ORCID Profile
0000-0003-2298-9908
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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)
Neurosciences Not Elsewhere Classified | Artificial Intelligence and Image Processing | Neurosciences not elsewhere classified | Signal Processing | Neurocognitive Patterns and Neural Networks | Simulation And Modelling | Biological psychology | Statistics | Biological Mathematics | Human Biophysics | Image Processing | Stochastic Analysis And Modelling | Signal Processing | Cognitive Science | Central Nervous System | Biological psychology not elsewhere classified |
ANZSRC Socio-Economic Objective (SEO)
Mental Health | Surgical methods and procedures | Mental health | Application packages | Nervous System and Disorders | Diagnostic Methods | Expanding Knowledge in the Biological Sciences | Expanding Knowledge in the Mathematical Sciences
Related Links
Publications
Altered functional brain connectivity in a non-clinical sample of young adults with attention-deficit/hyperactivity disorder
Publisher: Society for Neuroscience
Date: 05-12-2012
DOI: 10.1523/JNEUROSCI.3272-12.2012
Abstract: Attention-deficit/hyperactivity disorder (ADHD) is characterized by symptoms of inattention and hyperactivity/impulsivity that often persist in adulthood. There is a growing consensus that ADHD is associated with abnormal function of diffuse brain networks, but such alterations remain poorly characterized. Using resting-state functional magnetic resonance imaging, we characterized multivariate (complex network measures), bivariate (network-based statistic), and univariate (regional homogeneity) properties of brain networks in a non-clinical, drug-naive s le of high-functioning young men and women with ADHD (nine males, seven females) and a group of matched healthy controls. Data from our s le allowed the isolation of intrinsic functional connectivity alterations specific to ADHD diagnosis and symptoms that are not related to developmental delays, general cognitive dysfunction, or history of medication use. Multivariate results suggested that frontal, temporal, and occipital cortices were abnormally connected locally as well as with the rest of the brain in in iduals with ADHD. Results from the network-based statistic support and extend multivariate results by isolating two brain networks comprising regions between which inter-regional connectivity was significantly altered in the ADHD group namely, a frontal amygdala-occipital network and a frontal temporal-occipital network. Brain behavior correlations further highlighted the key role of altered orbitofrontal-temporal and frontal-amygdala connectivity for symptoms of inattention and hyperactivity/impulsivity. All univariate properties were similar between groups. Taken together, results from this study show that the diagnosis and the two main symptom dimensions of ADHD are related to altered intrinsic connectivity in orbitofrontal-temporal-occipital and fronto-amygdala-occipital networks. Accordingly, our findings highlight the importance of extending the conceptualization of ADHD beyond segregated fronto-striatal alterations.
Regional, circuit and network heterogeneity of brain abnormalities in psychiatric disorders
Publisher: Springer Science and Business Media LLC
Date: 14-08-2023
DOI: 10.1038/S41593-023-01404-6
Abstract: The substantial in idual heterogeneity that characterizes people with mental illness is often ignored by classical case–control research, which relies on group mean comparisons. Here we present a comprehensive, multiscale characterization of the heterogeneity of gray matter volume (GMV) differences in 1,294 cases diagnosed with one of six conditions (attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, depression, obsessive–compulsive disorder and schizophrenia) and 1,465 matched controls. Normative models indicated that person-specific deviations from population expectations for regional GMV were highly heterogeneous, affecting the same area in % of people with the same diagnosis. However, these deviations were embedded within common functional circuits and networks in up to 56% of cases. The salience–ventral attention system was implicated transdiagnostically, with other systems selectively involved in depression, bipolar disorder, schizophrenia and attention-deficit/hyperactivity disorder. Phenotypic differences between cases assigned the same diagnosis may thus arise from the heterogeneous localization of specific regional deviations, whereas phenotypic similarities may be attributable to the dysfunction of common functional circuits and networks.
Subgenual Functional Connectivity Predicts Antidepressant Treatment Response to Transcranial Magnetic Stimulation: Independent Validation and Evaluation of Personalization
Publisher: Elsevier BV
Date: 07-2019
Machine learning prediction of cognition from functional connectivity: Are feature weights reliable?
Publisher: Cold Spring Harbor Laboratory
Date: 29-05-2021
DOI: 10.1101/2021.05.27.446059
Abstract: Cognitive performance can be predicted from an in idual’s functional brain connectivity with modest accuracy using machine learning approaches. As yet, however, predictive models have arguably yielded limited insight into the neurobiological processes supporting cognition. To do so, feature selection and feature weight estimation need to be reliable to ensure that important connections and circuits with high predictive utility can be reliably identified. We comprehensively investigate feature weight test-retest reliability for various predictive models of cognitive performance built from resting-state functional connectivity networks in healthy young adults (n=400). Despite achieving modest prediction accuracies (r=0.2-0.4), we find that feature weight reliability is generally poor for all predictive models (ICC .3), and significantly poorer than predictive models for overt biological attributes such as sex (ICC ≈ 0.5). Larger s le sizes (n=800), the Haufe transformation, non-sparse feature selection/regularization and smaller feature spaces marginally improve reliability (ICC .4). We elucidate a tradeoff between feature weight reliability and prediction accuracy and find that univariate statistics are marginally more reliable than feature weights from predictive models. Finally, we show that measuring agreement in feature weights between cross-validation folds provides inflated estimates of feature weight reliability. We thus recommend for reliability to be estimated out-of-s le, if possible. We argue that rebalancing focus from prediction accuracy to model reliability may facilitate mechanistic understanding of cognition with machine learning approaches.
Default mode network anatomy and function is linked to pediatric concussion recovery
Publisher: Wiley
Date: 22-11-2019
DOI: 10.1002/ACN3.50951
Track-weighted dynamic functional connectivity (TW-dFC): a new method to study time-resolved functional connectivity
Publisher: Springer Science and Business Media LLC
Date: 26-04-2017
The Relationship Between White Matter Microstructure and General Cognitive Ability in Patients With Schizophrenia and Healthy Participants in the ENIGMA Consortium
Publisher: American Psychiatric Association Publishing
Date: 06-2020
Neighborhood disadvantage and longitudinal brain-predicted-age trajectory during adolescence
Publisher: Elsevier BV
Date: 10-2021
Cell type-specific manifestations of cortical thickness heterogeneity in schizophrenia
Publisher: Springer Science and Business Media LLC
Date: 10-02-2022
DOI: 10.1038/S41380-022-01460-7
Abstract: Brain morphology differs markedly between in iduals with schizophrenia, but the cellular and genetic basis of this heterogeneity is poorly understood. Here, we sought to determine whether cortical thickness (CTh) heterogeneity in schizophrenia relates to interregional variation in distinct neural cell types, as inferred from established gene expression data and person-specific genomic variation. This study comprised 1849 participants in total, including a discovery (140 cases and 1267 controls) and a validation cohort (335 cases and 185 controls). To characterize CTh heterogeneity, normative ranges were established for 34 cortical regions and the extent of deviation from these ranges was measured for each in idual with schizophrenia. CTh deviations were explained by interregional gene expression levels of five out of seven neural cell types examined: (1) astrocytes (2) endothelial cells (3) oligodendrocyte progenitor cells (OPCs) (4) excitatory neurons and (5) inhibitory neurons. Regional alignment between CTh alterations with cell type transcriptional maps distinguished broad patient subtypes, which were validated against genomic data drawn from the same in iduals. In a predominantly neuronal/endothelial subtype (22% of patients), CTh deviations covaried with polygenic risk for schizophrenia (sczPRS) calculated specifically from genes marking neuronal and endothelial cells ( r = −0.40, p = 0.010). Whereas, in a predominantly glia/OPC subtype (43% of patients), CTh deviations covaried with sczPRS calculated from glia and OPC-linked genes ( r = −0.30, p = 0.028). This multi-scale analysis of genomic, transcriptomic, and brain phenotypic data may indicate that CTh heterogeneity in schizophrenia relates to inter-in idual variation in cell-type specific functions. Decomposing heterogeneity in relation to cortical cell types enables prioritization of schizophrenia subsets for future disease modeling efforts.
Risk and resilience brain networks in treatment-resistant schizophrenia
Publisher: Elsevier BV
Date: 03-2018
DOI: 10.1016/J.SCHRES.2017.07.014
Abstract: Genes, molecules and neural circuits that are associated with, or confer risk to developing schizophrenia have been studied and mapped. It is hypothesized that certain neural systems may counterbalance familial risk of schizophrenia, and thus confer resilience to developing the disorder. This study sought to identify resting-state functional brain connectivity (rs-FC) representing putative risk or resilience endophenotypes in schizophrenia. Resting-state functional magnetic resonance imaging (rs-fMRI) was performed in 42 in iduals with treatment resistant schizophrenia (TRS), 16 unaffected first-degree family members (UFM) and 42 healthy controls. Whole-brain rs-FC networks were mapped for each in idual and analysed graph theoretically to identify network markers associated with schizophrenia risk or resilience. The ~900 functional connections showing between-group differences were operationalized as conferring: i) resilience, ii) risk, or iii) precipitating risk and/or illness effects. Approximately 95% of connections belonged to the latter two categories, with substantially fewer connections associated with resilience. Schizophrenia risk primarily involved reduced frontal and occipital rs-FC, with patients showing additional reduced frontal and temporal rs-FC. Functional brain networks were characterized by greater local efficiency in UFM, compared to TRS and controls. TRS and UFM share frontal and occipital rs-FC deficits, representing a 'risk' endophenotype. Additional reductions in frontal and temporal rs-FC appear to be associated with risk that precipitates psychosis in vulnerable in iduals, or may be due to other illness-related effects, such as medication. Functional brain networks are more topologically resilient in UFM compared to TRS, which may protect UFM from psychosis onset despite familial liability.
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.
Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors
Publisher: Wiley
Date: 29-05-2020
DOI: 10.1002/HBM.25020
Reducing module size bias of participation coefficient
Publisher: Cold Spring Harbor Laboratory
Date: 29-08-2019
DOI: 10.1101/747162
Abstract: Both natural and engineered networks are often modular. Whether a network node interacts with only nodes from its own module or nodes from multiple modules provides insight into its functional role. The participation coeffcient ( PC ) is typically used to measure this attribute although its value also depends on the size of the module it belongs to, often leading to non-intuitive identification of highly connected nodes. Here, we develop a normalized PC that overcomes the module size bias associated with the conventional PC . Using brain, C.elegans , airport and simulated networks, we show that our measure of participation alleviates the module size bias, while preserving conceptual and mathematical properties, of the classic formulation of PC . Unlike the conventional PC , we identify London and New York as high participators in the air traffic network and demonstrate stronger associations with working memory in human brain networks, yielding new insights into nodal participation across network modules.
Brain stimulation and brain lesions converge on common causal circuits in neuropsychiatric disease
Publisher: Springer Science and Business Media LLC
Date: 08-07-2021
Synchronization lag in post stroke: relation to motor function and structural connectivity
Publisher: MIT Press - Journals
Date: 2019
DOI: 10.1162/NETN_A_00105
Abstract: Stroke is characterized by delays in the resting-state hemodynamic response, resulting in synchronization lag in neural activity between brain regions. However, the structural basis of this lag remains unclear. In this study, we used resting-state functional MRI (rs-fMRI) to characterize synchronization lag profiles between homotopic regions in 15 in iduals (14 males, 1 female) with brain lesions consequent to stroke as well as a group of healthy comparison in iduals. We tested whether the network communication efficiency of each in idual’s structural brain network (connectome) could explain interin idual and interregional variation in synchronization lag profiles. To this end, connectomes were mapped using diffusion MRI data, and communication measures were evaluated under two schemes: shortest paths and navigation. We found that interin idual variation in synchronization lags was inversely associated with communication efficiency under both schemes. Interregional variation in lag was related to navigation efficiency and navigation distance, reflecting its dependence on both distance and structural constraints. Moreover, severity of motor deficits significantly correlated with average synchronization lag in stroke. Our results provide a structural basis for the delay of information transfer between homotopic regions inferred from rs-fMRI and provide insight into the clinical significance of structural-functional relationships in stroke in iduals.
Individual deviations from normative models of brain structure in a large cross-sectional schizophrenia cohort
Publisher: Cold Spring Harbor Laboratory
Date: 18-01-2020
DOI: 10.1101/2020.01.17.911032
Abstract: The heterogeneity of schizophrenia has defied efforts to derive reproducible and definitive anatomical maps of structural brain changes associated with the disorder. We aimed to map deviations from normative ranges of brain structure for in idual patients and evaluate whether the loci of in idual deviations recapitulated group-average brain maps of schizophrenia pathology. For each of 48 white matter tracts and 68 cortical regions, normative percentiles of variation in fractional anisotropy (FA) and cortical thickness (CT) were established using diffusion-weighted and structural MRI from healthy adults (n=195). In iduals with schizophrenia (n=322) were classified as either within the normative range for healthy in iduals of the same age and sex (5-95% percentiles), infra-normal ( % percentile) or supra-normal ( % percentile). Repeating this classification for each tract and region yielded a deviation map for each in idual. Compared to the healthy comparison group, the schizophrenia group showed widespread reductions in FA and CT, involving virtually all white matter tracts and cortical regions. Paradoxically, however, no more than 15-20% of patients deviated from the normative range for any single tract or region, whereas 79% of patients showed infra-normal deviations for at least one locus (healthy in iduals: 59±2%, p .001). Higher polygenic risk for schizophrenia associated with a greater number of regions with infra-normal deviations in CT (r=-0.17, p=0.006). Anatomical loci of schizophrenia-related changes are highly heterogeneous across in iduals to the extent that group-consensus pathological maps are not representative of most in idual patients. Normative modeling can aid in parsing schizophrenia heterogeneity and guiding personalized interventions.
The impact of premorbid and current intellect in schizophrenia
Publisher: Springer Science and Business Media LLC
Date: 04-11-2015
Abstract: Cognitive heterogeneity among people with schizophrenia has been defined on the basis of premorbid and current intelligence quotient (IQ) estimates. In a relatively large, community cohort, we aimed to independently replicate and extend cognitive subtyping work by determining the extent of symptom severity and functional deficits in each group. A total of 635 healthy controls and 534 patients with a diagnosis of schizophrenia or schizoaffective disorder were recruited through the Australian Schizophrenia Research Bank. Patients were classified into cognitive subgroups on the basis of the Wechsler Test of Adult Reading (a premorbid IQ estimate) and current overall cognitive abilities into preserved, deteriorated, and compromised groups using both clinical and empirical ( k -means clustering) methods. Additional cognitive, functional, and symptom outcomes were compared among the resulting groups. A total of 157 patients (29%) classified as ‘preserved’ performed within one s.d. of control means in all cognitive domains. Patients classified as ‘deteriorated’ ( n =239, 44%) performed more than one s.d. below control means in all cognitive domains except estimated premorbid IQ and current visuospatial abilities. A separate 138 patients (26%), classified as ‘compromised,’ performed more than one s.d. below control means in all cognitive domains and displayed greater impairment than other groups on symptom and functional measures. In the present study, we independently replicated our previous cognitive classifications of people with schizophrenia. In addition, we extended previous work by demonstrating worse functional outcomes and symptom severity in the compromised group.
