12 Functional connectivity of the brain has been studied by analyzing 13 correlation differences in time courses among seed voxels or regions 14 with other voxels of the brain in patients versus controls. The spatial 15 extent of strongly temporally coherent brain regions co-activated 16 during rest has also been examined using independent component 17 analysis (ICA). However, the weaker temporal relationships among 18 ICA component time courses, which we operationally define as a 19 measure of functional network connectivity (FNC), have not yet been 20 studied. In this study, we propose an approach for evaluating FNC and 21 apply it to functional magnetic resonance imaging (fMRI) data 22 collected from persons with schizophrenia and healthy controls. We 23 examined the connectivity and latency among ICA component time 24 courses to test the hypothesis that patients with schizophrenia would 25 show increased functional connectivity and increased lag among resting 26 state networks compared to controls. Resting state fMRI data were 27 collected and the inter-relationships among seven selected resting state 28 networks (identified using group ICA) were evaluated by correlating 29 each subject's ICA time courses with one another. Patients showed 30 higher correlation than controls among most of the dominant resting 31 state networks. Patients also had slightly more variability in functional 32 connectivity than controls. We present a novel approach for quantify- 33 ing functional connectivity among brain networks identified with 34 spatial ICA. Significant differences between patient and control 35 connectivity in different networks were revealed possibly reflecting 36 deficiencies in cortical processing in patients.
['Madiha Jafri', 'Godfrey D. Pearlson', 'Michael C. Stevens', 'Vince D. Calhoun']
A Method for Functional Network Connectivity Among Spatially Independent Resting-State Components in Schizophrenia