Dataset Information

State-related functional integration and functional segregation brain networks in schizophrenia.

ABSTRACT: Altered topological properties of brain connectivity networks have emerged as important features of schizophrenia. The aim of this study was to investigate how the state-related modulations to graph measures of functional integration and functional segregation brain networks are disrupted in schizophrenia. Firstly, resting state and auditory oddball discrimination (AOD) fMRI data of healthy controls (HCs) and schizophrenia patients (SZs) were decomposed into spatially independent components (ICs) by group independent component analysis (ICA). Then, weighted positive and negative functional integration (inter-component networks) and functional segregation (intra-component networks) brain networks were built in each subject. Subsequently, connectivity strength, clustering coefficient, and global efficiency of all brain networks were statistically compared between groups (HCs and SZs) in each state and between states (rest and AOD) within group. We found that graph measures of negative functional integration brain network and several positive functional segregation brain networks were altered in schizophrenia during AOD task. The metrics of positive functional integration brain network and one positive functional segregation brain network were higher during the resting state than during the AOD task only in HCs. These findings imply that state-related characteristics of both functional integration and functional segregation brain networks are impaired in schizophrenia which provides new insight into the altered brain performance in this brain disorder.

SUBMITTER: Yu Q

PROVIDER: S-EPMC3839349 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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State-related functional integration and functional segregation brain networks in schizophrenia.

Yu Qingbao Q Sui Jing J Kiehl Kent A KA Pearlson Godfrey G Calhoun Vince D VD

Schizophrenia research 20131002 2-3

Altered topological properties of brain connectivity networks have emerged as important features of schizophrenia. The aim of this study was to investigate how the state-related modulations to graph measures of functional integration and functional segregation brain networks are disrupted in schizophrenia. Firstly, resting state and auditory oddball discrimination (AOD) fMRI data of healthy controls (HCs) and schizophrenia patients (SZs) were decomposed into spatially independent components (ICs ...[more]

PMID: 24094882

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Project description:A critical feature of the human brain that gives rise to complex cognition is its ability to reconfigure its network structure dynamically and adaptively in response to the environment. Existing research probing task-related reconfiguration of brain network structure has concluded that, although there are many similarities in network structure during an intrinsic, resting state and during the performance of a variety of cognitive tasks, there are meaningful differences as well. In this study, we related intrinsic, resting state network organization to reconfigured network organization during the performance of two tasks: a sequence tapping task, which is thought to probe motor execution and likely engages a single brain network, and an n-back task, which is thought to probe working memory and likely requires coordination across multiple networks. We implemented graph theoretical analyses using functional connectivity data from fMRI scans to calculate whole-brain measures of network organization in healthy young adults. We focused on quantifying measures of network segregation (modularity, system segregation, local efficiency, number of provincial hub nodes) and measures of network integration (global efficiency, number of connector hub nodes). Using these measures, we found converging evidence that local, within-network communication is critical for motor execution, whereas integrative, between-network communication is critical for working memory. These results confirm that the human brain has the remarkable ability to reconfigure its large-scale organization dynamically in response to current cognitive demands and that interpreting reconfiguration in terms of network segregation and integration may shed light on the optimal network structures underlying successful cognition. SIGNIFICANCE STATEMENT:The dynamic nature of the human brain gives rise to the wide range of behaviors and cognition of which humans are capable. We collected fMRI data from healthy young adults and measured large-scale functional connectivity patterns between regions distributed across the entire brain. We implemented graph theoretical analyses to quantify network organization during two tasks hypothesized to require different combinations of brain networks. During motor execution, segregation of distinct networks increased. Conversely, during working memory, integration across networks increased. These changes in network organization were related to better behavioral performance. These results underscore the human brain's ability to reconfigure network organization selectively and adaptively when confronted with changing cognitive demands to achieve an optimal balance between segregation and integration.

Dataset Information

State-related functional integration and functional segregation brain networks in schizophrenia.

Publications

State-related functional integration and functional segregation brain networks in schizophrenia.

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