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

Highlights

• Develop a new method for characterizing time-varying brain graph in R-fMRI data.

• Apply the method in patients with schizophrenia.

• Dynamic properties of the time-varying brain graph are altered in schizophrenia.

Abstract

Graph theory-based analysis has been widely employed in brain imaging studies, and altered topological properties of brain connectivity have emerged as important features of mental diseases such as schizophrenia. However, most previous studies have focused on graph metrics of stationary brain graphs, ignoring that brain connectivity exhibits fluctuations over time. Here we develop a new framework for accessing dynamic graph properties of time-varying functional brain connectivity in resting-state fMRI data and apply it to healthy controls (HCs) and patients with schizophrenia (SZs). Specifically, nodes of brain graphs are defined by intrinsic connectivity networks (ICNs) identified by group independent component analysis (ICA). Dynamic graph metrics of the time-varying brain connectivity estimated by the correlation of sliding time-windowed ICA time courses of ICNs are calculated. First- and second-level connectivity states are detected based on the correlation of nodal connectivity strength between time-varying brain graphs. Our results indicate that SZs show decreased variance in the dynamic graph metrics. Consistent with prior stationary functional brain connectivity works, graph measures of identified first-level connectivity states show lower values in SZs. In addition, more first-level connectivity states are disassociated with the second-level connectivity state which resembles the stationary connectivity pattern computed by the entire scan. Collectively, the findings provide new evidence about altered dynamic brain graphs in schizophrenia, which may underscore the abnormal brain performance in this mental illness.

Introduction

Resting-state functional magnetic resonance imaging (R-fMRI) is a powerful technique to characterize functional organization of human brain. A number of resting-state brain networks, also called intrinsic connectivity networks (ICNs) such as the default mode network (Buckner et al., 2008, Greicius et al., 2003, Raichle et al., 2001), motor network (Biswal et al., 1995), ventral and dorsal attention networks (Fox et al., 2006, Ptak, 2012, Vincent et al., 2008, Viviani, 2013), and salience network (Seeley et al., 2007), have been widely recognized by functional connectivity analysis. The main relationship among these networks is that the rest-related default mode network which is thought to support internally oriented processing is anticorrelated with other task-related networks, which act as a generic external attention system (EAS) (Fornito et al., 2012a, Fox et al., 2005). Recently, a more refined and fine-grained parcellation of these large-scale networks into a multitude of smaller constituents (Abou-Elseoud et al., 2010, Allen et al., 2011, Kiviniemi et al., 2009) has been shown by independent component analysis (ICA) (Calhoun et al., 2008, McKeown et al., 1998) in fMRI data. To evaluate the connectivity between multiple brain networks, a method called functional network connectivity (FNC), which examines the temporal relationship among brain components, has been developed (Jafri et al., 2008). Most studies which implement this technique have discovered altered FNC in patients with brain disorder such as schizophrenia (Calhoun and Adali, 2012, Calhoun et al., 2009a, Yu et al., 2012).

Schizophrenia is a severe chronic, mental disease that causes significant social and work problems. Common symptoms include delusion, hallucinations, apathy, and social withdrawal (Marin, 2012). This illness impairs multiple cognitive domains including memory (He et al., 2012), attention, and executive function (Heinrichs and Zakzanis, 1998). Although the causes and mechanisms of schizophrenia are still unclear, graph theory-based analysis in brain imaging data suggest that the aberrant topological properties of brain connectivity are important features of this mental disorder (Fornito et al., 2012b, van den Heuvel and Fornito, 2014, Xia and He, 2011).

Graph theory-based analysis has become a powerful technique for analyzing brain imaging data. 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., 2011b, Yu et al., 2013a, Yu et al., 2013b). 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).

Dynamics of brain activation and connectivity have long been appreciated in electroencephalograms (EEGs) (Mutlu et al., 2012). Functional microstates which may correspond to basic building blocks of human information processing have been well-established in EEG data (Hennings et al., 2009, Koenig et al., 2002, Lehmann and Skrandies, 1984, Lehmann et al., 1998, Pascualmarqui et al., 1995). In the last decade, more and more fMRI studies are investigating the temporal dynamics of functional connectivity in the human brain (Hutchison et al., 2013a). Functional brain connectivity has been reported to exhibit changes due to task demands (Esposito et al., 2006, Fornito et al., 2012a, Fransson, 2006), learning (Bassett et al., 2011), maturation (Uddin et al., 2011), and large state transition such as sleep (Horovitz et al., 2008, Horovitz et al., 2009). Brain connectivity under dynamic changes within time scales of seconds to minutes has also been reported in fMRI data (Chang and Glover, 2010, Hutchison et al., 2013b, Kang et al., 2011, Kiviniemi et al., 2011, Li et al., 2013, Li et al., 2014, Sakoglu et al., 2010). Most recent time-varying brain connectivity studies with sliding time–window correlation analysis in R-fMRI data have reported brain connectivity states (patterns) reoccurring over time and subjects identified by a k-means clustering algorithm (Allen et al., 2014) and eigenconnectivities, which capture connectivity pairs with similar dynamics identified by principal component analysis (PCA) (Leonardi et al., 2013). However, topological metrics of the time-varying functional brain connectivity which may provide a quantified description of the dynamic mind-brain organization at a system level (Bassett and Gazzaniga, 2011, Fornito et al., 2013, Telesford et al., 2011) have been largely uninvestigated in both healthy controls (HCs) and patients with mental illness such as schizophrenia.

The aim of this study is to develop a framework for assessing dynamic graph properties of time-varying functional brain connectivity in R-fMRI data and apply it to HCs and SZs. This framework combines spatial ICA which is used to define nodes of brain graphs by decomposing the imaging data into functionally homogeneous brain regions (Abou-Elseoud et al., 2010, Kiviniemi et al., 2009, Yu et al., 2011a), sliding time–window correlation analysis, which is used to estimate time-varying brain connectivity, and graph theory-based analysis, which is used to evaluate dynamic graph metrics. Based on previous studies (Jones et al., 2012, Rottschy et al., 2012, Sakoglu et al., 2010, Wee et al., 2013), we predict that the dynamic properties of the time-varying brain graphs will differ from HCs to SZs. The findings could provide new insights into the biomarker of schizophrenia about impaired brain performance. The novel framework reported in this study is generalizable to other works of exploring group differences in dynamic brain graphs.