scholarly journals Age of onset modulates resting‐state brain network dynamics in Friedreich Ataxia

2021 ◽  
Author(s):  
Gilles Naeije ◽  
Nicolas Coquelet ◽  
Vincent Wens ◽  
Serge Goldman ◽  
Massimo Pandolfo ◽  
...  
Keyword(s):  
Resting State ◽  
Age Of Onset ◽  
Network Dynamics ◽  
Friedreich Ataxia ◽  
Brain Network

scholarly journals EEG resting-state large-scale brain network dynamics are related to depressive symptoms

2019 ◽  
Author(s):  
Alena Damborská ◽  
Miralena I. Tomescu ◽  
Eliška Honzírková ◽  
Richard Barteček ◽  
Jana Hořínková ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Resting State ◽  
Large Scale ◽  
Rating Scale ◽  
Depressive Symptomatology ◽  
Network Dynamics ◽  
Brain Network ◽  
Temporal Characteristics ◽  
Microstate Analysis ◽  
Depressive Episodes

AbstractBackgroundThe few previous studies on resting-state EEG microstates in depressive patients suggest altered temporal characteristics of microstates compared to those of healthy subjects. We tested whether resting-state microstate temporal characteristics could capture large-scale brain network dynamic activity relevant to depressive symptomatology.MethodsTo evaluate a possible relationship between the resting-state large-scale brain network dynamics and depressive symptoms, we performed EEG microstate analysis in patients with moderate to severe depression within bipolar affective disorder, depressive episode, and periodic depressive disorder, and in healthy controls.ResultsMicrostate analysis revealed six classes of microstates (A-F) in global clustering across all subjects. There were no between-group differences in the temporal characteristics of microstates. In the patient group, higher symptomatology on the Montgomery-Åsberg Depression Rating Scale, a questionnaire validated as measuring severity of depressive episodes in patients with mood disorders, correlated with higher occurrence of microstate A (Spearman’s rank correlation, r = 0.70, p < 0.01).ConclusionOur results suggest that the observed interindividual differences in resting-state EEG microstate parameters could reflect altered large-scale brain network dynamics relevant to depressive symptomatology during depressive episodes. These findings suggest the utility of the microstate analysis approach in an objective depression assessment.

scholarly journals Altered Electroencephalographic Resting-State Large-Scale Brain Network Dynamics in Euthymic Bipolar Disorder Patients

2019 ◽  
Vol 10 ◽  
Author(s):  
Alena Damborská ◽  
Camille Piguet ◽  
Jean-Michel Aubry ◽  
Alexandre G. Dayer ◽  
Christoph M. Michel ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Resting State ◽  
Large Scale ◽  
Network Dynamics ◽  
Brain Network

scholarly journals Altered EEG resting-state large-scale brain network dynamics in euthymic bipolar disorder patients

2019 ◽  
Author(s):  
Alena Damborská ◽  
Camille Piguet ◽  
Jean-Michel Aubry ◽  
Alexandre G. Dayer ◽  
Christoph M. Michel ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Resting State ◽  
Large Scale ◽  
Network Dynamics ◽  
Brain Network ◽  
Network Activity ◽  
Anxiety State ◽  
Temporal Characteristics ◽  
Functional Brain Network ◽  
And Gender

AbstractBackgroundNeuroimaging studies provided evidence for disrupted resting-state functional brain network activity in bipolar disorder (BD). Electroencephalographic (EEG) studies found altered temporal characteristics of functional EEG microstates during depressive episode within different affective disorders. Here we investigated whether euthymic patients with BD show deviant resting-state large-scale brain network dynamics as reflected by altered temporal characteristics of EEG microstates.MethodsWe used high-density EEG to explore between-group differences in duration, coverage and occurrence of the resting-state functional EEG microstates in 17 euthymic adults with BD in on-medication state and 17 age- and gender-matched healthy controls. Two types of anxiety, state and trait, were assessed separately with scores ranging from 20 to 80.ResultsMicrostate analysis revealed five microstates (A-E) in global clustering across all subjects. In patients compared to controls, we found increased occurrence and coverage of microstate A that did not significantly correlate with anxiety scores.ConclusionOur results provide neurophysiological evidence for altered large-scale brain network dynamics in BD patients and suggest the increased presence of A microstate to be an electrophysiological trait characteristic of BD.

scholarly journals EEG Resting-State Large-Scale Brain Network Dynamics Are Related to Depressive Symptoms

2019 ◽  
Vol 10 ◽  
Author(s):  
Alena Damborská ◽  
Miralena I. Tomescu ◽  
Eliška Honzírková ◽  
Richard Barteček ◽  
Jana Hořínková ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Resting State ◽  
Large Scale ◽  
Network Dynamics ◽  
Brain Network

scholarly journals Brain network dynamics fingerprints are resilient to data heterogeneity

2020 ◽  
Author(s):  
Tommaso Menara ◽  
Giuseppe Lisi ◽  
Fabio Pasqualetti ◽  
Aurelio Cortese
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Brain Activity ◽  
Network Dynamics ◽  
Brain Network ◽  
Fmri Data ◽  
Dynamical Models ◽  
Subject Specific ◽  
Data Heterogeneity ◽  
Data Collections

