depressive disorder within bipolar disorder pedigrees
Publisher: Physicians Postgraduate Press, Inc
Date: 28-01-2015
DOI: 10.4088/JCP.14M09293
Charting the landscape of priority problems in psychiatry, part 2: pathogenesis and aetiology
Publisher: Elsevier BV
Date: 2016
Network structure of the human musculoskeletal system shapes neural interactions on multiple timescales
Publisher: Cold Spring Harbor Laboratory
Date: 29-08-2017
DOI: 10.1101/181818
Abstract: Human motor control requires the coordination of muscle activity under the anatomical constraints imposed by the musculoskeletal system. Interactions within the central nervous system are fundamental to motor coordination, but the principles governing functional integration remain poorly understood. We used network analysis to investigate the relationship between anatomical and functional connectivity amongst 36 muscles. Anatomical networks were defined by the physical connections between muscles and functional networks were based on intermuscular coherence assessed during postural tasks. We found a modular structure of functional networks that was strongly shaped by the anatomical constraints of the musculoskeletal system. Changes in postural tasks were associated with a frequency-dependent reconfiguration of the coupling between functional modules. These findings reveal distinct patterns of functional interactions between muscles involved in flexibly organising muscle activity during postural control. Our network approach to the motor system offers a unique window into the neural circuitry driving the musculoskeletal system.
A brain model of disturbed self-appraisal in depression
Publisher: American Psychiatric Association Publishing
Date: 09-2017
DOI: 10.1176/APPI.AJP.2017.16080883
Abstract: A disturbed sense of self is a core feature of depression. The medial prefrontal cortex, which has a central role in self-appraisal processes, is often implicated in the illness, although it remains unclear how functional alterations of the region contribute to the observed disturbances. The aim of this study was to clarify the role of the medial prefrontal cortex in self-appraisal processes in depression. The authors applied a recently developed dynamic network model of self-directed cognition to functional MRI data from 71 adolescents and young adults with moderate to severe major depressive disorder, none of whom were being treated with medication, and 88 healthy control participants. Bayesian model averaging was used to determine parameter estimates for the dynamic causal models, which were compared between groups. While self-directed cognitive processes in the depression group were shown to rely on the same dynamic network as in the healthy control group, the medial prefrontal cortex had a "hyperregulatory" effect on the posterior cingulate cortex in the depressed group, with self-appraisal causing significantly more negative modulation of connectivity between the medial prefrontal cortex and the posterior cingulate cortex than in the control group (odds ratio=0.54, 95% CI=0.38, 0.77). This parameter was significantly inversely related with a depression factor related to poor concentration and inner tension (r=-0.32 95% CI=-0.51, -0.08). The exaggerated influence of the medial prefrontal cortex on the posterior cingulate cortex in depression is a neural correlate of the disturbed self-appraisal that is characteristic of the illness.
Neuroimaging, Neural Population Models for
Publisher: Springer New York
Date: 2015
Sifting through the surfeit of neuroinflammation tracers
Publisher: SAGE Publications
Date: 19-12-2017
Abstract: The first phase of molecular brain imaging of microglial activation in neuroinflammatory conditions began some 20 years ago with the introduction of [ 11 C]-( R)-PK11195, the prototype isoquinoline ligand for translocator protein (18 kDa) (TSPO). Investigations by positron emission tomography (PET) revealed microgliosis in numerous brain diseases, despite the rather low specific binding signal imparted by [ 11 C]-( R)-PK11195. There has since been enormous expansion of the repertoire of TSPO tracers, many with higher specific binding, albeit complicated by allelic dependence of the affinity. However, the specificity of TSPO PET for revealing microglial activation not been fully established, and it has been difficult to judge the relative merits of the competing tracers and analysis methods with respect to their sensitivity for detecting microglial activation. We therefore present a systematic comparison of 13 TSPO PET and single photon computed tomography (SPECT) tracers belonging to five structural classes, each of which has been investigated by compartmental analysis in healthy human brain relative to a metabolite-corrected arterial input. We emphasize the need to establish the non-displaceable binding component for each ligand and conclude with five recommendations for a standard approach to define the cellular distribution of TSPO signals, and to characterize the properties of candidate TSPO tracers.
Connectomics of bipolar disorder: a critical review, and evidence for dynamic instabilities within interoceptive networks
Publisher: Springer Science and Business Media LLC
Date: 02-10-2018
Reply to: Commentary on Pang et al. (2023) Nature
Publisher: Cold Spring Harbor Laboratory
Date: 09-10-2023
A hierarchy of timescales explains distinct effects of local inhibition of primary visual cortex and frontal eye fields
Publisher: eLife Sciences Publications, Ltd
Date: 06-09-2016
DOI: 10.7554/ELIFE.15252
Abstract: Within the primate visual system, areas at lower levels of the cortical hierarchy process basic visual features, whereas those at higher levels, such as the frontal eye fields (FEF), are thought to modulate sensory processes via feedback connections. Despite these functional exchanges during perception, there is little shared activity between early and late visual regions at rest. How interactions emerge between regions encompassing distinct levels of the visual hierarchy remains unknown. Here we combined neuroimaging, non-invasive cortical stimulation and computational modelling to characterize changes in functional interactions across widespread neural networks before and after local inhibition of primary visual cortex or FEF. We found that stimulation of early visual cortex selectively increased feedforward interactions with FEF and extrastriate visual areas, whereas identical stimulation of the FEF decreased feedback interactions with early visual areas. Computational modelling suggests that these opposing effects reflect a fast-slow timescale hierarchy from sensory to association areas.
Using the Virtual Brain to Reveal the Role of Oscillations and Plasticity in Shaping Brain's Dynamical Landscape
Publisher: Mary Ann Liebert Inc
Date: 12-2014
Abstract: Spontaneous brain activity, that is, activity in the absence of controlled stimulus input or an explicit active task, is topologically organized in multiple functional networks (FNs) maintaining a high degree of coherence. These "resting state networks" are constrained by the underlying anatomical connectivity between brain areas. They are also influenced by the history of task-related activation. The precise rules that link plastic changes and ongoing dynamics of resting-state functional connectivity (rs-FC) remain unclear. Using the framework of the open source neuroinformatics platform "The Virtual Brain," we identify potential computational mechanisms that alter the dynamical landscape, leading to reconfigurations of FNs. Using a spiking neuron model, we first demonstrate that network activity in the absence of plasticity is characterized by irregular oscillations between low- litude asynchronous states and high- litude synchronous states. We then demonstrate the capability of spike-timing-dependent plasticity (STDP) combined with intrinsic alpha (8-12 Hz) oscillations to efficiently influence learning. Further, we show how alpha-state-dependent STDP alters the local area dynamics from an irregular to a highly periodic alpha-like state. This is an important finding, as the cortical input from the thalamus is at the rate of alpha. We demonstrate how resulting rhythmic cortical output in this frequency range acts as a neuronal tuner and, hence, leads to synchronization or de-synchronization between brain areas. Finally, we demonstrate that locally restricted structural connectivity changes influence local as well as global dynamics and lead to altered rs-FC.
Brain–behavior patterns define a dimensional biotype in medication-naïve adults with attention-deficit hyperactivity disorder
Publisher: Cambridge University Press (CUP)
Date: 07-02-2018
DOI: 10.1017/S0033291718000028
Abstract: Childhood-onset attention-deficit hyperactivity disorder (ADHD) in adults is clinically heterogeneous and commonly presents with different patterns of cognitive deficits. It is unclear if this clinical heterogeneity expresses a dimensional or categorical difference in ADHD. We first studied differences in functional connectivity in multi-echo resting-state functional magnetic resonance imaging (rs-fMRI) acquired from 80 medication-naïve adults with ADHD and 123 matched healthy controls. We then used canonical correlation analysis (CCA) to identify latent relationships between symptoms and patterns of altered functional connectivity (dimensional biotype) in patients. Clustering methods were implemented to test if the in idual associations between resting-state brain connectivity and symptoms reflected a non-overlapping categorical biotype. Adults with ADHD showed stronger functional connectivity compared to healthy controls, predominantly between the default-mode, cingulo-opercular and subcortical networks. CCA identified a single mode of brain–symptom co-variation, corresponding to an ADHD dimensional biotype. This dimensional biotype is characterized by a unique combination of altered connectivity correlating with symptoms of hyperactivity-impulsivity, inattention, and intelligence. Clustering analyses did not support the existence of distinct categorical biotypes of adult ADHD. Overall, our data advance a novel finding that the reduced functional segregation between default-mode and cognitive control networks supports a clinically important dimensional biotype of childhood-onset adult ADHD. Despite the heterogeneity of its presentation, our work suggests that childhood-onset adult ADHD is a single disorder characterized by dimensional brain–symptom mediators.
A Canonical Model of Multistability and Scale-Invariance in Biological Systems
Publisher: Public Library of Science (PLoS)
Date: 09-08-2012
Sony Pictures imageworks celebrating 25 years of innovation, imagination and creativity
Publisher: ACM
Date: 30-07-2017
State switching and high-order spatiotemporal organization of dynamic Functional Connectivity are disrupted by Alzheimer’s Disease
Publisher: Cold Spring Harbor Laboratory
Date: 22-02-2023
DOI: 10.1101/2023.02.19.23285768
Abstract: Spontaneous activity during the resting state, tracked by BOLD fMRI imaging, or shortly rsfMRI, gives rise to brain-wide dynamic patterns of inter-regional correlations, whose structured flexibility relates to cognitive performance. Here we analyze resting state dynamic Functional Connectivity (dFC) in a cohort of older adults, including amnesic Mild Cognitive Impairment (aMCI, N = 34) and Alzheimer’s Disease (AD, N = 13) patients, as well as normal control (NC, N = 16) and cognitively “super-normal” (SN, N = 10) subjects. Using complementary state-based and state-free approaches, we find that resting state fluctuations of different functional links are not independent but are constrained by high-order correlations between triplets or quadruplets of functionally connected regions. When contrasting patients with healthy subjects, we find that dFC between cingulate and other limbic regions is increasingly bursty and intermittent when ranking the four groups from SNC to NC, aMCI and AD. Furthermore, regions affected at early stages of AD pathology are less involved in higher-order interactions in patient than in control groups, while pairwise interactions are not significantly reduced. Our analyses thus suggest that the spatiotemporal complexity of dFC organization is precociously degraded in AD and provides a richer window into the underlying neurobiology than time-averaged FC connections. Brain functions emerge from the coordinated dynamics of many brain regions. Dynamic Functional Connectivity (dFC) analyses are a key tool to describe such dynamic complexity and have been shown to be good predictors of cognitive performance. This is particularly true in the case of Alzheimer’s Disease (AD) in which an impoverished dFC could indicate compromised functional reserve due to the detrimental effects of neurodegeneration. Here we observe that in healthy ageing dFC is indeed spatiotemporally organized, as reflected by high-order correlations between multiple regions. However, in people with aMCI or AD, dFC becomes less “entangled”, more random-like, and intermittently bursty. We speculate that this degraded spatiotemporal coordination may reflect dysfunctional information processing, thus ultimately leading to worsening of cognitive deficits.
The rich club of the brain in bipolar disorder
Publisher: Frontiers Media SA
Date: 2015
Zero-lag synchronization in cortical motifs
Publisher: Springer Science and Business Media LLC
Date: 07-2013
Critical fluctuations in cortical models near instability
Publisher: Frontiers Media SA
Date: 2012
Muscle Co-Contraction Modulates Damping and Joint Stability in a Three-Link Biomechanical Limb
Publisher: Frontiers Media SA
Date: 2012
Putting the “dynamic” back into dynamic functional connectivity
Publisher: MIT Press - Journals
Date: 06-2018
DOI: 10.1162/NETN_A_00041
Abstract: The study of fluctuations in time-resolved functional connectivity is a topic of substantial current interest. As the term “dynamic functional connectivity” implies, such fluctuations are believed to arise from dynamics in the neuronal systems generating these signals. While considerable activity currently attends to methodological and statistical issues regarding dynamic functional connectivity, less attention has been paid toward its candidate causes. Here, we review candidate scenarios for dynamic (functional) connectivity that arise in dynamical systems with two or more subsystems generalized synchronization, itinerancy (a form of metastability), and multistability. Each of these scenarios arises under different configurations of local dynamics and intersystem coupling: We show how they generate time series data with nonlinear and/or nonstationary multivariate statistics. The key issue is that time series generated by coupled nonlinear systems contain a richer temporal structure than matched multivariate (linear) stochastic processes. In turn, this temporal structure yields many of the phenomena proposed as important to large-scale communication and computation in the brain, such as phase- litude coupling, complexity, and flexibility. The code for simulating these dynamics is available in a freeware software platform, the Brain Dynamics Toolbox.
Publisher Correction: Human cognition involves the dynamic integration of neural activity and neuromodulatory systems
Publisher: Springer Science and Business Media LLC
Date: 21-02-2019
DOI: 10.1038/S41593-019-0347-X
Abstract: In the version of this article initially published, Kaylena A. Ehgoetz Martens' name was misspelled as Kayla. The error has been corrected in the HTML and PDF versions of the article.
Graph analysis of the human connectome: Promise, progress, and pitfalls
Publisher: Elsevier BV
Date: 10-2013
DOI: 10.1016/J.NEUROIMAGE.2013.04.087
Abstract: The human brain is a complex, interconnected network par excellence. Accurate and informative mapping of this human connectome has become a central goal of neuroscience. At the heart of this endeavor is the notion that brain connectivity can be abstracted to a graph of nodes, representing neural elements (e.g., neurons, brain regions), linked by edges, representing some measure of structural, functional or causal interaction between nodes. Such a representation brings connectomic data into the realm of graph theory, affording a rich repertoire of mathematical tools and concepts that can be used to characterize erse anatomical and dynamical properties of brain networks. Although this approach has tremendous potential - and has seen rapid uptake in the neuroimaging community - it also has a number of pitfalls and unresolved challenges which can, if not approached with due caution, undermine the explanatory potential of the endeavor. We review these pitfalls, the prevailing solutions to overcome them, and the challenges at the forefront of the field.
Charting the landscape of priority problems in psychiatry, part 1: classification and diagnosis
Publisher: Elsevier BV
Date: 2016
Prior and present evidence: How prior experience interacts with present information in a perceptual decision making task
Publisher: Public Library of Science (PLoS)
Date: 29-05-2012
Clinical characteristics and temperament influences on 'happy' euphoric and 'snappy' irritable bipolar hypo/manic mood states
Publisher: Elsevier BV
Date: 03-2015
DOI: 10.1016/J.JAD.2014.11.042
Abstract: While mood elevation and euphoria are the most commonly described phenotypic descriptors of hypo/mania, irritability and anger may dominate. This study was designed to pursue possible determinants of such differing states. Patients with bipolar I or II disorder were assigned to an 'irritable/snappy' or 'euphoric/happy' sub-set on the basis of their dominant hypo/manic symptoms. Group differences were examined across clinical, personality, lifestyle and illness impact measures. The two sub-sets did not differ on age of depression onset, family history of mood disorders, or depression severity and impairment. The snappy sub-set reported higher levels of irritability in depressed phases and were more likely to have a comorbid anxiety disorder. Their hypo/manic episodes were shorter and they were more likely to be hospitalized at such times. On a temperament measure they scored as more irritable and self-focussed and as less cooperative and effective - indicative of higher levels of disordered personality functioning. Some comparison analyses were undertaken on a reduced s le size, giving rise to power issues. Our bipolar I and II diagnoses deviated to some extent from DSM-5 criteria in not imposing duration criteria for hypo/manic episodes. Findings support a spectrum model for the bipolar disorders linking temperament to bipolar symptomatic state and which may have treatment implications.
Computational models of the brain: From structure to function
Publisher: Elsevier BV
Date: 09-2010
Cortical burst dynamics predict clinical outcome early in extremely preterm infants
Publisher: Oxford University Press (OUP)
Date: 22-05-2015
DOI: 10.1093/BRAIN/AWV129
Abstract: Intermittent bursts of electrical activity are a ubiquitous signature of very early brain activity. Previous studies have largely focused on assessing the litudes of these transient cortical bursts or the intervals between them. Recent advances in basic neuroscience have identified the presence of scale-free 'avalanche' processes in bursting patterns of cortical activity in other clinical contexts. Here, we hypothesize that cortical bursts in human preterm infants also exhibit scale-free properties, providing new insights into the nature, temporal evolution, and prognostic value of spontaneous brain activity in the days immediately following preterm birth. We examined electroencephalographic recordings from 43 extremely preterm infants (gestational age 22-28 weeks) and demonstrated that their cortical bursts exhibit scale-free properties as early as 12 h after birth. The scaling relationships of cortical bursts correlate significantly with later mental development-particularly within the first 12 h of life. These findings show that early preterm brain activity is characterized by scale-free dynamics which carry developmental significance, hence offering novel means for rapid and early clinical prediction of neurodevelopmental outcomes.
Comparison of depressive episodes in bipolar disorder and in major depressive disorder within bipolar disorder pedigrees
Publisher: Royal College of Psychiatrists
Date: 10-2011
DOI: 10.1192/BJP.BP.110.088823
Abstract: Although genetic epidemiological studies have confirmed increased rates of major depressive disorder among the relatives of people with bipolar affective disorder, no report has compared the clinical characteristics of depression between these two groups. To compare clinical features of depressive episodes across participants with major depressive disorder and bipolar disorder from within bipolar disorder pedigrees, and assess the utility of a recently proposed probabilistic approach to distinguishing bipolar from unipolar depression. A secondary aim was to identify subgroups within the relatives with major depression potentially indicative of ‘genetic’ and ‘sporadic’ subgroups. Patients with bipolar disorder types 1 and 2 ( n = 246) and patients with major depressive disorder from bipolar pedigrees ( n = 120) were assessed using the Diagnostic Interview for Genetic Studies. Logistic regression was used to identify distinguishing clinical features and assess the utility of the probabilistic approach. Hierarchical cluster analysis was used to identify subgroups within the major depressive disorder s le. Bipolar depression was characterised by significantly higher rates of psychomotor retardation, difficulty thinking, early morning awakening, morning worsening and psychotic features. Depending on the threshold employed, the probabilistic approach yielded a positive predictive value ranging from 74% to 82%. Two clusters within the major depressive disorder s le were found, one of which demonstrated features characteristic of bipolar depression, suggesting a possible ‘genetic’ subgroup. A number of previously identified clinical differences between unipolar and bipolar depression were confirmed among participants from within bipolar disorder pedigrees. Preliminary validation of the probabilistic approach in differentiating between unipolar and bipolar depression is consistent with dimensional distinctions between the two disorders and offers clinical utility in identifying patients who may warrant further assessment for bipolarity. The major depressive disorder clusters potentially reflect genetic and sporadic subgroups which, if replicated independently, might enable an improved phenotypic definition of underlying bipolarity in genetic analyses.
