scholarly journals Three Large-Scale Functional Brain Networks from Resting-State Functional MRI in Subjects with Different Levels of Cognitive Impairment

2016 ◽  
Vol 13 (1) ◽  
pp. 1 ◽  
Author(s):  
Soo Hyun Joo ◽  
Hyun Kook Lim ◽  
Chang Uk Lee
Keyword(s):  
Cognitive Impairment ◽  
Functional Mri ◽  
Resting State ◽  
Large Scale ◽  
Brain Networks ◽  
Functional Brain ◽  
Functional Brain Networks ◽  
Different Levels

scholarly journals Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data

PLoS ONE ◽  
2011 ◽  
Vol 6 (7) ◽  
pp. e21976 ◽  
Author(s):  
Jin-Hui Wang ◽  
Xi-Nian Zuo ◽  
Suril Gohel ◽  
Michael P. Milham ◽  
Bharat B. Biswal ◽  
...  
Keyword(s):  
Theoretical Analysis ◽  
Functional Mri ◽  
Resting State ◽  
Brain Networks ◽  
Functional Brain ◽  
Graph Theoretical Analysis ◽  
Functional Brain Networks ◽  
Short And Long Term

scholarly journals The older adult brain is less modular, more integrated and less efficient at rest: A systematic review of large-scale resting-state functional brain networks across the adult lifespan.

2021 ◽  
Author(s):  
Hamish Deery ◽  
Rober Di Paolo ◽  
Chris Moran ◽  
Gary F. Egan ◽  
Sharna Jamadar
Keyword(s):  
Older Adults ◽  
Age Differences ◽  
Resting State ◽  
Large Scale ◽  
Brain Networks ◽  
Adult Brain ◽  
Functional Brain ◽  
Adult Lifespan ◽  
Age Related ◽  
Functional Brain Networks

The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2,938 records, 144 studies were included. Results on 11 network measures were summarised and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function. Higher-order networks reliably showed age differences, whereas basic processing and control networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for increased hemispheric lateralisation at rest, reduced BOLD signal variability within-subjects and altered patterns and speed of dynamic connectivity. Research on connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.

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.

scholarly journals Interactions Between Large-Scale Functional Brain Networks are Captured by Sparse Coupled HMMs

2018 ◽  
Vol 37 (1) ◽  
pp. 230-240 ◽  
Author(s):  
Thomas A. W. Bolton ◽  
Anjali Tarun ◽  
Virginie Sterpenich ◽  
Sophie Schwartz ◽  
Dimitri Van De Ville
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Functional Brain ◽  
Functional Brain Networks

scholarly journals Brain network dynamics correlate with personality traits

2019 ◽  
Author(s):  
Aya Kabbara ◽  
Veronique Paban ◽  
Arnaud Weill ◽  
Julien Modolo ◽  
Mahmoud Hassan
Keyword(s):  
Functional Connectivity ◽  
Personality Traits ◽  
Resting State ◽  
Brain Networks ◽  
Neural Substrates ◽  
Brain Network ◽  
Functional Brain ◽  
Human Connectome Project ◽  
Functional Brain Networks ◽  
The Brain

AbstractIntroductionIdentifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system which can be studied using functional connectivity techniques. However, much of the current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks.ObjectiveIn this study, we aimed to evaluate the feasibility of using dynamic network measures to predict personality traits.MethodUsing the EEG/MEG source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two datasets: 1) Resting state EEG data acquired from 56 subjects. 2) Resting state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated.ResultsSimilar observations were obtained by the two modalities (EEG and MEG) according to the neuroticism, which showed a negative correlation with the dynamic variability of resting state brain networks. In particular, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks.ConclusionThese findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

Sign in / Sign up

Export Citation Format

Share Document