scholarly journals The Acute Effects of Aerobic Exercise on the Functional Connectivity of Human Brain Networks

2017 ◽  
Vol 2 (2) ◽  
pp. 171-190 ◽  
Author(s):  
Timothy B. Weng ◽  
Gary L. Pierce ◽  
Warren G. Darling ◽  
Derik Falk ◽  
Vincent A. Magnotta ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Aerobic Exercise ◽  
Brain Networks ◽  
Acute Effects

scholarly journals Development and Emergence of Individual Variability in the Functional Connectivity Architecture of the Preterm Human Brain

2018 ◽  
Vol 29 (10) ◽  
pp. 4208-4222 ◽  
Author(s):  
Yuehua Xu ◽  
Miao Cao ◽  
Xuhong Liao ◽  
Mingrui Xia ◽  
Xindi Wang ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Network Architecture ◽  
Brain Networks ◽  
Individual Variability ◽  
Distance Constraint ◽  
Behavioral Differences ◽  
Sensorimotor Network ◽  
Middle Range ◽  
Brain Functional Connectivity

Abstract Individual variability in human brain networks underlies individual differences in cognition and behaviors. However, researchers have not conclusively determined when individual variability patterns of the brain networks emerge and how they develop in the early phase. Here, we employed resting-state functional MRI data and whole-brain functional connectivity analyses in 40 neonates aged around 31–42 postmenstrual weeks to characterize the spatial distribution and development modes of individual variability in the functional network architecture. We observed lower individual variability in primary sensorimotor and visual areas and higher variability in association regions at the third trimester, and these patterns are generally similar to those of adult brains. Different functional systems showed dramatic differences in the development of individual variability, with significant decreases in the sensorimotor network; decreasing trends in the visual, subcortical, and dorsal and ventral attention networks, and limited change in the default mode, frontoparietal and limbic networks. The patterns of individual variability were negatively correlated with the short- to middle-range connection strength/number and this distance constraint was significantly strengthened throughout development. Our findings highlight the development and emergence of individual variability in the functional architecture of the prenatal brain, which may lay network foundations for individual behavioral differences later in life.

scholarly journals Comparing the temporal relationship of structural and functional connectivity changes in different adult human brain networks: a single-case study

2018 ◽  
Vol 3 ◽  
pp. 50 ◽  
Author(s):  
Takamitsu Watanabe ◽  
Geraint Rees
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Large Scale ◽  
Single Case ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Time Lags ◽  
Single Case Study ◽  
Adult Human

Background: Despite accumulated evidence for adult brain plasticity, the temporal relationships between large-scale functional and structural connectivity changes in human brain networks remain unclear. Methods: By analysing a unique richly detailed 19-week longitudinal neuroimaging dataset, we tested whether macroscopic functional connectivity changes lead to the corresponding structural alterations in the adult human brain, and examined whether such time lags between functional and structural connectivity changes are affected by functional differences between different large-scale brain networks. Results: In this single-case study, we report that, compared to attention-related networks, functional connectivity changes in default-mode, fronto-parietal, and sensory-related networks occurred in advance of modulations of the corresponding structural connectivity with significantly longer time lags. In particular, the longest time lags were observed in sensory-related networks. In contrast, such significant temporal differences in connectivity change were not seen in comparisons between anatomically categorised different brain areas, such as frontal and occipital lobes. These observations survived even after multiple validation analyses using different connectivity definitions or using parts of the datasets. Conclusions: Although the current findings should be examined in independent datasets with different demographic background and by experimental manipulation, this single-case study indicates the possibility that plasticity of macroscopic brain networks could be affected by cognitive and perceptual functions implemented in the networks, and implies a hierarchy in the plasticity of functionally different brain systems.

scholarly journals Age-Related Reorganizational Changes in Modularity and Functional Connectivity of Human Brain Networks

2014 ◽  
Vol 4 (9) ◽  
pp. 662-676 ◽  
Author(s):  
Jie Song ◽  
Rasmus M. Birn ◽  
Mélanie Boly ◽  
Timothy B. Meier ◽  
Veena A. Nair ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Brain Networks ◽  
Age Related

scholarly journals Relationship of critical dynamics, functional connectivity, and states of consciousness in large-scale human brain networks

NeuroImage ◽  
2019 ◽  
Vol 188 ◽  
pp. 228-238 ◽  
Author(s):  
Heonsoo Lee ◽  
Daniel Golkowski ◽  
Denis Jordan ◽  
Sebastian Berger ◽  
Rüdiger Ilg ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Large Scale ◽  
Brain Networks ◽  
Critical Dynamics ◽  
States Of Consciousness ◽  
Relationship Of

scholarly journals Establishing the cognitive signature of human brain networks derived from structural and functional connectivity

2018 ◽  
Vol 223 (9) ◽  
pp. 4023-4038 ◽  
Author(s):  
JeYoung Jung ◽  
Maya Visser ◽  
Richard J. Binney ◽  
Matthew A. Lambon Ralph
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Brain Networks

scholarly journals Intracranial Electrophysiology Reveals Reproducible Intrinsic Functional Connectivity within Human Brain Networks

