ORCID Profile
0000-0002-7435-0236
Current Organisations
University of Sydney
,
Monash University
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Biological Psychology (Neuropsychology, Psychopharmacology, Physiological Psychology) | Psychology
Nervous System and Disorders | Expanding Knowledge in Psychology and Cognitive Sciences | Computer Software and Services not elsewhere classified |
Publisher: Cold Spring Harbor Laboratory
Date: 23-12-2021
DOI: 10.1101/2021.12.22.473927
Abstract: New brain atlases with high spatial resolution and whole-brain coverage have rapidly advanced our knowledge of the brain’s neural architecture, including the systematic variation of excitatory and inhibitory cell densities across the mammalian cortex. But understanding how the brain’s microscale physiology shapes brain dynamics at the macroscale has remained a challenge. While physiologically based mathematical models of brain dynamics are well placed to bridge this explanatory gap, their complexity can form a barrier to providing clear mechanistic interpretation of the dynamics they generate. In this work we develop a neural-mass model of the mouse cortex and show how bifurcation diagrams, which capture local dynamical responses to inputs and their variation across brain regions, can be used to understand the resulting whole-brain dynamics. We show that strong fits to resting-state functional magnetic resonance imaging (fMRI) data can be found in surprisingly simple dynamical regimes—including where all brain regions are confined to a stable fixed point—in which regions are able to respond strongly to variations in their inputs, consistent with direct structural connections providing a strong constraint on functional connectivity in the anesthetized mouse. We also use bifurcation diagrams to show how perturbations to local excitatory and inhibitory coupling strengths across the cortex, constrained by cell-density data, provide spatially dependent constraints on resulting cortical activity, and support a greater ersity of coincident dynamical regimes. Our work illustrates methods for visualizing and interpreting model performance in terms of underlying dynamical mechanisms, an approach that is crucial for building explanatory and physiologically grounded models of the dynamical principles that underpin large-scale brain activity.
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.
Publisher: Cold Spring Harbor Laboratory
Date: 06-06-2019
DOI: 10.1101/662726
Abstract: One of the most controversial procedures in the analysis of resting-state functional magnetic resonance imaging (rsfMRI) data is global signal regression (GSR): the removal, via linear regression, of the mean signal averaged over the entire brain, from voxel-wise or regional time series. On one hand, the global mean signal contains variance associated with respiratory, scanner-, and motion-related artifacts. Its removal via GSR improves various quality control metrics, enhances the anatomical specificity of functional connectivity patterns, and can increase the behavioural variance explained by such patterns. On the other hand, GSR alters the distribution of regional signal correlations in the brain, can induce artifactual anticorrelations, may remove real neural signal, and can distort case-control comparisons of functional-connectivity measures. Global signal fluctuations can be identified by visualizing a matrix of colour-coded signal intensities, called a carpet plot, in which rows represent voxels and columns represent time. Prior to GSR, large, periodic bands of coherent signal changes that affect most of the brain are often apparent after GSR, these apparent global changes are greatly diminished. Here, using three independent datasets, we show that reordering carpet plots to emphasize cluster structure in the data reveals a greater ersity of spatially widespread signal deflections (WSDs) than previously thought. Their precise form varies across time and participants and GSR is only effective in removing specific kinds of WSDs. We present an alternative, iterative correction method called Diffuse Cluster Estimation and Regression (DiCER), that identifies representative signals associated with large clusters of coherent voxels. DiCER is more effective than GSR at removing erse WSDs as visualized in carpet plots, reduces correlations between functional connectivity and head-motion estimates, reduces inter-in idual variability in global correlation structure, and results in comparable or improved identification of canonical functional-connectivity networks. All code for implementing DiCER and replicating our results is available at github.com/BMHLab/DiCER .
