Discriminative analysis of non-linear brain connectivity in schizophrenia: an fMRI Study - Su, Longfei; Wang, Lubin; Shen, Hui; Feng, Guiyu; Hu, Dewen (Frontiers in Human Neuroscience, 2013)
Background: Dysfunctional integration of distributed brain networks is believed to be the cause of schizophrenia, and resting-state functional connectivity analyses of schizophrenia have attracted considerable attention in recent years. Unfortunately, existing functional connectivity analyses of schizophrenia have been mostly limited to linear associations. Objective: The objective of the present study is to evaluate the discriminative power of nonlinear functional connectivity and identify its changes in schizophrenia. Method: A novel measure utilizing the extended maximal information coefficient was introduced to construct nonlinear functional connectivity. In conjunction with multivariate pattern analysis, the new functional connectivity successfully discriminated schizophrenic patients from healthy controls with relative higher accuracy rate than the linear measure. Result: We found that the strength of the identified nonlinear functional connections involved in the classification increased in patients with schizophrenia, which was opposed to its linear counterpart. Further functional network analysis revealed that the changes of the nonlinear and linear connectivity have similar but not completely the same spatial distribution in human brain. Conclusion: The classification results suggest that the nonlinear functional connectivity provided useful discriminative power in diagnosis of schizophrenia, and the inverse but similar spatial distributed changes between the nonlinear and linear measure may indicate the underlying compensatory mechanism and the complex neuronal synchronization underlying the symptom of schizophrenia.