scholarly journals Bout-associated intrinsic functional network changes in cluster headache: A longitudinal resting-state functional MRI study

Cephalalgia ◽  
2016 ◽  
Vol 37 (12) ◽  
pp. 1152-1163 ◽  
Author(s):  
Kun-Hsien Chou ◽  
Fu-Chi Yang ◽  
Jong-Ling Fuh ◽  
Chen-Yuan Kuo ◽  
Yi-Hsin Wang ◽  
...  
Keyword(s):  
Cluster Headache ◽  
Resting State ◽  
Large Scale ◽  
Brain Networks ◽  
Functional Network ◽  
Imaging Data ◽  
Dynamic Changes ◽  
Attention Networks ◽  
Clinical Observations ◽  
Mri Study

Background Previous imaging studies on the pathogenesis of cluster headache (CH) have implicated the hypothalamus and multiple brain networks. However, very little is known regarding dynamic bout-associated, large-scale resting state functional network changes related to CH. Methods Resting-state functional magnetic resonance imaging data were obtained from CH patients and matched controls. Data were analyzed using independent component analysis for exploratory assessment of the changes in intrinsic brain networks and their relationship between in-bout and out-of-bout periods, as well as correlations with clinical observations. Results Compared to healthy controls, CH patients had functional connectivity (FC) changes in the temporal, frontal, salience, default mode, somatosensory, dorsal attention, and visual networks, independent of bout period. Compared to out-of-bout scans, in-bout scans showed altered FC in the frontal and dorsal attention networks. Lower frontal network FC correlated with longer duration of CH. Conclusions The present findings suggest that episodic CH with dynamic bout period shifts may involve bout-associated FC changes in multiple discrete cortical areas within networks outside traditional pain processing areas. Dynamic changes in FC in frontal and dorsal attention networks between bout periods could be important for understanding episodic CH pathophysiology.

scholarly journals Functional Segregation of Human Brain Networks Across the Lifespan: An Exploratory Analysis of Static and Dynamic Resting-State Functional Connectivity

2020 ◽  
Vol 14 ◽  
Author(s):  
Benjamin M. Rosenberg ◽  
Eva Mennigen ◽  
Martin M. Monti ◽  
Roselinde H. Kaiser
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Dwell Time ◽  
Brain Networks ◽  
Older Age ◽  
Functional Network ◽  
Functional Networks ◽  
Resting State Functional Connectivity ◽  
Network State ◽  
High Connectivity

Prior research has shown that during development, there is increased segregation between, and increased integration within, prototypical resting-state functional brain networks. Functional networks are typically defined by static functional connectivity over extended periods of rest. However, little is known about how time-varying properties of functional networks change with age. Likewise, a comparison of standard approaches to functional connectivity may provide a nuanced view of how network integration and segregation are reflected across the lifespan. Therefore, this exploratory study evaluated common approaches to static and dynamic functional network connectivity in a publicly available dataset of subjects ranging from 8 to 75 years of age. Analyses evaluated relationships between age and static resting-state functional connectivity, variability (standard deviation) of connectivity, and mean dwell time of functional network states defined by recurring patterns of whole-brain connectivity. Results showed that older age was associated with decreased static connectivity between nodes of different canonical networks, particularly between the visual system and nodes in other networks. Age was not significantly related to variability of connectivity. Mean dwell time of a network state reflecting high connectivity between visual regions decreased with age, but older age was also associated with increased mean dwell time of a network state reflecting high connectivity within and between canonical sensorimotor and visual networks. Results support a model of increased network segregation over the lifespan and also highlight potential pathways of top-down regulation among networks.

scholarly journals Loss of integration of brain networks after sleep deprivation relates to the worsening of cognitive functions

2020 ◽  
Author(s):  
Pesoli Matteo ◽  
Rucco Rosaria ◽  
Liparoti Marianna ◽  
Lardone Anna ◽  
D’Aurizio Giula ◽  
...  
Keyword(s):  
Sleep Deprivation ◽  
Resting State ◽  
Large Scale ◽  
Brain Networks ◽  
Brain Network ◽  
Cognitive Tasks ◽  
Functional Networks ◽  
Long Sleep ◽  
Before And After ◽  
The Brain

AbstractThe topology of brain networks changes according to environmental demands and can be described within the framework of graph theory. We hypothesized that 24-hours long sleep deprivation (SD) causes functional rearrangements of the brain topology so as to impair optimal communication, and that such rearrangements relate to the performance in specific cognitive tasks, namely the ones specifically requiring attention. Thirty-two young men underwent resting-state MEG recording and assessments of attention and switching abilities before and after SD. We found loss of integration of brain network and a worsening of attention but not of switching abilities. These results show that brain network changes due to SD affect switching abilities, worsened attention and induce large-scale rearrangements in the functional networks.

scholarly journals Centralized and distributed cognitive task processing in the human connectome