Publisher: The Royal Society
Date: 12-2016
Abstract: It is shown that recently discovered haemodynamic waves can form shock-like fronts when driven by stimuli that excite the cortex in a patch that moves faster than the haemodynamic wave velocity. If stimuli are chosen in order to induce shock-like behaviour, the resulting blood oxygen level-dependent (BOLD) response is enhanced, thereby improving the signal to noise ratio of measurements made with functional magnetic resonance imaging. A spatio-temporal haemodynamic model is extended to calculate the BOLD response and determine the main properties of waves induced by moving stimuli. From this, the optimal conditions for stimulating shock-like responses are determined, and ways of inducing these responses in experiments are demonstrated in a pilot study.
Publisher: Frontiers Media SA
Date: 2013
Publisher: eLife Sciences Publications, Ltd
Date: 03-07-2022
Publisher: Cold Spring Harbor Laboratory
Date: 15-03-2021
DOI: 10.1101/2021.03.11.21253426
Abstract: Dysfunction of fronto-striato-thalamic (FST) circuits is thought to contribute to dopaminergic dysfunction and symptom onset in psychosis, but it remains unclear whether this dysfunction is driven by aberrant bottom-up subcortical signaling or impaired top-down cortical regulation. Here, we used spectral dynamic causal modelling (DCM) of resting-state functional magnetic resonance imaging (fMRI) to characterize the effective connectivity of dorsal and ventral FST circuits in a s le of 46 antipsychotic-naïve first-episode psychosis (FEP) patients and 23 controls and an independent s le of 36 patients with established schizophrenia (SCZ) patients and 100 controls. We found that midbrain and thalamic connectivity were implicated across both patient groups. Dysconnectivity in FEP patients was mainly restricted to the subcortex, with positive symptom severity being associated with midbrain connectivity. Dysconnectivity between the cortex and subcortical systems was only apparent in SCZ patients. In another independent s le of 33 healthy in iduals who underwent concurrent fMRI and [ 18 F]DOPA positron emission tomography, we found that striatal dopamine synthesis capacity was associated with the effective connectivity of nigrostriatal and striatothalamic pathways, implicating similar circuits as those associated with psychotic symptom severity in patients. Our findings thus indicate that subcortical dysconnectivity is salient in the early stages of psychosis, that cortical dysfunction may emerge later in the illness, and that nigrostriatal and striatothalamic signaling are closely related to striatal dopamine synthesis capacity, which is a robust risk marker for psychosis.
Publisher: Cold Spring Harbor Laboratory
Date: 10-2021
DOI: 10.1101/2021.09.29.462379
Abstract: The complex connectivity of nervous systems is thought to have been shaped by competitive selection pressures to minimize wiring costs and support adaptive function. Accordingly, recent modeling work indicates that stochastic processes, shaped by putative trade-offs between the cost and value of each connection, can successfully reproduce many topological properties of macroscale human connectomes measured with diffusion magnetic resonance imaging. Here, we derive a new formalism with the aim to more accurately capture the competing pressures of wiring cost minimization and topological complexity. We further show that model performance can be improved by accounting for developmental changes in brain geometry and associated wiring costs, and by using inter-regional transcriptional or microstructural similarity rather than topological wiring-rules. However, all models struggled to capture topologies spatial embedding. Our findings highlight an important role for genetics in shaping macroscale brain connectivity and indicate that stochastic models offer an incomplete account of connectome organization.
