Non-negative discriminative brain functional connectivity for identifying schizophrenia on resting-state fMRI

BACKGROUND: Schizophrenia is a clinical syndrome, and its causes have not been well determined. The objective of this study was to investigate the alteration of brain functional connectivity between schizophrenia and healthy control, and present a practical solution for accurately identifying schizophrenia at single-subject level. METHODS: 24 schizophrenia patients and 21 matched healthy subjects were recruited to undergo the resting-state functional magnetic resonance imaging (rs-fMRI) scanning. First, we constructed the brain network by calculating the Pearson correlation coefficient between each pair of the brain regions. Then, this study proposed a novel non-negative discriminant functional connectivity selection method, i.e. non-negative elastic-net based method (N2EN), to extract the alteration of brain functional connectivity between schizophrenia and healthy control. It ranks the significance of the connectivity with a uniform criterion by introducing the non-negative constraint. Finally, kernel discriminant analysis (KDA) is exploited to classify the subjects with the selected discriminant brain connectivity features. RESULTS: The proposed method is applied into schizophrenia classification, and achieves the sensitivity, specificity and accuracy of 100, 90.48 and 95.56%, respectively. Our findings also indicate the alteration of functional network can be used as the biomarks for guiding the schizophrenia diagnosis. The regions of cuneus, superior frontal gyrus, medial, paracentral lobule, calcarine fissure, surrounding cortex, etc. are highly relevant to schizophrenia. CONCLUSIONS: This study provides a method for accurately identifying schizophrenia, which outperforms several state-of-the-art methods, including conventional brain network classification, multi-threshold brain network based classification, frequent sub-graph based brain network classification and support vector machine. Our investigation suggested that the selected discriminant resting-state functional connectivities are meaningful features for classifying schizophrenia and healthy control.