2019 ◽  
Vol 3 (2) ◽  
pp. 455-474 ◽  
Author(s):  
Enrico Amico ◽  
Alex Arenas ◽  
Joaquín Goñi
Keyword(s):  
Cognitive Processing ◽  
Resting State ◽  
Large Scale ◽  
Cognitive Task ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Cognitive Changes ◽  
Human Connectome Project ◽  
Functional Brain Networks ◽  
Jensen Shannon Divergence

A key question in modern neuroscience is how cognitive changes in a human brain can be quantified and captured by functional connectivity (FC). A systematic approach to measure pairwise functional distance at different brain states is lacking. This would provide a straightforward way to quantify differences in cognitive processing across tasks; also, it would help in relating these differences in task-based FCs to the underlying structural network. Here we propose a framework, based on the concept of Jensen-Shannon divergence, to map the task-rest connectivity distance between tasks and resting-state FC. We show how this information theoretical measure allows for quantifying connectivity changes in distributed and centralized processing in functional networks. We study resting state and seven tasks from the Human Connectome Project dataset to obtain the most distant links across tasks. We investigate how these changes are associated with different functional brain networks, and use the proposed measure to infer changes in the information-processing regimes. Furthermore, we show how the FC distance from resting state is shaped by structural connectivity, and to what extent this relationship depends on the task. This framework provides a well-grounded mathematical quantification of connectivity changes associated with cognitive processing in large-scale brain networks.

scholarly journals Resting-State Functional MRI Study: Connection Strength of Brain Networks in DR Patients

2019 ◽  
Vol Volume 15 ◽  
pp. 3359-3366
Author(s):  
Lan Li ◽  
Hui Dai ◽  
Jun Ke ◽  
Cen Shi ◽  
Nan Jiang ◽  
...  
Keyword(s):  
Functional Mri ◽  
Resting State ◽  
Brain Networks ◽  
Connection Strength ◽  
Mri Study

scholarly journals Directionality of large-scale resting-state brain networks during eyes open and eyes closed conditions

2015 ◽  
Vol 9 ◽  
Author(s):  
Delong Zhang ◽  
Bishan Liang ◽  
Xia Wu ◽  
Zengjian Wang ◽  
Pengfei Xu ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Brain Networks ◽  
Eyes Closed ◽  
Eyes Open

scholarly journals Large-Scale Brain Networks in Board Game Experts: Insights from a Domain-Related Task and Task-Free Resting State

PLoS ONE ◽  
2012 ◽  
Vol 7 (3) ◽  
pp. e32532 ◽  
Author(s):  
Xujun Duan ◽  
Wei Liao ◽  
Dongmei Liang ◽  
Lihua Qiu ◽  
Qing Gao ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Brain Networks ◽  
Board Game ◽  
And Task

scholarly journals The Global Signal and Observed Anticorrelated Resting State Brain Networks

2009 ◽  
Vol 101 (6) ◽  
pp. 3270-3283 ◽  
Author(s):  
Michael D. Fox ◽  
Dongyang Zhang ◽  
Abraham Z. Snyder ◽  
Marcus E. Raichle
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Brain Networks ◽  
Blood Oxygen Level Dependent ◽  
Great Promise ◽  
Blood Oxygen Level ◽  
Whole Brain ◽  
Global Signal ◽  
Global Regression

Resting state studies of spontaneous fluctuations in the functional MRI (fMRI) blood oxygen level dependent (BOLD) signal have shown great promise in mapping the brain's intrinsic, large-scale functional architecture. An important data preprocessing step used to enhance the quality of these observations has been removal of spontaneous BOLD fluctuations common to the whole brain (the so-called global signal). One reproducible consequence of global signal removal has been the finding that spontaneous BOLD fluctuations in the default mode network and an extended dorsal attention system are consistently anticorrelated, a relationship that these two systems exhibit during task performance. The dependence of these resting-state anticorrelations on global signal removal has raised important questions regarding the nature of the global signal, the validity of global signal removal, and the appropriate interpretation of observed anticorrelated brain networks. In this study, we investigate several properties of the global signal and find that it is, indeed, global, not residing preferentially in systems exhibiting anticorrelations. We detail the influence of global signal removal on resting state correlation maps both mathematically and empirically, showing an enhancement in detection of system-specific correlations and improvement in the correspondence between resting-state correlations and anatomy. Finally, we show that several characteristics of anticorrelated networks including their spatial distribution, cross-subject consistency, presence with modified whole brain masks, and existence before global regression are not attributable to global signal removal and therefore suggest a biological basis.

scholarly journals Age-Related Declines in Motor Performance are Associated With Decreased Segregation of Large-Scale Resting State Brain Networks

2017 ◽  
Vol 28 (12) ◽  
pp. 4390-4402 ◽  
Author(s):  
B R King ◽  
P van Ruitenbeek ◽  
I Leunissen ◽  
K Cuypers ◽  
K -F Heise ◽  
...  
Keyword(s):  
Resting State ◽  
Motor Performance ◽  
Large Scale ◽  
Brain Networks ◽  
Age Related
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