Richness in Functional Connectivity Depends on the Neuronal Integrity within the Posterior Cingulate Cortex
Publisher: Frontiers Media SA
Date: 07-04-2017
The connectomics of brain disorders
Publisher: Springer Science and Business Media LLC
Date: 20-02-2015
DOI: 10.1038/NRN3901
Abstract: Pathological perturbations of the brain are rarely confined to a single locus instead, they often spread via axonal pathways to influence other regions. Patterns of such disease propagation are constrained by the extraordinarily complex, yet highly organized, topology of the underlying neural architecture the so-called connectome. Thus, network organization fundamentally influences brain disease, and a connectomic approach grounded in network science is integral to understanding neuropathology. Here, we consider how brain-network topology shapes neural responses to damage, highlighting key maladaptive processes (such as diaschisis, transneuronal degeneration and dedifferentiation), and the resources (including degeneracy and reserve) and processes (such as compensation) that enable adaptation. We then show how knowledge of network topology allows us not only to describe pathological processes but also to generate predictive models of the spread and functional consequences of brain disease.
Computational models link cellular mechanisms of neuromodulation to large-scale neural dynamics
Publisher: Springer Science and Business Media LLC
Date: 06-05-2021
DOI: 10.1038/S41593-021-00824-6
Abstract: Decades of neurobiological research have disclosed the erse manners in which the response properties of neurons are dynamically modulated to support adaptive cognitive functions. This neuromodulation is achieved through alterations in the biophysical properties of the neuron. However, changes in cognitive function do not arise directly from the modulation of in idual neurons, but are mediated by population dynamics in mesoscopic neural ensembles. Understanding this multiscale mapping is an important but nontrivial issue. Here, we bridge these different levels of description by showing how computational models parametrically map classic neuromodulatory processes onto systems-level models of neural activity. The ensuing critical balance of systems-level activity supports perception and action, although our knowledge of this mapping remains incomplete. In this way, quantitative models that link microscale neuronal neuromodulation to systems-level brain function highlight gaps in knowledge and suggest new directions for integrating theoretical and experimental work.
Changes in Community Structure of Resting State Functional Connectivity in Unipolar Depression
Publisher: Public Library of Science (PLoS)
Date: 20-08-2012
N-way decomposition: Towards linking concurrent EEG and fMRI analysis during natural stimulus
Publisher: Springer International Publishing
Date: 2017
Fragility and volatility of structural hubs in the human connectome
Publisher: Springer Science and Business Media LLC
Date: 23-07-2018
DOI: 10.1038/S41593-018-0188-Z
Abstract: Brain structure reflects the influence of evolutionary processes that pit the costs of its anatomical wiring against the computational advantages conferred by its complexity. We show that cost-neutral 'mutations' of the human connectome almost inevitably degrade its complexity and disconnect high-strength connections to prefrontal network hubs. Conversely, restoring the peripheral location and strong connectivity of empirically observed hubs confers a wiring cost that the brain appears to minimize. Progressive cost-neutral randomization yields daughter networks that differ substantially from one another and results in a topologically unstable phenomenon consistent with a phase transition in complex systems. The fragility of hubs to disconnection shows a significant association with the acceleration of gray matter loss in schizophrenia. Together with effects on wiring cost, we suggest that fragile prefrontal hub connections and topological volatility act as evolutionary influences on brain networks whose optimal set point may be perturbed in neuropsychiatric disorders.
Critical-like bistable dynamics in the resting-state human brain
Publisher: Cold Spring Harbor Laboratory
Date: 10-01-2022
DOI: 10.1101/2022.01.09.475554
Abstract: Brain activity exhibits scale-free avalanche dynamics and power-law long-range temporal correlations (LRTCs) across the nervous system. This has been thought to reflect “brain criticality”, i.e. , brains operating near a critical phase transition between disorder and excessive order. Neuronal activity is, however, metabolically costly and may be constrained by activity-limiting mechanisms and resource depletion, which could make the phase transition discontinuous and bistable. Observations of bistability in awake human brain activity have nonetheless remained scarce and its functional significance unclear. First, using computational modelling where bistable synchronization dynamics emerged through local positive feedback, we found bistability to occur exclusively in a regime of critical-like dynamics. We then assessed bistability in vivo with resting-state magnetoencephalography and stereo-encephalography. Bistability was a robust characteristic of cortical oscillations throughout frequency bands from δ (3-7 Hz) to high-γ (100-225 Hz). As predicted by modelling, bistability and LRTCs were positively correlated. Importantly, while moderate levels of bistability were positively correlated with executive functioning, excessive bistability was associated with epileptic pathophysiology and predictive of local epileptogenicity. Critical bistability is thus a salient feature of spontaneous human brain dynamics in awake resting-state and is both functionally and clinically significant. These findings expand the framework of brain criticality and show that critical-like neuronal dynamics in vivo involves both continuous and discontinuous phase transitions in a frequency-, neuroanatomy-, and state-dependent manner.
Author Correction: Computational models link cellular mechanisms of neuromodulation to large-scale neural dynamics
Publisher: Springer Science and Business Media LLC
Date: 21-06-2021
Optimising activity and diet compositions for dementia prevention: Protocol for the ACTIVate prospective longitudinal cohort study
Publisher: Cold Spring Harbor Laboratory
Date: 31-07-2021
DOI: 10.1101/2021.07.28.21261299
Abstract: Approximately 40% of late-life dementia may be prevented by addressing modifiable risk factors, including physical activity and diet. Yet, it is currently unknown how multiple lifestyle factors interact to influence cognition. The ACTIVate Study aims to 1) Explore associations between 24-hour time-use and diet compositions with changes in cognition and brain function and 2) Identify durations of time-use behaviours and the dietary compositions to optimise cognition and brain function. This three-year prospective longitudinal cohort study will recruit 448 adults aged 60-70 years across Adelaide and Newcastle, Australia. Time-use data will be collected through wrist-worn activity monitors and the Multimedia Activity Recall for Children and Adults (MARCA). Dietary intake will be assessed using the Australian Eating Survey food frequency questionnaire. The primary outcome will be cognitive function, assessed using the Addenbrooke’s Cognitive Examination-III (ACE-III). Secondary outcomes include structural and functional brain measures using Magnetic Resonance Imaging (MRI), cerebral arterial pulse measured with Diffuse Optical Tomography (Pulse-DOT), neuroplasticity using simultaneous Transcranial Magnetic Stimulation (TMS) and Electroencephalography (EEG), and electrophysiological markers of cognitive control using event-related potential (ERP) and time-frequency analyses. Compositional data analysis, testing for interactions between time-point and compositions, will assess longitudinal associations between dependent (cognition, brain function) and independent (time-use and diet compositions) variables. The ACTIVate Study will be the first to examine associations between time-use and diet compositions, cognition and brain function. Our findings will inform new avenues for multidomain interventions that may more effectively account for the co-dependence between activity and diet behaviours for dementia prevention. Ethics approval has been obtained from University of South Australia’s Human Research Ethics committee (202639). Findings will be disseminated through peer reviewed manuscripts, conference presentations, targeted media releases and community engagement events. Australia New Zealand Clinical Trials Registry (ACTRN12619001659190). The ACTIVate Study will collect comprehensive measures of lifestyle behaviours and dementia risk over time in 448 older adults aged 60-70 years. Using newly developed Compositional Data Analysis (CoDA) techniques we will examine the associations between time-use and diet compositions, cognition and brain function. Data will inform the development of a digital tool to help older adults obtain personalised information about how to reduce their risk of cognitive decline based on changes to time use and diet. Recruitment will be focussed on older adults to maximise the potential of making an impact on dementia prevention in the next 10 years. Findings may not be generalisable to younger adults.
Michael Breakspear: using imaging to unravel psychiatric mysteries
Publisher: Elsevier BV
Date: 07-2018
The modulation of neural gain facilitates a transition between functional segregation and integration in the brain
Publisher: Cold Spring Harbor Laboratory
Date: 30-08-2017
DOI: 10.1101/182444
Abstract: Cognitive function relies on a dynamic, context-sensitive balance between functional integration and segregation in the brain. Previous work has proposed that this balance is mediated by global fluctuations in neural gain by projections from ascending neuromodulatory nuclei. To test this hypothesis in silico , we studied the effects of neural gain on network dynamics in a model of large-scale neuronal dynamics. We found that increases in neural gain pushed the network through an abrupt dynamical transition, leading to an integrated network topology that was maximal in frontoparietal ‘rich club’ regions. This gain-mediated transition was also associated with increased topological complexity, as well as increased variability in time-resolved topological structure, further highlighting the potential computational benefits of the gain-mediated network transition. These results support the hypothesis that neural gain modulation has the computational capacity to mediate the balance between integration and segregation in the brain.
Complex mental activity and the aging brain: Molecular, cellular and cortical network mechanisms
Publisher: Elsevier BV
Date: 11-2007
DOI: 10.1016/J.BRAINRESREV.2007.07.007
Abstract: There is strong evidence to suggest that high levels of complex mental activity can improve clinical outcome from brain injury. What are the neurobiological mechanisms underlying this observation? This paper proposes that complex mental activity induces a spectrum of biological changes on brain structure and function which can be best understood in a multiscalar spatiotemporal framework. Short-term molecular changes may include induction of BDNF, NGF and endopeptidase genes and elevation of the high-energy phosphocreatine-creatine resting state equilibrium. Animal models have implicated these processes in the reduction and even reversal of neurodegenerative changes secondary to mental work. These mechanisms can therefore be described as neuroprotective. Medium-term cellular changes are erse and include neurogenesis, synaptogenesis, angiogenesis and formation of more complex dendritic branching patterns. Importantly, these effects parallel behavioral improvement, and thus a neurogenerative class of mechanisms is implicated. Finally, in the post-lesion context, computation principles such as efficiency, small world connectivity and functional adaptation are identified as important, with supportive clinical evidence from neuroimaging studies. Thus, dynamic compensatory cortical network mechanisms may also be relevant, yet take some time to evolve. This paper will explore the neurobiological and clinical implications of this framework, in particular in the context of age-related brain disease.
Novel features of early burst suppression predict outcome after birth asphyxia
Publisher: Wiley
Date: 18-02-2014
DOI: 10.1002/ACN3.32
Nonlinear dynamic causal models for fMRI
Publisher: Elsevier BV
Date: 08-2008
Distinct neurobiological signatures of brain connectivity in depression subtypes during natural viewing of emotionally salient films
Publisher: Cambridge University Press (CUP)
Date: 18-02-2016
DOI: 10.1017/S0033291716000179
Abstract: Establishing an evidence-based diagnostic system informed by the biological (dys)function of the nervous system is a major priority in psychiatry. This objective, however, is often challenged by difficulties in identifying homogeneous clinical populations. Melancholia, a biological and endogenous subtype for major depressive disorder, presents a canonical test case in the search of biological nosology. We employed a unique combination of naturalistic functional magnetic resonance imaging (fMRI) paradigms – resting state and free viewing of emotionally salient films – to search for neurobiological signatures of depression subtypes. fMRI data were acquired from 57 participants 17 patients with melancholia, 17 patients with (non-melancholic) major depression and 23 matched healthy controls. Patients with melancholia showed a prominent loss of functional connectivity in hub regions [including ventral medial prefrontal cortex, anterior cingulate cortex (ACC) and superior temporal gyrus] during natural viewing, and in the posterior cingulate cortex while at rest. Of note, the default mode network showed diminished reactivity to external stimuli in melancholia, which correlated with the severity of anhedonia. Intriguingly, the subgenual ACC, a potential target for treating depression with deep brain stimulation (DBS), showed ergent changes between the two depression subtypes, with increased connectivity in the non-melancholic and decreased connectivity in the melancholic subsets. These findings reveal neurobiological changes specific to depression subtypes during ecologically valid behavioural conditions, underscoring the critical need to respect differing neurobiological processes underpinning depressive subtypes.
A Dendritic Mechanism for Decoding Traveling Waves: Principles and Applications to Motor Cortex
Publisher: Public Library of Science (PLoS)
Date: 31-10-2013
Whole-brain analytic measures of network communication reveal increased structure-function correlation in right temporal lobe epilepsy
Publisher: Elsevier BV
Date: 2016
The effect of genetic predisposition to Alzheimer’s disease and related traits on recruitment bias in a study of cognitive ageing
Publisher: Cold Spring Harbor Laboratory
Date: 14-05-2023
DOI: 10.1101/2023.05.10.23289642
Abstract: The recruitment of participants for research studies may be subject to bias due to an overrepresentation of those more willing to participate voluntarily. No study has analysed the effect of genetic predisposition to Alzheimer’s disease (AD) on study participation. The Prospective Imaging Study of Ageing (PISA), aims to characterise the phenotype and natural history of healthy adult Australians at high future risk of AD. Participants approached to take part in PISA were selected from existing cohort studies with available genome-wide genetic data for both successfully and unsuccessfully recruited participants, allowing us to investigate the genetic contribution to voluntary recruitment. From a recruitment pool of 13,432 in iduals (age 40-80), 64% of participants were successfully recruited into the study. Polygenic risk scores (PRS) were computed in order to test to what extent the genetic risk for AD, and related risk factors (including educational attainment, household income and IQ), predicted participation in PISA. We examined the associations between PRS and APOE ε4 with recruitment using logistic regression models. We found significant associations of age and sex with study participation, where older and female participants were more likely to complete the core module. We did not identify a significant association of genetic risk for AD with study participation. Nonetheless, we identified significant associations with genetic scores for key causal risk factors for AD, such as IQ, household income and years of education. Our findings highlight the importance of considering bias in key risk factors for AD in the recruitment of in iduals for cohort studies.
Generalized time-frequency coherency for assessing neural interactions in electrophysiological recordings
Publisher: Springer Science and Business Media LLC
Date: 17-11-2011
Rejection sensitivity and pain in bipolar versus unipolar depression
Publisher: Wiley
Date: 25-11-2014
DOI: 10.1111/BDI.12147
Abstract: Recent neuroimaging studies support the contention that depression, pain distress, and rejection distress share the same neurobiological circuits. In two recently published studies we confirmed the hypothesis that the perception of increased pain during both treatment-refractory depression (predominantly unipolar) and difficult-to-treat bipolar depression was related to increased state rejection sensitivity (i.e., rejection sensitivity when depressed). In the present study, we aimed to compare the correlates of pain and rejection sensitivity in in iduals with bipolar versus unipolar depression and test the hypothesis that bipolar disorder may be distinguished from unipolar depression both by an increased perception of pain and heightened rejection sensitivity during depression. We analyzed data from 113 bipolar and 146 unipolar depressed patients presenting to the Black Dog Institute, Sydney, Australia. The patients all met DSM-IV criteria for bipolar disorder or unipolar depression (major depressive disorder). Bipolar disorder predicted a major increase in state rejection sensitivity when depressed (p = 0.001), whereas trait rejection sensitivity (i.e., a long-standing pattern of rejection sensitivity) was not predicted by polarity. A major increase in the experience of headaches (p = 0.007), chest pain (p < 0.001), and body aches and pains (p = 0.02) during depression was predicted by a major increase in state rejection sensitivity for both bipolar and unipolar depression. State, but not trait, rejection sensitivity is significantly predicted by bipolar depression, suggesting that this might be considered as a state marker for bipolar depression and taken into account in the clinical differentiation of bipolar and unipolar depression.
The site of stimulation moderates neuropsychiatric symptoms after subthalamic deep brain stimulation for Parkinson's disease
Publisher: Elsevier BV
Date: 2018
The dynamic basis of cognition: An integrative core under the control of the ascending neuromodulatory system
Publisher: Cold Spring Harbor Laboratory
Date: 18-02-2018
DOI: 10.1101/266635
Abstract: The human brain integrates erse cognitive processes into a coherent whole, shifting fluidly as a function of changing environmental demands. Despite recent progress, the neurobiological mechanisms responsible for this dynamic system-level integration remain poorly understood. Here, we used multi-task fMRI data from the Human Connectome Project to examine the spatiotemporal architecture of cognition in the human brain. By investigating the spatial, dynamic and molecular signatures of system-wide neural activity across a range of cognitive tasks, we show that large-scale neuronal activity converges onto a low dimensional manifold that facilitates the dynamic execution of erse task states. Flow within this attractor space is associated with dissociable cognitive functions, and with unique patterns of network-level topology and information processing complexity. The axes of the low-dimensional neurocognitive architecture align with regional differences in the density of neuromodulatory receptors, which in turn relate to distinct signatures of network controllability estimated from the structural connectome. These results advance our understanding of functional brain organization by emphasizing the interface between low dimensional neural activity, network topology, neuromodulatory systems and cognitive function. A erse set of neuromodulators facilitates the formation of a dynamic, low-dimensional integrative core in the brain that is recruited by erse cognitive demands
Reduced inferior frontal gyrus activation during response inhibition to emotional stimuli in youth at high risk of bipolar disorder
Publisher: Elsevier BV
Date: 07-2013
DOI: 10.1016/J.BIOPSYCH.2012.11.004
Abstract: Functional brain imaging of young people at increased genetic risk for bipolar disorder provides a means of identifying potential endophenotypes for this condition. Dysfunctional neural mechanisms for the cognitive control of emotion are implicated in the genetic predisposition to bipolar disorder, with aberrant activity in frontocortical, striatal, and limbic brain regions previously reported in subjects with established bipolar disorder during inhibitory and emotion processing tasks. Functional brain activity during inhibition of emotional material in young people at increased genetic risk for bipolar disorder was investigated using a facial-emotion go/no-go task during functional magnetic resonance imaging. Data from 47 genetically high-risk in iduals aged 18 to 30 years with at least one first-degree relative with bipolar disorder were compared with 49 control subjects (within the same age range but without a family history of bipolar disorder or other severe mental illness). Whole-brain corrected analyses revealed a highly specific and significant lack of recruitment of the inferior frontal gyrus when inhibiting responses to fearful faces in the high-risk participants compared with control subjects (p = .011, family-wise error, peak voxel). Impaired inhibitory function of the inferior frontal cortex may represent a trait marker of vulnerability to bipolar disorder. That this finding was revealed during inhibition of emotional material further implicates dysregulated frontolimbic brain networks as a potential neurocognitive endophenotype for bipolar disorder and provides evidence for pre-existing functional disturbances in those at high genetic risk for bipolar disorder.