Widespread white matter microstructural differences in schizophrenia across 4322 individuals: results from the ENIGMA Schizophrenia DTI Working Group
Publisher: Springer Science and Business Media LLC
Date: 17-10-2017
DOI: 10.1038/MP.2017.170
The Impact of Childhood Adversity on Cognitive Development in Schizophrenia
Publisher: Oxford University Press (OUP)
Date: 03-05-2020
Abstract: Childhood adversity, such as physical, sexual, and verbal abuse, as well as neglect and family conflict, is a risk factor for schizophrenia. Such adversity can lead to disruptions of cognitive function during development, undermining intellectual capabilities and academic achievement. Schizophrenia is a neurodevelopmental disorder that is associated with cognitive impairments that may become evident during childhood. The Australian Schizophrenia Research Bank database comprises a large community cohort (N = 1169) in which we previously identified 3 distinct cognitive groups among people with schizophrenia: (1) Compromised, current, and estimated premorbid cognitive impairment (2) Deteriorated, substantial decline from estimated premorbid function and (3) Preserved, performing in the normal cognitive range without decline. The compromised group displayed the worst functional and symptom outcomes. Here, we extend our previous work by assessing the relationship among these categories of cognitive abilities and reported childhood adversity in 836 patients and healthy controls. Exploratory factor analysis of the Childhood Adversity Questionnaire revealed 3 factors (lack of parental involvement overt abuse family breakdown and hardship). People with schizophrenia reported significantly more childhood adversity than healthy controls on all items and factors. People with schizophrenia in the compromised group reported significantly more lack of parental involvement and family breakdown and hardship and lower socioeconomic status than those in the deteriorated group. The cognitive groups were not related to family history of psychosis. These findings identify specific social and family factors that impact cognition, highlighting the important role of these factors in the development of cognitive and functional abilities in schizophrenia.
Performance analysis of an OBS edge router
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2004
Delayed Development of Brain Connectivity in Adolescents With Schizophrenia and Their Unaffected Siblings
Publisher: American Medical Association (AMA)
Date: 09-2015
DOI: 10.1001/JAMAPSYCHIATRY.2015.0226
Abstract: Abnormalities in structural brain connectivity have been observed in patients with schizophrenia. Mapping these abnormalities longitudinally and understanding their genetic risk via sibship studies will provide crucial insight into progressive developmental changes associated with schizophrenia. To identify corticocortical connections exhibiting an altered developmental trajectory in adolescents with childhood-onset schizophrenia (COS) and to determine whether similar alterations are found in patients' unaffected siblings. Using prospective structural brain magnetic resonance imaging, large-scale corticocortical connectivity was mapped from ages 12 to 24 years in 109 patients with COS (272 images), 86 of their unaffected siblings (184 images), and 102 healthy controls (262 images) over a 20-year period beginning January 1, 1991, through April 30, 2011, as part of the ongoing COS study at the National Institute of Mental Health. Structural connectivity between pairs of cortical regions was estimated using a validated technique based on across-subject covariation in magnetic resonance imaging-derived cortical thickness measurements. Compared with normally developing controls, significant left-hemisphere occipitotemporal deficits in cortical thickness correlations were found in patients with COS as well as their healthy siblings (P < .05). Deficits in siblings normalized by mid-adolescence, whereas patients with COS showed significantly longer maturational delays, with cortical thickness correlations between the left temporal lobe and left occipital cortex not showing evidence of development until early adulthood. The normalization of deficits with age in patients with COS correlated with improvement in symptoms. Compared with controls, left-hemisphere occipitotemporal thickness correlations in a subgroup of patients with high positive symptoms were significantly reduced from age 14 to 18 years (P < .05) however, other patients with low positive symptoms showed no significant deficits. Delayed maturation of occipitotemporal connectivity appears to be a trait marker in patients with COS, with a milder endophenotype in unaffected siblings associated with resilience to developing schizophrenia. These findings indicate genetically influenced and connection-specific developmental abnormalities in the schizophrenia connectome, and lead to the hypothesis that visual hallucinations in patients with COS may be because of delayed development of the inferior longitudinal fasciculus, a prominent occipitotemporal fiber.
Gray Matter Abnormalities in Idiopathic P arkinson's Disease: Evaluation by Diffusional Kurtosis Imaging and Neurite Orientation Dispersion and Density Imaging
Publisher: Wiley
Date: 04-05-2017
DOI: 10.1002/HBM.23628
Valuation system connectivity is correlated with poly-drug use in young adults
Publisher: Elsevier BV
Date: 12-2021
DOI: 10.1016/J.NEURES.2021.06.006
Abstract: Poly-drug consumption contributes to fatal overdose in more than half of all poly-drug users. Analyzing decision-making networks may give insight into the motivations behind poly-drug use. We correlated average functional connectivity of the valuation system (VS), executive control system (ECS) and valuation-control complex (VCC) in a large population s le (n = 992) with drug use behaviour. VS connectivity is correlated with sedative use, ECS connectivity is separately correlated with hallucinogens and opiates. Network connectivity is also correlated with drug use via two-way interactions with other substances including alcohol and tobacco. These preliminary findings can contribute to our understanding of the common combinations of substance co-use and associated neural patterns.
Development of Brain Network Connectivity in Risky Drinkers During Adolescence
Publisher: Cold Spring Harbor Laboratory
Date: 27-10-2020
DOI: 10.1101/2020.10.22.20217992
Abstract: Alcohol consumption is common in adolescence, a time when the human brain undergoes substantial development, raising concerns about the neurodevelopmental impact of drinking alcohol, especially at high levels. Risky drinking may adversely affect the developing white matter, comprised of axonal fibre pathways that integrate anatomically distributed, and functionally specialised, neural systems. We used diffusion-weighted magnetic resonance imaging (MRI) to perform the first prospective, comprehensive and regionally unbiased connectome-wide analysis of longitudinal changes in inter-regional structural connectivity between 16.5 and 18.8 years of age, comparing adolescents who reported frequent risky drinking ( n = 20) with those who reported limited risky drinking ( n = 19) across this developmental period. We found no difference in the development of structural connectivity between these groups, regardless of whether inter-regional connections were quantified using streamline count, fractional anisotropy, mean diffusivity, axial diffusivity, or radial diffusivity. These findings suggest that risky drinking may have limited adverse effects on the development of inter-regional structural connectivity during mid to late adolescence.
Rich club and reward network connectivity as endophenotypes for alcohol dependence: a diffusion tensor imaging study
Publisher: Wiley
Date: 27-12-2017
DOI: 10.1111/ADB.12599
Abstract: We aimed to examine the whole-brain white matter connectivity and local topology of reward system nodes in patients with alcohol use disorder (AUD) and unaffected siblings, relative to healthy comparison in iduals. Diffusion-weighted magnetic resonance imaging scans were acquired from 18 patients with AUD, 15 unaffected siblings of AUD patients and 15 healthy controls. Structural networks were examined using network-based statistic and connectomic analysis. Connectomic analysis showed a significant ordered difference in normalized rich club organization (AUD < Siblings Sibling > AUD) for both nodal clustering coefficient and nodal local efficiency in reward system nodes, particularly left caudate, right putamen and left hippoc us. Network-based statistic analyses showed that AUD group had significantly weaker connectivity than controls in the right hemisphere, mostly in the edges connecting putamen and hippoc us with other brain regions. Our results suggest that reward system network abnormalities, especially in subcortical structures, and impairments in rich-club organization might be related to the familial predisposition for AUD.
Large-scale brain network dynamics supporting adolescent cognitive control
Publisher: Society for Neuroscience
Date: 15-10-2014
DOI: 10.1523/JNEUROSCI.1634-14.2014
Abstract: Adolescence is a time when the ability to engage cognitive control is linked to crucial life outcomes. Despite a historical focus on prefrontal cortex functioning, recent evidence suggests that differences between in iduals may relate to interactions between distributed brain regions that collectively form a cognitive control network (CCN). Other research points to a spatially distinct and functionally antagonistic system—the default-mode network (DMN)—which typically deactivates during performance of control tasks. This literature implies that in idual differences in cognitive control are determined either by activation or functional connectivity of CCN regions, deactivation or functional connectivity of DMN regions, or some combination of both. We tested between these possibilities using a multilevel fMRI characterization of CCN and DMN dynamics, measured during performance of a cognitive control task and during a task-free resting state, in 73 human adolescents. Better cognitive control performance was associated with (1) reduced activation of CCN regions, but not deactivation of the DMN (2) variations in task-related, but not resting-state, functional connectivity within a distributed network involving both the CCN and DMN (3) functional segregation of core elements of these two systems and (4) task-dependent functional integration of a set of peripheral nodes into either one network or the other in response to prevailing stimulus conditions. These results indicate that in idual differences in adolescent cognitive control are not solely attributable to the functioning of any single region or network, but are instead dependent on a dynamic and context-dependent interplay between the CCN and DMN.
Connectome Spatial Smoothing (CSS): concepts, methods, and evaluation
Publisher: Cold Spring Harbor Laboratory
Date: 21-09-2021
DOI: 10.1101/2021.09.19.461011
Abstract: Structural connectomes are increasingly mapped at high spatial resolutions comprising many hundreds—if not thousands—of network nodes. However, high-resolution connectomes are particularly susceptible to image registration misalignment, tractography artifacts, and noise, all of which can lead to reductions in connectome accuracy and test-retest reliability. We investigate a network analogue of image smoothing to address these key challenges. Connectome Spatial Smoothing (CSS) involves jointly applying a carefully chosen smoothing kernel to the two endpoints of each tractography streamline, yielding a spatially smoothed connectivity matrix. We develop computationally efficient methods to perform CSS using a matrix congruence transformation and evaluate a range of different smoothing kernel choices on CSS performance. We find that smoothing substantially improves the identifiability, sensitivity, and test-retest reliability of high-resolution connectivity maps, though at a cost of increasing storage burden. For atlas-based connectomes (i.e. low-resolution connectivity maps), we show that CSS marginally improves the statistical power to detect associations between connectivity and cognitive performance, particularly for connectomes mapped using probabilistic tractography. CSS was also found to enable more reliable statistical inference compared to connectomes without any smoothing. We provide recommendations on optimal smoothing kernel parameters for connectomes mapped using both deterministic and probabilistic tractography. We conclude that spatial smoothing is particularly important for the reliability of high-resolution connectomes, but can also provide benefits at lower parcellation resolutions. We hope that our work enables computationally efficient integration of spatial smoothing into established structural connectome mapping pipelines. We establish a network equivalent of image smoothing for structural connectomes. Connectome Spatial Smoothing (CSS) improves connectome test-retest reliability, identifiability and sensitivity. CSS also facilitates reliable inference and improves power to detect statistical associations. Both high-resolution and atlas-based connectomes can benefit from CSS.
Spatio‐temporal dynamics of resting‐state brain networks improve single‐subject prediction of schizophrenia diagnosis
Publisher: Wiley
Date: 10-05-2018
DOI: 10.1002/HBM.24202
Insula Functional Connectivity in Schizophrenia: Subregions, Gradients, and Symptoms
Publisher: Elsevier BV
Date: 04-2019
DOI: 10.1016/J.BPSC.2018.12.003
Abstract: The insular cortex is connected to a erse network of cortical and subcortical areas. This study aimed to investigate whether the ersity in functional connectivity across the insula's topography is altered in in iduals with schizophrenia and relates to the clinical symptoms of the disorder. Insula-to-whole-brain functional connectivity was mapped using resting-state functional magnetic resonance imaging at the resolution of voxels in in iduals with schizophrenia (n = 49) and healthy comparison in iduals (n = 52). Diversity in functional connectivity across the insula's topography was represented as discrete subregions and gradients of continuous variation. Canonical correlation analysis was used to relate interin idual variation in insula connectivity to clinical symptoms. Insula connectional ersity was parcellated into two subregions: dorsoanterior and ventroposterior. Compared with the healthy comparison group, subjects with schizophrenia were associated with an overall reduction in insula functional connectivity as well as reduced differentiation in connectivity profiles between these subregions. A significant interaction effect between diagnosis and insula subregion indicated that the anterior subregion in schizophrenia was connected with increased strength to the somatosensory, motor, occipital, and parietal cortices, whereas the posterior subregion showed increased connectivity with the thalamus and prefrontal cortex. Insula connectivity with the anterior cingulate and auditory cortices was significantly associated with cognitive impairment, negative symptoms, poor psychosocial functioning, and longer duration of illness (r = .64, p = .03). Diversity in functional connectivity across the insula's rostrocaudal axis is reduced in schizophrenia, resulting in reduced differentiation between anterior and posterior insula. Interin idual variation in insula connectivity explains variability in some of the clinical symptoms of schizophrenia.
Multilayer network switching rate predicts brain performance
Publisher: Proceedings of the National Academy of Sciences
Date: 13-12-2018
Abstract: Large-scale brain dynamics are characterized by repeating spatiotemporal connectivity patterns that reflect a range of putative different brain states that underlie the dynamic repertoire of brain functions. The role of transition between brain networks is poorly understood, and whether switching between these states is important for behavior has been little studied. Our aim was to model switching between functional brain networks using multilayer network methods and test for associations between model parameters and behavioral measures. We calculated time-resolved fMRI connectivity in 1,003 healthy human adults from the Human Connectome Project. The time-resolved fMRI connectivity data were used to generate a spatiotemporal multilayer modularity model enabling us to quantify network switching, which we define as the rate at which each brain region transits between different networks. We found ( i ) an inverse relationship between network switching and connectivity dynamics, where the latter was defined in terms of time-resolved fMRI connections with variance in time that significantly exceeded phase-randomized surrogate data ( ii ) brain connectivity was lower during intervals of network switching ( iii ) brain areas with frequent network switching had greater temporal complexity ( iv ) brain areas with high network switching were located in association cortices and ( v ) using cross-validated elastic net regression, network switching predicted intersubject variation in working memory performance, planning/reasoning, and amount of sleep. Our findings shed light on the importance of brain dynamics predicting task performance and amount of sleep. The ability to switch between network configurations thus appears to be a fundamental feature of optimal brain function.
Competitive and cooperative dynamics of large-scale brain functional networks supporting recollection
Publisher: Proceedings of the National Academy of Sciences
Date: 17-07-2012
Abstract: Analyses of functional interactions between large-scale brain networks have identified two broad systems that operate in apparent competition or antagonism with each other. One system, termed the default mode network (DMN), is thought to support internally oriented processing. The other system acts as a generic external attention system (EAS) and mediates attention to exogenous stimuli. Reports that the DMN and EAS show anticorrelated activity across a range of experimental paradigms suggest that competition between these systems supports adaptive behavior. Here, we used functional MRI to characterize functional interactions between the DMN and different EAS components during performance of a recollection task known to coactivate regions of both networks. Using methods to isolate task-related, context-dependent changes in functional connectivity between these systems, we show that increased cooperation between the DMN and a specific right-lateralized frontoparietal component of the EAS is associated with more rapid memory recollection. We also show that these cooperative dynamics are facilitated by a dynamic reconfiguration of the functional architecture of the DMN into core and transitional modules, with the latter serving to enhance integration with frontoparietal regions. In particular, the right posterior cingulate cortex may act as a critical information-processing hub that provokes these context-dependent reconfigurations from an intrinsic or default state of antagonism. Our findings highlight the dynamic, context-dependent nature of large-scale brain dynamics and shed light on their contribution to in idual differences in behavior.
