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.

Assessing dynamic brain graphs of time-varying connectivity in fMRI data: Application to healthy controls and patients with schizophrenia

“…Recent works have indeed demonstrated the association between network flexibility and cognitive task performance (Garrett et al, 2013; Madhyastha et al, 2014; Spreng and Schacter, 2012; Thompson et al, 2013a). And other studies show a decrease in task-related changes in brain connectivity [AOD task vs Sternberg working memory task (Calhoun et al, 2006); rest vs AOD task (Yu et al, 2013a)]. To our knowledge, this is the first study to characterize the variance of the dynamic graph measures in time-varying fMRI brain connectivity.…”

“…Particularly, in R-fMRI data, nodes of brain graphs could be voxels, regions of interest (ROIs) parcellated from brain atlas, or spatially independent components (de Reus and van den Heuvel, 2013; Fornito et al, 2013; Yu et al, 2012); edges of brain graphs could be defined based on cross correlation between time series of nodes. Our and others’ previous work which implemented graph theory-based analysis in fMRI data have consistently shown disrupted graph metrics of whole brain connectivity in patients with schizophrenia (SZs) (Bassett et al, 2012; Liu et al, 2008; Lynall et al, 2010; Yu et al, 2011a; Yu et al, 2013a; Yu et al, 2013b; Yu et al, 2011b). However, all these studies assessed the graph metrics of stationary functional brain connectivity estimated by the full time series of signals over the entire scan, while brain networks are dynamically connected (Allen et al, 2014) and it has been proposed that quantifying time-varying functional connectivity may provide great insight into fundamental properties of brain networks (Hutchison et al, 2013a).…”

“…INRIalign (Freire et al, 2002) was used to realign the images. Then the data were spatially normalized to the standard Montreal Neurological Institute (MNI) space, resampled to 3 mm × 3 mm × 3 mm voxels using the nonlinear (affine + low frequency direct cosine transform basis functions) registration implemented in SPM8 toolbox (http://www.fil.ion.ucl.ac.uk/spm), and smoothed using a Gaussian kernel with a small full-width at half-maximum of 5 mm to reduce spurious correlations in further analysis (van den Heuvel et al, 2008; Yu et al, 2013a; Zalesky et al, 2012). …”

The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery

“…The dependency among ICA-based models (i.e., among network dependency) has also been quantified using static graph metrics on fMRI data (Yu et al, 2013, 2011). Changes in networks can also be directly assessed in the context of chronnectomics via the calculation of time-varying graph metrics.…”

Comparing brain graphs in which nodes are regions of interest or independent components: A simulation study

“…automated anatomical labeling, AAL template) to define the nodes based on brain structure. Alternatively, independent component analysis (ICA) can be run to detect independent components (ICs, spatial brain maps), which can be considered as graph nodes (He et al, 2016; Smith, 2012; Smith et al, 2011; Yu et al, 2015; Yu et al, 2011a; Yu et al, 2013a; Yu et al, 2013b; Yu et al, 2011b; Yu et al, 2016). While the “correct” method for defining the brain nodes remains an open question that deserves further extensive research (Stanley et al, 2013).…”