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.

Figures

Figure 1. Activation Maps for selected Components

Activation maps for 7 resting state networks selected for correlation and lag analysis

Figure 2. Functional Network Connectivity in Controls

Solid lines show the significant correlation connections in controls. The arrow represents the direction of the delay between two components. For example (a → b) represents that component a lags component b by some calculated seconds among all controls.

Figure 3. Functional Network Connectivity in Patients

Dotted lines show the significant correlation connections in patients from the 21 possible correlation combinations. The arrow represents the direction of the delay between two components. For example (a → b) represents that component a lags component b by some calculated seconds among all patients.

Figure 4. Significant Correlation between Group Differences

Out of 21 possible correlation combinations between 7 components, only 5 combinations passed the two sample t-test (p

Figure 5. Significant Lag between Group Differences

Two sample t-test was also performed for significant lag among the 21 possible networks (p

Figure 6. Connectivity for Patients over 20 trials

Histogram of significant connectivity found in patients over 20 trials. X-axis represents the 21 possible combinations for correlations, while the y –axis represents the number of times a particular combination appeared to be significant in the 20 trials.

Figure 7. Connectivity for Controls over 20 trials

Histogram of significant connectivity found in controls over 20 trials. X-axis represents the 21 possible combinations for correlations, while the y –axis represents the number of times a particular combination appeared to be significant in the 20 trials.

Figure 8. Group Difference Correlation over 20 trials

The number of times the 5 connections in Figure 4 were selected as significant during the 20 trials. The solid line represents the significant connectivity where controls have higher mean correlation than patients, while dotted line represents connectivity where patients have higher mean correlation.