Dynamic changes of spatial functional network connectivity in healthy individuals and schizophrenia patients using independent vector analysis

Abstract

Recent work on both task-induced and resting-state functional magnetic resonance imaging (fMRI) data suggests that functional connectivity may fluctuate, rather than being stationary during an entire scan. Most dynamic studies are based on second-order statistics between fMRI time series or time courses derived from blind source separation, e.g., independent component analysis (ICA), to investigate changes of temporal interactions among brain regions. However, fluctuations related to spatial components over time are of interest as well. In this paper, we examine higher-order statistical dependence between pairs of spatial components, which we define as spatial functional network connectivity (sFNC), and changes of sFNC across a resting-state scan. We extract time-varying components from healthy controls and patients with schizophrenia to represent brain networks using independent vector analysis (IVA), which is an extension of ICA to multiple data sets and enables one to capture spatial variations. Based on mutual information among IVA components, we perform statistical analysis and Markov modeling to quantify the changes in spatial connectivity. Our experimental results suggest significantly more fluctuations in patient group and show that patients with schizophrenia have more variable patterns of spatial concordance primarily between frontoparietal, cerebellum and temporal lobe regions. This study extends upon earlier studies showing temporal connectivity differences in similar areas on average by providing evidence that the dynamic spatial interplay between these regions is also impacted by schizophrenia.

Adaptive independent vector analysis for multi-subject complex-valued fMRI data

“…In addition, we focused on task fMRI data in this study. Recently, IVA has found promising applications for resting-state fMRI data due to superior performance in capturing subject variability over group ICA (Ma et al, 2014;Laney et al, 2015aLaney et al, , 2015bGopal et al, 2015Calhoun and Adali, 2016). In the future, we will extend the proposed method to analyze resting-state complex-valued fMRI data.…”

“…Compared to tensor decomposition, ICAbased analysis extracts subject-specific TCs and/or SMs for emphasizing inter-subject variability. Two such approaches are group ICA (Calhoun et al, 2001(Calhoun et al, , 2008Guo and Pagnonib, 2008;Erhardt et al, 2011;Calhoun and Adali, 2012b;Eloyan et al, 2013;Afshin-Pour et al, 2014) and independent vector analysis (IVA, a kind of joint ICA) (Lee et al, 2008a;Dea et al, 2011;Michael et al, 2014;Ma et al, 2014;Laney et al, 2015aLaney et al, , 2015bGopal et al, 2015Adali et al, 2015). While group ICA provides individual TCs or SMs via ICA of temporally or spatially concatenated multi-subject fMRI datasets, IVA generates individual TCs and SMs via joint ICA of multi-subject fMRI datasets where similar SMs among different subjects were concatenated as source component vectors (SCVs).…”

“…IVA-GL emphasizes both second-order and higher-order statistics (Anderson et al, 2012a), and thus tends to be more efficient than IVA-L and IVA-G for fMRI analysis. IVA-GL was first tested using simulated fMRI data (Dea et al, 2011;Michael et al, 2014), and then applied to real-valued fMRI data for diverse applications: e.g., producing discriminative features for quantifying motor recovery after stroke (Laney et al, 2015a(Laney et al, , 2015b; finding dynamic changes in spatial functional network connectivity in healthy individuals and schizophrenic patients (Ma et al, 2014;Calhoun and Adali, 2016); showing the spatial variation in fMRI brain networks (Gopal et al, 2015; fusing multimodal data ; and removing the gradient artifact in concurrently collected electroencephalogram (EEG) and fMRI data (Acharjee et al, 2015). In addition to IVA-GL, there are also other real-valued IVA algorithms that are promising for analyzing magnitude-only fMRI data, such as IVA with the Kotz family of distribution (Anderson et al, 2013), and IVA using an adaptive multivariate generalized Gaussian distribution (MGGD) (Boukouvalas et al, 2015).…”

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

“…However, a previous work (Kiviniemi et al, 2011) has reported substantial spatial dynamics when doing sliding time window ICA, though that study used low model order ICA decompositions (on average 15 components). In addition, a more recent study (Ma et al, 2014) demonstrated time-varying spatial brain connectivity in HCs and SZs using independent vector analysis (IVA). In contrast with our finding that patients show lower variation of the dynamic graph metrics, that work found significantly more fluctuations of spatial concordance in schizophrenia.…”

A Review: Detecting Alterations of Brain Connectivity in Schizophrenia based on Structural MRI

“…Besides, discoveries of structural MRI-based network in schizophrenia can complement studies which show disrupted white matter tracts [3] and functional connectivity [54] between brain regions in the disease [11]. Then, we will present the results of recent researches of sMRI-based network applied in the schizophrenia.…”