A multivariate neuroimaging biomarker of individual outcome to transcranial magnetic stimulation in depression
Publisher: Wiley
Date: 22-07-2019
DOI: 10.1002/HBM.24725
Functional Connectivity of Corticostriatal Circuitry and Psychosis-like Experiences in the General Community
Publisher: Elsevier BV
Date: 07-2019
DOI: 10.1016/J.BIOPSYCH.2019.02.013
Abstract: Psychotic symptoms are proposed to lie on a continuum, ranging from isolated psychosis-like experiences (PLEs) in nonclinical populations to frank disorder. Here, we investigated the neurobiological correlates of this continuum by examining whether functional connectivity of dorsal corticostriatal circuitry, which is disrupted in psychosis patients and in iduals at high risk for psychosis, is associated with the severity of subclinical PLEs. A community s le of 672 adults with no history of psychiatric or neurological illnesses completed a battery of seven questionnaires spanning various PLE domains. Principal component analysis of 12 subscales taken from seven questionnaires was used to estimate major dimensions of PLEs. Dimension scores from principal component analysis were then correlated with whole-brain voxelwise functional connectivity maps of the dorsal striatum in a subset of 353 participants who completed a resting-state neuroimaging protocol. Principal component analysis identified two dimensions of PLEs that accounted for 62.57% of variance in the measures, corresponding to positive (i.e., subthreshold delusions and hallucinations) and negative (i.e., subthreshold social and physical anhedonia) symptom-like PLEs. Reduced functional connectivity between the dorsal striatum and prefrontal and motor cortices correlated with more severe positive PLEs. Increased functional connectivity between the dorsal striatum and motor cortex was associated with more severe negative PLEs. Consistent with past findings in patients and in iduals at high risk for psychosis, subthreshold positive symptomatology is associated with reduced functional connectivity of the dorsal circuit. This finding suggests that the connectivity of this circuit tracks the expression of psychotic phenomena across a broad spectrum of severity, extending from the subclinical domain to clinical diagnosis.
Reply to Yang et al.: Multilayer network switching and behavior.
Publisher: Proceedings of the National Academy of Sciences
Date: 13-08-2019
Brain charts for the human lifespan
Publisher: Cold Spring Harbor Laboratory
Date: 10-06-2021
DOI: 10.1101/2021.06.08.447489
Abstract: Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify in idual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight 1 . Here, we built an interactive resource to benchmark brain morphology, www.brainchart.io , derived from any current or future s le of magnetic resonance imaging (MRI) data. With the goal of basing these reference charts on the largest and most inclusive dataset available, we aggregated 123,984 MRI scans from 101,457 participants aged from 115 days post-conception through 100 postnatal years, across more than 100 primary research studies. Cerebrum tissue volumes and other global or regional MRI metrics were quantified by centile scores, relative to non-linear trajectories 2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones 3 showed high stability of in idual centile scores over longitudinal assessments and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared to non-centiled MRI phenotypes, and provided a standardised measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In sum, brain charts are an essential first step towards robust quantification of in idual deviations from normative trajectories in multiple, commonly-used neuroimaging phenotypes. Our collaborative study proves the principle that brain charts are achievable on a global scale over the entire lifespan, and applicable to analysis of erse developmental and clinical effects on human brain structure. Furthermore, we provide open resources to support future advances towards adoption of brain charts as standards for quantitative benchmarking of typical or atypical brain MRI scans.
Disruptions in white matter microstructure associated with impaired visual associative memory in schizophrenia-spectrum illness
Publisher: Springer Science and Business Media LLC
Date: 24-09-2021
DOI: 10.1007/S00406-021-01333-0
Abstract: Episodic memory ability relies on hippoc al-prefrontal connectivity. However, few studies have examined relationships between memory performance and white matter (WM) microstructure in hippoc al-prefrontal pathways in schizophrenia-spectrum disorder (SSDs). Here, we investigated these relationships in in iduals with first-episode psychosis (FEP) and chronic schizophrenia-spectrum disorders (SSDs) using tractography analysis designed to interrogate the microstructure of WM tracts in the hippoc al-prefrontal pathway. Measures of WM microstructure (fractional anisotropy [FA], radial diffusivity [RD], and axial diffusivity [AD]) were obtained for 47 in iduals with chronic SSDs, 28 FEP in iduals, 52 older healthy controls, and 27 younger healthy controls. Tractography analysis was performed between the hippoc us and three targets involved in hippoc al-prefrontal connectivity (thalamus, amygdala, nucleus accumbens). Measures of WM microstructure were then examined in relation to episodic memory performance separately across each group. Both those with FEP and chronic SSDs demonstrated impaired episodic memory performance. However, abnormal WM microstructure was only observed in in iduals with chronic SSDs. Abnormal WM microstructure in the hippoc al-thalamic pathway in the right hemisphere was associated with poorer memory performance in in iduals with chronic SSDs. These findings suggest that disruptions in WM microstructure in the hippoc al-prefrontal pathway may contribute to memory impairments in in iduals with chronic SSDs but not FEP.
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.
Complexity in relational processing predicts changes in functional brain network dynamics
Publisher: Oxford University Press (OUP)
Date: 05-04-2014
Abstract: The ability to link variables is critical to many high-order cognitive functions, including reasoning. It has been proposed that limits in relating variables depend critically on relational complexity, defined formally as the number of variables to be related in solving a problem. In humans, the prefrontal cortex is known to be important for reasoning, but recent studies have suggested that such processes are likely to involve widespread functional brain networks. To test this hypothesis, we used functional magnetic resonance imaging and a classic measure of deductive reasoning to examine changes in brain networks as a function of relational complexity. As expected, behavioral performance declined as the number of variables to be related increased. Likewise, increments in relational complexity were associated with proportional enhancements in brain activity and task-based connectivity within and between 2 cognitive control networks: A cingulo-opercular network for maintaining task set, and a fronto-parietal network for implementing trial-by-trial control. Changes in effective connectivity as a function of increased relational complexity suggested a key role for the left dorsolateral prefrontal cortex in integrating and implementing task set in a trial-by-trial manner. Our findings show that limits in relational processing are manifested in the brain as complexity-dependent modulations of large-scale networks.
Brain-predicted age associates with psychopathology dimensions in youth
Publisher: Cold Spring Harbor Laboratory
Date: 14-06-2020
DOI: 10.1101/2020.06.13.149658
Abstract: This study aims to investigate whether dimensional constructs of psychopathology relate to advanced, attenuated or normal patterns of brain development, and to determine whether these constructs share common neurodevelopmental profiles. Psychiatric symptom ratings from 9312 youths (8-21 years) were parsed into 7 independent dimensions of clinical psychopathology representing conduct, anxiety, obsessive-compulsive, attention, depression, bipolar, and psychosis symptoms. Using a subset of this cohort with structural MRI ( n =1313), a normative model of brain morphology was established and the model was then applied to predict the age of youth with clinical symptoms. We investigated whether the deviation of brain-predicted age from true chronological age, called the brain age gap, explained in idual variation in each psychopathology dimension. In idual variation in the brain age gap significantly associated with clinical dimensions representing psychosis ( t =3.16, p =0.0016), obsessive-compulsive symptoms ( t =2.5, p =0.01), and general psychopathology ( t =4.08, p .0001). Greater symptom severity along these dimensions was associated with brain morphology that appeared older than expected for typically developing youth of the same age. Psychopathology dimensions clustered into two modules based on shared brain loci where putative accelerated neurodevelopment was most prominent. Patterns of morphological development were accelerated in frontal cortices for depression, psychosis and conduct symptoms (Module I), whereas acceleration was most evident in subcortex and insula for the remaining dimensions (Module II). Our findings suggest that advanced brain development, particularly in frontal cortex and subcortical nuclei, underpins clinical psychosis and obsessive-compulsive symptoms in youth. Psychopathology dimensions share common neural substrates, despite representing clinically independent symptom profiles.
White Matter Pathology in Schizophrenia
Publisher: Springer International Publishing
Date: 2020
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.
Communication dynamics in the human connectome shape the cortex-wide propagation of direct electrical stimulation
Publisher: Cold Spring Harbor Laboratory
Date: 06-07-2022
DOI: 10.1101/2022.07.05.498875
Abstract: Communication between gray matter regions underpins all facets of brain function. To date, progress in understanding large-scale neural communication has been h ered by the inability of current neuroimaging techniques to track signaling at whole-brain, high-spatiotemporal resolution. Here, we use 2.77 million intracranial EEG recordings, acquired following 29,055 single-pulse electrical stimulations in a total of 550 in iduals, to study inter-areal communication in the human brain. We found that network communication models—computed on structural connectivity inferred from diffusion MRI—can explain the propagation of direct, focal electrical stimulation through white matter, measured at millisecond time scales. Building on this finding, we show that a parsimonious statistical model comprising structural, functional and spatial factors can accurately and robustly predict cortex-wide effects of brain stimulation (out-of-s le R 2 =54%). Our work contributes towards the biological validation of concepts in network neuroscience and provides insight into how white matter connectivity shapes inter-areal signaling. We anticipate that our findings will have implications for research on macroscale neural information processing and the design of brain stimulation paradigms.
Tractography-Guided Deep Brain Stimulation of the Anteromedial Globus Pallidus Internus for Refractory Obsessive-Compulsive Disorder: Case Report
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 17-07-2019
Abstract: Obsessive-compulsive disorder (OCD) is a disabling psychiatric disorder, mainly treated with psychotherapy and pharmacotherapy. Surgical intervention may be appropriate for patients with treatment-refractory OCD. Deep brain stimulation (DBS) is an alternative for previously common ablative surgical procedures. Tractography has been proposed as a method for in idualizing DBS treatment and may have the potential to improve efficacy. We present a patient with treatment-refractory OCD previously treated with bilateral leucotomies, who underwent DBS surgery with targeting informed by tractography. Preoperative tractography to identify suitable DBS targets was undertaken. Structural images were also utilized for standard stereotactic surgical planning. The anteromedial globus pallidus internus (amGPi) was chosen as the target bilaterally after consideration of white matter projections to frontal cortical regions and neurosurgical approach. Bilateral amGPi DBS surgery was undertaken without adverse events. At 16-mo follow-up, there was a 48.5% reduction in OCD symptom severity as measured by the Yale-Brown Obsessive Compulsive Scale. The amGPi can be a successful DBS target for OCD. This is the first known case to report on DBS surgery postleucotomies for OCD and highlights the utility of tractography for surgical planning in OCD.
Stabilizing deflection routing in optical burst switched networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2007
On the relationship between instantaneous phase synchrony and correlation-based sliding windows for time-resolved fMRI connectivity analysis
Publisher: Elsevier BV
Date: 11-2018
DOI: 10.1016/J.NEUROIMAGE.2018.06.020
Abstract: Correlation-based sliding window analysis (CSWA) is the most commonly used method to estimate time-resolved functional MRI (fMRI) connectivity. However, instantaneous phase synchrony analysis (IPSA) is gaining popularity mainly because it offers single time-point resolution of time-resolved fMRI connectivity. We aim to provide a systematic comparison between these two approaches, on temporal, topological and anatomical levels. For this purpose, we used resting-state fMRI data from two separate cohorts with different temporal resolutions (45 healthy subjects from Human Connectome Project fMRI data with repetition time of 0.72 s and 25 healthy subjects from a separate validation fMRI dataset with a repetition time of 3 s). For time-resolved functional connectivity analysis, we calculated tapered CSWA over a wide range of different window lengths that were compared to IPSA. We found a strong association in connectivity dynamics between IPSA and CSWA when considering the absolute values of CSWA. The association between CSWA and IPSA was stronger for a window length of ∼20 s (shorter than filtered fMRI wavelength) than ∼100 s (longer than filtered fMRI wavelength), irrespective of the s ling rate of the underlying fMRI data. Narrow-band filtering of fMRI data (0.03-0.07 Hz) yielded a stronger relationship between IPSA and CSWA than wider-band (0.01-0.1 Hz). On a topological level, time-averaged IPSA and CSWA nodes were non-linearly correlated for both short (∼20 s) and long (∼100 s) windows, mainly because nodes with strong negative correlations (CSWA) displayed high phase synchrony (IPSA). IPSA and CSWA were anatomically similar in the default mode network, sensory cortex, insula and cerebellum. Our results suggest that IPSA and CSWA provide comparable characterizations of time-resolved fMRI connectivity for appropriately chosen window lengths. Although IPSA requires narrow-band fMRI filtering, it does not mandate a (semi-)arbitrary choice of window length and window overlap. A code for calculating IPSA is provided.
Proportional thresholding in resting-state fMRI functional connectivity networks and consequences for patient-control connectome studies: Issues and recommendations
Publisher: Elsevier BV
Date: 05-2017
DOI: 10.1016/J.NEUROIMAGE.2017.02.005
Abstract: Graph theoretical analysis has become an important tool in the examination of brain dysconnectivity in neurological and psychiatric brain disorders. A common analysis step in the construction of the functional graph or network involves "thresholding" of the connectivity matrix, selecting the set of edges that together form the graph on which network organization is evaluated. To avoid systematic differences in absolute number of edges, studies have argued against the use of an "absolute threshold" in case-control studies and have proposed the use of "proportional thresholding" instead, in which a pre-defined number of strongest connections are selected as network edges, ensuring equal network density across datasets. Here, we systematically studied the effect of proportional thresholding on the construction of functional matrices and subsequent graph analysis in patient-control functional connectome studies. In a few simple experiments we show that differences in overall strength of functional connectivity (FC) - as often observed between patients and controls - can have predictable consequences for between-group differences in network organization. In in idual networks with lower overall FC the proportional thresholding algorithm has to select more edges based on lower correlations, which have (on average) a higher probability of being spurious, and thus introduces a higher degree of randomness in the resulting network. We show across both empirical and artificial patient-control datasets that lower levels of overall FC in either the patient or control group will most often lead to differences in network efficiency and clustering, suggesting that differences in FC across subjects will be artificially inflated or translated into differences in network organization. Based on the presented case-control findings we inform about the caveats of proportional thresholding in patient-control studies in which groups show a between-group difference in overall FC. We make recommendations on how to examine, report and to take into account overall FC effects in future patient-control functional connectome studies.
Increased peripheral inflammation in schizophrenia is associated with worse cognitive performance and related cortical thickness reductions.
Publisher: Springer Science and Business Media LLC
Date: 24-03-2021
DOI: 10.1007/S00406-021-01237-Z
Abstract: While the biological substrates of brain and behavioural changes in persons with schizophrenia remain unclear, increasing evidence implicates that inflammation is involved. In schizophrenia, including first-episode psychosis and anti-psychotic naïve patients, there are numerous reports of increased peripheral inflammation, cognitive deficits and neuropathologies such as cortical thinning. Research defining the relationship between inflammation and schizophrenia symptomatology and neuropathology is needed. Therefore, we analysed the level of C-reactive protein (CRP), a peripheral inflammation marker, and its relationship with cognitive functioning in a cohort of 644 controls and 499 schizophrenia patients. In a subset of in iduals who underwent MRI scanning (99 controls and 194 schizophrenia cases), we tested if serum CRP was associated with cortical thickness. CRP was significantly increased in schizophrenia patients compared to controls, co-varying for age, sex, overweight/obesity and diabetes (p < 0.006E-10). In schizophrenia, increased CRP was mildly associated with worse performance in attention, controlling for age, sex and education (R =- 0.15, p = 0.001). Further, increased CRP was associated with reduced cortical thickness in three regions related to attention: the caudal middle frontal, the pars opercularis and the posterior cingulate cortices, which remained significant after controlling for multiple comparisons (all p < 0.05). Together, these findings indicate that increased peripheral inflammation is associated with deficits in cognitive function and brain structure in schizophrenia, especially reduced attention and reduced cortical thickness in associated brain regions. Using CRP as a biomarker of peripheral inflammation in persons with schizophrenia may help to identify vulnerable patients and those that may benefit from adjunctive anti-inflammatory treatments.
