Altered resting state complexity in schizophrenia

Abstract

The complexity of the human brain's activity and connectivity varies over temporal scales and is altered in disease states such as schizophrenia. Using a multi-level analysis of spontaneous low-frequency fMRI data stretching from the activity of individual brain regions to the coordinated connectivity pattern of the whole brain, we investigate the role of brain signal complexity in schizophrenia. Specifically, we quantitatively characterize the univariate wavelet entropy of regional activity, the bivariate pairwise functional connectivity between regions, and the multivariate network organization of connectivity patterns. Our results indicate that univariate measures of complexity are less sensitive to disease state than higher level bivariate and multivariate measures. While wavelet entropy is unaffected by disease state, the magnitude of pairwise functional connectivity is significantly decreased in schizophrenia and the variance is increased. Furthermore, by considering the network structure as a function of correlation strength, we find that network organization specifically of weak connections is strongly correlated with attention, memory, and negative symptom scores and displays potential as a clinical biomarker, providing up to 75% classification accuracy and 85% sensitivity. We also develop a general statistical framework for the testing of group differences in network properties, which is broadly applicable to studies where changes in network organization are crucial to the understanding of brain function.

Introduction

Recent evidence suggests that resting-state brain function measured by functional magnetic resonance imaging (fMRI) (Raichle and Snyder, 2007) is a sensitive marker of disease (Auer, 2008, Broyd et al., 2009) and a potentially important tool for the discovery of quantitative genetic phenotypes (Biswal et al., 2010). Resting-state functional connectivity, measured by correlations between blood oxygen level dependent (BOLD) time series, is profoundly disturbed in schizophrenia (Lynall et al., 2010, Skudlarski et al., 2010), supporting the dysconnectivity hypothesis (Friston, 1998) of the disease. Dysconnectivity refers to a change in the complex pattern of healthy functional interactions between brain regions, likely driven by alterations in underlying neurophysiological processes (Valli et al., 2011) such as synaptic plasticity or developmental wiring (Stephan et al., 2006).

Resting state brain function in both health and disease can be characterized at multiple levels, from the activity of single brain regions (univariate), to the functional interactions between a pair of regions (bivariate), to the coordinated pattern of connectivity between many brain regions (multivariate). Each provides a window into a unique dimension of brain function complexity that can be linked to cognitive function and its alteration in disease. At the lowest level, the predictability of a single brain signal can be examined using Lyapunov exponents, Hurst exponents, dimensional complexity, and multi-scale entropy (Subha et al., 2010), which vary with age and development (McIntosh et al., 2010, Meyer-Lindenberg and Bassett, 2008), task (Barnes et al., 2009, Meyer-Lindenberg and Bassett, 2008, Misic et al., 2010), and disease (Bosl et al., 2011, Breakspear, 2006). In schizophrenia, studies have reported both increases (Bob et al., 2009) and decreases (Meyer-Lindenberg and Bassett, 2008) in EEG signal complexity relative to healthy controls, and modulation by antipsychotics (Takahashi et al., 2010). At intermediate levels, pairwise functional connectivity can be measured by simple linear correlations or more complex synchronization or causal properties. Mounting evidence suggests that pairwise functional connectivity is particularly critical for the understanding of schizophrenia as it represents a neurogenetic risk mechanism for psychosis (Esslinger et al., 2009, Esslinger et al., 2011). At higher levels, complex network theory provides a framework in which to examine the multivariate pattern of functional interactions between brain signals, where the human brain is represented as a network whose nodes are brain regions and whose edges are their functional connections (Bassett and Bullmore, 2006, Bassett and Bullmore, 2009, Bullmore and Bassett, 2011, Bullmore and Sporns, 2009). In schizophrenia, network organization extracted from multiple imaging modalities (EEG, fMRI, and MEG) appears to be both randomized (Bassett et al., 2008, Lynall et al., 2010, Rubinov and Sporns, 2009) and less cost-efficient (Bassett et al., 2009) compared to controls.

Here we examine the advantages of and relationships between uni-, bi-, and multi-variate properties in identifying abnormalities in resting state function in schizophrenia. We hypothesize that regional activity directly constrains higher-order connectivity properties, and suggest that understanding this link is critical for the development of a neurophysiological interpretation of altered functional connectivity in schizophrenia. We further hypothesize that the added power of multivariate network measures will provide enhanced sensitivity to detecting disease effects on brain function, and that the patterns of both strong and weak connections will be differentially sensitive to disease state. In the context of our analysis, we introduce a statistical framework for the examination of group differences in functional connectivity based on functional data analysis (FDA) (Ramsay and Silverman, 2005) that has significant advantages over previously used methods of network comparison.