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.

The effect of inebriation on human brain functional connectivity

2012 ◽  
Vol 85 (3) ◽  
pp. 388-389
Author(s):  
C. Lithari ◽  
M.A. Klados ◽  
C. Pappas ◽  
M. Albani ◽  
D. Kapoukranidou ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Brain Functional Connectivity

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 Heritability of the network architecture of intrinsic brain functional connectivity

NeuroImage ◽  
2015 ◽  
Vol 121 ◽  
pp. 243-252 ◽  
Author(s):  
Benjamin Sinclair ◽  
Narelle K. Hansell ◽  
Gabriëlla A.M. Blokland ◽  
Nicholas G. Martin ◽  
Paul M. Thompson ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Network Architecture ◽  
Brain Functional Connectivity

Isolating human brain functional connectivity associated with a specific cognitive process

2010 ◽  
Author(s):  
Michael A. Silver ◽  
Ayelet N. Landau ◽  
Thomas Z. Lauritzen ◽  
William Prinzmetal ◽  
Lynn C. Robertson
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Cognitive Process ◽  
Brain Functional Connectivity

scholarly journals Behavioural relevance of spontaneous, transient brain network interactions in fMRI

2019 ◽  
Author(s):  
D. Vidaurre ◽  
A. Llera ◽  
S.M. Smith ◽  
M.W. Woolrich
Keyword(s):  
Functional Connectivity ◽  
Network Architecture ◽  
Brain Networks ◽  
Structural Data ◽  
Brain Regions ◽  
Brain Network ◽  
Time Varying ◽  
Human Connectome Project ◽  
Brain Data ◽  
Structural Brain

AbstractHow spontaneously fluctuating functional magnetic resonance imaging (fMRI) signals in different brain regions relate to behaviour has been an open question for decades. Correlations in these signals, known as functional connectivity, can be averaged over several minutes of data to provide a stable representation of the functional network architecture for an individual. However, associations between these stable features and behavioural traits have been shown to be dominated by individual differences in anatomy. Here, using kernel learning tools, we propose methods to assess and compare the relation between time-varying functional connectivity, time-averaged functional connectivity, structural brain data, and non-imaging subject behavioural traits. We applied these methods on Human Connectome Project resting-state fMRI data to show that time-varying fMRI functional connectivity, detected at time-scales of a few seconds, has associations with some behavioural traits that are not dominated by anatomy. Despite time-averaged functional connectivity accounting for the largest proportion of variability in the fMRI signal between individuals, we found that some aspects of intelligence could only be explained by time-varying functional connectivity. The finding that time-varying fMRI functional connectivity has a unique relationship to population behavioural variability suggests that it might reflect transient neuronal communication fluctuating around a stable neural architecture.Significance statementComplex cognition is dynamic and emerges from the interaction between multiple areas across the whole brain, i.e. from brain networks. Hence, the utility of functional MRI to investigate brain activity depends on how well it can capture time-varying network interactions. Here, we develop methods to predict behavioural traits of individuals from either time-varying functional connectivity, time-averaged functional connectivity, or structural brain data. We use these to show that the time-varying nature of functional brain networks in fMRI can be reliably measured and can explain aspects of behaviour not captured by structural data or time-averaged functional connectivity. These results provide important insights to the question of how the brain represents information and how these representations can be measured with fMRI.

scholarly journals The Degree Distribution of Human Brain Functional Connectivity is Generalized Pareto: A Multi-Scale Analysis

2019 ◽  
Author(s):  
Riccardo Zucca ◽  
Xerxes D. Arsiwalla ◽  
Hoang Le ◽  
Mikail Rubinov ◽  
Antoni Gurguí ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Power Law ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Cross Model ◽  
Generalized Pareto ◽  
Heavy Tailed ◽  
And Function ◽  
Brain Functional Connectivity