Differential effect of disease-associated ST8SIA2 haplotype on cerebral white matter diffusion properties in schizophrenia and healthy controls
Publisher: Springer Science and Business Media LLC
Date: 22-01-2018
DOI: 10.1038/S41398-017-0052-Z
Abstract: Brain white matter abnormalities are evident in in iduals with schizophrenia, and also their first-degree relatives, suggesting that some alterations may relate to underlying genetic risk. The ST8 alpha-N-acetyl-neuraminide alpha-2,8-sialyltransferase 2 ( ST8SIA2) gene, which encodes the alpha-2,8-sialyltransferase 8B enzyme that aids neuronal migration and synaptic plasticity, was previously implicated as a schizophrenia susceptibility gene. This study examined the extent to which specific haplotypes in ST8SIA2 influence white matter microstructure using diffusion-weighted imaging of in iduals with schizophrenia ( n = 281) and healthy controls ( n = 172), recruited across five Australian sites. Interactions between diagnostic status and the number of haplotype copies (0 or ≥1) were tested across all white matter voxels with cluster-based statistics. Fractional anisotropy (FA) in the right parietal lobe was found to show a significant interaction between diagnosis and ST8SIA2 protective haplotype ( p 0.05, family-wise error rate (FWER) cluster-corrected). The protective haplotype was associated with increased FA in controls, but this effect was reversed in people with schizophrenia. White matter fiber tracking revealed that the region-of-interest was traversed by portions of the superior longitudinal fasciculus, corona radiata, and posterior limb of internal capsule. Post hoc analysis revealed that reduced FA in this regional juncture correlated with reduced IQ in people with schizophrenia. The ST8SIA2 risk haplotype copy number did not show any differential effects on white matter. This study provides a link between a common disease-associated haplotype and specific changes in white matter microstructure, which may relate to resilience or risk for mental illness, providing further compelling evidence for involvement of ST8SIA2 in the pathophysiology of schizophrenia.
Sparse coupled logistic regression to estimate co-activation and modulatory influences of brain regions
Publisher: IOP Publishing
Date: 19-11-2020
Abstract: Accurate mapping of the functional interactions between remote brain areas with resting-state functional magnetic resonance imaging requires the quantification of their underlying dynamics. In conventional methodological pipelines, a spatial scale of interest is first selected and dynamic analysis then proceeds at this hypothesised level of complexity. If large-scale functional networks or states are studied, more local regional rearrangements are then not described, potentially missing important neurobiological information. Here, we propose a novel mathematical framework that jointly estimates resting-state functional networks and spatially more localised cross-regional modulations. To do so, the changes in activity of each brain region are modelled by a logistic regression including co-activation coefficients (reflective of network assignment, as they highlight simultaneous activations across areas) and causal interplays (denoting finer regional cross-talks, when one region active at time t modulates the t to t + 1 transition likelihood of another area). A two-parameter ℓ 1 regularisation scheme is used to make these two sets of coefficients sparse: one controls overall sparsity, while the other governs the trade-off between co-activations and causal interplays, enabling to properly fit the data despite the yet unknown balance between both types of couplings. Across a range of simulation settings, we show that the framework successfully retrieves the two types of cross-regional interactions at once. Performance across noise and s le size settings was globally on par with that of other existing methods, with the potential to reveal more precise information missed by alternative approaches. Preliminary application to experimental data revealed that in the resting brain, co-activations and causal modulations co-exist with a varying balance across regions. Our methodological pipeline offers a conceptually elegant alternative for the assessment of functional brain dynamics and can be downloaded at c4science.ch/source/Sparse_logistic_regression.git .
Connectomes for 40,000 UK Biobank participants: A multi-modal, multi-scale brain network resource
Publisher: Cold Spring Harbor Laboratory
Date: 10-03-2023
DOI: 10.1101/2023.03.10.532036
Abstract: We mapped functional and structural brain networks for more than 40,000 UK Biobank participants. Structural connectivity was estimated with tractography and diffusion MRI. Resting-state functional MRI was used to infer regional functional connectivity. We provide high-quality structural and functional connectomes for multiple parcellation granularities, several alternative measures of interregional connectivity, and a variety of common data pre-processing techniques, yielding more than one million connectomes in total and requiring more than 200,000 hours of compute time. For a single subject, we provide 28 out-of-the-box versions of structural and functional brain networks, allowing users to select, e.g., the parcellation and connectivity measure that best suit their research goals. Furthermore, we provide code and intermediate data for the time-efficient reconstruction of more than 1,000 different versions of a subject’s connectome based on an array of methodological choices. All connectomes are available via the UK Biobank data sharing platform and our connectome mapping pipelines are openly available. In this report, we describe our connectome resource in detail for users, outline key considerations in developing an efficient pipeline to map an unprecedented number of connectomes, and report on the quality control procedures that were completed to ensure connectome reliability and accuracy. We demonstrate that our structural and functional connectivity matrices meet a number of quality control checks and replicate previously established findings in network neuroscience. We envisage that our resource will enable new studies of the human connectome in health, disease and aging at an unprecedented scale.
Machine learning prediction of cognition from functional connectivity: Are feature weights reliable?
Publisher: Elsevier BV
Date: 12-2021
DOI: 10.1016/J.NEUROIMAGE.2021.118648
Abstract: Cognitive performance can be predicted from an in idual's functional brain connectivity with modest accuracy using machine learning approaches. As yet, however, predictive models have arguably yielded limited insight into the neurobiological processes supporting cognition. To do so, feature selection and feature weight estimation need to be reliable to ensure that important connections and circuits with high predictive utility can be reliably identified. We comprehensively investigate feature weight test-retest reliability for various predictive models of cognitive performance built from resting-state functional connectivity networks in healthy young adults (n=400). Despite achieving modest prediction accuracies (r=0.2-0.4), we find that feature weight reliability is generally poor for all predictive models (ICC< 0.3), and significantly poorer than predictive models for overt biological attributes such as sex (ICC≈0.5). Larger s le sizes (n=800), the Haufe transformation, non-sparse feature selection/regularization and smaller feature spaces marginally improve reliability (ICC< 0.4). We elucidate a tradeoff between feature weight reliability and prediction accuracy and find that univariate statistics are marginally more reliable than feature weights from predictive models. Finally, we show that measuring agreement in feature weights between cross-validation folds provides inflated estimates of feature weight reliability. We thus recommend for reliability to be estimated out-of-s le, if possible. We argue that rebalancing focus from prediction accuracy to model reliability may facilitate mechanistic understanding of cognition with machine learning approaches.
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.
Valuation system connectivity is correlated with poly-drug use in young adults
Publisher: Cold Spring Harbor Laboratory
Date: 18-11-2020
DOI: 10.1101/2020.11.17.20233601
Abstract: Poly-drug consumption contributes to fatal overdose in more than half of all poly-drug users. Analyzing decision-making networks may give insight into the motivations behind poly-drug use. We correlated average functional connectivity of the valuation system (VS), executive control system (ECS) and valuation-control complex (VCC) in a large population s le (n=992) with drug use behaviour. VS connectivity is correlated with sedative use, ECS connectivity is separately correlated with hallucinogens and opiates. Network connectivity is also correlated with drug use via two-way interactions with other substances including alcohol and tobacco. These preliminary findings can contribute to our understanding of the common combinations of substance co-use and associated neural patterns.
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.
Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality
Publisher: Springer Science and Business Media LLC
Date: 06-04-2023
Network Analysis of Symptom Comorbidity in Schizophrenia: Relationship to Illness Course and Brain White Matter Microstructure
Publisher: Oxford University Press (OUP)
Date: 08-03-2021
Abstract: Recent network-based analyses suggest that schizophrenia symptoms are intricately connected and interdependent, such that central symptoms can activate adjacent symptoms and increase global symptom burden. Here, we sought to identify key clinical and neurobiological factors that relate to symptom organization in established schizophrenia. A symptom comorbidity network was mapped for a broad constellation of symptoms measured in 642 in iduals with a schizophrenia-spectrum disorder. Centrality analyses were used to identify hub symptoms. The extent to which each patient’s symptoms formed clusters in the comorbidity network was quantified with cluster analysis and used to predict (1) clinical features, including illness duration and psychosis (positive symptom) severity and (2) brain white matter microstructure, indexed by the fractional anisotropy (FA), in a subset (n = 296) of in iduals with diffusion-weighted imaging (DWI) data. Global functioning, substance use, and blunted affect were the most central symptoms within the symptom comorbidity network. Symptom profiles for some patients formed highly interconnected clusters, whereas other patients displayed unrelated and disconnected symptoms. Stronger clustering among an in idual’s symptoms was significantly associated with shorter illness duration (t = 2.7 P = .0074), greater psychosis severity (ie, positive symptoms expression) (t = −5.5 P & 0.0001) and lower fractional anisotropy in fibers traversing the cortico-cerebellar-thalamic-cortical circuit (r = .59, P & 0.05). Symptom network structure varies over the course of schizophrenia: symptom interactions weaken with increasing illness duration and strengthen during periods of high positive symptom expression. Reduced white matter coherence relates to stronger symptom clustering, and thus, may underlie symptom cascades and global symptomatic burden in in iduals with schizophrenia.
Analysis of OBS Networks With Limited Wavelength Conversion
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2006
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.
Network communication models improve the behavioral and functional predictive utility of the human structural connectome
Publisher: MIT Press - Journals
Date: 2020
DOI: 10.1162/NETN_A_00161
Abstract: The connectome provides the structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for polysynaptic communication in structural connectomes would improve prediction of interin idual variation in behavior as well as increase structure-function coupling strength. Connectomes were mapped for 889 healthy adults participating in the Human Connectome Project. To account for polysynaptic signaling, connectomes were transformed into communication matrices for each of 15 different network communication models. Communication matrices were (a) used to perform predictions of five data-driven behavioral dimensions and (b) correlated to resting-state functional connectivity (FC). While FC was the most accurate predictor of behavior, communication models, in particular communicability and navigation, improved the performance of structural connectomes. Communication also strengthened structure-function coupling, with the navigation and shortest paths models leading to 35–65% increases in association strength with FC. We combined behavioral and functional results into a single ranking that provides insight into which communication models may more faithfully recapitulate underlying neural signaling patterns. Comparing results across multiple connectome mapping pipelines suggested that modeling polysynaptic communication is particularly beneficial in sparse high-resolution connectomes. We conclude that network communication models can augment the functional and behavioral predictive utility of the human structural connectome.
Reducing Spare Capacity Through Traffic Splitting
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2004
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.
A framework for solving logical topology design problems within constrained computation time
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2003
Disrupted axonal fiber connectivity in schizophrenia
Publisher: Elsevier BV
Date: 2011
The structural connectome constrains fast brain dynamics
Publisher: Cold Spring Harbor Laboratory
Date: 25-11-2020
DOI: 10.1101/2020.11.25.393017
Abstract: Brain activity during rest displays complex, rapidly evolving patterns in space and time. Structural connections comprising the human connectome are hypothesized to impose constraints on the dynamics of this activity. Here, we use magnetoencephalography (MEG) to quantify the extent to which fast neural dynamics in the human brain are constrained by structural connections inferred from diffusion MRI tractography. We characterize the spatio-temporal unfolding of whole-brain activity at the millisecond scale from source-reconstructed MEG data, estimating the probability that any two brain regions will significantly deviate from baseline activity in consecutive time epochs. We find that the structural connectome relates to, and likely affects, the rapid spreading of neuronal avalanches, evidenced by a significant association between these transition probabilities and structural connectivity strengths (r=0.37, p .0001). This finding opens new avenues to study the relationship between brain structure and neural dynamics.
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.
Brain Network Connectivity in Adolescents At Risk for Alcohol Use Problems
Publisher: Cold Spring Harbor Laboratory
Date: 27-10-2020
DOI: 10.1101/2020.10.22.20217976
Abstract: Imaging studies of young people with a family history of alcohol use disorder (AUD) have found structural and/or functional differences within and between anatomically distributed and functionally specialised systems throughout the brain. Differences in brain connectivity among adolescents with a family history of AUD may account for the increased risk of later alcohol use problems however, to date, no prospective studies have directly examined this hypothesis across the entire connectome in a regionally unbiased way. Our analysis included 52 adolescents ( M age = 16.5 years ± 0.6 SD ) assessed with diffusion-weighted magnetic resonance imaging, of whom 20 had a family history of AUD and 32 did not. All participants were followed-up 2.3 years later and completed a questionnaire measuring past year alcohol use and alcohol-related harms. Subject-specific connectomic maps of structural connectivity were constructed using two parcellation schemes (82-node anatomical and 530-node random) and five measures of connectivity weight (streamline count, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity), and a connectome-wide network-based statistic analysis was used to compare group differences at each and every connection between adolescents with and without a family history of AUD. Baseline connectivity measures did not differentiate these groups, and we did not find an association between baseline connectivity measures and alcohol outcomes at follow-up. These findings suggest that atypical inter-regional structural connectivity may not contribute to the risk of developing alcohol use problems in this particular age group, or during this particular period of development.
Cerebello-cerebral connectivity deficits in Friedreich ataxia
Publisher: Springer Science and Business Media LLC
Date: 07-04-2014
DOI: 10.1007/S00429-013-0547-1
Abstract: Brain pathology in Friedreich ataxia is characterized by progressive degeneration of nervous tissue in the brainstem, cerebellum and cerebellar peduncles. Evidence of cerebral involvement is however equivocal. This brain imaging study investigates cerebello-cerebral white matter connectivity in Friedreich ataxia with diffusion MRI and tractography performed in 13 in iduals homozygous for a GAA expansion in intron one of the frataxin gene and 14 age- and gender-matched control participants. New evidence is presented for disrupted cerebello-cerebral connectivity in the disease, leading to secondary effects in distant cortical and subcortical regions. Remote regions affected by primary cerebellar and brainstem pathology include the supplementary motor area, cingulate cortex, frontal cortices, putamen and other subcortical nuclei. The connectivity disruptions identified provide an explanation for some of the non-ataxic symptoms observed in the disease and support the notion of reverse cerebellar diaschisis. This is the first study to comprehensively map white matter connectivity disruptions in Friedreich ataxia using tractography, connectomic techniques and super-resolution track density imaging.
Temporal complexity of fMRI is reproducible and correlates with higher order cognition
Publisher: Cold Spring Harbor Laboratory
Date: 16-09-2019
DOI: 10.1101/770826
Abstract: It has been hypothesized that resting state networks (RSNs) likely display unique temporal complexity fingerprints, quantified by their multi-scale entropy patterns [1]. This is a hypothesis with a potential capacity for developing digital biomarkers of normal brain function, as well as pathological brain dysfunction. Nevertheless, a limitation of [1] was that resting state functional magnetic resonance imaging (rsfMRI) data from only 20 healthy in iduals was used for the analysis. To validate this hypothesis in a larger cohort, we used rsfMRI datasets of 1000 healthy young adults from the Human Connectome Project (HCP), aged 22-35, each with four 14.4-minute rsfMRI recordings and parcellated into 379 brain regions. We quantified multi-scale entropy of rsfMRI time series averaged at different cortical and sub-cortical regions. We performed effect-size analysis on the data in 8 RSNs. Given that the morphology of multi-scale entropy is affected by the choice of its tolerance parameter ( r ) and embedding dimension ( m ), we repeated the analyses at multiple values of r and m including the values used in [1]. Our results reinforced high temporal complexity in the default mode and frontoparietal networks. Lowest temporal complexity was observed in the sub-cortical areas and limbic system. We investigated the effect of temporal resolution (determined by the repetition time T R ) after downs ling of rsfMRI time series at two rates. At a low temporal resolution, we observed increased entropy and variance across datasets. Test-retest analysis showed that findings were likely reproducible across in iduals over four rsfMRI runs, especially when the tolerance parameter r is equal to 0.5. A strong relationship was observed between temporal complexity of RSNs and fluid intelligence (people’s capacity to reason and think flexibly) through step-wise regression analysis suggesting that complex dynamics of the human brain is an important attribute of high-level brain function. Finally, the results confirmed that the relationship between functional brain connectivity strengths and rsfMRI temporal complexity changes over time scales, likely due to the regulation of neural synchrony at local and global network levels.
