Graph theoretical methods have been widely applied to study the modular organization of functional connectivity networks in neuropsychiatric disorders like Schizophrenia. However, current methods are affected by a resolution limit that prevents detection of modules that are smaller than a scale determined by the size of the entire network. We have developed a resolution-limit-free method, dubbed Surprise, and applied it to study resting state functional connectivity networks in a large cohort of Schizophrenia patients and matched controls. Improved resolution reveals substantial reorganization of resting state connectivity structure in patients, with previously undetected fragmentation and merging of sensory and associative modules.
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