Longitudinal changes in neural gain and its relationship to cognitive control trajectory in young adults with early psychosis
Publisher: Springer Science and Business Media LLC
Date: 03-03-2023
DOI: 10.1038/S41398-023-02381-X
Abstract: The mixed cognitive outcomes in early psychosis (EP) have important implications for recovery. In this longitudinal study, we asked whether baseline differences in the cognitive control system (CCS) in EP participants would revert toward a normative trajectory seen in healthy controls (HC). Thirty EP and 30 HC undertook functional MRI at baseline using the multi-source interference task—a paradigm that selectively introduces stimulus conflict—and 19 in each group repeated the task at 12 months. Activation of the left superior parietal cortex normalized over time for the EP group, relative to HC, coincident with improvements in reaction time and social-occupational functioning. To examine these group and timepoint differences, we used dynamic causal modeling to infer changes in effective connectivity between regions underlying the MSIT task execution, namely visual, anterior insula, anterior cingulate, and superior parietal cortical regions. To resolve stimulus conflict, EP participants transitioned from an indirect to a direct neuromodulation of sensory input to the anterior insula over timepoints, though not as strongly as HC participants. Stronger direct nonlinear modulation of the anterior insula by the superior parietal cortex at follow-up was associated with improved task performance. Overall, normalization of the CCS through adoption of more direct processing of complex sensory input to the anterior insula, was observed in EP after 12 months of treatment. Such processing of complex sensory input reflects a computational principle called gain control, which appears to track changes in cognitive trajectory within the EP group.
An approach for automatically measuring facial activity in depressed subjects
Publisher: IEEE
Date: 09-2009
Dose‐dependent modulation of the visually evoked N1/N170 by perceptual surprise: a clear demonstration of prediction‐error signalling
Publisher: Wiley
Date: 16-04-2018
DOI: 10.1111/EJN.13920
Abstract: Prediction-error checking processes play a key role in predictive coding models of perception. However, neural indices of such processes have yet to be unambiguously demonstrated. To date, experimental paradigms aiming to study such phenomena have relied upon the relative frequency of stimulus repeats and/or 'unexpected' events that are physically different from 'expected' events. These features of experimental design leave open alternative explanations for the observed effects. A definitive demonstration requires that presumed prediction error-related responses should show contextual dependency (rather than simply effects of frequency or repetition) and should not be attributable to low-level stimulus differences. Most importantly, prediction-error signals should show dose dependency with respect to the degree to which expectations are violated. Here, we exploit a novel experimental paradigm specifically designed to address these issues, using it to interrogate early latency event-related potentials (ERPs) to contextually expected and unexpected visual stimuli. In two electroencephalography (EEG) experiments, we demonstrate that an N1/N170 evoked potential is robustly modulated by unexpected perceptual events ('perceptual surprise') and shows dose-dependent sensitivity with respect to both the influence of prior information and the extent to which expectations are violated. This advances our understanding of perceptual predictions in the visual domain by clearly identifying these evoked potentials as an index of visual surprise.
Quantifying dynamic facial expressions under naturalistic conditions
Publisher: eLife Sciences Publications, Ltd
Date: 31-08-2022
DOI: 10.7554/ELIFE.79581
Abstract: Facial affect is expressed dynamically – a giggle, grimace, or an agitated frown. However, the characterisation of human affect has relied almost exclusively on static images. This approach cannot capture the nuances of human communication or support the naturalistic assessment of affective disorders. Using the latest in machine vision and systems modelling, we studied dynamic facial expressions of people viewing emotionally salient film clips. We found that the apparent complexity of dynamic facial expressions can be captured by a small number of simple spatiotemporal states – composites of distinct facial actions, each expressed with a unique spectral fingerprint. Sequential expression of these states is common across in iduals viewing the same film stimuli but varies in those with the melancholic subtype of major depressive disorder. This approach provides a platform for translational research, capturing dynamic facial expressions under naturalistic conditions and enabling new quantitative tools for the study of affective disorders and related mental illnesses.
Metastable brain waves
Publisher: Springer Science and Business Media LLC
Date: 05-03-2019
DOI: 10.1038/S41467-019-08999-0
Abstract: Traveling patterns of neuronal activity—brain waves—have been observed across a breadth of neuronal recordings, states of awareness, and species, but their emergence in the human brain lacks a firm understanding. Here we analyze the complex nonlinear dynamics that emerge from modeling large-scale spontaneous neural activity on a whole-brain network derived from human tractography. We find a rich array of three-dimensional wave patterns, including traveling waves, spiral waves, sources, and sinks. These patterns are metastable, such that multiple spatiotemporal wave patterns are visited in sequence. Transitions between states correspond to reconfigurations of underlying phase flows, characterized by nonlinear instabilities. These metastable dynamics accord with empirical data from multiple imaging modalities, including electrical waves in cortical tissue, sequential spatiotemporal patterns in resting-state MEG data, and large-scale waves in human electrocorticography. By moving the study of functional networks from a spatially static to an inherently dynamic (wave-like) frame, our work unifies apparently erse phenomena across functional neuroimaging modalities and makes specific predictions for further experimentation.
Cortical responses to a graded working memory challenge predict functional decline in mild cognitive impairment
Publisher: Elsevier BV
Date: 07-2011
DOI: 10.1016/J.BIOPSYCH.2011.03.006
Abstract: Early detection of progressive cognitive decline offers an opportunity for preventative interventions with enormous public health implications. Functional neuroimaging during cognitive activity in in iduals at risk of dementia has the potential to advance this objective. In a prior study, we evaluated the utility of a novel functional magnetic resonance imaging paradigm that incorporated a graded working memory (WM) task to detect changes associated with mild cognitive impairment (MCI). We observed greater deactivation of posteromedial cortex (PMC) under conditions of increased WM load in MCI compared with control subjects. Our objective here is to test whether this paradigm can predict ensuing functional decline. Thirty in iduals with MCI who underwent baseline functional magnetic resonance image scanning were followed clinically for 2 years. Multiple linear regression analyses were used to determine whether deactivation in PMC under increased load at baseline independently predicted decline in instrumental activities of daily living (IADL). Greater deactivation in PMC to increased load predicted greater decline in IADL after controlling for baseline clinical severity, MCI subtype, apolipoprotein ε4 carrier status, gray matter, PMC and hippoc al volumes, and task performance. Increased deactivation observed at baseline was a harbinger of subsequent functional decline as measured by IADL in a cohort with MCI. This graded WM challenge may operate like a memory stress test by producing a threshold effect beyond which abnormal deactivation is elicited in MCI subjects who are at greatest risk of functional decline.
The heavy tail of the human brain
Publisher: Elsevier BV
Date: 04-2015
DOI: 10.1016/J.CONB.2014.10.014
Abstract: Fluctuating oscillations are a ubiquitous feature of neurophysiology. Are the litude fluctuations of neural oscillations chance excursions drawn randomly from a normal distribution, or do they tell us more? Recent empirical research suggests that the occurrence of 'anomalous' (high litude) oscillations imbues their probability distributions with a heavier tail than the standard normal distribution. However, not all heavy tails are the same. We provide canonical ex les of different heavy-tailed distributions in cortical oscillations and discuss the corresponding mechanisms that each suggest, ranging from criticality to multistability, memory, bifurcations, and multiplicative noise. Their existence suggests that the brain is a strongly correlated complex system that employs many different functional mechanisms, and that likewise, we as scientists should refrain from methodological monism.
A comparative study of different classifiers for detecting depression from spontaneous speech
Publisher: IEEE
Date: 05-2013
Nonlinear phase desynchronization in human electroencephalographic data
Publisher: Wiley
Date: 08-01-2002
DOI: 10.1002/HBM.10011
Effective connectivity of the anterior hippocampus predicts recollection confidence during natural memory retrieval
Publisher: Springer Science and Business Media LLC
Date: 19-11-2018
DOI: 10.1038/S41467-018-07325-4
Abstract: Human interactions with the world are influenced by memories of recent events. This effect, often triggered by perceptual cues, occurs naturally and without conscious effort. However, the neuroscience of involuntary memory in a dynamic milieu has received much less attention than the mechanisms of voluntary retrieval with deliberate purpose. Here, we investigate the neural processes driven by naturalistic cues that relate to, and presumably trigger the retrieval of recent experiences. Viewing the continuation of recently viewed clips evokes greater bilateral activation in anterior hippoc us, precuneus and angular gyrus than naïve clips. While these regions manifest reciprocal connectivity, continued viewing specifically modulates the effective connectivity from the anterior hippoc us to the precuneus. The strength of this modulation predicts participants’ confidence in later voluntary recall of news details. Our study reveals network mechanisms of dynamic, involuntary memory retrieval and its relevance to metacognition in a rich context resembling everyday life.
Functional connectivity analysis of multiplex muscle network across frequencies
Publisher: IEEE
Date: 07-2017
A Video-Based Facial Behaviour Analysis Approach to Melancholia
Publisher: IEEE
Date: 05-2017
DOI: 10.1109/FG.2017.94
Scale-free bursting in human cortex following hypoxia at birth
Publisher: Society for Neuroscience
Date: 07-05-2014
DOI: 10.1523/JNEUROSCI.4701-13.2014
Abstract: The human brain is fragile in the face of oxygen deprivation. Even a brief interruption of metabolic supply at birth challenges an otherwise healthy neonatal cortex, leading to a cascade of homeostatic responses. During recovery from hypoxia, cortical activity exhibits a period of highly irregular electrical fluctuations known as burst suppression. Here we show that these bursts have fractal properties, with power-law scaling of burst sizes across a remarkable 5 orders of magnitude and a scale-free relationship between burst sizes and durations. Although burst waveforms vary greatly, their average shape converges to a simple form that is asymmetric at long time scales. Using a simple computational model, we argue that this asymmetry reflects activity-dependent changes in the excitatory–inhibitory balance of cortical neurons. Bursts become more symmetric following the resumption of normal activity, with a corresponding reorganization of burst scaling relationships. These findings place burst suppression in the broad class of scale-free physical processes termed crackling noise and suggest that the resumption of healthy activity reflects a fundamental reorganization in the relationship between neuronal activity and its underlying metabolic constraints.
Spatiotemporal wavelet resampling for functional neuroimaging data
Publisher: Wiley
Date: 16-06-2004
DOI: 10.1002/HBM.20045
The independent influences of age and education on functional brain networks and cognition in healthy older adults
Publisher: Cold Spring Harbor Laboratory
Date: 25-06-2017
DOI: 10.1101/154898
Abstract: Healthy ageing is accompanied by a constellation of changes in cognitive processes and alterations in functional brain networks. The relationships between brain networks and cognition during ageing in later life are moderated by demographic and environmental factors, such as prior education, in a poorly understood manner. Using multivariate analyses, we identify three latent patterns (or modes) linking resting-state functional connectivity to demographic and cognitive measures in 101 cognitively-normal elders. The first mode ( p =0.00043) captures an opposing association between age and core cognitive processes such as attention and processing speed on functional connectivity patterns. The functional subnetwork expressed by this mode links bilateral sensorimotor and visual regions through key areas such as the parietal operculum. A strong, independent association between years of education and functional connectivity loads onto a second mode ( p =0.012), characterised by the involvement of key hub-regions. A third mode ( p =0.041) captures weak, residual brain-behaviour relations. Our findings suggest that circuits supporting lower-level cognitive processes are most sensitive to the influence of age in healthy older adults. Education, and to a lesser extent, executive functions, load independently onto functional networks - suggesting that the moderating effect of education acts upon networks distinct from those vulnerable with ageing. This has important implications in understanding the contribution of education to cognitive reserve during healthy ageing.
Using Computational Neuroscience to Define Common Input to Spinal Motor Neurons
Publisher: Frontiers Media SA
Date: 21-06-2016
Construction of multivariate surrogate sets from nonlinear data using the wavelet transform
Publisher: Elsevier BV
Date: 08-2003
Author response: Quantifying dynamic facial expressions under naturalistic conditions
Publisher: eLife Sciences Publications, Ltd
Date: 21-07-2022
Questions and controversies in the study of time-varying functional connectivity in resting fMRI
Publisher: MIT Press - Journals
Date: 2020
DOI: 10.1162/NETN_A_00116
Abstract: The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain’s functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as “dynamic” or time-varying functional connectivity TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.
Modeling the Impact of Lesions in the Human Brain
Publisher: Public Library of Science (PLoS)
Date: 12-06-2009
Dynamic and stochastic models of neuroimaging data: A comment on Lohmann et al.
Publisher: Elsevier BV
Date: 07-2013
DOI: 10.1016/J.NEUROIMAGE.2012.02.047
Abstract: DCM is a platform for inferring the architecture of dynamical systems, combining a user-dependent model specification step with a Bayesian model selection scheme. In their critique of the model selection procedure, Lohmann et al confine themselves to models generated from the classic bilinear deterministic DCM. Although brief reference is made to recent modeling advances, such as stochastic DCM and nonlinear DCM, these are negatively cast as guilty of further exploding the combinatorial problem that is proposed to plague model selection. Yet this is only a problem if a naïve approach is adopted towards the model generation process. Where the user draws on prior knowledge of the system being modeled and the statistical properties of the particular data set, these advances can be employed precisely to address the type of concerns Lohmann et al raise in their exemplar analysis (Fig. 6). This note provides a putative generative model for their data by adding stochastic effects, using independent evidence to increase its biological plausibility and challenging the notion that model fit should be assessed using simple linear correlations. Rather than encouraging reliance on future developments in imaging hardware and data-driven multivariate algorithms, informed engagement with causal models of neuronal dynamics allows imaging researchers develop detailed theories of brain function across a broad range of data sets and cognitive phenomena.
Detection and description of non-linear interdependence in normal multichannel human EEG data
Publisher: Elsevier BV
Date: 05-2002
DOI: 10.1016/S1388-2457(02)00051-2
Abstract: This study examines human scalp electroencephalographic (EEG) data for evidence of non-linear interdependence between posterior channels. The spectral and phase properties of those epochs of EEG exhibiting non-linear interdependence are studied. Scalp EEG data was collected from 40 healthy subjects. A technique for the detection of non-linear interdependence was applied to 2.048 s segments of posterior bipolar electrode data. Amplitude-adjusted phase-randomized surrogate data was used to statistically determine which EEG epochs exhibited non-linear interdependence. Statistically significant evidence of non-linear interactions were evident in 2.9% (eyes open) to 4.8% (eyes closed) of the epochs. In the eyes-open recordings, these epochs exhibited a peak in the spectral and cross-spectral density functions at about 10 Hz. Two types of EEG epochs are evident in the eyes-closed recordings one type exhibits a peak in the spectral density and cross-spectrum at 8 Hz. The other type has increased spectral and cross-spectral power across faster frequencies. Epochs identified as exhibiting non-linear interdependence display a tendency towards phase interdependencies across and between a broad range of frequencies. Non-linear interdependence is detectable in a small number of multichannel EEG epochs, and makes a contribution to the alpha rhythm. Non-linear interdependence produces spatially distributed activity that exhibits phase synchronization between oscillations present at different frequencies. The possible physiological significance of these findings are discussed with reference to the dynamical properties of neural systems and the role of synchronous activity in the neocortex.
The rhythms of steady posture: Motor commands as spatially organized oscillation patterns
Publisher: Elsevier BV
Date: 12-2015
Detergency Requirements of Future Diesel Injection Systems
Publisher: SAE International
Date: 24-10-2005
DOI: 10.4271/2005-01-3901
The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields
Publisher: Public Library of Science (PLoS)
Date: 29-08-2008
Possibilities for the prevention and treatment of cognitive impairment and dementia
Publisher: Royal College of Psychiatrists
Date: 05-2007
DOI: 10.1192/BJP.BP.106.033407
Abstract: The human brain has a remarkable capacity for plasticity, but does it have the capacity for repair and/or regeneration? On the basis of controversial new evidence we speculate that the answer may be ‘yes', and suggest that clinicians should therefore approach cognitive impairment and dementia with a new, cautious optimism.
Modeling magnification and anisotropy in the primate foveal confluence
Publisher: Public Library of Science (PLoS)
Date: 29-01-2010
Neurobiologically Realistic Determinants of Self-Organized Criticality in Networks of Spiking Neurons
Publisher: Public Library of Science (PLoS)
Date: 02-06-2011
Simulation of Neuronal Death and Network Recovery in a Computational Model of Distributed Cortical Activity
Publisher: Elsevier BV
Date: 03-2009
How Synchronization Protects from Noise
Publisher: Public Library of Science (PLoS)
Date: 15-01-2010
Interhemispheric white matter integrity in young people with bipolar disorder and at high genetic risk
Publisher: Cambridge University Press (CUP)
Date: 13-06-2016
DOI: 10.1017/S0033291716001161
Abstract: White matter (WM) impairments have been reported in patients with bipolar disorder (BD) and those at high familial risk of developing BD. However, the distribution of these impairments has not been well characterized. Few studies have examined WM integrity in young people early in the course of illness and in in iduals at familial risk who have not yet passed the peak age of onset. WM integrity was examined in 63 BD subjects, 150 high-risk (HR) in iduals and 111 participants with no family history of mental illness (CON). All subjects were aged 12 to 30 years. This young BD group had significantly lower fractional anisotropy within the genu of the corpus callosum (CC) compared with the CON and HR groups. Moreover, the abnormality in the genu of the CC was also present in HR participants with recurrent major depressive disorder (MDD) ( n = 16) compared with CON participants. Our findings provide important validation of interhemispheric abnormalities in BD patients. The novel finding in HR subjects with recurrent MDD – a group at particular risk of future hypo/manic episodes – suggests that this may potentially represent a trait marker for BD, though this will need to be confirmed in longitudinal follow-up studies.
Head Pose and Movement Analysis as an Indicator of Depression
Publisher: IEEE
Date: 09-2013
DOI: 10.1109/ACII.2013.53
Effects of mnemonic load on cortical activity during visual working memory: Linking ongoing brain activity with evoked responses
Publisher: Elsevier BV
Date: 09-2013
DOI: 10.1016/J.IJPSYCHO.2013.04.001
Abstract: The mechanisms generating task-locked changes in cortical potentials remain poorly understood, despite a wealth of research. It has recently been proposed that ongoing brain oscillations are not symmetric, so that task-related litude modulations generate a baseline shift that does not average out, leading to slow event-related potentials. We test this hypothesis using multivariate methods to formally assess the co-variation between task-related evoked potentials and spectral changes in scalp EEG during a visual working memory task, which is known to elicit both evoked and sustained cortical activities across broadly distributed cortical regions. 64-channel EEG data were acquired from eight healthy human subjects who completed a visuo-spatial associative working memory task as memory load was parametrically increased from easy to hard. As anticipated, evoked activity showed a complex but robust spatio-temporal waveform maximally expressed bilaterally in the parieto-occipital and anterior midline regions, showing robust effects of memory load that were specific to the stage of the working memory trial. Similarly, memory load was associated with robust spectral changes in the theta and alpha range, throughout encoding in posterior regions and through maintenance and retrieval in anterior regions, consistent with the additional resources required for decision making in prefrontal cortex. Analysis of the relationship between event-related changes in slow potentials and cortical rhythms, using partial least squares, is indeed consistent with the notion that the former make a causal contribution to the latter.