ABSTRACTAre degree distributions of human brain functional connectivity networks heavy-tailed? Initial claims based on least-square fitting suggested that brain functional connectivity networks obey power law scaling in their degree distributions. This interpretation has been challenged on methodological grounds. Subsequently, estimators based on maximum-likelihood and non-parametric tests involving surrogate data have been proposed. No clear consensus has emerged as results especially depended on data resolution. To identify the underlying topological distribution of brain functional connectivity calls for a closer examination of the relationship between resolution and statistics of model fitting. In this study, we analyze high-resolution functional magnetic resonance imaging (fMRI) data from the Human Connectome Project to assess its degree distribution across resolutions. We consider resolutions from one thousand to eighty thousand regions of interest (ROIs) and test whether they follow a heavy or short-tailed distribution. We analyze power law, exponential, truncated power law, log-normal, Weibull and generalized Pareto probability distributions. Notably, the Generalized Pareto distribution is of particular interest since it interpolates between heavy-tailed and short-tailed distributions, and it provides a handle on estimating the tail’s heaviness or shortness directly from the data. Our results show that the statistics support the short-tailed limit of the generalized Pareto distribution, rather than a power law or any other heavy-tailed distribution. Working across resolutions of the data and performing cross-model comparisons, we further establish the overall robustness of the generalized Pareto model in explaining the data. Moreover, we account for earlier ambiguities by showing that down-sampling the data systematically affects statistical results. At lower resolutions models cannot easily be differentiated on statistical grounds while their plausibility consistently increases up to an upper bound. Indeed, more power law distributions are reported at low resolutions (5K) than at higher ones (50K or 80K). However, we show that these positive identifications at low resolutions fail cross-model comparisons and that down-sampling data introduces the risk of detecting spurious heavy-tailed distributions. This dependence of the statistics of degree distributions on sampling resolution has broader implications for neuroinformatic methodology, especially, when several analyses rely on down-sampled data, for instance, due to a choice of anatomical parcellations or measurement technique. Our findings that node degrees of human brain functional networks follow a short-tailed distribution have important implications for claims of brain organization and function. Our findings do not support common simplistic representations of the brain as a generic complex system with optimally efficient architecture and function, modeled with simple growth mechanisms. Instead these findings reflect a more nuanced picture of a biological system that has been shaped by longstanding and pervasive developmental and architectural constraints, including wiring-cost constraints on the centrality architecture of individual nodes.

scholarly journals State-Unspecific Modes of Whole-Brain Functional Connectivity Predict Intelligence and Life Outcomes

2018 ◽  
Author(s):  
Yu Takagi ◽  
Jun-ichiro Hirayama ◽  
Saori C Tanaka
Keyword(s):  
Functional Connectivity ◽  
Individual Variation ◽  
Brain Networks ◽  
Life Outcomes ◽  
Behavioral Measures ◽  
Experimental Conditions ◽  
Whole Brain ◽  
Human Connectome Project ◽  
Functional Brain Networks ◽  
Brain Functional Connectivity

AbstractRecent functional magnetic resonance imaging (fMRI) studies have increasingly revealed potential neural substrates of individual differences in diverse types of brain function and dysfunction. Although most previous studies have been inherently limited to state-specific characterizations of related brain networks and their functions, several recent studies have examined the potential state-unspecific nature of functional brain networks, such as their global similarities across different experimental conditions (i.e., states) including both task and rest. However, no previous studies have carried out direct, systematic characterizations of state-unspecific brain networks, or their functional implications. Here, we quantitatively identified several modes of state-unspecific individual variation in whole-brain functional connectivity patterns, called “Common Neural Modes (CNMs)”, from a large fMRI dataset including eight task/rest states, obtained from the Human Connectome Project. Furthermore, we tested how CNMs account for variability in individual behavioral measures. The results revealed that three CNMs were robustly extracted under various different preprocessing conditions. Each of these CNMs was significantly correlated with different aspects of behavioral measures of both fluid and crystalized intelligence. The three CNMs were also able to predict several life outcomes, such as income and life satisfaction, achieving the highest performance when combined with behavioral intelligence measures as inputs. Our findings highlight the importance of state-unspecific brain networks to characterize fundamental individual variation.

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