A method for functional network connectivity among spatially independent resting-state components in schizophrenia

Abstract

Functional connectivity of the brain has been studied by analyzing correlation differences in time courses among seed voxels or regions with other voxels of the brain in healthy individuals as well as in patients with brain disorders. The spatial extent of strongly temporally coherent brain regions co-activated during rest has also been examined using independent component analysis (ICA). However, the weaker temporal relationships among ICA component time courses, which we operationally define as a measure of functional network connectivity (FNC), have not yet been studied. In this study, we propose an approach for evaluating FNC and apply it to functional magnetic resonance imaging (fMRI) data collected from persons with schizophrenia and healthy controls. We examined the connectivity and latency among ICA component time courses to test the hypothesis that patients with schizophrenia would show increased functional connectivity and increased lag among resting state networks compared to controls. Resting state fMRI data were collected and the inter-relationships among seven selected resting state networks (identified using group ICA) were evaluated by correlating each subject's ICA time courses with one another. Patients showed higher correlation than controls among most of the dominant resting state networks. Patients also had slightly more variability in functional connectivity than controls. We present a novel approach for quantifying functional connectivity among brain networks identified with spatial ICA. Significant differences between patient and control connectivity in different networks were revealed possibly reflecting deficiencies in cortical processing in patients.

Introduction

Developments in functional imaging in the past two decades have allowed for significant advances in our understanding of the complex relationships and interactions among distributed brain regions underlying cognition. An active area of neuroimaging research involves examining the “functional connectivity” of spatially remote brain regions. Functional connectivity analyses allow the characterization of inter-regional neural interactions during particular cognitive or motor tasks or merely from spontaneous activity during rest. Previous functional connectivity analysis approaches have relied on choosing individual seed voxels and subsequently constructing cross-correlation maps of other voxels with respect to the chosen seed voxels (Biswal et al., 1995, Biswal et al., 1997, Cordes et al., 2002, Cordes et al., 2000, Lowe et al., 1998).

Another useful method to examine functional connectivity is independent component analysis (ICA) (Calhoun et al., 2001b, Esposito et al., 2005, Garrity et al., 2007, McKeown et al., 1998), particularly as applied to “resting state” scans, which are relatively easy to obtain and do not suffer from performance confounds in cognitively-impaired patient groups (Beckmann et al., 2005, Greicius et al., 2004, Kivinienri et al., 2001). ICA is a method for recovering underlying signals from linear mixtures of these signals and draws upon higher-order signal statistics to determine a set of “components” that are maximally independent of each other (Calhoun and Adali, 2006). The use of ICA in these studies effectively finds and characterizes functional networks in data collected during the performance of a task as well as in resting state fMRI data. For instance, van de Ven et al., used spatial differences across ICA-generated components' intensity to determine functional connectivity levels (Van de Ven et al., 2004). ICA has been found to be useful and able to capture the complex nature of fMRI time courses while also producing consistent spatial components (Turner and Twieg, 2005). Rakapakse et al., performed analyses using structural equation modeling to find connectivity between regions identified using spatial ICA in healthy individuals while performing a task (Rajapakse et al., 2006).

While these techniques are effective for analyzing dysfunctional integration of activations in various regions' time series in brains, to date there has been no study of group differences in the temporal relationship among spatial components. Within a given component, the regions are by definition strongly temporally coherent due to the ICA assumption of linear mixing. In this paper we focus not upon these strongly coherent time courses, rather we consider weaker dependencies among components. In spatial ICA the images are maximally independent, but the time courses are not independent and can exhibit considerable temporal dependencies. These temporal dependencies among components are significant, but not as large as those between regions within a component (were this the case they would likely have been included within a single component) (Calhoun et al., 2003). A technique to determine functional temporal connectivity among components and to evaluate group differences in these relatively weaker connections is proposed in this paper. In this paper, we define functional network connectivity (FNC) as the temporal dependency among the ICA components. In contrast to connectivity studies which focus upon the correlation between a single seed region of interest and the other brain regions, we focus instead upon the temporal connectivity among functional networks (components) estimated from ICA.