PET imaging of putative microglial activation in individuals at ultra-high risk for psychosis, recently diagnosed and chronically ill with schizophrenia
Publisher: Springer Science and Business Media LLC
Date: 29-08-2017
DOI: 10.1038/TP.2017.193
Effect of long-term cannabis use on axonal fibre connectivity
Publisher: Oxford University Press (OUP)
Date: 04-06-2012
DOI: 10.1093/BRAIN/AWS136
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.
Neuregulin-1 (NRG1) polymorphisms linked with psychosis transition are associated with enlarged lateral ventricles and white matter disruption in schizophrenia.
Publisher: Cambridge University Press (CUP)
Date: 22-08-2017
DOI: 10.1017/S0033291717002173
Abstract: Two single-nucleotide polymorphisms (SNPs) (rs4281084 and rs12155594) within the neuregulin-1 (NRG1) gene have been associated with psychosis transition. However, the neurobiological changes associated with these SNPs remain unclear. We aimed to determine what relationship these two SNPs have on lateral ventricular volume and white matter integrity, as abnormalities in these brain structures are some of the most consistent in schizophrenia. Structural ( n = 370) and diffusion ( n = 465) magnetic resonance imaging data were obtained from affected and unaffected in iduals predominantly of European descent. The SNPs rs4281084, rs12155594, and their combined allelic load were examined for their effects on lateral ventricular volume, fractional anisotropy (FA) as well as axial (AD) and radial (RD) diffusivity. Additional exploratory analyses assessed NRG1 effects on gray matter volume, cortical thickness, and surface area throughout the brain. In iduals with a schizophrenia age of onset ⩽25 and a combined allelic load ⩾3 NRG1 risk alleles had significantly larger right (up to 50%, p adj = 0.01) and left (up to 45%, p adj = 0.05) lateral ventricle volumes compared with those with allelic loads of less than three. Furthermore, carriers of three or more risk alleles, regardless of age of onset and case status, had significantly reduced FA and elevated RD but stable AD in the frontal cortex compared with those carrying fewer than three risk alleles. Our findings build on a growing body of research supporting the functional importance of genetic variation within the NRG1 gene and complement previous findings implicating the rs4281084 and rs12155594 SNPs as markers for psychosis transition.
Investigation of peripheral complement factors across stages of psychosis
Publisher: Elsevier BV
Date: 02-2019
DOI: 10.1016/J.SCHRES.2018.11.035
Abstract: The complement cascade has been proposed to contribute to the pathogenesis of schizophrenia. However, it remains unclear whether peripheral complement levels differ in cases compared to controls, change over the course of illness and whether they are associated with current symptomatology. This study aimed to: i) investigate whether peripheral complement protein levels are altered at different stages of illness, and ii) identify patterns among complement protein levels that predict clinical symptoms. Complement factors C1q, C3 and C4 were quantified in 183 participants [n = 83 Healthy Controls (HC), n = 10 Ultra-High Risk (UHR) for psychosis, n = 40 First Episode Psychosis (FEP), n = 50 Chronic schizophrenia] using Multiplex ELISA. Permutation-based t-tests were used to assess between-group differences in complement protein levels at each of the three illness stages, relative to age- and gender-matched healthy controls. Canonical correlation analysis was used to identify patterns of complement protein levels that correlated with clinical symptoms. C4 was significantly increased in chronic schizophrenia patients, while C3 and C4 were significantly increased in UHR patients. There were no differences in C1q, C3 and C4 in FEP patients when adjusting for BMI. A molecular pattern of increased C4 and decreased C3 was associated with positive and negative symptom severity in the pooled patient s le. Our findings indicate that peripheral complement concentration is increased across specific stages of psychosis and its imbalance may be associated with symptom severity. Given the small s le size of the UHR group, these findings should be regarded as exploratory, requiring replication.
Altered resting functional connectivity patterns associated with problematic substance use and substance use disorders during adolescence
Publisher: Elsevier BV
Date: 2021
Evidence for network-based cortical thickness reductions in schizophrenia
Publisher: American Psychiatric Association Publishing
Date: 07-2019
DOI: 10.1176/APPI.AJP.2019.18040380
Abstract: Cortical thickness reductions in schizophrenia are irregularly distributed across multiple loci. The authors hypothesized that cortical connectivity networks would explain the distribution of cortical thickness reductions across the cortex, and, specifically, that cortico-cortical connectivity between loci with these reductions would be exceptionally strong and form an interconnected network. This hypothesis was tested in three cross-sectional schizophrenia cohorts: first-episode psychosis, chronic schizophrenia, and treatment-resistant schizophrenia. Structural brain images were acquired for 70 patients with first-episode psychosis, 153 patients with chronic schizophrenia, and 47 patients with treatment-resistant schizophrenia and in matching healthy control groups (N=57, N=168, and N=54, respectively). Cortical thickness was compared between the patient and respective control groups at 148 regions spanning the cortex. Structural connectivity strength between pairs of cortical regions was quantified with structural covariance analysis. Connectivity strength between regions with cortical thickness reductions was compared with connectivity strength between 5,000 sets of randomly chosen regions to establish whether regions with reductions were interconnected more strongly than would be expected by chance. Significant (false discovery rate corrected) and widespread cortical thickness reductions were found in the chronic schizophrenia (79 regions) and treatment-resistant schizophrenia (106 regions) groups, with more circumscribed reductions in the first-episode psychosis group (34 regions). Cortical thickness reductions with the largest effect sizes were found in frontal, temporal, cingulate, and insular regions. In all cohorts, both the patient and healthy control groups showed significantly increased structural covariance between regions with cortical thickness reductions compared with randomly selected regions. Brain network architecture can explain the irregular topographic distribution of cortical thickness reductions in schizophrenia. This finding, replicated in three distinct schizophrenia cohorts, suggests that the effect is robust and independent of illness stage.
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.
Functional Connectivity of Corticostriatal Circuitry and Psychosis-Like Experiences in the General Community
Publisher: Cold Spring Harbor Laboratory
Date: 07-10-2018
DOI: 10.1101/437376
Abstract: Psychotic symptoms are proposed lie on a continuum, ranging from isolated psychosis-like experiences (PLEs) in non-clinical populations to frank disorder. Here, we investigate neurobiological correlates of this symptomatologic continuum by examining whether functional connectivity of dorsal corticostriatal circuitry, which is disrupted in patients and high-risk in iduals, is associated with the severity of subclinical PLEs. A community s le of 672 adults with no history of psychiatric or neurological illnesses completed a battery of seven questionnaires spanning various PLE domains. Principal component analysis (PCA) estimated major dimensions of PLEs from the questionnaires. PCA dimension scores were then correlated with whole-brain voxelwise functional connectivity (FC) maps of the striatum in a subset of 353 participants who completed a resting-state neuroimaging protocol. PCA identified two dimensions of PLEs accounting for 62.57% of variance in the measures, corresponding to positive and negative PLEs. Reduced FC between the dorsal striatum and prefrontal cortex correlated with higher positive PLEs. Negative PLEs correlated with increased FC between the dorsal striatum and visual and sensorimotor areas. In the ventral corticostriatal system, positive and negative PLEs were both associated with FC between the ventro-rostral putamen and sensorimotor cortices. Consistent with past findings in patients and high-risk in iduals, subthreshold positive symptomatology is associated with reduced FC of the dorsal circuit. These findings suggest that the connectivity of this circuit tracks the expression of psychotic phenomena across a broad spectrum of severity, extending from the subclinical domain to clinical diagnosis.
Connectivity differences in brain networks
Publisher: Elsevier BV
Date: 04-2012
DOI: 10.1016/J.NEUROIMAGE.2012.01.068
Abstract: The scenario considered here is one where brain connectivity is represented as a network and an experimenter wishes to assess the evidence for an experimental effect at each of the typically thousands of connections comprising the network. To do this, a univariate model is independently fitted to each connection. It would be unwise to declare significance based on an uncorrected threshold of α=0.05, since the expected number of false positives for a network comprising N=90 nodes and N(N-1)/2=4005 connections would be 200. Control of Type I errors over all connections is therefore necessary. The network-based statistic (NBS) and spatial pairwise clustering (SPC) are two distinct methods that have been used to control family-wise errors when assessing the evidence for an experimental effect with mass univariate testing. The basic principle of the NBS and SPC is the same as supra-threshold voxel clustering. Unlike voxel clustering, where the definition of a voxel cluster is unambiguous, 'clusters' formed among supra-threshold connections can be defined in different ways. The NBS defines clusters using the graph theoretical concept of connected components. SPC on the other hand uses a more stringent pairwise clustering concept. The purpose of this article is to compare the pros and cons of the NBS and SPC, provide some guidelines on their practical use and demonstrate their utility using a case study involving neuroimaging data.
Decreased Functional Brain Connectivity in Adolescents with Internet Addiction
Publisher: Public Library of Science (PLoS)
Date: 25-02-2013
Characterizing the functional connectivity diversity of the insula cortex: Subregions, diversity curves and behavior
Publisher: Elsevier BV
Date: 12-2018
DOI: 10.1016/J.NEUROIMAGE.2018.08.055
Abstract: The connectivity of the insula cortex is erse. We present new models to characterize the resting-state connectional ersity of the human insula cortex and perform model selection using high-quality fMRI data from the Human Connectome Project. We first attempt to parcellate the insula into distinct subregions using traditional clustering methods, but find that the resulting subregions are not homogeneous and that the optimal number of subregions is substantially influenced by data smoothness. We then introduce the concept of a ersity curve, which we use to continuously parameterize the insula's Laplacian eigenmap with respect to streamlines propagated through the eigenmap's gradient field. To perform model selection, we compare the insula's ersity curve to benchmark ersity curves for: i) two distinct regions ii) a continuum of gradual change and, iii) an absence of any connectional ersity (i.e. homogenous region). Of the three benchmarks tested, we find that the insula's connectional ersity is most parsimoniously modeled as continuum of gradual change, from dorsal-posterior to ventral-anterior. We find that in iduals who score high on measures of positive affect, self-efficacy, emotion recognition, motor dexterity and gustation show greater ersity within the anterior insula. Our findings are replicated using data from a second fMRI session. We conclude that the functional connectivity ersity of the insula can be characterized parsimoniously as a continuum, avoiding the vexed task of determining an optimal number of insula subregions, and that inter-in idual variation in this continuum can explain significant variation in behavior.
Dynamic cooperation and competition between brain systems during cognitive control
Publisher: Elsevier BV
Date: 10-2013
DOI: 10.1016/J.TICS.2013.08.006
Abstract: The human brain is characterized by a remarkable ability to adapt its information processing based on current goals. This ability, which is encompassed by the psychological construct of cognitive control, involves activity throughout large-scale, specialized brain systems that support segregated functions at rest and during active task performance. Based on recent research, we propose an account in which control functions rely on transitory changes in patterns of cooperation and competition between neural systems. This account challenges current conceptualizations of control as relying on segregated or antagonistic activity of specialized brain systems. Accordingly, we argue that the study of transitory task-based interactions between brain systems is critical to understanding the flexibility of normal cognitive control and its disruption in pathological conditions.
OBS contention resolution performance
Publisher: Elsevier BV
Date: 05-2007
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.
Associations Between Neighborhood Disadvantage, Resting-State Functional Connectivity, and Behavior in the Adolescent Brain Cognitive Development Study: The Moderating Role of Positive Family and School Environments.
Publisher: Elsevier BV
Date: 09-2021
Widespread Volumetric Reductions in Schizophrenia and Schizoaffective Patients Displaying Compromised Cognitive Abilities
Publisher: Oxford University Press (OUP)
Date: 21-08-2017
Imaging human brain networks to improve the clinical efficacy of non-invasive brain stimulation
Publisher: Elsevier BV
Date: 10-2015
DOI: 10.1016/J.NEUBIOREV.2015.09.010
Abstract: The flexible integration of segregated neural processes is essential to healthy brain function. Advances in neuroimaging techniques have revealed that psychiatric and neurological disorders are characterized by anomalies in the dynamic integration of widespread neural populations. Re-establishing optimal neural activity is an important component of the treatment of such disorders. Non-invasive brain stimulation is emerging as a viable tool to selectively restore both local and widespread neural activity in patients affected by psychiatric and neurological disorders. Importantly, the different forms of non-invasive brain stimulation affect neural activity in distinct ways, which has important ramifications for their clinical efficacy. In this review, we discuss how non-invasive brain stimulation techniques influence widespread neural integration across brain regions. We suggest that the efficacy of such techniques in the treatment of psychiatric and neurological conditions is contingent on applying the appropriate stimulation paradigm to restore specific aspects of altered neural integration.
Reply to Muscoloni and Cannistraci: Navigation performance measures
Publisher: Proceedings of the National Academy of Sciences
Date: 10-01-2019
Dynamic coupling between fMRI local connectivity and interictal EEG in focal epilepsy: A wavelet analysis approach
Publisher: Wiley
Date: 02-2016
DOI: 10.1002/HBM.23723
Using Brain Imaging to Improve Spatial Targeting of Transcranial Magnetic Stimulation for Depression
Publisher: Elsevier BV
Date: 11-2021
Brain-Predicted Age Associates With Psychopathology Dimensions in Youths
Publisher: Elsevier BV
Date: 04-2021
Neural Correlates of Sleep Recovery following Melatonin Treatment for Pediatric Concussion: A Randomized Controlled Trial
Publisher: Mary Ann Liebert Inc
Date: 15-12-2020
Connectome analysis with diffusion MRI in idiopathic Parkinson's disease: Evaluation using multi-shell, multi-tissue, constrained spherical deconvolution
Publisher: Elsevier BV
Date: 2018
Meditation attenuates Default-mode activity: a pilot study using ultra-high strength MRI
Publisher: Cold Spring Harbor Laboratory
Date: 03-01-2023
DOI: 10.1101/2023.01.02.522524
Abstract: Mapping the neurobiology of meditation using 3 Tesla functional MRI (fMRI) has burgeoned recently. However, limitations in signal quality and neuroanatomical resolution have impacted reliability and precision of extant findings. Although ultra-high strength 7 Tesla MRI overcomes these limitations, investigation of meditation using 7 Tesla fMRI is still in its infancy. In this feasibility study, we scanned 10 in iduals who were beginner meditators using 7 Tesla fMRI while they performed focused attention meditation and non-focused rest. We also measured and adjusted the fMRI signal for key physiological differences between meditation and rest. Finally, we explored the 2-week impact of the single fMRI meditation session on mindfulness, anxiety and focused attention attributes. Group-level task fMRI analyses revealed significant reductions in activity during meditation relative to rest in Default-mode network hubs, i.e., antero-medial prefrontal and posterior cingulate cortices, precuneus, as well as visual and thalamic regions. These findings survived stringent statistical corrections for fluctuations in physiological responses which demonstrated significant differences (p 0.05/n, Bonferroni controlled) between meditation and rest. Compared to baseline, State Mindfulness Scale (SMS) scores were significantly elevated (F = 8.16, p .05/n, Bonferroni controlled) following the fMRI meditation session, and were closely maintained at 2-week follow up. This pilot study establishes the feasibility and utility of investigating focused attention meditation using ultra-high strength (7 Tesla) fMRI, by supporting widespread evidence that focused attention meditation attenuates Default-mode activity responsible for self-referential processing. Future functional neuroimaging studies of meditation should control for physiological confounds and include behavioural assessments.