AbstractLarge multi-site neuroimaging datasets have significantly advanced our quest to understand brain-behaviour relationships and to develop biomarkers of psychiatric and neurodegenerative disorders. Yet, such data collections come at a cost, as the inevitable differences across samples may lead to biased or erroneous conclusions. Previous work has investigated this critical issue in resting-state functional magnetic resonance imaging (rs-fMRI) data in terms of effects on static measures, such as functional connectivity and brain parcellations. Here, we depart from prior approaches and utilize dynamical models to examine how diverse scanning factors in multi-site fMRI recordings affect our ability to infer the brain’s spatiotemporal wandering between large-scale networks of activity. Building upon this premise, we first confirm the emergence of robust subject-specific dynamical patterns of brain activity. Next, we exploit these individual fingerprints to show that scanning sessions belonging to different sites and days tend to induce high variability, while other factors, such as the scanner manufacturer or the number of coils, affect the same metrics to a lesser extent. These results concurrently indicate that we can recover the unique trajectories of brain activity changes in each individual, but also that our ability to infer such patterns is affected by how, where and when we try to do so.Author summaryWe investigate the important issue of data heterogeneity in large multi-site data collections of brain activity recordings. At a time in which appraising the source of variability in large datasets is gaining increasing attention, this study provides a novel point of view based on data-driven dynamical models. By employing subject-specific signatures of brain network dynamics, we find that certain scanning factors significantly affect the quality of resting-state fMRI data. More specifically, we first validate the existence of subject-specific brain dynamics fingerprints. As a proof of concept, we show that dynamical states can be estimated reliably, even across different datasets. Finally, we assess which scanning factors, and to what extent, influence the variability of such fingerprints.

scholarly journals The unbalanced reorganization of weaker functional connections induces the altered brain network topology in schizophrenia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rossana Mastrandrea ◽  
Fabrizio Piras ◽  
Andrea Gabrielli ◽  
Nerisa Banaj ◽  
Guido Caldarelli ◽  
...  
Keyword(s):  
Resting State ◽  
Functional Organization ◽  
Functional Integration ◽  
Brain Network ◽  
Modular Organization ◽  
Node Degree ◽  
Imaging Data ◽  
Brain Disorder ◽  
Functional Connections ◽  
The Brain

AbstractNetwork neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robustness of the brain functional architecture in 44 medicated schizophrenic patients and 40 healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal functional organization between the two groups, the expected hierarchy of healthy brains' modular organization is crumbled in schizophrenia, showing a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones. Thus, the manifold nature of the functional organization’s basal scheme, together with its altered hierarchical modularity, may be crucial in the pathogenesis of schizophrenia. This result fits the disconnection hypothesis that describes schizophrenia as a brain disorder characterized by an abnormal functional integration among brain regions.

scholarly journals Reconfiguration of Brain Network Architectures between Resting-State and Complexity-Dependent Cognitive Reasoning

2017 ◽  
Vol 37 (35) ◽  
pp. 8399-8411 ◽  
Author(s):  
Luke J. Hearne ◽  
Luca Cocchi ◽  
Andrew Zalesky ◽  
Jason B. Mattingley
Keyword(s):  
Resting State ◽  
Brain Network ◽  
Network Architectures ◽  
Cognitive Reasoning

scholarly journals Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study

2021 ◽  
Vol 11 (1) ◽  
pp. 118
Author(s):  
Blake R. Neyland ◽  
Christina E. Hugenschmidt ◽  
Robert G. Lyday ◽  
Jonathan H. Burdette ◽  
Laura D. Baker ◽  
...  
Keyword(s):  
Older Adults ◽  
Graph Theory ◽  
Community Structure ◽  
Resting State ◽  
Brain Networks ◽  
Brain Network ◽  
Functional Brain ◽  
Network Community ◽  
Functional Brain Networks ◽  
The Brain

Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. In the current study, we applied graph theory to cross-sectional data to characterize functional brain networks generated from functional magnetic resonance imaging data both at rest and during a motor imagery (MI) task. Our MI task is derived from the Mobility Assessment Tool–short form (MAT-sf), which predicts performance on a 400 m walk, and the Short Physical Performance Battery (SPPB). Participants (n = 157) were from the Brain Networks and Mobility (B-NET) Study (mean age = 76.1 ± 4.3; % female = 55.4; % African American = 8.3; mean years of education = 15.7 ± 2.5). We used community structure analyses to partition functional brain networks into communities, or subnetworks, of highly interconnected regions. Global brain network community structure decreased during the MI task when compared to the resting state. We also examined the community structure of the default mode network (DMN), sensorimotor network (SMN), and the dorsal attention network (DAN) across the study population. The DMN and SMN exhibited a task-driven decline in consistency across the group when comparing the MI task to the resting state. The DAN, however, displayed an increase in consistency during the MI task. To our knowledge, this is the first study to use graph theory and network community structure to characterize the effects of a MI task, such as the MAT-sf, on overall brain network organization in older adults.

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