Towards a statistical test for functional connectivity dynamics
Publisher: Elsevier BV
Date: 07-2015
DOI: 10.1016/J.NEUROIMAGE.2015.03.047
Abstract: Sliding-window correlation is an emerging method for mapping time-resolved, resting-state functional connectivity. To avoid mapping spurious connectivity fluctuations (false positives), Leonardi and Van De Ville recently recommended choosing a window length exceeding the longest wavelength composing the BOLD signal, usually assumed to be ~100s. Here, we provide further statistical support for this rule of thumb. However, we demonstrate that non-stationary fluctuations in functional connectivity can in theory be detected with much shorter window lengths (e.g. 40s), while maintaining nominal control of false positives. We find that statistical power is near-maximal for window lengths chosen according to Leonardi and Van De Ville's rule of thumb. Furthermore, we lay some foundations for a parametric test to identify non-stationary fluctuations in functional connectivity, also noting limitations of the sinusoidal model upon which our work, and the work of Leonardi and Van De Ville, is based. Most notably, our analytical results pertain to covariances, as does our statistical test, whereas functional connectivity is more commonly measured using correlations.
The Brain Dynamics Toolbox for Matlab
Publisher: Cold Spring Harbor Laboratory
Date: 14-11-2017
DOI: 10.1101/219329
Abstract: Nonlinear dynamical systems are increasingly informing both theoretical and empirical branches of neuroscience. The Brain Dynamics Toolbox provides an interactive simulation platform for exploring such systems in M ATLAB . It supports the major classes of differential equations that arise in computational neuroscience: Ordinary Differential Equations, Delay Differential Equations and Stochastic Differential Equations. The design of the graphical interface fosters intuitive exploration of the dynamics while still supporting scripted parameter explorations and large-scale simulations. Although the toolbox is intended for dynamical models in computational neuroscience, it can be applied to dynamical systems from any domain.
Eye movement analysis for depression detection
Publisher: IEEE
Date: 09-2013
The foveal confluence in human visual cortex
Publisher: Society for Neuroscience
Date: 15-07-2009
The reorganization of corticomuscular coherence during a transition between sensorimotor states
Publisher: Elsevier BV
Date: 10-2014
DOI: 10.1016/J.NEUROIMAGE.2014.06.050
Abstract: Recent research suggests that neural oscillations in different frequency bands support distinct and sometimes parallel processing streams in neural circuits. Studies of the neural dynamics of human motor control have primarily focused on oscillations in the beta band (15-30 Hz). During sustained muscle contractions, corticomuscular coherence is mainly present in the beta band, while coherence in the alpha (8-12 Hz) and gamma (30-80 Hz) bands has not been consistently found. Here we test the hypothesis that the frequency of corticomuscular coherence changes during transitions between sensorimotor states. Corticomuscular coherence was investigated in twelve participants making rapid transitions in force output between two targets. Corticomuscular coherence was present in the beta band during sustained contractions but vanished before movement onset, being replaced by transient synchronization in the alpha and gamma bands during dynamic force output. Analysis of the phase spectra suggested a time delay from muscle to cortex for alpha-band coherence, by contrast to a time delay from cortex to muscle for gamma-band coherence, indicating afferent and efferent corticospinal interactions respectively. Moreover, alpha and gamma-band coherence revealed distinct spatial topologies, suggesting different generative mechanisms. Coherence in the alpha and gamma bands was almost exclusively confined to trials showing a movement overshoot, suggesting a functional role related to error correction. We interpret the dual-band synchronization in the alpha and gamma bands as parallel streams of corticospinal processing involved in parsing prediction errors and generating new motor predictions.
Editorial: State-dependent brain computation
Publisher: Frontiers Media SA
Date: 23-06-2015
A Novel Method for the Topographic Analysis of Neural Activity Reveals Formation and Dissolution of ‘Dynamic Cell Assemblies’
Publisher: Springer Science and Business Media LLC
Date: 2004
DOI: 10.1023/B:JCNS.0000004841.66897.7D
Abstract: The study of synchronous oscillations in neural systems is a very active area of research. However, cognitive function may depend more crucially upon a dynamic alternation between synchronous and desynchronous activity rather than synchronous behaviour per se. The principle aim of this study is to develop and validate a novel method of quantifying this complex process. The method permits a direct mapping of phase synchronous dynamics and desynchronizing bursts in the spatial and temporal domains. Two data sets are analyzed: Numeric data from a model of a sparsely coupled neural cell assembly and experimental data consisting of scalp-recorded EEG from 40 human subjects. In the numeric data, the approach enables the demonstration of complex relationships between cluster size and temporal duration that cannot be detected with other methods. Dynamic patterns of phase-clustering and desynchronization are also demonstrated in the experimental data. It is further shown that in a significant proportion of the recordings, the pattern of dynamics exhibits nonlinear structure. We argue that this procedure provides a 'natural partitioning' of ongoing brain dynamics into topographically distinct synchronous epochs which may be integral to the brain's adaptive function. In particular, the character of transitions between consecutive synchronous epochs may reflect important aspects of information processing and cognitive flexibility.
Evaluating the first 1000 patients referred to a specialist depression clinic: A case for tertiary referral facilities
Publisher: Elsevier BV
Date: 06-1970
DOI: 10.1016/J.JAD.2010.11.004
Abstract: We report on the assessment and outcome of the first 1000 patients referred to a tertiary referral depression clinic established to assess the utility of diagnostic sub-typing on clinical course of illness. Diagnostic, treatment recommendations, prognostic judgments and 12-week outcome data were examined. Nearly 40% of those with a primary mood disorder were diagnosed with bipolar disorder, of whom three-quarters received such a diagnosis for the first time. Alternative diagnoses or formulations were provided for 68% of the total s le, with the therapeutic paradigm altered for the majority (86%) of patients. Improvement rates were indicative of a higher level of improvement in those diagnosed with bipolar disorder (some 70%) compared to those with unipolar disorders (some 60%). Overall, however, rates of 'full remission' were low, being 2% and up to 12% for bipolar and unipolar patients respectively and perhaps reflecting the tertiary nature of the assessing clinical facility. Baseline clinician predictions were in the order of 60% accuracy in predicting outcome, irrespective of diagnostic grouping. Anticipation factors (e.g. attending a specialist tertiary referral service) may have contributed non-specifically to outcome. Use of clinician-derived diagnoses rather than strict DSM-IV criteria limits comparisons to other studies. The high rates of a first-time bipolar diagnosis suggest that detection and diagnosis of this condition continues to be problematic. Low remission rates underline the chronic nature of many mood disorders, and the need for ongoing management given the high risk of relapse. Our findings offer support for the importance of identifying bipolar disorder and distinguishing depressive sub-types in order to shape more targeted treatments, a task that might be advanced by the establishment of more tertiary referral services.
A Discrete Model to Study Reaction-Diffusion-Mechanics Systems
Publisher: Public Library of Science (PLoS)
Date: 11-07-2011
Screening for bipolar disorder: The utility and comparative properties of the MSS and MDQ measures
Publisher: Elsevier BV
Date: 07-2008
DOI: 10.1016/J.JAD.2007.11.003
Abstract: Our objective was to further determine the diagnostic utility of the Mood Swings Survey (MSS) in distinguishing bipolar and unipolar disorders, and draw comparisons between this measure and the widely-used Mood Disorder Questionnaire (MDQ). A total of 247 consecutively recruited patients attending the Black Dog Institute Depression Clinic were administered the Mood Swings Survey (MSS) as part of a computerized Mood Assessment Program (MAP), in addition to undergoing clinical assessment by two independent psychiatrists. The MDQ, along with a structured interview assessing DSM-IV criteria for bipolar disorder, was administered to a sub-s le of patients. The MSS-46 demonstrates comparable sensitivity and specificity to the MDQ (86.5% and 60.0% vs. 78.8% and 71.4%) when using pre-established cut-off scores. MSS diagnoses embedded within the computerized program correctly classified 82.2% of cases when compared to clinician diagnosis. Optimal cut-off scores derived in the current s le were > or = 35 (Se=88.5%, Sp=60.0%) for the MSS-46, and > or = 7 (Se=78.8%, Sp=71.4%) for the MDQ, indicating acceptable stability of cut-off scores in differing s les for both measures. ROC analyses compromised 'true' estimates of MSS sensitivity and specificity as a number of patients who did not affirm the initial screener question were excluded from these analyses. Further work is required to evaluate the diagnostic utility of the MSS in differing clinical and community s les to determine the stability of its cut-off score and to refine the item set.
On the genesis of spike-wave oscillations in a mean-field model of human thalamic and corticothalamic dynamics
Publisher: Elsevier BV
Date: 07-2006
Fixational eye movements during viewing of dynamic natural scenes
Publisher: Frontiers Media SA
Date: 2013
Topographic organization of the human subcortex unveiled with functional connectivity gradients
Publisher: Springer Science and Business Media LLC
Date: 28-09-2020
Inching toward Bethlehem: Mapping melancholia
Publisher: Elsevier BV
Date: 06-2010
DOI: 10.1016/J.JAD.2009.10.001
Abstract: As melancholia has resisted symptom-based definition, this report considers possible explanations and options for moving forward. Clinician-assigned melancholic and non-melancholic groups were initially compared to refine a candidate set of differentiating symptoms alone for examination against a set of non-clinical validators. Analyses then examined the capacity of both the refined symptom and validator sets to discriminate the assigned melancholic and non-melancholic subjects. Subjects completed measures assessing symptoms and correlates (putative validators) of diagnostic sub-type, and were assessed independently by two psychiatrists. Analyses identified 14 severity-based symptoms as discriminating clinically-diagnosed groups - with melancholic subjects differing significantly from non-melancholic subjects across a number of validators. Such symptom-based discrimination was superior to DSM-IV and Newcastle Index assignment in a study sub-set. While the refined symptom set had an overall accurate classificatory rate of 68%, use of the combined sets of refined symptoms and validators improved classification to 80%. Melancholia definition is improved by the use of correlates in addition to depressive symptoms, suggesting that melancholia may be mapped more precisely by use of multiple co-ordinates or data sources.
Bias and discriminability during emotional signal detection in melancholic depression
Publisher: Springer Science and Business Media LLC
Date: 27-04-2014
Understanding the Brain, By Default
Publisher: Elsevier BV
Date: 05-2018
DOI: 10.1016/J.TINS.2018.03.004
Abstract: In 2001 Raichle and colleagues showed that, at rest, brain activity fluctuates near a metabolically active equilibrium: a 'default mode' of brain function. This finding broke ranks with the prevailing 'task-rest' dichotomy to position the brain as continuously active, balancing the deployment of resources according to current and anticipated needs.
White matter alterations in the internal capsule and psychomotor impairment in melancholic depression
Publisher: Public Library of Science (PLoS)
Date: 19-04-2018
Posterior compensatory network in cognitively intact elders with hippocampal atrophy
Publisher: Wiley
Date: 11-02-2015
DOI: 10.1002/HIPO.22395
Abstract: Functional compensation in late life is poorly understood but may be vital to understanding long-term cognitive trajectories. To study this we first established an empirically derived threshold to distinguish hippoc al atrophy in those with Mild Cognitive Impairment (MCI n = 34) from those with proficient cognition (PRO n = 22), using data from a population-based cohort. Next, to identify compensatory networks we compared cortical activity patterns during a graded spatial working memory (SWM) task in only cognitively proficient in iduals, either with (PROATR ) or without hippoc al atrophy (PRONIL ). Multivariate Partial Least Squares analyses revealed that these groups engaged spatially distinct SWM-related networks. In those with hippoc al atrophy and under conditions of basic-SWM demand, expression of a posterior compensatory network (PCN) comprised calcarine and posterior parietal cortex strongly correlated with superior SWM performance (r = -0.96). In these in iduals, basic level SWM response times were faster and no less accurate than in those with no hippoc al atrophy. Cognitively proficient older in iduals with hippoc al atrophy may, therefore, uniquely engage posterior brain areas when performing simple spatial working memory tasks.
Author response: Reconfiguration of functional brain networks and metabolic cost converge during task performance
Publisher: eLife Sciences Publications, Ltd
Date: 20-03-2020
Small-world networks and disturbed functional connectivity in schizophrenia
Publisher: Elsevier BV
Date: 10-2006
DOI: 10.1016/J.SCHRES.2006.06.028
Abstract: Disturbances in "functional connectivity" have been proposed as a major pathophysiological mechanism for schizophrenia, and in particular, for cognitive disorganization. Detection and estimation of these disturbances would be of clinical interest. Here we characterize the spatial pattern of functional connectivity by computing the "synchronization likelihood" (SL) of EEG at rest and during performance of a 2Back working memory task using letters of the alphabet presented on a PC screen in subjects with schizophrenia and healthy controls. The spatial patterns of functional connectivity were then characterized with graph theoretical measures to test whether a disruption of an optimal spatial pattern ("small-world") of the functional connectivity network underlies schizophrenia. Twenty stabilized patients with schizophrenia, who were able to work, and 20 healthy controls participated in the study. During the working memory (WM) task healthy subjects exhibited small-world properties (a combination of local clustering and high overall integration of the functional networks) in the alpha, beta and gamma bands. These properties were not present in the schizophrenia group. These findings are in accordance with a partially inadequate organization of neuronal networks in subjects with schizophrenia. This method could be helpful for diagnosis and evaluation of the severity of the disease, as well as understanding the pathophysiologic mechanisms underlying cognitive dysfunction in schizophrenia.
Differentiation of Alzheimer's disease based on local and global parameters in personalized Virtual Brain models
Publisher: Elsevier BV
Date: 2018
Fragmentation: loss of global coherence or breakdown of modularity in functional brain architecture?
Publisher: Frontiers Media SA
Date: 2012
Quantifying dynamic facial expressions under naturalistic conditions
Publisher: Cold Spring Harbor Laboratory
Date: 10-05-2022
DOI: 10.1101/2022.05.08.490793
Abstract: Facial affect is expressed dynamically – a giggle, grimace, or an agitated frown. However, the characterization of human affect has relied almost exclusively on static images. This approach cannot capture the nuances of human communication or support the naturalistic assessment of affective disorders. Using the latest in machine vision and systems modelling, we studied dynamic facial expressions of people viewing emotionally salient film clips. We found that the apparent complexity of dynamic facial expressions can be captured by a small number of simple spatiotemporal states - composites of distinct facial actions, each expressed with a unique spectral fingerprint. Sequential expression of these states is common across in iduals viewing the same film stimuli but varies in those with the melancholic subtype of major depressive disorder. This approach provides a platform for translational research, capturing dynamic facial expressions under naturalistic conditions and enabling new quantitative tools for the study of affective disorders and related mental illnesses.
State-switching and high-order spatiotemporal organization of dynamic functional connectivity are disrupted by Alzheimer’s disease
Publisher: MIT Press
Date: 17-10-2023
DOI: 10.1162/NETN_A_00332
Abstract: Spontaneous activity during the resting state, tracked by BOLD fMRI imaging, or shortly rsfMRI, gives rise to brain-wide dynamic patterns of inter-regional correlations, whose structured flexibility relates to cognitive performance. Here we analyze resting state dynamic Functional Connectivity (dFC) in a cohort of older adults, including amnesic Mild Cognitive Impairment (aMCI, N = 34) and Alzheimer’s Disease (AD, N = 13) patients, as well as normal control (NC, N = 16) and cognitively “super-normal” (SN, N = 10) subjects. Using complementary state-based and state-free approaches, we find that resting state fluctuations of different functional links are not independent but are constrained by high-order correlations between triplets or quadruplets of functionally connected regions. When contrasting patients with healthy subjects, we find that dFC between cingulate and other limbic regions is increasingly bursty and intermittent when ranking the four groups from SNC to NC, aMCI and AD. Furthermore, regions affected at early stages of AD pathology are less involved in higher-order interactions in patient than in control groups, while pairwise interactions are not significantly reduced. Our analyses thus suggest that the spatiotemporal complexity of dFC organization is precociously degraded in AD and provides a richer window into the underlying neurobiology than time-averaged FC connections.
Putting the “dynamic” back into dynamic functional connectivity
Publisher: Cold Spring Harbor Laboratory
Date: 28-08-2017
DOI: 10.1101/181313
Abstract: The study of fluctuations in time-resolved functional connectivity is a topic of substantial current interest. As the term “ dynamic functional connectivity” implies, such fluctuations are believed to arise from dynamics in the neuronal systems generating these signals. While considerable activity currently attends to methodological and statistical issues regarding dynamic functional connectivity, less attention has been paid toward its candidate causes. Here, we review candidate scenarios for dynamic (functional) connectivity that arise in dynamical systems with two or more subsystems generalized synchronization, itinerancy (a form of metastability), and multistability. Each of these scenarios arise under different configurations of local dynamics and inter-system coupling: We show how they generate time series data with nonlinear and/or non-stationary multivariate statistics. The key issue is that time series generated by coupled nonlinear systems contain a richer temporal structure than matched multivariate (linear) stochastic processes. In turn, this temporal structure yields many of the phenomena proposed as important to large-scale communication and computation in the brain, such as phase- litude coupling, complexity and flexibility. The code for simulating these dynamics is available in a freeware software platform, the “Brain Dynamics Toolbox”.
Movie viewing elicits rich and reliable brain state dynamics
Publisher: Springer Science and Business Media LLC
Date: 05-10-2020
DOI: 10.1038/S41467-020-18717-W
Abstract: Adaptive brain function requires that sensory impressions of the social and natural milieu are dynamically incorporated into intrinsic brain activity. While dynamic switches between brain states have been well characterised in resting state acquisitions, the remodelling of these state transitions by engagement in naturalistic stimuli remains poorly understood. Here, we show that the temporal dynamics of brain states, as measured in fMRI, are reshaped from predominantly bistable transitions between two relatively indistinct states at rest, toward a sequence of well-defined functional states during movie viewing whose transitions are temporally aligned to specific features of the movie. The expression of these brain states covaries with different physiological states and reflects subjectively rated engagement in the movie. In sum, a data-driven decoding of brain states reveals the distinct reshaping of functional network expression and reliable state transitions that accompany the switch from resting state to perceptual immersion in an ecologically valid sensory experience.