Minimum spanning tree analysis of the human connectome
Publisher: Wiley
Date: 21-02-2018
DOI: 10.1002/HBM.24014
Functional brain networks in treatment-resistant schizophrenia
Publisher: Elsevier BV
Date: 06-2017
DOI: 10.1016/J.SCHRES.2016.12.008
Abstract: Up to 20% of in iduals with schizophrenia show minimal or no response to medication and are considered to have 'treatment-resistant' schizophrenia (TRS). Unlike early and established schizophrenia, few studies have investigated resting-state functional connectivity (rs-FC) in TRS. Here, we test for disruptions in FC and altered efficiency of functional brain networks in a well-characterized cohort of TRS patients. Resting-state functional magnetic resonance imaging was used to investigate functional brain networks in 42 TRS participants prescribed clozapine (30 males, mean age=41.3(10)) and 42 healthy controls (24 males, mean age=38.4(10)). Graph analysis was used to characterize between-group differences in local and global efficiency of functional brain network organization as well as the strength of FC. Global brain FC was reduced in TRS patients (p=0.0001). Relative to controls, 3.4% of all functional connections showed reduced strength in TRS (p<0.001), predominantly involving fronto-temporal, fronto-occipital and temporo-occipital connections. Global efficiency was reduced in TRS (p=0.0015), whereas local efficiency was increased (p=0.0042). TRS is associated with widespread reductions in rs-FC and altered network topology. Increased local functional network efficiency coupled with decreased global efficiency suggests that hub-to-hub connections are preferentially affected in TRS. These findings further our understanding of the neurobiological impairments in TRS.
Dynamic changes in brain functional connectivity during concurrent dual-task performance
Publisher: Public Library of Science (PLoS)
Date: 29-11-2011
Connectomes for 40,000 UK Biobank participants: A multi-modal, multi-scale brain network resource
Publisher: Elsevier BV
Date: 12-2023
How can connectomics advance our knowledge of psychiatric disorders?
Publisher: EDITORA SCIENTIFIC
Date: 06-2012
Unraveling the Consequences of Childhood Maltreatment: Deviations From Typical Functional Neurodevelopment Mediate the Relationship Between Maltreatment History and Depressive Symptoms
Publisher: Elsevier BV
Date: 03-2021
Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network
Publisher: Wiley
Date: 21-01-2019
DOI: 10.1002/HBM.24519
Whole-brain anatomical networks: Does the choice of nodes matter?
Publisher: Elsevier BV
Date: 04-2010
DOI: 10.1016/J.NEUROIMAGE.2009.12.027
Abstract: Whole-brain anatomical connectivity in living humans can be modeled as a network with diffusion-MRI and tractography. Network nodes are associated with distinct grey-matter regions, while white-matter fiber bundles serve as interconnecting network links. However, the lack of a gold standard for regional parcellation in brain MRI makes the definition of nodes arbitrary, meaning that network nodes are defined using templates employing either random or anatomical parcellation criteria. Consequently, the number of nodes included in networks studied by different authors has varied considerably, from less than 100 up to more than 10(4). Here, we systematically and quantitatively assess the behavior, structure and topological attributes of whole-brain anatomical networks over a wide range of nodal scales, a variety of grey-matter parcellations as well as different diffusion-MRI acquisition protocols. We show that simple binary decisions about network organization, such as whether small-worldness or scale-freeness is evident, are unaffected by spatial scale, and that the estimates of various organizational parameters (e.g. small-worldness, clustering, path length, and efficiency) are consistent across different parcellation scales at the same resolution (i.e. the same number of nodes). However, these parameters vary considerably as a function of spatial scale for ex le small-worldness exhibited a difference of 95% between the widely-used automated anatomical labeling (AAL) template (approximately 100 nodes) and a 4000-node random parcellation (sigma(AAL)=1.9 vs. sigma(4000)=53.6+/-2.2). These findings indicate that any comparison of network parameters across studies must be made with reference to the spatial scale of the nodal parcellation.
Individual deviations from normative models of brain structure in a large cross-sectional schizophrenia cohort
Publisher: Springer Science and Business Media LLC
Date: 22-09-2021
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.
Reconfiguration of brain network architectures between resting-state and complexity-dependent cognitive reasoning
Publisher: Society for Neuroscience
Date: 31-07-2017
White matter changes in psychosis risk relate to development and are not impacted by the transition to psychosis
Publisher: Springer Science and Business Media LLC
Date: 24-05-2021
Neural correlates of sleep recovery following melatonin treatment for pediatric concussion: a randomized control trial
Publisher: Cold Spring Harbor Laboratory
Date: 04-08-2020
DOI: 10.1101/2020.08.02.20166918
Abstract: Evidence-based treatments for children with persistent post-concussion symptoms (PPCS) are few and limited. Common PPCS complaints such as sleep disturbance and fatigue could be ameliorated via the supplementation of melatonin, which has significant neuroprotective and anti-inflammatory properties. This study aims to identify neural correlates of melatonin treatment with changes in sleep disturbances and clinical recovery in a pediatric cohort with PPCS. We examined structural and functional neuroimaging (MRI) in 62 children with PPCS in a randomized, double-blind, placebo-controlled trial of 3mg or 10mg of melatonin ( NCT01874847 ). The primary outcome was the total youth self-report Post-Concussion Symptom Inventory (PCSI) score after 28 days of treatment. Secondary outcomes included the change in the sleep domain PCSI score and sleep-wake behavior (assessed using wrist-worn actigraphy). Whole-brain analyses of (i) functional connectivity (FC) of resting-state fMRI, and (ii) structural grey matter (GM) volumes via voxel-based morphometry were assessed immediately before and after melatonin treatment and compared to placebo in order to identify neural effects of melatonin treatment. Increased FC of posterior default mode network (DMN) regions with visual, somatosensory and dorsal networks was detected in the melatonin groups over time. FC increases also corresponded with reduced wake periods ( r= −0.27, p= 0.01). Children who did not recover ( n =39) demonstrated significant FC increases within anterior DMN and limbic regions compared to those that did recover (i.e. PCSI scores returned to pre-injury level n =23) over time, ( p= 0.026). Increases in GM volume within the posterior cingulate cortex were found to correlate with reduced wakefulness after sleep onset ( r= −0.32, p= 0.001) and sleep symptom improvement ( r= 0.29, p= 0.02). Although the melatonin treatment trial was negative and did not result in PPCS recovery (with or without sleep problems), the relationship between melatonin and improvement in sleep parameters were linked to changes in function-structure within and between brain regions interacting with the DMN.
Multi-timepoint pattern analysis: Influence of personality and behavior on decoding context-dependent brain connectivity dynamics
Publisher: Wiley
Date: 03-12-2022
DOI: 10.1002/HBM.25732
Abstract: Behavioral traits are rarely considered in task‐evoked functional magnetic resonance imaging (MRI) studies, yet these traits can affect how an in idual engages with the task, and thus lead to heterogeneity in task‐evoked brain responses. We aimed to investigate whether interin idual variation in behavior associates with the accuracy of predicting task‐evoked changes in the dynamics of functional brain connectivity measured with functional MRI. We developed a novel method called multi‐timepoint pattern analysis (MTPA), in which binary logistic regression classifiers were trained to distinguish rest from each of 7 tasks (i.e., social cognition, working memory, language, relational, motor, gambling, emotion) based on functional connectivity dynamics measured in 1,000 healthy adults. We found that connectivity dynamics for multiple pairs of large‐scale networks enabled in idual classification between task and rest with accuracies exceeding 70%, with the most discriminatory connections relatively unique to each task. Crucially, interin idual variation in classification accuracy significantly associated with several behavioral, cognition and task performance measures. Classification between task and rest was generally more accurate for in iduals with higher intelligence and task performance. Additionally, for some of the tasks, classification accuracy improved with lower perceived stress, lower aggression, higher alertness, and greater endurance. We conclude that heterogeneous dynamic adaptations of functional brain networks to changing cognitive demands can be reliably captured as linearly separable patterns by MTPA. Future studies should account for interin idual variation in behavior when investigating context‐dependent dynamic functional connectivity.
Exploring the moderating effects of dopaminergic polymorphisms and childhood adversity on brain morphology in schizophrenia-spectrum disorders
Publisher: Elsevier BV
Date: 11-2018
Disruption of brain white matter microstructure in women with anorexia nervosa
Publisher: CMA Joule Inc.
Date: 11-2014
DOI: 10.1503/JPN.130135
White-matter abnormalities in adolescents with long-term inhalant and cannabis use: a diffusion magnetic resonance imaging study
Publisher: CMA Joule Inc.
Date: 11-2010
DOI: 10.1503/JPN.090177
Personalized connectivity‐guided DLPFC‐TMS for depression: Advancing computational feasibility, precision and reproducibility
Publisher: Wiley
Date: 05-02-2021
DOI: 10.1002/HBM.25330
Abstract: Repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex (DLPFC) is an established treatment for refractory depression, however, therapeutic outcomes vary. Mounting evidence suggests that clinical response relates to functional connectivity with the subgenual cingulate cortex (SGC) at the precise DLPFC stimulation site. Critically, SGC‐related network architecture shows considerable interin idual variation across the spatial extent of the DLPFC, indicating that connectivity‐based target personalization could potentially be necessary to improve treatment outcomes. However, to date accurate personalization has not appeared feasible, with recent work indicating that the intrain idual reproducibility of optimal targets is limited to 3.5 cm. Here we developed reliable and accurate methodologies to compute in idualized connectivity‐guided stimulation targets. In resting‐state functional MRI scans acquired across 1,000 healthy adults, we demonstrate that, using this approach, personalized targets can be reliably and robustly pinpointed, with a median accuracy of ~2 mm between scans repeated across separate days. These targets remained highly stable, even after 1 year, with a median intrain idual distance between coordinates of only 2.7 mm. Interin idual spatial variation in personalized targets exceeded intrain idual variation by a factor of up to 6.85, suggesting that personalized targets did not trivially converge to a group‐average site. Moreover, personalized targets were heritable, suggesting that connectivity‐guided rTMS personalization is stable over time and under genetic control. This computational framework provides capacity for personalized connectivity‐guided TMS targets to be robustly computed with high precision and has the flexibly to advance research in other basic research and clinical applications.
Transcranial magnetic stimulation in obsessive-compulsive disorder: A focus on network mechanisms and state dependence
Publisher: Elsevier BV
Date: 2018
The relationship between regional and inter-regional functional connectivity deficits in schizophrenia
Publisher: Wiley
Date: 16-09-2012
DOI: 10.1002/HBM.21379
Developmental changes in brain network hub connectivity in late adolescence
Publisher: Society for Neuroscience
Date: 17-06-2015
Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models
Publisher: Cold Spring Harbor Laboratory
Date: 11-02-2021
DOI: 10.1101/2021.02.09.430363
Abstract: The potential of normative modeling to make in idualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach. However, site effects are often confounded with variables of interest in a complex manner and can bias estimates of normative models, which has impeded the application of normative models to large multi-site neuroimaging data sets. In this study, we suggest accommodating for these site effects by including them as random effects in a hierarchical Bayesian model. We compared the performance of a linear and a non-linear hierarchical Bayesian model in modeling the effect of age on cortical thickness. We used data of 570 healthy in iduals from the ABIDE (autism brain imaging data exchange) data set in our experiments. In addition, we used data from in iduals with autism to test whether our models are able to retain clinically useful information while removing site effects. We compared the proposed single stage hierarchical Bayesian method to several harmonization techniques commonly used to deal with additive and multiplicative site effects using a two stage regression, including regressing out site and harmonizing for site with ComBat, both with and without explicitly preserving variance related to age and sex as biological variation of interest. In addition, we made predictions from raw data, in which site has not been accommodated for. The proposed hierarchical Bayesian method showed the best predictive performance according to multiple metrics. Beyond that, the resulting z-scores showed little to no residual site effects, yet still retained clinically useful information. In contrast, performance was particularly poor for the regression model and the ComBat model in which age and sex were not explicitly modeled. In all two stage harmonization models, predictions were poorly scaled, suffering from a loss of more than 90 % of the original variance. Our results show the value of hierarchical Bayesian regression methods for accommodating site variation in neuroimaging data, which provides an alternative to harmonization techniques. While the approach we propose may have broad utility, our approach is particularly well suited to normative modelling where the primary interest is in accurate modelling of inter-subject variation and statistical quantification of deviations from a reference model. 1 Development and presentation of normative modeling approach based on hierarchical Bayesian modeling that can be applied to large multi-site neuroimaging data sets. Comparison of performance of Hierarchical Bayesian model including site as predictor to several common ways to harmonize for multi-site effects. Presentation of normative modeling as site correction tool.
Towards deep learning for connectome mapping: A block decomposition framework
Publisher: Elsevier BV
Date: 05-2020
Temporal complexity of fMRI is reproducible and correlates with higher order cognition
Publisher: Elsevier BV
Date: 04-2021
Adolescent Cognitive Control: Brain Network Dynamics
Publisher: Elsevier
Date: 2016
Navigation of brain networks
Publisher: Proceedings of the National Academy of Sciences
Date: 30-05-2018
Abstract: We show that the combination of topology and geometry in mammalian cortical networks allows for near-optimal decentralized communication under navigation routing. Following a simple propagation rule based on local knowledge of the distance between cortical regions, we demonstrate that brain networks can be successfully navigated with efficiency that is comparable to shortest paths routing. This finding helps to conciliate the major progress achieved over more than a decade of connectomics research, under the assumption of communication via shortest paths, with recent questions raised by the biologically unrealistic requirements involved in the computation of optimal routes. Our results reiterate the importance of the brain’s spatial embedding, suggesting a three-way relationship between connectome geometry, topology, and communication.
Designing an Optimal Scheduler Buffer in OBS Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2008
A longitudinal study investigating sub-threshold symptoms and white matter changes in individuals with an ‘at risk mental state’ (ARMS)
Publisher: Elsevier BV
Date: 03-2015
DOI: 10.1016/J.SCHRES.2015.01.002
Abstract: Evidence supports disruption in white matter (WM) connectivity in established schizophrenia, however, it is unclear when these abnormalities occur during the course of illness and if they are progressive. Here we investigated whether WM abnormalities predate illness onset by examining a group of in iduals with an 'at risk mental state' (ARMS) and assess whether there is evidence of progressive change. We hypothesized that WM abnormalities are associated with symptom change. Sixteen healthy controls and 41 ARMS subjects at baseline underwent Diffusion Tensor Imaging (DTI). Sub-threshold positive symptoms were measured using the Scale of Prodromal Symptoms (SOPS). Imaging and symptoms were re-administered in the ARMS group after one year (52weeks). Fractional anisotropy (FA) value differences between ARMS and control groups at baseline were localized using the method of Tract-Based Spatial Statistics (TBSS). At baseline, FA was significantly reduced in a sub-region of the corpus callosum (CC) in the ARMS group as a whole compared to controls. This reduction was also found in the 34 in iduals who did not transition (ARMS-N) during the one-year follow-up. However, the ARMS-N group showed a significant improvement in sub-threshold positive symptoms at follow-up, which was correlated with an increase in FA in the same CC region (r=-0.664, p<0.001). There was a significant FA reduction in the CC in in iduals at high risk for psychosis regardless of transition status at one year. This suggests that WM abnormalities in the CC may represent a biological vulnerability to psychosis. Improvement in sub-threshold positive symptoms was associated with improvement in measures of WM integrity in the CC. This may suggest that neurobiological 'resilience' is associated with improved outcomes, although this notion requires future study.