Publisher: Cold Spring Harbor Laboratory
Date: 03-08-2018
DOI: 10.1101/384065
Abstract: Directing attention helps to extract relevant information and suppress distracters. Alpha brain oscillations (8-12Hz) play a fundamental role in this process, with a power decrease facilitating processing of important information and power increase inhibiting brain regions processing irrelevant information. Evidence for this phenomenon arises from visual attention studies (Worden et al., 2000), however, the effect also exists in other modalities, including the somatosensory system (Haegens et al., 2011) and inter-sensory attention tasks (Foxe and Snyder, 2011). We investigated what happens when attention is ided between two modalities using both a multi- and unimodal attention paradigm while recording EEG over 128 scalp electrodes in two separate experiments. In Experiment 1 participants ided their attention between the visual and somatosensory modality to determine the temporal or spatial frequency of a target stimulus (vibrotactile stimulus or Gabor grating). In Experiment 2, participants ided attention between two visual hemifields to identify the orientation of a target Gabor grating. In both experiments, pre-stimulus alpha power in visual areas decreased linearly with increasing attention to visual stimuli. In contrast, alpha power in parietal areas showed lower pre-stimulus alpha power when attention was ided between modalities, compared to unimodal attention. These results suggest that there are two different alpha sources, where one reflects the ‘visual spotlight of attention’ and the other reflects attentional effort. To our knowledge, this is the first study to show that attention recruits two spatially distinct alpha sources in occipital and parietal brain regions, which act simultaneously but serve different functions in attention. Attention to one spatial location/sensory modality leads to power changes of alpha oscillations ( ~ 10Hz) with decreased power over regions processing relevant information and power increases to actively inhibit areas processing ‘to-be-ignored’ information. Here, we used detailed source modelling to investigate EEG data recorded during separate uni-modal (visual) and multi- (visual and somatosensory) attention tasks. Participants either focused their attention on one modality/spatial location or directed it to both. We show for the first time two distinct alpha sources are active simultaneously but play different roles. A sensory (visual) alpha source was linearly modulated by attention representing the ‘visual spotlight of attention’. In contrast, a parietal alpha source was modulated by attentional effort, showing lowest alpha power when attention was ided.
Publisher: Elsevier BV
Date: 08-2010
DOI: 10.1016/J.JTBI.2010.05.026
Abstract: A quantitative theory is developed for the relationship between stimulus and the resulting blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) signal, including both spatial and temporal dynamics for the first time. The brain tissue is modeled as a porous elastic medium, whose interconnected pores represent the vasculature. The model explicitly incorporates conservation of blood mass, interconversion of oxygenated and deoxygenated hemoglobin, force balance within the blood and of blood pressure with vessel walls, and blood flow modulation due to neuronal activity. In appropriate limits it is shown to reproduce prior Balloon models of hemodynamic response, which do not include spatial variations. The regime of validity of such models is thereby clarified by elucidating their assumptions, and when these break down, for ex le when voxel sizes become small.
Publisher: Cold Spring Harbor Laboratory
Date: 18-03-2020
DOI: 10.1101/2020.03.16.993154
Abstract: A physiologically based three-dimensional (3D) hemodynamic model is used to predict the experimentally observed blood oxygen level dependent (BOLD) responses versus the cortical depth induced by visual stimuli. Prior 2D approximations are relaxed in order to analyze 3D blood flow dynamics as a function of cortical depth. Comparison of the predictions with experimental data for typical stimuli demonstrates that the full 3D model matches at least as well as previous approaches while requiring significantly fewer assumptions and model parameters (e.g., there is no more need to define depth-specific parameter values for spatial spreading, peak litude, and hemodynamic velocity).
Publisher: Cold Spring Harbor Laboratory
Date: 23-07-2018
DOI: 10.1101/373977
Abstract: Functional MRI at ultra-high field (UHF, ≥7T) provides significant increases in BOLD contrast-to-noise ratio (CNR) compared with conventional field strength (3T), and has been exploited for reduced field-of-view, high spatial resolution mapping of primary sensory areas. Applying these high spatial resolution methods to investigate whole brain functional responses to higher-order cognitive tasks leads to a number of challenges, in particular how to perform robust group-level statistical analyses. This study addresses these challenges using an inter-sensory cognitive task which modulates top-down attention at graded levels between the visual and somatosensory domains. At the in idual level, highly focal functional activation to the task and task difficulty (modulated by attention levels) were detectable due to the high CNR at UHF. However, to assess group level effects, both anatomical and functional variability must be considered during analysis. We demonstrate the importance of surface over volume normalization and the requirement of no spatial smoothing when assessing highly focal activity. Using novel group analysis on anatomically parcellated brain regions, we show that in higher cognitive areas (parietal and dorsal-lateral-prefrontal cortex) fMRI responses to graded attention levels were modulated quadratically, whilst in visual cortex and VIP, responses were modulated linearly. These group fMRI responses were not seen clearly using conventional second-level GLM analyses, illustrating the limitations of a conventional approach when investigating such focal responses in higher cognitive regions which are more anatomically variable. The approaches demonstrated here complement other advanced analysis methods such as multi-variate pattern analysis, allowing UHF to be fully exploited in cognitive neuroscience.