Network structure of the human musculoskeletal system shapes neural interactions on multiple time scales
Publisher: American Association for the Advancement of Science (AAAS)
Date: 06-2018
Abstract: Changes in postural task result in a frequency-dependent reconfiguration of the multiplex muscle network.
Unexpected Course of Nonlinear Cardiac Interbeat Interval Dynamics during Childhood and Adolescence
Publisher: Public Library of Science (PLoS)
Date: 20-05-2011
Development of frontoparietal connectivity predicts longitudinal symptom changes in young people with autism spectrum disorder
Publisher: Springer Science and Business Media LLC
Date: 12-02-2019
DOI: 10.1038/S41398-019-0418-5
Abstract: Structural neuroimaging studies suggest altered brain maturation in autism spectrum disorder (ASD) compared with typically developing controls (TDC). However, the prognostic value of whole-brain structural connectivity analysis in ASD has not been established. Diffusion magnetic imaging data were acquired in 27 high-functioning young ASD participants (2 females) and 29 age-matched TDC (12 females age 8–18 years) at baseline and again following 3–7 years. Whole-brain structural connectomes were reconstructed from these data and analyzed using a longitudinal statistical model. We identified distinct patterns of widespread brain connections that exhibited either significant increases or decreases in connectivity over time ( p 0.001). There was a significant interaction between diagnosis and time in brain development ( p 0.001). This was expressed by a decrease in structural connectivity within the frontoparietal network—and its broader connectivity—in ASD during adolescence and early adulthood. Conversely, these connections increased with time in TDC. Crucially, stronger baseline connectivity in this subnetwork predicted a lower symptom load at follow-up ( p = 0.048), independent of the expression of symptoms at baseline. Our findings suggest a clinically meaningful relationship between the atypical development of frontoparietal structural connections and the dynamics of the autism phenotype through early adulthood. These results highlight a potential marker of future outcome.
Functional Dysconnection of the Inferior Frontal Gyrus in Young People With Bipolar Disorder or at Genetic High Risk
Publisher: Elsevier BV
Date: 04-2017
DOI: 10.1016/J.BIOPSYCH.2016.08.018
Abstract: Bipolar disorder (BD) is characterized by a dysregulation of affect and impaired integration of emotion with cognition. These traits are also expressed in probands at high genetic risk of BD. The inferior frontal gyrus (IFG) is a key cortical hub in the circuits of emotion and cognitive control, and it has been frequently associated with BD. Here, we studied resting-state functional connectivity of the left IFG in participants with BD and in those at increased genetic risk. Using resting-state functional magnetic resonance imaging we compared 49 young BD participants, 71 in iduals with at least one first-degree relative with BD (at-risk), and 80 control subjects. We performed between-group analyses of the functional connectivity of the left IFG and used graph theory to study its local functional network topology. We also used machine learning to study classification based solely on the functional connectivity of the IFG. In BD, the left IFG was functionally dysconnected from a network of regions, including bilateral insulae, ventrolateral prefrontal gyri, superior temporal gyri, and the putamen (p < .001). A small network incorporating neighboring insular regions and the anterior cingulate cortex showed weaker functional connectivity in at-risk than control participants (p < .006). These constellations of regions overlapped with frontolimbic regions that a machine learning classifier selected as predicting group membership with an accuracy significantly greater than chance. Functional dysconnectivity of the IFG from regions involved in emotional regulation may represent a trait abnormality for BD and could potentially aid clinical diagnosis.
A Unifying Explanation of Primary Generalized Seizures Through Nonlinear Brain Modeling and Bifurcation Analysis
Publisher: Oxford University Press (OUP)
Date: 09-11-2005
Abstract: The aim of this paper is to explain critical features of the human primary generalized epilepsies by investigating the dynamical bifurcations of a nonlinear model of the brain's mean field dynamics. The model treats the cortex as a medium for the propagation of waves of electrical activity, incorporating key physiological processes such as propagation delays, membrane physiology, and corticothalamic feedback. Previous analyses have demonstrated its descriptive validity in a wide range of healthy states and yielded specific predictions with regards to seizure phenomena. We show that mapping the structure of the nonlinear bifurcation set predicts a number of crucial dynamic processes, including the onset of periodic and chaotic dynamics as well as multistability. Quantitative study of electrophysiological data supports the validity of these predictions. Hence, we argue that the core electrophysiological and cognitive differences between tonic-clonic and absence seizures are predicted and interrelated by the global bifurcation diagram of the model's dynamics. The present study is the first to present a unifying explanation of these generalized seizures using the bifurcation analysis of a dynamical model of the brain.
Detecting depression: A comparison between spontaneous and read speech
Publisher: IEEE
Date: 05-2013
Altered neural synchronisation in major depressive disorders during emotional video viewing
Publisher: Frontiers Media SA
Date: 2015
Blankets at birth: Transitional objects
Publisher: Elsevier BV
Date: 09-2021
DOI: 10.1016/J.PLREV.2020.12.001
Abstract: Birth is accompanied by a complete reset of metabolic flows in the neonate, challenging the brain to fulfill the basic needs of life through action - breathing, feeding, crying. The perinatal period is fundamentally a transitional one, such that the basic conditions for thermodynamic self-regulation are re-established ex utero. Wright and Bourke lay out the core tenants of these conditions [1] the emergence of regularities in cortical geometry and activity that allow "crisp" states. Before this can occur - in the immediate perinatal phase - electrical recordings of neonatal cortex suggest it passes through a highly critical regime - a phase transition - with disordered statistical fingerprints. The resolution of this state is a necessary condition for the more stable metabolic conditions that support the conjectures of Wright and Bourke.
Human cognition involves the dynamic integration of neural activity and neuromodulatory systems
Publisher: Springer Science and Business Media LLC
Date: 21-01-2019
DOI: 10.1038/S41593-018-0312-0
Abstract: The human brain integrates erse cognitive processes into a coherent whole, shifting fluidly as a function of changing environmental demands. Despite recent progress, the neurobiological mechanisms responsible for this dynamic system-level integration remain poorly understood. Here we investigated the spatial, dynamic, and molecular signatures of system-wide neural activity across a range of cognitive tasks. We found that neuronal activity converged onto a low-dimensional manifold that facilitates the execution of erse task states. Flow within this attractor space was associated with dissociable cognitive functions, unique patterns of network-level topology, and in idual differences in fluid intelligence. The axes of the low-dimensional neurocognitive architecture aligned with regional differences in the density of neuromodulatory receptors, which in turn relate to distinct signatures of network controllability estimated from the structural connectome. These results advance our understanding of functional brain organization by emphasizing the interface between neural activity, neuromodulatory systems, and cognitive function.
Nonlinear interdependence in neural systems: Motivation, theory, and relevance
Publisher: Informa UK Limited
Date: 2002
DOI: 10.1080/00207450290026193
Abstract: In this article, we motivate models of medium to large-scale neural activity that place an emphasis on the modular nature of neocortical organization and discuss the occurrence of nonlinear interdependence in such models. On the basis of their functional, anatomical, and physiological properties, it is argued that cortical columns may be treated as the basic dynamical modules of cortical systems. Coupling between these columns is introduced to represent sparse long-range cortical connectivity. Thus, neocortical activity can be modeled as an array of weakly coupled dynamical subsystems. The behavior of such systems is represented by dynamical attractors, which may be fixed point, limit cycle, or chaotic in nature. If all the subsystems are perfectly identical, then the state of identical chaotic synchronization is a possible attractor for the array. Following the introduction of parameter variation across the array, such a state is not possible, although two other important nonlinear interdependences--generalized and phase synchronized--are possible. We suggest that an understanding of nonlinear interdependence may assist advances in models of neural activity and neuroscience time series analysis.
Mode-based morphometry: A multiscale approach to mapping human neuroanatomy
Publisher: Cold Spring Harbor Laboratory
Date: 27-02-2023
DOI: 10.1101/2023.02.26.529328
Abstract: Voxel-based morphometry (VBM) and surface-based morphometry (SBM) are two widely used neuroimaging techniques for investigating brain anatomy. These techniques rely on statistical inferences at in idual points (voxels or vertices), clusters of points, or a priori regions-of-interest. They are powerful tools for describing brain anatomy, but offer little insights into the generative processes that shape a particular set of findings. Moreover, they are restricted to a single spatial resolution scale, precluding the opportunity to distinguish anatomical variations that are expressed across multiple scales. Drawing on concepts from classical physics, here we develop an approach, called mode-based morphometry (MBM), that can describe any empirical map of anatomical variations in terms of the fundamental, resonant modes––eigenmodes––of brain anatomy, each tied to a specific spatial scale. Hence, MBM naturally yields a multiscale characterization of the empirical map, affording new opportunities for investigating the spatial frequency content of neuroanatomical variability. Using simulated and empirical data, we show that the validity and reliability of MBM are either comparable or superior to classical vertex-based SBM for capturing differences in cortical thickness maps between two experimental groups. Our approach thus offers a robust, accurate, and informative method for characterizing empirical maps of neuroanatomical variability that can be directly linked to a generative physical process.
Early detection of preterm intraventricular hemorrhage from clinical electroencephalography
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 10-2015
Muscle networks: Connectivity analysis of EMG activity during postural control
Publisher: Springer Science and Business Media LLC
Date: 04-12-2015
DOI: 10.1038/SREP17830
Abstract: Understanding the mechanisms that reduce the many degrees of freedom in the musculoskeletal system remains an outstanding challenge. Muscle synergies reduce the dimensionality and hence simplify the control problem. How this is achieved is not yet known. Here we use network theory to assess the coordination between multiple muscles and to elucidate the neural implementation of muscle synergies. We performed connectivity analysis of surface EMG from ten leg muscles to extract the muscle networks while human participants were standing upright in four different conditions. We observed widespread connectivity between muscles at multiple distinct frequency bands. The network topology differed significantly between frequencies and between conditions. These findings demonstrate how muscle networks can be used to investigate the neural circuitry of motor coordination. The presence of disparate muscle networks across frequencies suggests that the neuromuscular system is organized into a multiplex network allowing for parallel and hierarchical control structures.
Recent progress in the pharmacotherapy of bipolar disorder
Publisher: Future Medicine Ltd
Date: 07-2009
DOI: 10.2217/FNL.09.18
Abstract: Bipolar disorder is a common and highly disabling condition necessitating early and effective therapeutic intervention. This review focuses on recent progress in pharmacotherapies reported in the last few years. The recent literature suggests two distinct developmental themes. The first is the consolidation of knowledge concerning the role of the atypical antipsychotics and anticonvulsants in bipolar disorder, with increasing clarity regarding which actions are ‘class effects’ and which actions are, in contrast, specific to particular agents. The second theme is the first ‘glimmerings’ of the mood stabilizing efficacy of compounds with ‘novel’ actions, with tamoxifen being perhaps the agent of most interest. While demonstration of the efficacy of truly innovative compounds developed specifically for bipolar disorder has yet to occur, the gradual understanding of some of the critical pharmacological mechanisms of action of current agents suggests that this may not be too distant a reality.
Clinical Applications of Stochastic Dynamic Models of the Brain, Part I: A Primer
Publisher: Elsevier BV
Date: 04-2017
DOI: 10.1016/J.BPSC.2017.01.010
Abstract: Biological phenomena arise through interactions between an organism's intrinsic dynamics and stochastic forces-random fluctuations due to external inputs, thermal energy, or other exogenous influences. Dynamic processes in the brain derive from neurophysiology and anatomical connectivity stochastic effects arise through sensory fluctuations, brainstem discharges, and random microscopic states such as thermal noise. The dynamic evolution of systems composed of both dynamic and random effects can be studied with stochastic dynamic models (SDMs). This article, Part I of a two-part series, offers a primer of SDMs and their application to large-scale neural systems in health and disease. The companion article, Part II, reviews the application of SDMs to brain disorders. SDMs generate a distribution of dynamic states, which (we argue) represent ideal candidates for modeling how the brain represents states of the world. When augmented with variational methods for model inversion, SDMs represent a powerful means of inferring neuronal dynamics from functional neuroimaging data in health and disease. Together with deeper theoretical considerations, this work suggests that SDMs will play a unique and influential role in computational psychiatry, unifying empirical observations with models of perception and behavior.
Universal Entropy of Word Ordering Across Linguistic Families
Publisher: Public Library of Science (PLoS)
Date: 13-05-2011
Multivariate time–frequency analysis of electromagnetic brain activity during bimanual motor learning
Publisher: Elsevier BV
Date: 06-2007
DOI: 10.1016/J.NEUROIMAGE.2007.03.012
Abstract: Although the relationship between brain activity and motor performance is reasonably well established, the manner in which this relationship changes with motor learning remains incompletely understood. This paper presents a study of cortical modulations of event-related beta activity when participants learned to perform a complex bimanual motor task: 151 channel MEG data were acquired from nine healthy adults whilst learning a bimanual 3:5 polyrhythm. Sources of MEG activity were determined by means of synthetic aperture magnetometry that yielded locations and time courses of beta activities. The relationship between changes in performance and corresponding changes in event-related power were assessed using partial least squares. Behavioral data revealed that participants successfully learned to perform the 3:5 polyrhythm and that performance improvement was mainly achieved through the proper timing of the finger producing the slow rhythm. We found event-related modulation of beta power in the contralateral motor cortex that was inversely related to force output. The degree of beta modulation increased during the experiment - although the force level remained constant - and was positively correlated with motor performance, in particular for the motor cortex contralateral to the slow hand. These electrophysiological findings support the view that activity in motor cortex co-varies closely with behavioral changes over the course of learning.
Effective connectivity within the frontoparietal control network differentiates cognitive control and working memory
Publisher: Elsevier BV
Date: 02-2015
DOI: 10.1016/J.NEUROIMAGE.2014.11.039
Abstract: Cognitive control and working memory rely upon a common fronto-parietal network that includes the inferior frontal junction (IFJ), dorsolateral prefrontal cortex (dlPFC), pre-supplementary motor area/dorsal anterior cingulate cortex (pSMA/dACC), and intraparietal sulcus (IPS). This network is able to flexibly adapt its function in response to changing behavioral goals, mediating a wide range of cognitive demands. Here we apply dynamic causal modeling to functional magnetic resonance imaging data to characterize task-related alterations in the strength of network interactions across distinct cognitive processes. Evidence in favor of task-related connectivity dynamics was accrued across a very large space of possible network structures. Cognitive control and working memory demands were manipulated using a factorial combination of the multi-source interference task and a verbal 2-back working memory task, respectively. Both were found to alter the sensitivity of the IFJ to perceptual information, and to increase IFJ-to-pSMA/dACC connectivity. In contrast, increased connectivity from the pSMA/dACC to the IPS, as well as from the dlPFC to the IFJ, was uniquely driven by cognitive control demands a task-induced negative influence of the dlPFC on the pSMA/dACC was specific to working memory demands. These results reflect a system of both shared and unique context-dependent dynamics within the fronto-parietal network. Mechanisms supporting cognitive engagement, response selection, and action evaluation may be shared across cognitive domains, while dynamic updating of task and context representations within this network are potentially specific to changing demands on cognitive control.
Disrupted Effective Connectivity of Cortical Systems Supporting Attention and Interoception in Melancholia
Publisher: American Medical Association (AMA)
Date: 04-2015
DOI: 10.1001/JAMAPSYCHIATRY.2014.2490
Abstract: Patients with melancholia report a distinct and intrusive dysphoric state during internally generated thought. Melancholia has long been considered to have a strong biological component, but evidence for its specific neurobiological origins is limited. The distinct neurocognitive, psychomotor, and mood disturbances observed in melancholia do, however, suggest aberrant coordination of frontal-subcortical circuitry, which may best be captured through analysis of complex brain networks. To investigate the effective connectivity between spontaneous (resting-state) brain networks in melancholia, focusing on networks underlying attention and interoception. We performed a cross-sectional, observational, resting-state functional magnetic resonance imaging study of 16 participants with melancholia, 16 with nonmelancholic depression, and 16 in iduals serving as controls at a hospital-based research institute between August 30, 2010, and June 27, 2012. We identified 5 canonical resting-state networks (default mode, executive control, left and right frontoparietal attention, and bilateral anterior insula) and inferred spontaneous interactions among these networks using dynamic causal modeling. Graph theoretic measures of brain connectivity, namely, in-degree and out-degree of each network and edge connectivity, between regions composed our principal between-group contrasts. Melancholia was characterized by a pervasive disconnection involving anterior insula and attentional networks compared with participants in the control (Mann-Whitney, 189.00 z = 2.38 P = .02) and nonmelancholic depressive (Mann-Whitney, 203.00 z = 2.93 P = .004) groups. Decreased effective connectivity between the right frontoparietal and insula networks was present in participants with melancholic depression compared with those with nonmelancholic depression (χ2 = 8.13 P = .004). Reduced effective connectivity between the insula and executive networks was found in in iduals with melancholia compared with healthy controls (χ2 = 8.96 P = .003). We observed reduced effective connectivity in resting-state functional magnetic resonance imaging between key networks involved in attention and interoception in melancholia. We propose that these abnormalities underlie the impoverished variety and affective quality of internally generated thought in this disorder.
Dynamics of brain-muscle networks reveal effects of age and somatosensory function on gait
Publisher: Cold Spring Harbor Laboratory
Date: 03-02-2023
DOI: 10.1101/2023.02.02.526912
Abstract: Walking is a complex motor activity that requires coordinated interactions between sensory and motor systems. We used mobile EEG and EMG to investigate the brain-muscle networks involved in gait control during overground walking in young, older and in iduals with Parkinson’s Disease. Dynamic interactions between the sensorimotor cortices and eight leg muscles within a gait cycle were assessed using multivariate analysis. We identified three distinct brain-muscle networks during a gait cycle. These networks include a bilateral network, a left-lateralised network activated during the left swing phase, and a right-lateralised network active during right swing. The trajectories of these networks are contracted in older adults, indicating a reduction in neuromuscular connectivity with age. In iduals with impaired tactile sensitivity of the foot showed a selective enhancement of the bilateral network, possibly reflecting a compensation strategy to maintain gait stability. These findings provide a parsimonious description of interin idual differences in neuromuscular connectivity during gait. Dynamic network analysis shows how brain-muscle connectivity during gait varies with age and somatosensory function.
Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations
Publisher: The Royal Society
Date: 19-05-2015
Abstract: For more than a century, cerebral cartography has been driven by investigations of structural and morphological properties of the brain across spatial scales and the temporal/functional phenomena that emerge from these underlying features. The next era of brain mapping will be driven by studies that consider both of these components of brain organization simultaneously—elucidating their interactions and dependencies. Using this guiding principle, we explored the origin of slowly fluctuating patterns of synchronization within the topological core of brain regions known as the rich club, implicated in the regulation of mood and introspection. We find that a constellation of densely interconnected regions that constitute the rich club (including the anterior insula, amygdala and precuneus) play a central role in promoting a stable, dynamical core of spontaneous activity in the primate cortex. The slow timescales are well matched to the regulation of internal visceral states, corresponding to the somatic correlates of mood and anxiety. In contrast, the topology of the surrounding ‘feeder’ cortical regions shows unstable, rapidly fluctuating dynamics likely to be crucial for fast perceptual processes. We discuss these findings in relation to psychiatric disorders and the future of connectomics.
Nonlinear synchronization in EEG and whole‐head MEG recordings of healthy subjects
Publisher: Wiley
Date: 12-03-2003
DOI: 10.1002/HBM.10106
Low-dimensional dynamics of resting-state cortical activity
Publisher: Springer Science and Business Media LLC
Date: 09-10-2014
DOI: 10.1007/S10548-013-0319-5
Abstract: Endogenous brain activity supports spontaneous human thought and shapes perception and behavior. Connectivity-based analyses of endogenous, or resting-state, functional magnetic resonance imaging (fMRI) data have revealed the existence of a small number of robust networks which have a rich spatial structure. Yet the temporal information within fMRI data is limited, motivating the complementary analysis of electrophysiological recordings such as electroencephalography (EEG). Here we provide a novel method based on multivariate time-frequency interdependence to reconstruct the principal resting-state network dynamics in human EEG data. The stability of network expression across subjects is assessed using res ling techniques. We report the presence of seven robust networks, with distinct topographic organizations and high frequency (∼ 5-45 Hz) fingerprints, nested within slow temporal sequences that build up and decay over several orders of magnitude. Interestingly, all seven networks are expressed concurrently during these slow dynamics, although there is a temporal asymmetry in the pattern of their formation and dissolution. These analyses uncover the complex temporal character of endogenous cortical fluctuations and, in particular, offer an opportunity to reconstruct the low dimensional linear subspace in which they unfold.
Wavelets and statistical analysis of functional magnetic resonance images of the human brain
Publisher: SAGE Publications
Date: 10-2003
DOI: 10.1191/0962280203SM339RA
Abstract: Wavelets provide an orthonormal basis for multiresolution analysis and decorrelation or ‘whitening’ of nonstationary time series and spatial processes. Wavelets are particularly well suited to analysis of biological signals and images, such as human brain imaging data, which often have fractal or scale-invariant properties. We briefly define some key properties of the discrete wavelet transform (DWT) and review its applications to statistical analysis of functional magnetic resonance imaging (fMRI) data. We focus on time series res ling by ‘wavestrapping’ of wavelet coefficients, methods for efficient linear model estimation in the wavelet domain, and wavelet-based methods for multiple hypothesis testing, all of which are somewhat simplified by the decorrelating property of the DWT.
PERCEPTION OF ODORS BY A NONLINEAR MODEL OF THE OLFACTORY BULB
Publisher: World Scientific Pub Co Pte Lt
Date: 04-2001
DOI: 10.1142/S0129065701000564
Abstract: The behavior of the olfactory bulb is modeled as a network of interconnected cells with nonlinear dynamics. External inputs from sensory neurons are introduced as perturbations to subsets of cells within the network. We describe the attractors of the system and show how they can be classified and ordered according to their varying degrees of symmetry. By studying networks of attractors in the system's phase space, it is shown how different perturbations may evoke specific switches between various patterns of behavior. This ensures that different odors, even if present at extremely low concentrations, are able to evoke a specific spatio-temporal behavior in the olfactory bulb, permitting their unique perception. The model incorporates many of the processes proposed to mediate perception, such as the topographic organisation of sensory systems, destabilization of cortex by sensory input and synchronisation between neurons. It is also consistent with the character of the olfactory electroencephalogram.
White Matter Correlates of Neuropsychological Dysfunction in Systemic Lupus Erythematosus
Publisher: Public Library of Science (PLoS)
Date: 26-01-2012
Anisotropic properties of riddled basins
Publisher: Elsevier BV
Date: 02-2001
Extensive functional repertoire underpins complex behaviours: insights from Parkinson’s disease
Publisher: Cold Spring Harbor Laboratory
Date: 30-10-2019
DOI: 10.1101/823849
Abstract: Rapid reconfigurations of brain activity support efficient neuronal communication and flexible behaviour. Suboptimal brain dynamics impair this adaptability, possibly leading to functional deficiencies. We hypothesize that impaired flexibility in brain activity can lead to motor and cognitive symptoms of Parkinson’s disease (PD). To test this hypothesis, we studied the ‘functional repertoire’ – the number of distinct configurations of neural activity – using source-reconstructed magnetoencephalography in PD patients and controls. We found stereotyped brain dynamics and reduced flexibility in PD. The intensity of this reduction was proportional to symptoms severity, which can be explained by beta-band hyper-synchronization. Moreover, the basal ganglia were prominently involved in the abnormal patterns of brain activity. Our findings support the hypotheses that: symptoms in PD reflect impaired brain flexibility, this impairment preferentially involves the basal ganglia, and beta-band hypersynchronization is associated with reduced brain flexibility. These findings highlight the importance of extensive functional repertoires for behaviour and motor.
Neuroscience: Modeling the Brain on Acid
Publisher: Elsevier BV
Date: 10-2018
DOI: 10.1016/J.CUB.2018.08.008
Abstract: A receptor map of serotonin distribution is integrated into a model of the dynamic activity of the brain under the effects of LSD. The approach opens new avenues to understand experimental manipulations of healthy brain activity and offers a novel drug-discovery platform.
Large-scale brain modes reorganize between infant sleep states and carry prognostic information for preterms
Publisher: Springer Science and Business Media LLC
Date: 13-06-2019
DOI: 10.1038/S41467-019-10467-8
Abstract: Sleep architecture carries vital information about brain health across the lifespan. In particular, the ability to express distinct vigilance states is a key physiological marker of neurological wellbeing in the newborn infant although systems-level mechanisms remain elusive. Here, we demonstrate that the transition from quiet to active sleep in newborn infants is marked by a substantial reorganization of large-scale cortical activity and functional brain networks. This reorganization is attenuated in preterm infants and predicts visual performance at two years. We find a striking match between these empirical effects and a computational model of large-scale brain states which uncovers fundamental biophysical mechanisms not evident from inspection of the data. Active sleep is defined by reduced energy in a uniform mode of neural activity and increased energy in two more complex anteroposterior modes. Preterm-born infants show a deficit in this sleep-related reorganization of modal energy that carries novel prognostic information.
Modulation of excitatory synaptic coupling facilitates synchronization and complex dynamics in a nonlinear model of neuronal dynamics
Publisher: Elsevier BV
Date: 06-2003
Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality
Publisher: Cold Spring Harbor Laboratory
Date: 04-09-2022
DOI: 10.1101/2022.09.03.22279337
Abstract: Biological aging of human organ systems reflects the interplay of age, chronic disease, lifestyle and genetic risk. Using longitudinal brain imaging and physiological phenotypes from the UK Biobank, we establish normative models of biological age for 3 brain and 7 body systems. We find that an organ’s biological age selectively influences the aging of other organ systems, revealing a multiorgan aging network. We report organ age profiles for 16 chronic diseases, where advanced biological aging extends from the organ of primary disease to multiple systems. Advanced body age associates with several lifestyle and environmental factors, leucocyte telomere lengths and mortality risk, and predicts survival time (AUC=0.77) and premature death (AUC=0.86). Our work reveals the multisystem nature of human aging in health and chronic disease. It may enable early identification of in iduals at increased risk of aging-related morbidity and inform new strategies to potentially limit organ-specific aging in such in iduals.
Bistability and Non-Gaussian Fluctuations in Spontaneous Cortical Activity
Publisher: Society for Neuroscience
Date: 07-2009
Pain and rejection sensitivity in bipolar depression
Publisher: Wiley
Date: 02-2011
DOI: 10.1111/J.1399-5618.2011.00892.X
Abstract: Little is known regarding the correlates of pain in bipolar disorder. Recent neuroimaging studies support the contention that depression, as well as pain distress and rejection distress, share the same neurobiological circuits. In a recently published study, we confirmed the hypothesis that perception of increased pain during treatment-refractory depression, predominantly unipolar, was related to increased rejection sensitivity. In the present study, we aimed to test this same hypothesis for bipolar depression. The present study analysed data from 67 patients presenting to the Black Dog Institute Bipolar Disorders Clinic in Sydney, Australia. The patients all met DSM-IV criteria for bipolar disorder and had completed a self-report questionnaire regarding perceived pain and rejection sensitivity during depression. A significant increase in the experience of headaches (p=0.003) as well as chest pain (p=0.004) during bipolar depression was predicted by a major increase in rejection sensitivity when depressed, i.e., state rejection sensitivity. Being rejection sensitive in general, i.e., trait rejection sensitivity, did not predict pain during depression. The experience of increased headaches and chest pain during bipolar depression is related to increased rejection sensitivity during depression. Research to further elucidate this relationship is required.
Understanding cortical networks involved in the preparation of voluntary movement using simultaneous EEG-fMRI
Publisher: Frontiers Media SA
Date: 2013
Geometric constraints on human brain function
Publisher: Cold Spring Harbor Laboratory
Date: 05-10-2022
DOI: 10.1101/2022.10.04.510897
Abstract: The brain’s anatomy constrains its function, but precisely how remains unclear. Here, we show that human cortical and subcortical activity, measured with magnetic resonance imaging under spontaneous and erse task-evoked conditions, can be parsimoniously understood as resulting from excitations of fundamental, resonant modes of the brain’s geometry (i.e., its shape) rather than modes from complex inter-regional connectivity, as classically assumed. We then use these modes to show that task-evoked activations across ,000 brain maps are not confined to focal areas, as widely believed, but instead excite brain-wide modes with wavelengths spanning mm. Finally, we confirm theoretical predictions that the close link between geometry and function is explained by a dominant role for wave-like dynamics, showing that such dynamics can reproduce numerous canonical spatiotemporal properties of spontaneous and evoked recordings. Our findings challenge prevailing views of brain function and identify a previously under-appreciated role of brain geometry that is predicted by a unifying and physically principled approach.
Exploiting the potential of three dimensional spatial wavelet analysis to explore nesting of temporal oscillations and spatial variance in simultaneous EEG-fMRI data
Publisher: Elsevier BV
Date: 03-2011
DOI: 10.1016/J.PBIOMOLBIO.2010.11.003
Abstract: Synchronization of the activity in neural networks is a fundamental mechanism of brain function, putatively serving the integration of computations on multiple spatial and temporal scales. Time scales are thought to be nested within distinct spatial scales, so that whereas fast oscillations may integrate local networks, slow oscillations might integrate computations across distributed brain areas. We here describe a newly developed approach that provides potential for the further substantiation of this hypothesis in future studies. We demonstrate the feasibility and important caveats of a novel wavelet-based means of relating time series of three-dimensional spatial variance (energy) of fMRI data to time series of temporal variance of EEG. The spatial variance of fMRI data was determined by employing the three-dimensional dual-tree complex wavelet transform. The temporal variance of EEG data was estimated by using traditional continuous complex wavelets. We tested our algorithm on artificial signals with known signal-to-noise ratios and on empirical resting state EEG-fMRI data obtained from four healthy human subjects. By employing the human posterior alpha rhythm as an exemplar, we demonstrated face validity of the approach. We believe that the proposed method can serve as a suitable tool for future research on the spatiotemporal properties of brain dynamics, hence moving beyond analyses based exclusively in one domain or the other.
Structural dysconnectivity of key cognitive and emotional hubs in young people at high genetic risk for bipolar disorder
Publisher: Springer Science and Business Media LLC
Date: 20-12-2016
DOI: 10.1038/MP.2016.216
A computational role for bistability and traveling waves in motor cortex
Publisher: Frontiers Media SA
Date: 2012
Cross-sectional associations between 24-hour time-use composition, grey matter volume and cognitive function in healthy older adults
Publisher: Cold Spring Harbor Laboratory
Date: 16-05-2023
DOI: 10.1101/2023.05.15.23289982
Abstract: Increasing physical activity (PA) is an effective strategy to slow reductions in cortical volume and maintain cognitive function in older adulthood. However, PA does not exist in isolation, but coexists with sleep and sedentary behaviour to make up the 24-hour day. We investigated how the balance of all three behaviours (24-hour time-use composition) is associated with grey matter volume in healthy older adults, and whether grey matter volume influences the relationship between 24-hour time-use composition and cognitive function. This cross-sectional study included 378 older adults (65.6 ± 3.0 years old, 123 male) from the ACTIVate study across two Australian sites (Adelaide and Newcastle). Time-use composition was captured using 7-day accelerometry, and T1-weighted magnetic resonance imaging was used to measure grey matter volume both globally and across regions of interest (ROI: frontal lobe, temporal lobe, hippoc i, and lateral ventricles). Pairwise correlations were used to explore univariate associations between time-use variables, grey matter volumes and cognitive outcomes. Compositional data analysis linear regression models were used to quantify associations between ROI volumes and time-use composition, and explore potential associations between the interaction between ROI volumes and time-use composition with cognitive outcomes. After adjusting for covariates (age, sex, education), there were no significant associations between time-use composition and any volumetric outcomes. There were significant interactions between time-use composition and frontal lobe volume for long-term memory (p=0.018) and executive function (p=0.018), and between time-use composition and total grey matter volume for executive function (p=0.028). Spending more time in moderate-vigorous PA was associated with better long-term memory scores, but only for those with smaller frontal lobe volume (below the s le mean). Conversely, spending more time in sleep and less time in sedentary behaviour was associated with better executive function in those with smaller total grey matter volume. Although 24-hour time use was not associated with total or regional grey matter independently, total grey matter and frontal lobe grey matter volume mediated the relationship between time-use composition and several cognitive outcomes. Future studies should investigate these relationships longitudinally to assess whether changes in time-use composition correspond to changes in grey matter volume and cognition.
The independent influences of age and education on functional brain networks and cognition in healthy older adults
Publisher: Wiley
Date: 07-07-2017
DOI: 10.1002/HBM.23717
Ventilatory Chaos Is Impaired in Carotid Atherosclerosis
Publisher: Public Library of Science (PLoS)
Date: 28-01-2011
Metastable brain waves
Publisher: Cold Spring Harbor Laboratory
Date: 14-06-2018
DOI: 10.1101/347054
Abstract: Traveling patterns of neuronal activity – brain waves – have been observed across a breadth of neuronal recordings, states of awareness, and species, but their emergence in the human brain lacks a firm understanding. Here, we analyze the complex nonlinear dynamics that emerge from modeling large-scale spontaneous neural activity on a whole-brain network derived from human tractography. We find a rich array of three-dimensional wave patterns, including traveling waves, spiral waves, sources, and sinks. These patterns are metastable, such that multiple spatiotemporal wave patterns are visited in sequence. Transitions between states correspond to reconfigurations of underlying phase flows, characterized by nonlinear instabilities. These metastable dynamics accord with empirical data from multiple imaging modalities, including electrical waves in cortical tissue, sequential spatiotemporal patterns in resting-state MEG data, and large-scale waves in human electrocorticography. By moving the study of functional networks from a spatially static to an inherently dynamic (wave-like) frame, our work unifies apparently erse phenomena across functional neuroimaging modalities and makes specific predictions for further experimentation.
Subdiffusive Dynamics of Bump Attractors: Mechanisms and Functional Roles
Publisher: MIT Press - Journals
Date: 02-2015
DOI: 10.1162/NECO_A_00698
Abstract: Bump attractors are localized activity patterns that can self-sustain after stimulus presentation, and they are regarded as the neural substrate for a host of perceptual and cognitive processes. One of the characteristic features of bump attractors is that they are neutrally stable, so that noisy inputs cause them to drift away from their initial locations, severely impairing the accuracy of bump location-dependent neural coding. Previous modeling studies of such noise-induced drifting activity of bump attractors have focused on normal diffusive dynamics, often with an assumption that noisy inputs are uncorrelated. Here we show that long-range temporal correlations and spatial correlations in neural inputs generated by multiple interacting bumps cause them to drift in an anomalous subdiffusive way. This mechanism for generating subdiffusive dynamics of bump attractors is further analyzed based on a generalized Langevin equation. We demonstrate that subdiffusive dynamics can significantly improve the coding accuracy of bump attractors, since the variance of the bump displacement increases sublinearly over time and is much smaller than that of normal diffusion. Furthermore, we reanalyze existing psychophysical data concerning the spread of recalled cue position in spatial working memory tasks and show that its variance increases sublinearly with time, consistent with subdiffusive dynamics of bump attractors. Based on the probability density function of bump position, we also show that the subdiffusive dynamics result in a long-tailed decay of firing rate, greatly extending the duration of persistent activity.
The frustrated brain: From dynamics on motifs to communities and networks
Publisher: The Royal Society
Date: 05-10-2014
Abstract: Cognitive function depends on an adaptive balance between flexible dynamics and integrative processes in distributed cortical networks. Patterns of zero-lag synchrony likely underpin numerous perceptual and cognitive functions. Synchronization fulfils integration by reducing entropy, while adaptive function mandates that a broad variety of stable states be readily accessible. Here, we elucidate two complementary influences on patterns of zero-lag synchrony that derive from basic properties of brain networks. First, mutually coupled pairs of neuronal subsystems—resonance pairs—promote stable zero-lag synchrony among the small motifs in which they are embedded, and whose effects can propagate along connected chains. Second, frustrated closed-loop motifs disrupt synchronous dynamics, enabling metastable configurations of zero-lag synchrony to coexist. We document these two complementary influences in small motifs and illustrate how these effects underpin stable versus metastable phase-synchronization patterns in prototypical modular networks and in large-scale cortical networks of the macaque (CoCoMac). We find that the variability of synchronization patterns depends on the inter-node time delay, increases with the network size and is maximized for intermediate coupling strengths. We hypothesize that the dialectic influences of resonance versus frustration may form a dynamic substrate for flexible neuronal integration, an essential platform across erse cognitive processes.
Modulation of Fronto-Parietal Connectivity by Cognitive Interference and Working Memory: A Dynamic Causal Modelling Study
Publisher: Frontiers Media SA
Date: 2012
Time-resolved resting-state brain networks
Publisher: Proceedings of the National Academy of Sciences
Date: 30-06-2014
Abstract: Large-scale organizational properties of brain networks mapped with functional magnetic resonance imaging have been studied in a time-averaged sense. This is an oversimplification. We demonstrate that brain activity between multiple pairs of spatially distributed regions spontaneously fluctuates in and out of correlation over time in a globally coordinated manner, giving rise to sporadic intervals during which information can be efficiently exchanged between neuronal populations. We argue that dynamic fluctuations in the brain’s organizational properties may minimize metabolic requirements while maintaining the brain in a responsive state.