Structural connectivity and weight loss in children with obesity: a study of the “connectobese”
Publisher: Springer Science and Business Media LLC
Date: 26-07-2019
DOI: 10.1038/S41366-019-0380-6
Abstract: Previous studies suggest that obesity (OB) is associated with disrupted brain network organization however, it remains unclear whether these differences already exist during childhood. Moreover, it should be investigated whether deviant network organization may be susceptible to treatment. Here, we compared the structural connectomes of children with OB with age-matched healthy weight (HW) controls (aged 7-11 years). In addition, we examined the effect of a multidisciplinary treatment program, consisting of diet restriction, cognitive behavioral therapy, and physical activity for children with OB on brain network organization. After stringent quality assessment criteria, 40 (18 OB, 22 HW) data sets of the total s le of 51 participants (25 OB, 26 HW) were included in further analyses. For all participants, anthropometric measurements were administered twice, with a 5-month interval between pre- and post tests. Pre- and post T1- and diffusion-weighted imaging scans were also acquired and analyzed using a graph-theoretical approach and network-based statistics. Global network analyses revealed a significantly increased normalized clustering coefficient and small-worldness in children with OB compared with HW controls. In addition, regional analyses revealed increased betweenness centrality, reduced clustering coefficient, and increased structural network strength in children with OB, mainly in the motor cortex and reward network. Importantly, children with OB lost a considerable amount of their body mass after the treatment however, no changes were observed in the organization of their brain networks. This is the first study showing disrupted structural connectomes of children with OB, especially in the motor and reward network. These results provide new insights into the pathophysiology underlying childhood obesity. The treatment did result in a significant weight loss, which was however not associated with alterations in the brain networks. These findings call for larger s les to examine the impact of short-term and long-term weight loss (treatment) on children's brain network organization.
Reconfiguration of brain network architectures between resting state and complexity-dependent cognitive reasoning
Publisher: Cold Spring Harbor Laboratory
Date: 14-07-2017
DOI: 10.1101/163022
Abstract: Our capacity for higher cognitive reasoning has a measureable limit. This limit is thought to arise from the brain’s capacity to flexibly reconfigure interactions between spatially distributed networks. Recent work, however, has suggested that reconfigurations of task-related networks are modest when compared with intrinsic ‘resting state’ network architecture. Here we combined resting state and task-driven functional magnetic resonance imaging to examine how flexible, task-specific reconfigurations associated with increasing reasoning demands are integrated within a stable intrinsic brain topology. Human participants (21 males and 28 females) underwent an initial resting state scan, followed by a cognitive reasoning task involving different levels of complexity, followed by a second resting state scan. The reasoning task required participants to deduce the identity of a missing element in a 4 × 4 matrix, and item difficulty was scaled parametrically as determined by relational complexity theory. Analyses revealed that external task engagement was characterized by a significant change in functional brain modules. Specifically, resting state and null-task demand conditions were associated with more segregated brain network topology, whereas increases in reasoning complexity resulted in merging of resting state modules. Further increments in task complexity did not change the established modular architecture, but impacted selective patterns of connectivity between fronto-parietal, subcortical, cingulo-opercular and default-mode networks. Larger increases in network efficiency within the newly established task modules were associated with higher reasoning accuracy. Our results shed light on the network architectures that underlie external task engagement, and highlight selective changes in brain connectivity supporting increases in task complexity. Humans have clear limits in their ability to solve complex reasoning problems. It is thought that such limitations arise from flexible, moment-to-moment reconfigurations of functional brain networks. It is less clear how such task-driven adaptive changes in connectivity relate to stable, intrinsic networks of the brain and behavioral performance. We found that increased reasoning demands rely on selective patterns of connectivity within cortical networks that emerged in addition to a more general, task-induced modular architecture. This task-driven architecture reverted to a more segregated resting state architecture both immediately before and after the task. These findings reveal how flexibility in human brain networks is integral to achieving successful reasoning performance across different levels of cognitive demand.
Interactions between default mode and control networks as a function of increasing cognitive reasoning complexity
Publisher: Wiley
Date: 02-04-2015
DOI: 10.1002/HBM.22802
Scalable performance evaluation of a hybrid optical switch
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2005
Neural decoding of visual stimuli varies with fluctuations in global network efficiency
Publisher: Cold Spring Harbor Laboratory
Date: 12-02-2017
DOI: 10.1101/107888
Abstract: Functional magnetic resonance imaging (fMRI) studies have shown that neural activity fluctuates spontaneously between different states of global synchronization over a timescale of several seconds. Such fluctuations generate transient states of high and low correlation across distributed cortical areas. It has been hypothesized that such fluctuations in global efficiency might alter patterns of activity in local neuronal populations elicited by changes in incoming sensory stimuli. To test this prediction, we used a linear decoder to discriminate patterns of neural activity elicited by face and motion stimuli presented periodically while participants underwent time-resolved fMRI. As predicted, decoding was reliably higher during states of high global efficiency than during states of low efficiency, and this difference was evident across both visual and non-visual cortical regions. The results indicate that slow fluctuations in global network efficiency are associated with variations in the pattern of activity across widespread cortical regions responsible for representing distinct categories of visual stimulus. More broadly, the findings highlight the importance of understanding the impact of global fluctuations in functional connectivity on specialised, stimulus driven neural processes.
Relating brain connectivity with persistent symptoms in pediatric concussion
Publisher: Cold Spring Harbor Laboratory
Date: 04-02-2019
DOI: 10.1101/539825
Abstract: Persistent post-concussion symptoms (PCS) in children following a mild traumatic brain injury (mTBI) is a growing public health concern. There is a pressing need to understand the neural underpinning of PCS. Here, we examined whole-brain functional connectivity from resting-state fMRI with behavioral assessments in a cohort of 110 children with mTBI. Children with mTBI and controls had similar levels of connectivity. PCS symptoms and behaviors including poor cognition and sleep were associated with connectivity within functional brain networks. The identification of a single “positive-negative” dimension linking connectivity with behaviors enables better prognosis and stratification towards personalized therapeutic interventions.
The structural connectome constrains fast brain dynamics
Publisher: eLife Sciences Publications, Ltd
Date: 09-07-2021
DOI: 10.7554/ELIFE.67400
Abstract: Brain activity during rest displays complex, rapidly evolving patterns in space and time. Structural connections comprising the human connectome are hypothesized to impose constraints on the dynamics of this activity. Here, we use magnetoencephalography (MEG) to quantify the extent to which fast neural dynamics in the human brain are constrained by structural connections inferred from diffusion MRI tractography. We characterize the spatio-temporal unfolding of whole-brain activity at the millisecond scale from source-reconstructed MEG data, estimating the probability that any two brain regions will significantly deviate from baseline activity in consecutive time epochs. We find that the structural connectome relates to, and likely affects, the rapid spreading of neuronal avalanches, evidenced by a significant association between these transition probabilities and structural connectivity strengths (r = 0.37, p .0001). This finding opens new avenues to study the relationship between brain structure and neural dynamics.
Genetic influences on cost-efficient organization of human cortical functional networks
Publisher: Society for Neuroscience
Date: 02-03-2011
DOI: 10.1523/JNEUROSCI.4858-10.2011
Abstract: The human cerebral cortex is a complex network of functionally specialized regions interconnected by axonal fibers, but the organizational principles underlying cortical connectivity remain unknown. Here, we report evidence that one such principle for functional cortical networks involves finding a balance between maximizing communication efficiency and minimizing connection cost, referred to as optimization of network cost-efficiency. We measured spontaneous fluctuations of the blood oxygenation level-dependent signal using functional magnetic resonance imaging in healthy monozygotic (16 pairs) and dizygotic (13 pairs) twins and characterized cost-efficient properties of brain network functional connectivity between 1041 distinct cortical regions. At the global network level, 60% of the interin idual variance in cost-efficiency of cortical functional networks was attributable to additive genetic effects. Regionally, significant genetic effects were observed throughout the cortex in a largely bilateral pattern, including bilateral posterior cingulate and medial prefrontal cortices, dorsolateral prefrontal and superior parietal cortices, and lateral temporal and inferomedial occipital regions. Genetic effects were stronger for cost-efficiency than for other metrics considered, and were more clearly significant in functional networks operating in the 0.09–0.18 Hz frequency interval than at higher or lower frequencies. These findings are consistent with the hypothesis that brain networks evolved to satisfy competitive selection criteria of maximizing efficiency and minimizing cost, and that optimization of network cost-efficiency represents an important principle for the brain's functional organization.
Co-existence of Network Architectures Supporting the Human Gut Microbiome
Publisher: Elsevier BV
Date: 12-2019
Default mode network anatomy and function is linked to pediatric concussion recovery
Publisher: Cold Spring Harbor Laboratory
Date: 07-10-2019
DOI: 10.1101/795740
Abstract: To determine whether anatomical and functional brain features relate to key persistent post-concussion symptoms (PPCS) in children recovering from mild traumatic brain injuries (mTBI), and whether such brain indices can predict in idual recovery from PPCS. 110 children with mixed recovery following mTBI were seen at the concussion clinic at Neurology department Alberta Children’s Hospital. The primary outcome was the Post-Concussion Symptom Inventory (PCSI, parent proxy). Sleep disturbance scores (PCSI sub-domain) and the Neurocognition Index (CNS Vital Signs) were also measured longitudinally. PPCS was assessed at 4 weeks post-injury and 8-10 weeks post-injury. Grey matter volumes were assessed using magnetic resonance imaging (MRI) and voxel-based morphometry. Functional connectivity was estimated using resting-state MRI. Two complementary machine learning methods were used to assess if the combination of grey matter and functional connectivity indices carried meaningful prognostic information. Higher scores on a composite index of sleep disturbance, including fatigue, were associated with converging decreases in grey matter volume and local functional connectivity in two key nodes of the default mode network: the posterior cingulate cortex and the medial prefrontal cortex. Sleep-related disturbances also significantly correlated with reductions in functional connectivity between these brain regions. The combination of structural and functional brain indices associated to in idual variations in the default mode network accurately predicted clinical outcomes at follow-up (area under the curve = 0.86). These results highlight that the function-structure profile of core default mode regions underpins sleep-related problems following mTBI and carries meaningful prognostic information for pediatric concussion recovery.
Aberrant Metabolic Patterns Networks in Insular Epilepsy
Publisher: Frontiers Media SA
Date: 23-12-2020
DOI: 10.3389/FNEUR.2020.605256
Abstract: Introduction: Insular epilepsy is clinically challenging. This study aimed to map cerebral metabolic networks in insular epilepsy and investigate their graph-theoretic properties, with the goal of elucidating altered metabolic network architectures that underlie interictal hypometabolism. Aims: Fluorine-18-fluorodeoxyglucose positron emission tomography ( 18 F-FDG-PET) imaging was performed in 17 in iduals with a stereoelectroencephalography (SEEG) confirmed diagnosis of insula epilepsy and 14 age- and sex-matched healthy comparison in iduals. Metabolic covariance networks were mapped for each group and graph theoretical analyses of these networks were undertaken. For each pair of regions comprising a whole-brain parcellation, regionally-averaged FDG uptake values were correlated across in iduals to estimate connection weights. Results: Correlation in regionally-averaged FDG uptake values in the insular epilepsy group was substantially increased for several pairs of regions compared to the healthy comparison group, particularly for the opercular cortex and subcortical structures. This effect was less prominent in brainstem structures. Metabolic covariance networks in the epilepsy group showed reduced small-worldness as well as altered nodal properties in the ipsilateral hemisphere, compared to the healthy comparison group. Conclusions: Cerebral glucose metabolism in insular epilepsy is marked by a lack of normal regional heterogeneity in metabolic patterns, resulting in metabolic covariance networks that are more tightly coupled between regions than healthy comparison in iduals. Metabolic networks in insular epilepsy exhibit altered topological properties and evidence of potentially compensatory formation of aberrant local connections. Taken together, these results demonstrate that insular epilepsy is a systemic neurological disorder with widespread disruption to cerebral metabolic networks.
White matter microstructure in anorexia nervosa
Publisher: Wiley
Date: 02-07-2018
DOI: 10.1002/HBM.24279
Brain morphology is differentially impacted by peripheral cytokines in schizophrenia-spectrum disorder
Publisher: Elsevier BV
Date: 07-2021
DOI: 10.1016/J.BBI.2021.04.002
Abstract: Deficits in brain morphology are one of the most widely replicated neuropathological features in schizophrenia-spectrum disorder (SSD), although their biological underpinnings remain unclear. Despite the existence of hypotheses by which peripheral inflammation may impact brain structure, few studies have examined this relationship in SSD. This study aimed to establish the relationship between peripheral markers of inflammation and brain morphology and determine whether such relationships differed across healthy controls and in iduals with first episode psychosis (FEP) and chronic schizophrenia. A panel of 13 pro- and anti-inflammatory cytokines were quantified from serum in 175 participants [n = 84 Healthy Controls (HC), n = 40 FEP, n = 51 Chronic SCZ]. We first performed a series of permutation tests to identify the cytokines most consistently associated with brain structural regions. Using moderation analysis, we then determined the extent to which in idual variation in select cytokines, and their interaction with diagnostic status, predicted variation in brain structure. We found significant interactions between cytokine level and diagnosis on brain structure. Diagnostic status significantly moderated the relationship of IFNγ, IL4, IL5 and IL13 with frontal thickness, and of IFNγ and IL5 and total cortical volume. Specifically, frontal thickness was positively associated with IFNγ, IL4, IL5 and IL13 cytokine levels in the healthy control group, whereas pro-inflammatory cytokines IFNγ and IL5 were associated with lower total cortical volume in the FEP group. Our findings suggest that while there were no relationships detected in chronic schizophrenia, the relationship between peripheral inflammatory markers and select brain regions are differentially impacted in FEP and healthy controls. Longitudinal investigations are required to determine whether the relationship between brain structure and peripheral inflammation changes over time.
The impact of smoking status on cognition and brain morphology in schizophrenia spectrum disorders
Publisher: Cambridge University Press (CUP)
Date: 14-01-2022
DOI: 10.1017/S0033291720005152
Abstract: Cigarette smoking is associated with worse cognition and decreased cortical volume and thickness in healthy cohorts. Chronic cigarette smoking is prevalent in schizophrenia spectrum disorders (SSD), but the effects of smoking status on the brain and cognition in SSD are not clear. This study aimed to understand whether cognitive performance and brain morphology differed between smoking and non-smoking in iduals with SSD compared to healthy controls. Data were obtained from the Australian Schizophrenia Research Bank. Cognitive functioning was measured in 299 controls and 455 SSD patients. Cortical volume, thickness and surface area data were analysed from T1-weighted structural scans obtained in a subset of the s le ( n = 82 controls, n = 201 SSD). Associations between smoking status (cigarette smoker/non-smoker), cognition and brain morphology were tested using analyses of covariance, including diagnosis as a moderator. No smoking by diagnosis interactions were evident, and no significant differences were revealed between smokers and non-smokers across any of the variables measured, with the exception of a significantly thinner left posterior cingulate in smokers compared to non-smokers. Several main effects of smoking in the cognitive, volume and thickness analyses were initially significant but did not survive false discovery rate (FDR) correction. Despite the general absence of significant FDR-corrected findings, trend-level effects suggest the possibility that subtle smoking-related effects exist but were not uncovered due to low statistical power. An investigation of this topic is encouraged to confirm and expand on our findings.
Spontaneous brain network activity: Analysis of its temporal complexity.
Publisher: MIT Press - Journals
Date: 06-2017
DOI: 10.1162/NETN_A_00006
Abstract: The brain operates in a complex way. The temporal complexity underlying macroscopic and spontaneous brain network activity is still to be understood. In this study, we explored the brain’s complexity by combining functional connectivity, graph theory, and entropy analyses in 25 healthy people using task-free functional magnetic resonance imaging. We calculated the pairwise instantaneous phase synchrony between 8,192 brain nodes for a total of 200 time points. This resulted in graphs for which time series of clustering coefficients (the “cliquiness” of a node) and participation coefficients (the between-module connectivity of a node) were estimated. For these two network metrics, s le entropy was calculated. The procedure produced a number of results: (1) Entropy is higher for the participation coefficient than for the clustering coefficient. (2) The average clustering coefficient is negatively related to its associated entropy, whereas the average participation coefficient is positively related to its associated entropy. (3) The level of entropy is network-specific to the participation coefficient, but not to the clustering coefficient. High entropy for the participation coefficient was observed in the default-mode, visual, and motor networks. These results were further validated using an independent replication dataset. Our work confirms that brain networks are temporally complex. Entropy is a good candidate metric to explore temporal network alterations in diseases with paroxysmal brain disruptions, including schizophrenia and epilepsy.