Publisher: Wiley
Date: 15-11-2018
DOI: 10.1002/HBM.24450
Publisher: Cold Spring Harbor Laboratory
Date: 09-10-2023
Publisher: Association for Research in Vision and Ophthalmology (ARVO)
Date: 09-2015
DOI: 10.1167/15.12.583
Publisher: Public Library of Science (PLoS)
Date: 22-03-2012
Publisher: Elsevier BV
Date: 04-2014
DOI: 10.1016/J.JTBI.2013.12.027
Abstract: Probing neural activity with functional magnetic resonance imaging (fMRI) relies upon understanding the hemodynamic response to changes in neural activity. Although existing studies have extensively characterized the temporal hemodynamic response, less is understood about the spatial and spatiotemporal hemodynamic responses. This study systematically characterizes the spatiotemporal response by deriving the hemodynamic response due to a short localized neural drive, i.e., the spatiotemporal hemodynamic response function (stHRF) from a physiological model of hemodynamics based on a poroelastic model of cortical tissue. In this study, the model's boundary conditions are clarified and a resulting nonlinear hemodynamic wave equation is derived. From this wave equation, d ed linear hemodynamic waves are predicted from the stHRF. The main features of these waves depend on two physiological parameters: wave propagation speed, which depends on mean cortical stiffness, and d ing which depends on effective viscosity. Some of these predictions were applied and validated in a companion study (Aquino et al., 2012). The advantages of having such a theory for the stHRF include improving the interpretation of spatiotemporal dynamics in fMRI data improving estimates of neural activity with fMRI spatiotemporal deconvolution and enabling wave interactions between hemodynamic waves to be predicted and exploited to improve the signal to noise ratio of fMRI.
Publisher: Cold Spring Harbor Laboratory
Date: 25-05-2021
DOI: 10.1101/2021.05.23.445373
Abstract: Large-scale dynamics of the brain are routinely modelled using systems of nonlinear dynamical equations that describe the evolution of population-level activity, with distinct neural populations often coupled according to an empirically measured structural connection matrix. This modelling approach has been used to generate insights into the neural underpinnings of spontaneous brain dynamics, as recorded with techniques such as resting state functional MRI (fMRI). In fMRI, researchers have many degrees of freedom in the way that they can process the data and recent evidence indicates that the choice of pre-processing steps can have a major effect on empirical estimates of functional connectivity. However, the potential influence of such variations on modelling results are seldom considered. Here we show, using three popular whole-brain dynamical models, that different choices during fMRI preprocessing can dramatically affect model fits and interpretations of findings. Critically, we show that the ability of these models to accurately capture patterns in fMRI dynamics is mostly driven by the degree to which they fit global signals rather than interesting sources of coordinated neural dynamics. We show that widespread deflections can arise from simple global synchronisation. We introduce a simple two-parameter model that captures these fluctuations and which performs just as well as more complex, multi-parameter biophysical models. From our combined analyses of data and simulations, we describe benchmarks to evaluate model fit and validity. Although most models are not resilient to denoising, we show that relaxing the approximation of homogeneous neural populations by more explicitly modelling inter-regional effective connectivity can improve model accuracy at the expense of increased model complexity. Our results suggest that many complex biophysical models may be fitting relatively trivial properties of the data, and underscore a need for tighter integration between data quality assurance and model development.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 16-07-2021
Abstract: Regional heterogeneity in the brain’s transcriptional landscape supports complex neuronal dynamics.