Neuronal Dynamics and Brain Connectivity
Publisher: Springer Berlin Heidelberg
Date: 2007
Network structure of cerebral cortex shapes functional connectivity on multiple time scales
Publisher: Proceedings of the National Academy of Sciences
Date: 12-06-2007
Abstract: Neuronal dynamics unfolding within the cerebral cortex exhibit complex spatial and temporal patterns even in the absence of external input. Here we use a computational approach in an attempt to relate these features of spontaneous cortical dynamics to the underlying anatomical connectivity. Simulating nonlinear neuronal dynamics on a network that captures the large-scale interregional connections of macaque neocortex, and applying information theoretic measures to identify functional networks, we find structure–function relations at multiple temporal scales. Functional networks recovered from long windows of neural activity (minutes) largely overlap with the underlying structural network. As a result, hubs in these long-run functional networks correspond to structural hubs. In contrast, significant fluctuations in functional topology are observed across the sequence of networks recovered from consecutive shorter (seconds) time windows. The functional centrality of in idual nodes varies across time as interregional couplings shift. Furthermore, the transient couplings between brain regions are coordinated in a manner that reveals the existence of two anticorrelated clusters. These clusters are linked by prefrontal and parietal regions that are hub nodes in the underlying structural network. At an even faster time scale (hundreds of milliseconds) we detect in idual episodes of interregional phase-locking and find that slow variations in the statistics of these transient episodes, contingent on the underlying anatomical structure, produce the transfer entropy functional connectivity and simulated blood oxygenation level-dependent correlation patterns observed on slower time scales.
Critical Slowing and Perception
Publisher: Wiley
Date: 28-03-2014
An improved algorithm for the detection of dynamical interdependence in bivariate time-series
Publisher: Springer Science and Business Media LLC
Date: 02-2003
DOI: 10.1007/S00422-002-0368-4
Abstract: We describe a new algorithm for the detection of dynamical interdependence in bivariate time-series data sets. By using geometrical and dynamical arguments, we produce a method that can detect dynamical interdependence in weakly coupled systems where previous techniques have failed. We illustrate this by comparison of our algorithm with another commonly used technique when applied to a system of coupled Hénon maps. In addition, an improvement of approximately 20% in the detection rate is observed when the technique is applied to human scalp EEG data, as compared with existing techniques. Such an improvement may assist an understanding of the role of large-scale nonlinear processes in normal brain function.
Multimodal assistive technologies for depression diagnosis and monitoring
Publisher: Springer Science and Business Media LLC
Date: 07-09-2013
Phase-locked cluster oscillations in periodically forced integrate-and-fire-or-burst neuronal populations
Publisher: American Physical Society (APS)
Date: 07-12-2012
Non-identical smoothing operators for estimating time-frequency interdependence in electrophysiological recordings
Publisher: Springer Science and Business Media LLC
Date: 10-04-2013
Mechanisms of Zero-Lag Synchronization in Cortical Motifs
Publisher: Public Library of Science (PLoS)
Date: 24-04-2014
Caregiver Burden and Caregiver Appraisal of Psychiatric Symptoms are not Modulated by Subthalamic Deep Brain Stimulation for Parkinson’s Disease
Publisher: Center for Open Science
Date: 18-12-2017
Abstract: Subthalamic deep brain stimulation is an advanced therapy that typically improves quality of life for persons with Parkinson’s disease (PD). However, the effect on caregiver burden is unclear. We recruited sixty-four persons with PD and their caregivers from a movement disorders clinic during the assessment of eligibility for subthalamic DBS. We used clinician, patient and caregiver-rated instruments to follow the patient-caregiver dyad from pre- to postoperative status, s ling repeatedly in the postoperative period to ascertain fluctuations in phenotypic variables. We employed multivariate models to identify key drivers of burden. We clustered caregiver-rated variables into ‘high’ and ‘low’ symptom groups and examined whether postoperative cluster assignment could be predicted from baseline values. Psychiatric symptoms in the postoperative period made a substantial contribution to longitudinal caregiver burden. The development of stimulation-dependent mood changes was also associated with increased burden. However, caregiver burden and caregiver-rated psychiatric symptom clusters were temporally stable and thus predicted only by their baseline values. We confirmed this finding using frequentist and Bayesian statistics, concluding that in our s le, subthalamic DBS for PD did not significantly influence caregiver burden or caregiver-rated psychiatric symptoms. Specifically, patient-caregiver dyads with high burden and high levels of psychiatric symptoms at baseline were likely to maintain this profile during follow up. These findings support the importance of assessing caregiver burden prior to functional neurosurgery. Furthermore, they suggest that interventions addressing caregiver burden in this population should target those with greater symptomatology at baseline and may usefully prioritise psychiatric symptoms reported by the caregiver.
Deconvolution of neural dynamics from fMRI data using a spatiotemporal hemodynamic response function
Publisher: Elsevier BV
Date: 07-2014
DOI: 10.1016/J.NEUROIMAGE.2014.03.001
Abstract: Functional magnetic resonance imaging (fMRI) is a powerful and broadly used means of non-invasively mapping human brain activity. However fMRI is an indirect measure that rests upon a mapping from neuronal activity to the blood oxygen level dependent (BOLD) signal via hemodynamic effects. The quality of estimated neuronal activity hinges on the validity of the hemodynamic model employed. Recent work has demonstrated that the hemodynamic response has non-separable spatiotemporal dynamics, a key property that is not implemented in existing fMRI analysis frameworks. Here both simulated and empirical data are used to demonstrate that using a physiologically based model of the spatiotemporal hemodynamic response function (stHRF) results in a quantitative improvement of the estimated neuronal response relative to unphysical space-time separable forms. To achieve this, an integrated spatial and temporal deconvolution is established using a recently developed stHRF. Simulated data allows the variation of key parameters such as noise and the spatial complexity of the neuronal drive, while knowing the neuronal input. The results demonstrate that the use of a spatiotemporally integrated HRF can avoid "ghost" neuronal responses that can otherwise be falsely inferred. Applying the spatiotemporal deconvolution to high resolution fMRI data allows the recovery of neuronal responses that are consistent with independent electrophysiological measures.
The contribution of geometry to the human connectome
Publisher: Elsevier BV
Date: 2016
DOI: 10.1016/J.NEUROIMAGE.2015.09.009
Abstract: The human connectome is a topologically complex, spatially embedded network. While its topological properties have been richly characterized, the constraints imposed by its spatial embedding are poorly understood. By applying a novel res ling method to tractography data, we show that the brain's spatial embedding makes a major, but not definitive, contribution to the topology of the human connectome. We first identify where the brain's structural hubs would likely be located if geometry was the sole determinant of brain topology. Empirical networks show a widespread shift away from this geometric center toward more peripheral interconnected skeletons in each hemisphere, with discrete clusters around the anterior insula, and the anterior and posterior midline regions of the cortex. A relatively small number of strong inter-hemispheric connections assimilate these intra-hemispheric structures into a rich club, whose connections are locally more clustered but globally longer than predicted by geometry. We also quantify the extent to which the segregation, integration, and modularity of the human brain are passively inherited from its geometry. These analyses reveal novel insights into the influence of spatial geometry on the human connectome, highlighting specific topological features that likely confer functional advantages but carry an additional metabolic cost.
Are there subtypes of bipolar depression?
Publisher: Wiley
Date: 20-06-2016
DOI: 10.1111/ACPS.12615
Abstract: To investigate for subtypes of bipolar depression using latent class analysis (LCA). Participants were recruited through a bipolar disorder (BD) clinic. LCA was undertaken using: (i) symptoms reported on the SCID-IV for the most severe lifetime depressive episode (ii) lifetime illness features such as age at first depressive and hypo/manic episodes and (iii) family history of BD and unipolar depression. To explore the validity of any demonstrated 'classes', clinical, demographic and treatment correlates were investigated. A total of 243 BD subjects (170 with BD-I and 73 with BD-II) were included. For the combined s le, we found two robust LCA solutions, with two and three classes respectively. There were no consistent solutions when the BD-I and BD-II s les were considered separately. Subjects in class 2 of the three-class solution (characterised by anxiety, insomnia, reduced appetite/weight loss, irritability, psychomotor retardation, suicidal ideation, guilt, worthlessness and evening worsening) were significantly more likely to be in receipt of government financial support, suggesting a particularly malign pattern of symptoms. Our study suggests the existence of two or three distinct classes of bipolar depression and a strong association with functional outcome.
Connectome sensitivity or specificity: which is more important?
Publisher: Elsevier BV
Date: 11-2016
DOI: 10.1016/J.NEUROIMAGE.2016.06.035
Abstract: Connectomes with high sensitivity and high specificity are unattainable with current axonal fiber reconstruction methods, particularly at the macro-scale afforded by magnetic resonance imaging. Tensor-guided deterministic tractography yields sparse connectomes that are incomplete and contain false negatives (FNs), whereas probabilistic methods steered by crossing-fiber models yield dense connectomes, often with low specificity due to false positives (FPs). Densely reconstructed probabilistic connectomes are typically thresholded to improve specificity at the cost of a reduction in sensitivity. What is the optimal tradeoff between connectome sensitivity and specificity? We show empirically and theoretically that specificity is paramount. Our evaluations of the impact of FPs and FNs on empirical connectomes indicate that specificity is at least twice as important as sensitivity when estimating key properties of brain networks, including topological measures of network clustering, network efficiency and network modularity. Our asymptotic analysis of small-world networks with idealized modular structure reveals that as the number of nodes grows, specificity becomes exactly twice as important as sensitivity to the estimation of the clustering coefficient. For the estimation of network efficiency, the relative importance of specificity grows linearly with the number of nodes. The greater importance of specificity is due to FPs occurring more prevalently between network modules rather than within them. These spurious inter-modular connections have a dramatic impact on network topology. We argue that efforts to maximize the sensitivity of connectome reconstruction should be realigned with the need to map brain networks with high specificity.
Metabolic demands of the posteromedial default mode network are shaped by dorsal attention and frontoparietal control networks
Publisher: Cold Spring Harbor Laboratory
Date: 12-08-2022
DOI: 10.1101/2022.08.12.503715
Abstract: Although BOLD signal decreases in the default mode network (DMN) are commonly observed during attention-demanding tasks, their neurobiological underpinnings are not fully understood. Previous work has shown decreases but also increases in glucose metabolism that match with or dissociate from these BOLD signal decreases, respectively. To resolve this discrepancy, we analyzed functional PET/MRI data from 50 healthy subjects during the performance of the visuo-spatial processing game Tetris® and combined this with previously published data sets of working memory as well as visual and motor stimulation. Our findings show that the glucose metabolism of the posteromedial DMN is dependent on the metabolic demands of the correspondingly engaged task-positive brain networks. Specifically, the dorsal attention (involved in Tetris®) and frontoparietal networks (engaged during working memory) shape the glucose metabolism of the posteromedial DMN in opposing directions. External attention-demanding tasks lead to a downregulation of the posteromedial DMN with consistent decreases in the BOLD signal and glucose metabolism, whereas working memory is subject to metabolically expensive mechanisms of BOLD signal suppression. We suggest that the former finding is mediated by decreased glutamate signaling, while the latter results from active GABAergic inhibition, regulating the competition between self-generated and task-driven internal demands. The results demonstrate that the DMN relates to cognitive processing in a flexible manner and does not always act as a cohesive task-negative network in isolation.
What clinical features precede the onset of bipolar disorder?
Publisher: Elsevier BV
Date: 03-2015
DOI: 10.1016/J.JPSYCHIRES.2015.01.017
Abstract: Despite a growing number of reports, there is still limited knowledge of the clinical features that precede the onset of bipolar disorder (BD). To explore this, we investigated baseline data from a prospectively evaluated longitudinal cohort of subjects aged 12-30 years to compare: first, lifetime rates of clinical features between a) subjects at increased genetic risk for developing BD ('AR'), b) participants from families without mental illness ('controls'), and c) those with established BD and, second, prior clinical features that predict the later onset of affective disorders in these same three groups. This is the first study to report such comparisons between these three groups (though certainly not the first to compare AR and control s les). 118 AR participants with a parent or sibling with BD (including 102 with a BD parent), 110 controls, and 44 BD subjects were assessed using semi-structured interviews. AR subjects had significantly increased lifetime risks for depressive, anxiety and behavioural disorders compared to controls. Unlike prior reports, preceding anxiety and behavioural disorders were not found to increase risk for later onset of affective disorders in the AR s le, perhaps due to limited s le size. However, preceding behavioural disorders did predict later onset of affective disorders in the BD s le. The findings that i) AR subjects had higher rates of depressive, anxiety and behavioural disorders compared to controls, and ii) prior behavioural disorders increased the risk to later development of affective disorders in the BD group, suggest the possibility of therapeutic targeting for these disorders in those at high genetic risk for BD.
Generative Models of Cortical Oscillations: Neurobiological Implications of the Kuramoto Model
Publisher: Frontiers Media SA
Date: 2010
Upregulation of cortico-cerebellar functional connectivity after motor learning
Publisher: Elsevier BV
Date: 03-2016
DOI: 10.1016/J.NEUROIMAGE.2015.12.052
Abstract: Interactions between the cerebellum and primary motor cortex are crucial for the acquisition of new motor skills. Recent neuroimaging studies indicate that learning motor skills is associated with subsequent modulation of resting-state functional connectivity in the cerebellar and cerebral cortices. The neuronal processes underlying the motor-learning-induced plasticity are not well understood. Here, we investigate changes in functional connectivity in source-reconstructed electroencephalography (EEG) following the performance of a single session of a dynamic force task in twenty young adults. Source activity was reconstructed in 112 regions of interest (ROIs) and the functional connectivity between all ROIs was estimated using the imaginary part of coherence. Significant changes in resting-state connectivity were assessed using partial least squares (PLS). We found that subjects adapted their motor performance during the training session and showed improved accuracy but with slower movement times. A number of connections were significantly upregulated after motor training, principally involving connections within the cerebellum and between the cerebellum and motor cortex. Increased connectivity was confined to specific frequency ranges in the mu- and beta-bands. Post hoc analysis of the phase spectra of these cerebellar and cortico-cerebellar connections revealed an increased phase lag between motor cortical and cerebellar activity following motor practice. These findings show a reorganization of intrinsic cortico-cerebellar connectivity related to motor adaptation and demonstrate the potential of EEG connectivity analysis in source space to reveal the neuronal processes that underpin neural plasticity.
Predicting tDCS treatment outcomes of patients with major depressive disorder using automated EEG classification
Publisher: Elsevier BV
Date: 2017
DOI: 10.1016/J.JAD.2016.10.021
Abstract: Transcranial direct current stimulation (tDCS) is a promising treatment for major depressive disorder (MDD). Standard tDCS treatment involves numerous sessions running over a few weeks. However, not all participants respond to this type of treatment. This study aims to investigate the feasibility of identifying MDD patients that respond to tDCS treatment based on resting-state electroencephalography (EEG) recorded prior to treatment commencing. We used machine learning to predict improvement in mood and cognition during tDCS treatment from baseline EEG power spectra. Ten participants with a current diagnosis of MDD were included. Power spectral density was assessed in five frequency bands: delta (0.5-4Hz), theta (4-8Hz), alpha (8-12Hz), beta (13-30Hz) and gamma (30-100Hz). Improvements in mood and cognition were assessed using the Montgomery-Åsberg Depression Rating Scale and Symbol Digit Modalities Test, respectively. We trained the classifiers using three algorithms (support vector machine, extreme learning machine and linear discriminant analysis) and a leave-one-out cross-validation approach. Mood labels were accurately predicted in 8 out of 10 participants using EEG channels FC4-AF8 (accuracy=76%, p=0.034). Cognition labels were accurately predicted in 10 out of 10 participants using channels pair CPz-CP2 (accuracy=92%, p=0.004). Due to the limited number of participants (n=10), the presented results mainly aim to serve as a proof of concept. These finding demonstrate the feasibility of using machine learning to identify patients that will respond to tDCS treatment. These promising results warrant a larger study to determine the clinical utility of this approach.
Transcranial magnetic stimulation in obsessive-compulsive disorder: A focus on network mechanisms and state dependence
Publisher: Elsevier BV
Date: 2018
Multi-frequency phase locking in human somatosensory cortex
Publisher: Elsevier BV
Date: 03-2011
DOI: 10.1016/J.PBIOMOLBIO.2010.09.015
Abstract: Cortical population responses to sensory input arise from the interaction between external stimuli and the intrinsic dynamics of the densely interconnected neuronal population. Although there is a large body of knowledge regarding single neuron responses to periodic stimuli, responses at the scale of cortical populations are incompletely understood. The characteristics of large-scale neuronal activity during periodic stimulation speak directly to the mechanisms underlying collective neuronal activity. Their accurate elucidation is hence a vital prelude to constructing and evaluating large-scale computational and biophysical models of the brain. Electroencephalographic data was recorded from eight human subjects while periodic vibrotactile stimuli were applied to the fingertip. Time-frequency decomposition was performed on the multi-channel data in order to investigate relative changes in the power and phase distributions at stimulus-related frequencies. We observed phase locked oscillatory activity at multiple stimulus-specific frequencies, in particular at ratios of 1:1, 2:1 and 2:3 to the stimulus frequency. These phase locked components were found to be modulated differently across the range of stimulus frequencies, with oscillatory responses most robustly sustained around 30 Hz. In contrast, no robust frequency-locked responses were apparent in the power changes. These results demonstrate n:m phase synchronization between cortical oscillations in the somatosensory system and an external periodic signal. We argue that neuronal populations evidence a collective nonlinear response to periodic sensory input. The existence of n:m phase synchronization demonstrates the contribution of intrinsic cortical dynamics to stimulus encoding and provides a novel phenomenological criteria for the validation of large-scale models of the brain.
Synaptic assays: Using biophysical models to infer neuronal dysfunction from non-invasive EEG
Publisher: Oxford University Press (OUP)
Date: 25-05-2018
DOI: 10.1093/BRAIN/AWY136
The modulation of neural gain facilitates a transition between functional segregation and integration in the brain
Publisher: eLife Sciences Publications, Ltd
Date: 29-01-2018
DOI: 10.7554/ELIFE.31130
Abstract: Cognitive function relies on a dynamic, context-sensitive balance between functional integration and segregation in the brain. Previous work has proposed that this balance is mediated by global fluctuations in neural gain by projections from ascending neuromodulatory nuclei. To test this hypothesis in silico, we studied the effects of neural gain on network dynamics in a model of large-scale neuronal dynamics. We found that increases in neural gain directed the network through an abrupt dynamical transition, leading to an integrated network topology that was maximal in frontoparietal ‘rich club’ regions. This gain-mediated transition was also associated with increased topological complexity, as well as increased variability in time-resolved topological structure, further highlighting the potential computational benefits of the gain-mediated network transition. These results support the hypothesis that neural gain modulation has the computational capacity to mediate the balance between integration and segregation in the brain.
