Modulations of functional connectivity in the healthy and schizophrenia groups during task and rest

Abstract

Connectivity analysis using functional magnetic resonance imaging (fMRI) data is an important area, useful for the identification of biomarkers for various mental disorders, including schizophrenia. Most studies to date have focused on resting data, while the study of functional connectivity during task and the differences between task and rest are of great interest as well. In this work, we examine the graph-theoretical properties of the connectivity maps constructed using spatial components derived from independent component analysis (ICA) for healthy controls and patients with schizophrenia during an auditory oddball task (AOD) and at extended rest. We estimate functional connectivity using the higher-order statistical dependence, i.e., mutual information among the ICA spatial components, instead of the typically used temporal correlation. We also define three novel topological metrics based on the modules of brain networks obtained using a clustering approach. Our experimental results show that although the schizophrenia patients preserve the small-world property, they present a significantly lower small-worldness during both AOD task and rest when compared to the healthy controls, indicating a consistent tendency towards a more random organization of brain networks. In addition, the task-induced modulations to topological measures of several components involving motor, cerebellum and parietal regions are altered in patients relative to controls, providing further evidence for the aberrant connectivity in schizophrenia.

Introduction

In the past two decades, the human brain as a complex system has been successfully analyzed using functional magnetic resonance imaging (fMRI). One of the most active areas in current fMRI research involves examining the functional connectivity, i.e., interactions among distributed regions. More interestingly, dysconnectivity or abnormal connectivity has been hypothesized as the major pathophysiological mechanism of various mental disorders, especially schizophrenia (Bullmore et al., 1997, Friston and Frith, 1995, Palmer et al., 2009, Stephan et al., 2009). Evidences for dysconnectivity in schizophrenia have been found between several brain regions (Honey et al., 2005, Lawrie et al., 2002, Liang et al., 2006, Rotarska-Jagiela et al., 2010, Zhou et al., 2007). For example, (Friston and Frith, 1995) discovered profound disruption of prefrontal–temporal interaction; (Rotarska-Jagiela et al., 2010) reported that schizophrenia patients showed aberrant connectivity in the default-mode network and decreased frontoparietal activity. Recently, further studies using graph-theoretical analysis methods suggested that patients with schizophrenia often present abnormalities in topological properties of the brain network connectivity, including small-worldness, efficiency and modularity (Alexander-Bloch et al., 2010, Bullmore and Sporns, 2009, Guye et al., 2010, Liu et al., 2008, Lynall et al., 2010, Wang et al., 2010, Yu et al., 2011b, Zalesky et al., 2010). For example, significantly decreased local and global efficiency in schizophrenia has been shown in a resting-state fMRI study (Liu et al., 2008); a reduced small-worldness in schizophrenic group has been reported in (Lynall et al., 2010). In general, most previous studies using graph-theoretical analysis methods have been conducted with participants during the resting state (Beckmann et al., 2005, Camchong et al., 2011, De Luca et al., 2005, Jafri et al., 2008, van de Ven et al., 2004, Yu et al., 2011b); while the topological properties of network connectivity as a function of task performance (Rajapakse et al., 2006, Yu et al., 2011a) and also the differences between task and rest have not been studied to the same degree. In this work, we examine the graph-theoretical properties of the brain network connectivity for the patients with schizophrenia and healthy controls during both an auditory oddball task (AOD) and an extended rest.

For fMRI data analysis, functional connectivity can be investigated using seed-based method that relies on temporal correlation between a few predefined seed regions of interest and other remaining regions (Biswal et al., 1995, Biswal et al., 1997, Cordes et al., 2000, Cordes et al., 2002, Moussa et al., 2011). However, the seed-based approach usually requires prior knowledge, for example an anatomical model, to determine the seed regions.

An alternative approach to estimate functional connectivity is using independent component analysis (ICA) (McKeown and Sejnowski, 1998). ICA, as applied to fMRI, separates data into a set of maximally independent components and associated time courses, where each component is a spatially distinct network containing temporally coherent voxels (Calhoun et al., 2001). Without an explicit prior knowledge, ICA provides a promising way to study connectivity on a multivariate level. Based on an ICA decomposition, functional connectivity is generally defined as the correlation between the ICA time courses (Jafri et al., 2008, van de Ven et al., 2004).

However, the time dimension of fMRI data and thus of the ICA time courses typically contain a significantly smaller set of samples relative to the spatial components (hundreds compared to tens of thousands). The inherent small sample size of time courses may reduce the accuracy of the computed statistics. Also, the second-order statistics such as correlation do not take full-order statistical information into account. We note that ICA uses diversity to take higher-order statistics into consideration. Based on its generative model, ICA also naturally takes temporal information into account by providing a clustering of spatial components through the temporal modulations. Hence, we measure the functional connectivity using ICA spatial components and in terms of higher-order statistical dependence, instead of the typically used temporal correlation.

In this study, we examine the functional connectivity in patients with schizophrenia and healthy controls acquired during both an auditory oddball task (AOD) and an extended rest. We construct connectivity maps using the mutual information among the ICA spatial components and calculate the graph-theoretical metrics of these maps. We also define three novel metrics based on the modules of components obtained using our previously proposed clustering approach (Ma et al., 2011a). According to the dysconnectivity hypothesis in schizophrenia, we assume in our study that the graph-theoretical properties of the network connectivity observed in the healthy group would be altered in the schizophrenia group, not only at resting state but also during the AOD task. Our experimental results show that regardless of the brain activation states (AOD task versus rest), the connectivity networks derived from spatial dependencies in schizophrenia patients preserve the small-world property. However, patients present a significantly lower small-worldness during both AOD task and rest relative to controls, indicating a consistent tendency towards a more random network organization. These two groups also present some different task-induced changes in the topological features, for example healthy controls show higher efficiency in the motor regions during the AOD task than at rest while patients exhibit reverse trend. These altered connectivity modulations in patients during different activation states may provide further evidence for the cortical processing deficiency in schizophrenia.