Publisher: Oxford University Press (OUP)
Date: 16-03-2023
Abstract: Schizophrenia is a debilitating neuropsychiatric disorder whose underlying correlates remain unclear despite decades of neuroimaging investigation. One contentious topic concerns the role of global signal (GS) fluctuations and how they affect more focal functional changes. Moreover, it has been difficult to pinpoint causal mechanisms of circuit disruption. Here, we analyzed resting-state fMRI data from 47 schizophrenia patients and 118 age-matched healthy controls and used dynamical analyses to investigate how global fluctuations and other functional metastable states are affected by this disorder. We found that brain dynamics in the schizophrenia group were characterized by an increased probability of globally coherent states and reduced recurrence of a substate dominated by coupled activity in the default mode and limbic networks. We then used the in silico perturbation of a whole-brain model to identify critical areas involved in the disease. Perturbing a set of temporo-parietal sensory and associative areas in a model of the healthy brain reproduced global pathological dynamics. Healthy brain dynamics were instead restored by perturbing a set of medial fronto-temporal and cingulate regions in the model of pathology. These results highlight the relevance of GS alterations in schizophrenia and identify a set of vulnerable areas involved in determining a shift in brain state.
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.
Publisher: Elsevier BV
Date: 08-2022
DOI: 10.1016/J.NEUROIMAGE.2022.119051
Abstract: Large-scale dynamics of the brain are routinely modelled using systems of nonlinear dynamical equations that describe the evolution of population-level activity, with distinct neural populations often coupled according to an empirically measured structural connectivity matrix. This modelling approach has been used to generate insights into the neural underpinnings of spontaneous brain dynamics, as recorded with techniques such as resting state functional MRI (fMRI). In fMRI, researchers have many degrees of freedom in the way that they can process the data and recent evidence indicates that the choice of pre-processing steps can have a major effect on empirical estimates of functional connectivity. However, the potential influence of such variations on modelling results are seldom considered. Here we show, using three popular whole-brain dynamical models, that different choices during fMRI preprocessing can dramatically affect model fits and interpretations of findings. Critically, we show that the ability of these models to accurately capture patterns in fMRI dynamics is mostly driven by the degree to which they fit global signals rather than interesting sources of coordinated neural dynamics. We show that widespread deflections can arise from simple global synchronisation. We introduce a simple two-parameter model that captures these fluctuations and performs just as well as more complex, multi-parameter biophysical models. From our combined analyses of data and simulations, we describe benchmarks to evaluate model fit and validity. Although most models are not resilient to denoising, we show that relaxing the approximation of homogeneous neural populations by more explicitly modelling inter-regional effective connectivity can improve model accuracy at the expense of increased model complexity. Our results suggest that many complex biophysical models may be fitting relatively trivial properties of the data, and underscore a need for tighter integration between data quality assurance and model development.
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.
Publisher: Elsevier BV
Date: 11-2016
DOI: 10.1016/J.NEUROIMAGE.2016.04.050
Abstract: Neural field theory of the corticothalamic system is applied to predict and analyze the activity eigenmodes of the bihemispheric brain, focusing particularly on their spatial structure. The eigenmodes of a single brain hemisphere are found to be close analogs of spherical harmonics, which are the natural modes of the sphere. Instead of multiple eigenvalues being equal, as in the spherical case, cortical folding splits them to have distinct values. Inclusion of interhemispheric connections between homologous regions via the corpus callosum leads to further splitting that depends on symmetry or antisymmetry of activity between brain hemispheres, and the strength and sign of the interhemispheric connections. Symmetry properties of the lowest observed eigenmodes strongly constrain the interhemispheric connectivity strengths and unihemispheric mode spectra, and it is predicted that most spontaneous brain activity will be symmetric between hemispheres, consistent with observations. Comparison with the eigenmodes of an experimental anatomical connectivity matrix confirms these results, permits the relative strengths of intrahemispheric and interhemispheric connectivities to be approximately inferred from their eigenvalues, and lays the foundation for further experimental tests. The results are consistent with brain activity being in corticothalamic eigenmodes, rather than discrete "networks" and open the way to new approaches to brain analysis.