2018 ◽  
Vol 38 (17) ◽  
pp. 4230-4242 ◽  
Author(s):  
Aaron Kucyi ◽  
Jessica Schrouff ◽  
Stephan Bickel ◽  
Brett L. Foster ◽  
James M. Shine ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Brain Networks ◽  
Intrinsic Functional Connectivity

scholarly journals Decoding task-specific cognitive states with slow, directed functional networks in the human brain

2019 ◽  
Author(s):  
Devarajan Sridharan ◽  
Shagun Ajmera ◽  
Hritik Jain ◽  
Mali Sundaresan
Keyword(s):  
Machine Learning ◽  
Functional Connectivity ◽  
Human Brain ◽  
Brain Networks ◽  
Fmri Data ◽  
Invasive Technique ◽  
Functional Interactions ◽  
Cognitive States ◽  
Human Connectome Project ◽  
The Brain

AbstractFlexible functional interactions among brain regions mediate critical cognitive functions. Such interactions can be measured from functional magnetic resonance imaging (fMRI) data with either instantaneous (zero-lag) or lag-based (time-lagged) functional connectivity; only the latter approach permits inferring directed functional interactions. Yet, the fMRI hemodynamic response is slow, and sampled at a timescale (seconds) several orders of magnitude slower than the underlying neural dynamics (milliseconds). It is, therefore, widely held that lag-based fMRI functional connectivity, measured with approaches like as Granger-Geweke causality (GC), provides spurious and unreliable estimates of underlying neural interactions. Experimental verification of this claim has proven challenging because neural ground truth connectivity is often unavailable concurrently with fMRI recordings. We address this challenge by combining machine learning with GC functional connectivity estimation. We estimated instantaneous and lag-based GC functional connectivity networks using fMRI data from 1000 participants, drawn from the Human Connectome Project database. A linear classifier, trained on either instantaneous or lag-based GC, reliably discriminated among seven different task and resting brain states, with over 80% cross-validation accuracy. With network simulations, we demonstrate that instantaneous and lag-based GC exploited interactions at fast and slow timescales, respectively, to achieve robust classification. With human fMRI data, instantaneous and lag-based GC identified distinct, cognitive core networks. Finally, variations in GC connectivity explained inter-individual variations in a variety of cognitive scores. Our findings show that instantaneous and lag-based methods reveal complementary aspects of functional connectivity in the brain, and suggest that slow, directed functional interactions, estimated with fMRI, provide robust markers of behaviorally relevant cognitive states.Author SummaryFunctional MRI (fMRI) is a leading, non-invasive technique for mapping networks in the human brain. Yet, fMRI signals are noisy and sluggish, and fMRI scans are acquired at a timescale of seconds, considerably slower than the timescale of neural spiking (milliseconds). Can fMRI, then, be used to infer dynamic processes in the brain such as the direction of information flow among brain networks? We sought to answer this question by applying machine learning to fMRI scans acquired from 1000 participants in the Human Connectome Project (HCP) database. We show that directed brain networks, estimated with a technique known as Granger-Geweke Causality (GC), accurately predicts individual subjects’ task-specific cognitive states inside the scanner, and also explains variations in a variety of behavioral scores across individuals. We propose that directed functional connectivity, as estimated with fMRI-GC, is relevant for understanding human cognitive function.

scholarly journals The Acute Effects of the Atypical Dissociative Hallucinogen Salvinorin A on Functional Connectivity in the Human Brain

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Manoj K. Doss ◽  
Darrick G. May ◽  
Matthew W. Johnson ◽  
John M. Clifton ◽  
Sidnee L. Hedrick ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Brain Function ◽  
Salvia Divinorum ◽  
Salvinorin A ◽  
Acute Effects ◽  
Opioid Receptor Agonist ◽  
Strength Variability ◽  
First Time ◽  
Better Than

Abstract Salvinorin A (SA) is a κ-opioid receptor agonist and atypical dissociative hallucinogen found in Salvia divinorum. Despite the resurgence of hallucinogen studies, the effects of κ-opioid agonists on human brain function are not well-understood. This placebo-controlled, within-subject study used functional magnetic resonance imaging for the first time to explore the effects of inhaled SA on strength, variability, and entropy of functional connectivity (static, dynamic, and entropic functional connectivity, respectively, or sFC, dFC, and eFC). SA tended to decrease within-network sFC but increase between-network sFC, with the most prominent effect being attenuation of the default mode network (DMN) during the first half of a 20-min scan (i.e., during peak effects). SA reduced brainwide dFC but increased brainwide eFC, though only the former effect survived multiple comparison corrections. Finally, using connectome-based classification, most models trained on dFC network interactions could accurately classify the first half of SA scans. In contrast, few models trained on within- or between-network sFC and eFC performed above chance. Notably, models trained on within-DMN sFC and eFC performed better than models trained on other network interactions. This pattern of SA effects on human brain function is strikingly similar to that of other hallucinogens, necessitating studies of direct comparisons.

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