In order to show the practical relevance of our technique, we apply the methods described in this paper to compare FNC in patients with schizophrenia versus healthy controls. Schizophrenia is a chronic, disabling mental disorder that is diagnosed on the basis of a constellation of psychiatric symptoms and longitudinal course (APA, 2000). The disease impairs multiple cognitive domains including memory, attention and executive function (Heinrichs and Zakzanis, 1998). Although the causes and mechanisms of schizophrenia are still unclear, a hypothesis of neural network ‘disconnection’ has been proposed. This proposal assumes that schizophrenia arises from dysfunctional integration of a distributed network of brain regions (Friston and Frith, 1995) or a misconnection syndrome of neural circuitry leading to an impairment in the smooth coordination of mental processes, sometimes described as “cognitive dysmetria” (Andreasen et al., 1999).

Many researchers have examined the possibility of ‘disconnection’ in psychiatric groups by analyzing brain function with functional connectivity methods (Bokde et al., 2006, Friston, 1995, Friston and Frith, 1995, Frith et al., 1995, Herbster et al., 1996, Josin and Liddle, 2001, Liang et al., 2006, Liddle et al., 1992, Mikula and Niebur, 2006). For example, in a sample of patients with schizophrenia, Liang et al. found disrupted functional integration of widespread brain areas, including a decreased connectivity among insula, temporal lobe, prefrontal lobe and corpus striatum and an increased connectivity between the cerebellum and other brain areas, during resting-state by analyzing correlations between brain regions (Liang et al., 2006). Similarly, Meyer-Lindenberg et al. reported pronounced disruptions of distributed cooperative activity in frontotemporal interactions in schizophrenia in selected regions of interest in positron emission tomography (PET) brain scans on working memory task (Meyer-Lindenberg et al., 2001). Other task-related studies reported a lack of interaction between right anterior cingulate and other brain regions (Boksman et al., 2005), disrupted integration between medial superior frontal gyrus and both the anterior cingulate and the cerebellum (Honey et al., 2005), as well as reduced functional connectivity in frontotemporal regions of subjects with schizophrenia (Lawrie et al., 2002). Although these studies help identify problems with typical functional integration among important brain regions, they do not examine patients to see if there is disruption in the relationship of activity within one large networks of brain regions with another. It follows that patients with schizophrenia may not only have deficits in the relationship of one brain region to another, but that their cognitive and behavioral deficits might be related to dysfunction of entire networks of regions failing to properly communicate with one another.

In the study, we focused on examining FNC differences between a group of patients with schizophrenia and a demographically-matched control sample. Based upon two of our recent studies showing less task-specific activation (Calhoun et al., 2006a) and more high frequency fluctuations in the default mode (Garrity et al., 2007) in schizophrenia patients versus healthy controls, we hypothesized that patients would show increased connectivity among ICA component networks, possibly reflecting less specialized cognitive processing. Also, based on a prior study showing delayed hemodynamic brain activity in schizophrenia (Ford et al., 2005), we predicted that patients would show increased latency of peak hemodynamic signal change in each network compared to activity changes in healthy controls. Although we do not directly test the disconnection hypothesis in this work, the proposed methods would permit such tests using the proper experimental paradigm. Indeed, the results of this analysis are consistent with the existence of misconnected neural circuitry in subjects with schizophrenia, through findings that likely depict an increased dependence among a wider array of less efficient brain regions in schizophrenia, a possible manifestation of a generalized cognitive deficit.

Our general approach can be outlined as follows: we first use ICA to extract resting state network time courses in patients with schizophrenia and healthy controls (Beckmann et al., 2005, Garrity et al., 2007). After additional data filtering for noise removal, the time series between all pair-wise combinations of these networks are further analyzed to determine the maximal lagged correlation between networks (this was done both to mitigate the impact of latency difference upon the correlation and also to provide the possibility of evaluating the latency differences). Significant correlations within groups, differences in correlation between groups, and differences in lags between groups were computed. A resampling technique was used to validate the significance of the group differences. We also tested the robustness of the detected differences in connectivity by testing multiple independent sub-samples of patients and controls.