Publisher: Society for Neuroscience
Date: 24-07-2019
Publisher: The Royal Society
Date: 05-2016
Abstract: The blood oxygen-level dependent (BOLD) response to a neural stimulus is analysed using the transfer function derived from a physiologically based poroelastic model of cortical tissue. The transfer function is decomposed into components that correspond to distinct poles, each related to a response mode with a natural frequency and dispersion relation together these yield the total BOLD response. The properties of the decomposed components provide a deeper understanding of the nature of the BOLD response, via the components' frequency dependences, spatial and temporal power spectra, and resonances. The transfer function components are then used to separate the BOLD response to a localized impulse stimulus, termed the Green function or spatio-temporal haemodynamic response function, into component responses that are explicitly related to underlying physiological quantities. The analytical results also provide a quantitative tool to calculate the linear BOLD response to an arbitrary neural drive, which is faster to implement than direct Fourier transform methods. The results of this study can be used to interpret functional magnetic resonance imaging data in new ways based on physiology, to enhance deconvolution methods and to design experimental protocols that can selectively enhance or suppress particular responses, to probe specific physiological phenomena.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 03-06-2022
Abstract: The complex connectivity of nervous systems is thought to have been shaped by competitive selection pressures to minimize wiring costs and support adaptive function. Accordingly, recent modeling work indicates that stochastic processes, shaped by putative trade-offs between the cost and value of each connection, can successfully reproduce many topological properties of macroscale human connectomes measured with diffusion magnetic resonance imaging. Here, we derive a new formalism that more accurately captures the competing pressures of wiring cost minimization and topological complexity. We further show that model performance can be improved by accounting for developmental changes in brain geometry and associated wiring costs, and by using interregional transcriptional or microstructural similarity rather than topological wiring rules. However, all models struggled to capture topographical (i.e., spatial) network properties. Our findings highlight an important role for genetics in shaping macroscale brain connectivity and indicate that stochastic models offer an incomplete account of connectome organization.
Publisher: Society for Neuroscience
Date: 07-2009
Publisher: eLife Sciences Publications, Ltd
Date: 05-10-2022
DOI: 10.7554/ELIFE.75056
Abstract: Asymmetries of the cerebral cortex are found across erse phyla and are particularly pronounced in humans, with important implications for brain function and disease. However, many prior studies have confounded asymmetries due to size with those due to shape. Here, we introduce a novel approach to characterize asymmetries of the whole cortical shape, independent of size, across different spatial frequencies using magnetic resonance imaging data in three independent datasets. We find that cortical shape asymmetry is highly in idualized and robust, akin to a cortical fingerprint, and identifies in iduals more accurately than size-based descriptors, such as cortical thickness and surface area, or measures of inter-regional functional coupling of brain activity. In idual identifiability is optimal at coarse spatial scales (~37 mm wavelength), and shape asymmetries show scale-specific associations with sex and cognition, but not handedness. While unihemispheric cortical shape shows significant heritability at coarse scales (~65 mm wavelength), shape asymmetries are determined primarily by subject-specific environmental effects. Thus, coarse-scale shape asymmetries are highly personalized, sexually dimorphic, linked to in idual differences in cognition, and are primarily driven by stochastic environmental influences.
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.
Publisher: Cold Spring Harbor Laboratory
Date: 29-10-2020
DOI: 10.1101/2020.10.28.359943
Abstract: Brain regions vary in their molecular and cellular composition, but how this heterogeneity shapes neuronal dynamics is unclear. Here, we investigate the dynamical consequences of regional heterogeneity using a biophysical model of whole-brain functional magnetic resonance imaging (MRI) dynamics in humans. We show that models in which transcriptional variations in excitatory and inhibitory receptor (E:I) gene expression constrain regional heterogeneity more accurately reproduce the spatiotemporal structure of empirical functional connectivity estimates than do models constrained by global gene expression profiles and MRI-derived estimates of myeloarchitecture. We further show that regional heterogeneity is essential for yielding both ignition-like dynamics, which are thought to support conscious processing, and a wide variance of regional activity timescales, which supports a broad dynamical range. We thus identify a key role for E:I heterogeneity in generating complex neuronal dynamics and demonstrate the viability of using transcriptional data to constrain models of large-scale brain function.
Start Date: 2020
End Date: 12-2023
Amount: $509,561.00
Funder: Australian Research Council
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