High-resolution connectomic fingerprints: Mapping neural identity and behavior
Publisher: Elsevier BV
Date: 04-2021
Reducing the influence of intramodular connectivity in participation coefficient
Publisher: MIT Press - Journals
Date: 2020
DOI: 10.1162/NETN_A_00127
Abstract: Both natural and engineered networks are often modular. Whether a network node interacts with only nodes from its own module or nodes from multiple modules provides insight into its functional role. The participation coefficient ( PC) is typically used to measure this attribute, although its value also depends on the size and connectedness of the module it belongs to and may lead to nonintuitive identification of highly connected nodes. Here, we develop a normalized PC that reduces the influence of intramodular connectivity compared with the conventional PC. Using brain, C. elegans, airport, and simulated networks, we show that our measure of participation is not influenced by the size or connectedness of modules, while preserving conceptual and mathematical properties, of the classic formulation of PC. Unlike the conventional PC, we identify London and New York as high participators in the air traffic network and demonstrate stronger associations with working memory in human brain networks, yielding new insights into nodal participation across network modules.
Topological Cluster Statistic (TCS): Towards structural-connectivity-guided fMRI cluster enhancement
Publisher: Research Square Platform LLC
Date: 15-09-2022
DOI: 10.21203/RS.3.RS-2059418/V1
Abstract: Functional magnetic resonance imaging (fMRI) studies use cluster-based inference to detect local changes in brain activity. Insufficient statistical power and disproportionate false-positive rates reportedly hinder optimal inference. We propose a structural-connectivity-guided clustering framework, called TCS, that enhances sensitivity by leveraging white matter anatomical connectivity information. TCS harnesses multimodal information from diffusion tractography and functional imaging to improve task fMRI activation inference. Compared to conventional approaches, TCS consistently improves power over a wide range of effects. This improvement results in a 10%-50% local sensitivity increase with the greatest gains for medium-sized effects. TCS additionally enables inspection of underlying anatomical networks and thus uncovers knowledge regarding the anatomical underpinnings of brain activation. This novel approach is made available in FSL PALM software to facilitate future usability. Given the increasing recognition that activation reflects widespread, coordinated processes, TCS provides a way to integrate the known structure underlying widespread activations into neuroimaging analyses moving forward.
Resting-state functional brain networks in first-episode psychosis: A 12-month follow-up study
Publisher: SAGE Publications
Date: 27-05-2018
Abstract: Schizophrenia is increasingly conceived as a disorder of brain network connectivity and organization. However, reports of network abnormalities during the early illness stage of psychosis are mixed. This study adopted a data-driven whole-brain approach to investigate functional connectivity and network architecture in a first-episode psychosis cohort relative to healthy controls and whether functional network properties changed abnormally over a 12-month period in first-episode psychosis. Resting-state functional connectivity was performed at two time points. At baseline, 29 first-episode psychosis in iduals and 30 healthy controls were assessed, and at 12 months, 14 first-episode psychosis in iduals and 20 healthy controls completed follow-up. Whole-brain resting-state functional connectivity networks were mapped for each in idual and analyzed using graph theory to investigate whether network abnormalities associated with first-episode psychosis were evident and whether functional network properties changed abnormally over 12 months relative to controls. This study found no evidence of abnormal resting-state functional connectivity or topology in first-episode psychosis in iduals relative to healthy controls at baseline or at 12-months follow-up. Furthermore, longitudinal changes in network properties over a 12-month period did not significantly differ between first-episode psychosis in iduals and healthy control. Network measures did not significantly correlate with symptomatology, duration of illness or antipsychotic medication. This is the first study to show unaffected resting-state functional connectivity and topology in the early psychosis stage of illness. In light of previous literature, this suggests that a subgroup of first-episode psychosis in iduals who have a neurotypical resting-state functional connectivity and topology may exist. Our preliminary longitudinal analyses indicate that there also does not appear to be deterioration in these network properties over a 12-month period. Future research in a larger s le is necessary to confirm our longitudinal findings.
Cognitive reserve attenuates age-related cognitive decline in the context of putatively accelerated brain ageing in schizophrenia-spectrum disorders
Publisher: Cambridge University Press (CUP)
Date: 09-07-2020
DOI: 10.1017/S0033291719001417
Abstract: In schizophrenia, relative stability in the magnitude of cognitive deficits across age and illness duration is inconsistent with the evidence of accelerated deterioration in brain regions known to support these functions. These discrepant brain–cognition outcomes may be explained by variability in cognitive reserve (CR), which in neurological disorders has been shown to buffer against brain pathology and minimize its impact on cognitive or clinical indicators of illness. Age-related change in fluid reasoning, working memory and frontal brain volume, area and thickness were mapped using regression analysis in 214 in iduals with schizophrenia or schizoaffective disorder and 168 healthy controls. In patients, these changes were modelled as a function of CR. Patients showed exaggerated age-related decline in brain structure, but not fluid reasoning compared to controls. In the patient group, no moderation of age-related brain structural change by CR was evident. However, age-related cognitive change was moderated by CR, such that only patients with low CR showed evidence of exaggerated fluid reasoning decline that paralleled the exaggerated age-related deterioration of underpinning brain structures seen in all patients . In schizophrenia-spectrum illness, CR may negate ageing effects on fluid reasoning by buffering against pathologically exaggerated structural brain deterioration through some form of compensation. CR may represent an important modifier that could explain inconsistencies in brain structure – cognition outcomes in the extant literature.
Vitamin D deficiency is associated with reduced hippocampal volume and disrupted structural connectivity in patients with mild cognitive impairment
Publisher: Wiley
Date: 25-09-2019
DOI: 10.1002/HBM.24380
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.
Linking Cortical and Connectional Pathology in Schizophrenia
Publisher: Oxford University Press (OUP)
Date: 12-09-2019
Functional Magnetic Resonance Imaging–Guided Personalization of Transcranial Magnetic Stimulation Treatment for Depression
Publisher: American Medical Association (AMA)
Date: 03-2021
White matter connectivity disruptions in early and chronic schizophrenia
Publisher: Cambridge University Press (CUP)
Date: 22-05-2017
DOI: 10.1017/S0033291717001313
Abstract: White matter disruptions in schizophrenia have been widely reported, but it remains unclear whether these abnormalities differ between illness stages. We mapped the connectome in patients with recently diagnosed and chronic schizophrenia and investigated the extent and overlap of white matter connectivity disruptions between these illness stages. Diffusion-weighted magnetic resonance images were acquired in recent-onset ( n = 19) and chronic patients ( n = 45) with schizophrenia, as well as age-matched controls ( n = 87). Whole-brain fiber tracking was performed to quantify the strength of white matter connections. Connections were tested for significant streamline count reductions in recent-onset and chronic groups, relative to separate age-matched controls. Permutation tests were used to assess whether disrupted connections significantly overlapped between chronic and recent-onset patients. Linear regression was performed to test whether connectivity was strongest in controls, weakest in chronic patients, and midway between these extremities in recent-onset patients (controls recent-onset chronic). Compared with controls, chronic patients displayed a widespread network of connectivity disruptions ( p 0.01). In contrast, connectivity reductions were circumscribed to the anterior fibers of the corpus callosum in recent-onset patients ( p 0.01). A significant proportion of disrupted connections in recent-onset patients (86%) coincided with disrupted connections in chronic patients ( p 0.01). Linear regression revealed that chronic patients displayed reduced connectivity relative to controls, while recent-onset patients showed an intermediate reduction compared with chronic patients ( p 0.01). Connectome pathology in recent-onset patients with schizophrenia is confined to select tracts within a more extensive network of white matter connectivity disruptions found in chronic illness. These findings may suggest a trajectory of progressive deterioration of connectivity in schizophrenia.
Accelerated gray and white matter deterioration with age in schizophrenia
Publisher: American Psychiatric Association Publishing
Date: 03-2017
Personalized Transcranial Magnetic Stimulation in Psychiatry
Publisher: Elsevier BV
Date: 09-2018
DOI: 10.1016/J.BPSC.2018.01.008
Abstract: Transcranial magnetic stimulation (TMS) is a noninvasive brain stimulation technique that allows for modulating the activity of local neural populations and related neural networks. TMS is touted as a viable intervention to normalize brain activity and alleviate some psychiatric symptoms. However, TMS interventions are known to be only moderately reliable, and the efficacy of such therapies remains to be proven for psychiatric disorders other than depression. We review new opportunities to personalize TMS interventions using neuroimaging and computational modeling, aiming to optimize treatment to suit particular in iduals and clinical subgroups. Specifically, we consider the prospect of improving the efficacy of existing TMS interventions by parsing broad diagnostic categories into biologically and clinically homogeneous biotypes. Biotypes can provide distinct treatment targets for optimized TMS interventions. We further discuss the utility of computational models in refining TMS personalization and efficiently establishing optimal cortical targets for distinct biotypes. Personalizing cortical stimulation targets, treatment frequencies, and intensities can improve the therapeutic efficacy of TMS and potentially establish noninvasive brain stimulation as a viable treatment for psychiatric symptoms.
Hippocampal harms, protection and recovery following regular cannabis use
Publisher: Springer Science and Business Media LLC
Date: 12-01-2016
DOI: 10.1038/TP.2015.201
Abstract: Shifting policies towards legalisation of cannabis for therapeutic and recreational use raise significant ethical issues for health-care providers seeking evidence-based recommendations. We investigated whether heavy cannabis use is associated with persistent harms to the hippoc us, if exposure to cannabidiol offers protection, and whether recovery occurs with abstinence. To do this, we assessed 111 participants: 74 long-term regular cannabis users (with an average of 15.4 years of use) and 37 non-user healthy controls. Cannabis users included subgroups of participants who were either exposed to Δ9-tetrahydrocannabinol (THC) but not to cannabidiol (CBD) or exposed to both, and former users with sustained abstinence. Participants underwent magnetic resonance imaging from which three measures of hippoc al integrity were assessed: (i) volume (ii) fractional anisotropy and (iii) N -acetylaspartate (NAA). Three curve-fitting models across the entire s le were tested for each measure to examine whether cannabis-related hippoc al harms are persistent, can be minimised (protected) by exposure to CBD or recovered through long-term abstinence. These analyses supported a protection and recovery model for hippoc al volume ( P =0.003) and NAA ( P =0.001). Further pairwise analyses showed that cannabis users had smaller hippoc al volumes relative to controls. Users not exposed to CBD had 11% reduced volumes and 15% lower NAA concentrations. Users exposed to CBD and former users did not differ from controls on any measure. Ongoing cannabis use is associated with harms to brain health, underpinned by chronic exposure to THC. However, such harms are minimised by CBD, and can be recovered with extended periods of abstinence.
Relating brain connectivity with persistent symptoms in pediatric concussion
Publisher: Wiley
Date: 22-03-2019
DOI: 10.1002/ACN3.764
Structural connectivity in adolescent synthetic cannabinoid users with and without ADHD
Publisher: Springer Science and Business Media LLC
Date: 07-01-2019
DOI: 10.1007/S11682-018-0023-X
Abstract: Synthetic cannabinoids (SC) have become increasingly popular in the last few years, especially among adolescents. Given ADHD is overrepresented in patients with substance use across adolescents compared to the general population, the current study aims were two-fold: i) examine structural brain network topology in SC users compared to healthy controls and, ii) examine the influence of ADHD on network topology in SC users. Diffusion-weighted magnetic resonance imaging scans were acquired from 27 SC users (14 without ADHD and 13 with ADHD combined type) and 13 controls. Structural networks were examined using network-based statistic and connectomic analysis. We found that SC users without ADHD had significantly weaker connectivity compared to controls in bilateral hemispheres, most notably in edges connecting the left parietal and occipital regions. In contrast, SC users with ADHD showed stronger structural connectivity compared to controls. In addition, adolescent SC users with ADHD, but not without ADHD, displayed reduced network organization, indicated by lower clustering coefficient and modularity, suggesting that poor structural network segregation and preserved structural network integration. These results suggest that comorbidity of ADHD and substance dependence may show different structural connectivity alterations than substance use alone. Therefore, future connectivity studies in the substance use population should account for the presence of ADHD in their s les, which may be associated with disparate connectivity profiles.
Structure-function coupling in the human connectome: A machine learning approach
Publisher: Elsevier BV
Date: 02-2021
Topographic organization of the human subcortex unveiled with functional connectivity gradients
Publisher: Springer Science and Business Media LLC
Date: 28-09-2020
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.
Structural connectivity relates to perinatal factors and functional impairment at 7 years in children born very preterm
Publisher: Elsevier BV
Date: 07-2016
White Matter Disruptions in Schizophrenia Are Spatially Widespread and Topologically Converge on Brain Network Hubs
Publisher: Oxford University Press (OUP)
Date: 17-08-2017
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.
Maternal parenting behavior and functional connectivity development in children: A longitudinal fMRI study.
Publisher: Elsevier BV
Date: 04-2021
Disruption of structure-function coupling in the schizophrenia connectome
Publisher: Elsevier BV
Date: 2014
Associations Between Delay Discounting and Connectivity of the Valuation-control System in Healthy Young Adults
Publisher: Elsevier BV
Date: 2021
Cost Comparison of Optical Circuit-Switched and Burst-Switched Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2009
White matter microstructure and connectivity in patients with obsessive‐compulsive disorder and their unaffected siblings
Publisher: Wiley
Date: 22-10-2020
DOI: 10.1111/ACPS.13241
Chronic Cannabis Use and Axonal Fiber Connectivity
Publisher: Elsevier
Date: 2017
Neural decoding of visual stimuli varies with fluctuations in global network efficiency
Publisher: Wiley
Date: 25-03-2017
DOI: 10.1002/HBM.23574
Assessment of temporal complexity in functional MRI between rest and task conditions
Publisher: Cold Spring Harbor Laboratory
Date: 20-11-2021
DOI: 10.1101/2021.11.20.469367
Abstract: Dynamic models of cortical activity, as measured by functional magnetic resonance imaging (fMRI), have recently brought out important insights into the organization of brain function. In terms of temporal complexity, these hemodynamic signals have been shown to exhibit critical behaviour at the edge between order and disorder. In this study, we aimed to revisit the properties and spatial distribution of temporal complexity in resting state and task fMRI of 100 unrelated subjects from the Human Connectome Project (HCP). First, we compared two common choices of complexity measures (i.e., Hurst exponent versus multiscale entropy) and reported high similarity between them. Second, we investigated the influence of experimental paradigms and found high task-specific complexity. We considered four mental tasks in the HCP database for the analysis: Emotion, Working memory, Social, and Language. Third, we tailored a recently-proposed statistical framework that incorporates the structural connectome, to assess the spatial distribution of complexity measures. These results highlight brain regions including parts of the default mode network and cingulate cortex with significantly stronger complex behaviour than the rest of the brain, irrespective of task. In sum, temporal complexity measures of fMRI are reliable markers of the cognitive status.
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End Date: 12-2022
Amount: $410,500.00
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View Funded ActivityDiscovery Projects - Grant ID: DP0986320
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View Funded ActivityARC Future Fellowships - Grant ID: FT220100091
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End Date: 06-2027
Amount: $1,035,760.00
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