Impact of load-related neural processes on feature binding in visuospatial working memory
Publisher: Public Library of Science (PLoS)
Date: 24-08-2011
Small-World Networks and Functional Connectivity in Alzheimer's Disease
Publisher: Oxford University Press (OUP)
Date: 02-2006
Abstract: We investigated whether functional brain networks are abnormally organized in Alzheimer's disease (AD). To this end, graph theoretical analysis was applied to matrices of functional connectivity of beta band-filtered electroencephalography (EEG) channels, in 15 Alzheimer patients and 13 control subjects. Correlations between all pairwise combinations of EEG channels were determined with the synchronization likelihood. The resulting synchronization matrices were converted to graphs by applying a threshold, and cluster coefficients and path lengths were computed as a function of threshold or as a function of degree K. For a wide range of thresholds, the characteristic path length L was significantly longer in the Alzheimer patients, whereas the cluster coefficient C showed no significant changes. This pattern was still present when L and C were computed as a function of K. A longer path length with a relatively preserved cluster coefficient suggests a loss of complexity and a less optimal organization. The present study provides further support for the presence of "small-world" features in functional brain networks and demonstrates that AD is characterized by a loss of small-world network characteristics. Graph theoretical analysis may be a useful approach to study the complexity of patterns of interrelations between EEG channels.
Developing a Diagnostic Brain Imaging Protocol for Recent Onset Psychosis which Embodies Principles of Predictive Coding and Free Energy
Publisher: Pion Ltd
Date: 05-2011
DOI: 10.1068/IC342
Functional alterations in brain activation and deactivation in mild cognitive impairment in response to a graded working memory challenge
Publisher: S. Karger AG
Date: 2011
DOI: 10.1159/000322112
Abstract: i Aim: /i To investigate dynamic changes in functional brain activity in mild cognitive impairment (MCI) in response to a graded working memory (WM) challenge with increasing memory load. i Methods: /i In an event-related functional magnetic resonance imaging (fMRI) study, 35 MCI and 22 cognitively normal subjects performed a visuospatial associative WM task with 3 load levels. Potential performance differences were controlled for by in idually calibrating the number of items presented at each load. i Results: /i An interaction between group and WM load was observed during stimulus encoding. At lower loads, greater activity in the right anterior cingulate and right precuneus was observed in MCI subjects. As the load increased to higher levels, reduced activation in these regions and greater deactivation in the posterior cingulate-medial precuneus were observed in MCI compared to control subjects. Stronger expression of load-related patterns of activation and deactivation in MCI subjects was associated with greater clinical severity and a more abnormal pattern of performance variability. i Conclusion: /i Patterns of overactivation, underactivation and deactivation during successful encoding in MCI subjects were dependent on WM load. This type of graded cognitive challenge may operate like a ‘memory stress test’ in MCI and may be a useful biomarker of disease at the predementia stage.
Perception and self-organized instability
Publisher: Frontiers Media SA
Date: 2012
Impact of In Utero Exposure to Antiepileptic Drugs on Neonatal Brain Function
Publisher: Oxford University Press (OUP)
Date: 29-09-2021
Abstract: In utero brain development underpins brain health across the lifespan but is vulnerable to physiological and pharmacological perturbation. Here, we show that antiepileptic medication during pregnancy impacts on cortical activity during neonatal sleep, a potent indicator of newborn brain health. These effects are evident in frequency-specific functional brain networks and carry prognostic information for later neurodevelopment. Notably, such effects differ between different antiepileptic drugs that suggest neurodevelopmental adversity from exposure to antiepileptic drugs and not maternal epilepsy per se. This work provides translatable bedside metrics of brain health that are sensitive to the effects of antiepileptic drugs on postnatal neurodevelopment and carry direct prognostic value.
Building capacity in academic psychiatry: The Queensland Mental Health Research Alliance.
Publisher: SAGE Publications
Date: 23-03-2015
Unified Modeling and Analysis of Primary Generalized Seizures
Publisher: Elsevier
Date: 2008
Generalized time-frequency coherency for assessing neural interactions in electrophysiological recordings
Publisher: Springer Science and Business Media LLC
Date: 17-11-2011
Abstract: Time-frequency coherence has been widely used to quantify statistical dependencies in bivariate data and has proven to be vital for the study of neural interactions in electrophysiological recordings. Conventional methods establish time-frequency coherence by smoothing the cross and power spectra using identical smoothing procedures. Smoothing entails a trade-off between time-frequency resolution and statistical consistency and is critical for detecting instantaneous coherence in single-trial data. Here, we propose a generalized method to estimate time-frequency coherency by using different smoothing procedures for the cross spectra versus power spectra. This novel method has an improved trade-off between time resolution and statistical consistency compared to conventional methods, as verified by two simulated data sets. The methods are then applied to single-trial surface encephalography recorded from human subjects for comparative purposes. Our approach extracted robust alpha- and gamma-band synchronization over the visual cortex that was not detected by conventional methods, demonstrating the efficacy of this method.
Neural mechanisms of intermuscular coherence: implications for the rectification of surface electromyography
Publisher: American Physiological Society
Date: 02-2012
Abstract: Oscillatory activity plays a crucial role in corticospinal control of muscle synergies and is widely investigated using corticospinal and intermuscular synchronization. However, the neurophysiological mechanisms that translate these rhythmic patterns into surface electromyography (EMG) are not well understood. This is underscored by the ongoing debate on the rectification of surface EMG before spectral analysis. Whereas empirical studies commonly rectify surface EMG, computational approaches have argued against it. In the present study, we employ a computational model to investigate the role of the motor unit action potential (MAUP) on the translation of oscillatory activity. That is, erse MUAP shapes may distort the transfer of common input into surface EMG. We test this in a computational model consisting of two motor unit pools receiving common input and compare it to empirical results of intermuscular coherence between bilateral leg muscles. The shape of the MUAP was parametrically varied, and power and coherence spectra were investigated with and without rectification. The model shows that the effect of EMG rectification depends on the uniformity of MUAP shapes. When output spikes of different motor units are convolved with identical MUAPs, oscillatory input is evident in both rectified and nonrectified EMG. In contrast, a heterogeneous MAUP distribution distorts common input and oscillatory components are only manifest as periodic litude modulations, i.e., in rectified EMG. The experimental data showed that intermuscular coherence was mainly discernable in rectified EMG, hence providing empirical support for a heterogeneous distribution of MUAPs. These findings implicate that the shape of MUAPs is an essential parameter to reconcile experimental and computational approaches.
The Brain Dynamics Toolbox for Matlab
Publisher: Elsevier BV
Date: 11-2018
Perceptions as Hypotheses: Saccades as Experiments
Publisher: Frontiers Media SA
Date: 2012
Predicting Brain Stimulation Treatment Outcomes of Depressed Patients Through the Classification of EEG Oscillations
Publisher: IEEE
Date: 08-2016
Kinetic Models of Brain Activity
Publisher: Springer Science and Business Media LLC
Date: 26-09-2008
BrainModes: A principled approach to modeling and measuring large-scale neuronal activity
Publisher: Elsevier BV
Date: 09-2009
DOI: 10.1016/J.JNEUMETH.2009.07.008
Abstract: Complex systems, such as the brain, exhibit multiple levels of organization due to processes which support the separation of scales across time and/or space. That is, cooperative phenomena--or "modes" of activity--occurring at one scale give rise to coherent spatiotemporal structures at a coarser scale. In turn, structures at the coarser scale constrain--and hence influence--emerging activity at a finer scale. BrainModes is an annual scientific summit which seeks to bring together experimental, computational and theoretical neuroscientists engaged at different levels of organization, with the goal of advancing a principled approach to understanding brain function based on the concept of cooperative phenomena in complex systems. Phenomena of particular interest include synchronization, stochastic influences, and spatiotemporal processes in both healthy and pathological states such as seizures. This Special Issue reports the 2008 BrainModes Workshop, held in Amsterdam (December 2008) which focused on the application of this framework to the analysis of brain oscillations and synchronization phenomena across time scales.
Symbiotic relationship between brain structure and dynamics
Publisher: Springer Science and Business Media LLC
Date: 02-06-2009
Neuroimaging, Neural Population Models for
Publisher: Springer New York
Date: 2013
Modulation of excitatory synaptic coupling facilitates synchronization and complex dynamics in a biophysical model of neuronal dynamics
Publisher: Informa UK Limited
Date: 2003
Towards a complete forward prediction from visual stimulus to BOLD.
Publisher: Association for Research in Vision and Ophthalmology (ARVO)
Date: 09-2015
DOI: 10.1167/15.12.583
Hemodynamic Traveling Waves in Human Visual Cortex
Publisher: Public Library of Science (PLoS)
Date: 22-03-2012
The spatiotemporal hemodynamic response function for depth-dependent functional imaging of human cortex
Publisher: Elsevier BV
Date: 10-2016
DOI: 10.1016/J.NEUROIMAGE.2016.06.019
Abstract: The gray matter of human cortex is characterized by depth-dependent differences in neuronal activity and connections (Shipp, 2007) as well as in the associated vasculature (Duvernoy et al., 1981). The resolution limit of functional magnetic resonance imaging (fMRI) measurements is now below a millimeter, promising the non-invasive measurement of these properties in awake and behaving humans (Muckli et al., 2015 Olman et al., 2012 Ress et al., 2007). To advance this endeavor, we present a detailed spatiotemporal hemodynamic response function (HRF) reconstructed through the use of high-resolution, submillimeter fMRI. We decomposed the HRF into directions tangential and perpendicular to the cortical surface and found that key spatial properties of the HRF change significantly with depth from the cortical surface. Notably, we found that the spatial spread of the HRF increases linearly from 4.8mm at the gray/white matter boundary to 6.6mm near the cortical surface. Using a hemodynamic model, we posit that this effect can be explained by the depth profile of the cortical vasculature, and as such, must be taken into account to properly estimate the underlying neuronal responses at different cortical depths.
Cortical Representations of Cognitive Control and Working Memory are Dependent yet Non-Interacting
Publisher: Frontiers Media SA
Date: 2015
The organisation of the elderly connectome
Publisher: Elsevier BV
Date: 07-2015
DOI: 10.1016/J.NEUROIMAGE.2015.04.009
Abstract: Investigations of the human connectome have elucidated core features of adult structural networks, particularly the crucial role of hub-regions. However, little is known regarding network organisation of the healthy elderly connectome, a crucial prelude to the systematic study of neurodegenerative disorders. Here, whole-brain probabilistic tractography was performed on high-angular diffusion-weighted images acquired from 115 healthy elderly subjects (age 76-94 years 65 females). Structural networks were reconstructed between 512 cortical and subcortical brain regions. We sought to investigate the architectural features of hub-regions, as well as left-right asymmetries, and sexual dimorphisms. We observed that the topology of hub-regions is consistent with a young adult population, and previously published adult connectomic data. More importantly, the architectural features of hub connections reflect their ongoing vital role in network communication. We also found substantial sexual dimorphisms, with females exhibiting stronger inter-hemispheric connections between cingulate and prefrontal cortices. Lastly, we demonstrate intriguing left-lateralized subnetworks consistent with the neural circuitry specialised for language and executive functions, whilst rightward subnetworks were dominant in visual and visuospatial streams. These findings provide insights into healthy brain ageing and provide a benchmark for the study of neurodegenerative disorders such as Alzheimer's disease (AD) and frontotemporal dementia (FTD).
Dynamic models of large-scale brain activity
Publisher: Springer Science and Business Media LLC
Date: 23-02-2017
DOI: 10.1038/NN.4497
Abstract: Movement, cognition and perception arise from the collective activity of neurons within cortical circuits and across large-scale systems of the brain. While the causes of single neuron spikes have been understood for decades, the processes that support collective neural behavior in large-scale cortical systems are less clear and have been at times the subject of contention. Modeling large-scale brain activity with nonlinear dynamical systems theory allows the integration of experimental data from multiple modalities into a common framework that facilitates prediction, testing and possible refutation. This work reviews the core assumptions that underlie this computational approach, the methodological framework that fosters the translation of theory into the laboratory, and the emerging body of supporting evidence. While substantial challenges remain, evidence supports the view that collective, nonlinear dynamics are central to adaptive cortical activity. Likewise, aberrant dynamic processes appear to underlie a number of brain disorders.
Intrinsic Coupling Modes in Source-Reconstructed Electroencephalography
Publisher: Mary Ann Liebert Inc
Date: 12-2014
Quantifying Sub-Optimal Decision Making in Depression
Publisher: Frontiers Media SA
Date: 2012
Detection of cross-frequency coupling between brain areas: an extension of phase-linearity measurement
Publisher: Cold Spring Harbor Laboratory
Date: 27-11-2020
DOI: 10.1101/2020.11.25.398628
Abstract: The current paper proposes a method to estimate phase to phase cross-frequency coupling between brain areas, applied to broadband signals, without any a priori hypothesis about the frequency of the synchronized components. N:m synchronization is the only form of cross-frequency synchronization that allows the exchange of information at the time resolution of the faster signal, hence likely to play a fundamental role in large-scale coordination of brain activity. The proposed method, named cross-frequency phase linearity measurement (CF-PLM), builds and expands upon the phase linearity measurement, an iso-frequency connectivity metrics previously published by our group. The main idea lies in using the shape of the interferometric spectrum of the two analyzed signals in order to estimate the strength of cross-frequency coupling. Here, we demonstrate that the CF-PLM successfully retrieves the (different) frequencies of the original broad-band signals involved in the connectivity process. Furthermore, if the broadband signal has some frequency components that are synchronized in iso-frequency and some others that are synchronized in cross-frequency, our methodology can successfully disentangle them and describe the behaviour of each frequency component separately. We first provide a theoretical explanation of the metrics. Then, we test the proposed metric on simulated data from coupled oscillators synchronized in iso- and cross-frequency (using both Rössler and Kuramoto oscillator models), and subsequently apply it on real data from brain activity, using source-reconstructed Magnetoencephalography (MEG) data. In the synthetic data, our results show reliable estimates even in the presence of noise and limited s le sizes. In the real signals, components synchronized in cross-frequency are retrieved, together with their oscillation frequencies. All in all, our method is useful to estimate n:m synchronization, based solely on the phase of the signals (independently of the litude), and no a-priori hypothesis is available about the expected frequencies. Our method can be exploited to more accurately describe patterns of cross-frequency synchronization and determine the central frequencies involved in the coupling.
Networks, noise and models: Reconceptualizing the brain as a complex, distributed system
Publisher: Elsevier BV
Date: 09-2011
Perceptual decision making and the time-order effect: A neural circuit model of biased vibrotactile discrimination
Publisher: Frontiers Media SA
Date: 2012
Revisiting deficits in threat and safety appraisal in obsessive-compulsive disorder
Publisher: Cold Spring Harbor Laboratory
Date: 02-09-2022
DOI: 10.1101/2022.09.01.22279518
Abstract: Current behavioural treatment of obsessive-compulsive disorder (OCD) is informed by fear conditioning and involves iteratively re-evaluating previously threatening stimuli as safe. However, there is limited research investigating the neurobiological response to conditioning and reversal of threatening stimuli in in iduals with OCD. A clinical s le of in iduals with OCD (N=45) and matched healthy controls (N=45) underwent functional Magnetic Resonance Imaging (fMRI). While in the scanner, participants completed a well-validated fear reversal task and a resting-state scan. We found no evidence for group differences in task-evoked brain activation or functional connectivity in OCD. Multivariate analyses encompassing all participants in the clinical and control groups suggested that subjective appraisal of threatening and safe stimuli were associated with a larger difference in brain activity than the contribution of OCD symptoms. In particular, we observed a brain-behaviour continuum whereby heightened affective appraisal was related to increased bilateral insula activation during the task ( r = 0.39, p FWE = 0.001). These findings suggest that changes in conditioned threat-related processes may not be a core neurobiological feature of OCD and encourage further research on the role of subjective experience in fear conditioning.
The Impact of Detecting Bipolar Disorder in Previously Diagnosed Unipolar Patients at a Specialist Depression Clinic
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 06-2011
Dissociable effects of local inhibitory and excitatory theta-burst stimulation on large-scale brain dynamics
Publisher: American Physiological Society
Date: 05-2015
Abstract: Normal brain function depends on a dynamic balance between local specialization and large-scale integration. It remains unclear, however, how local changes in functionally specialized areas can influence integrated activity across larger brain networks. By combining transcranial magnetic stimulation with resting-state functional magnetic resonance imaging, we tested for changes in large-scale integration following the application of excitatory or inhibitory stimulation on the human motor cortex. After local inhibitory stimulation, regions encompassing the sensorimotor module concurrently increased their internal integration and decreased their communication with other modules of the brain. There were no such changes in modular dynamics following excitatory stimulation of the same area of motor cortex nor were there changes in the configuration and interactions between core brain hubs after excitatory or inhibitory stimulation of the same area. These results suggest the existence of selective mechanisms that integrate local changes in neural activity, while preserving ongoing communication between brain hubs.
Topographic organization of nonlinear interdependence in multichannel human EEG
Publisher: Elsevier BV
Date: 07-2002
Abstract: This paper investigates the spatial organization of nonlinear interactions between different brain regions in healthy human subjects. This is achieved by studying the topography of nonlinear interdependence in multichannel EEG data, acquired from 40 healthy human subjects at rest. An algorithm for the detection and quantification of nonlinear interdependence is applied to four pairs of bipolar electrode derivations to detect posterior and anterior interhemispheric and left and right intrahemispheric interdependences. Multivariate surrogate data sets are constructed to control for linear coherence and finite s le size. Nonlinear interdependence is shown to occur in a small but statistically robust number of epochs. The occurrence of nonlinear interdependence in any region is correlated with the concurrent presence of nonlinear interdependence in other regions at high levels of significance. The strength, direction and topography of the interdependences are also correlated. For ex le, posterior interhemispheric interdependence from right-to-left is strongly correlated with right intrahemispheric interdependence from back-to-front. There is a subtle change in these correlations when subjects open their eyes. These results suggest that nonlinear interdependence in the human brain has a specific topographic organization which reflects simple cognitive changes. It sometimes occurs as an isolated phenomenon between two brain regions, but often involves concurrent interdependences between multiple brain regions.
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