Resting-state anticorrelated networks in Schizophrenia

• Published in: Psychiatry research. Neuroimaging Vol. 284; pp. 1 - 8
• Main Authors: Ramkiran, Shukti, Sharma, Abhinav, Rao, Naren P.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 28.02.2019
• Subjects: Adult; Anticorrelated network; Brain - physiopathology; Brain Mapping - methods; Case-Control Studies; Female; Gyrus Cinguli - physiopathology; Humans; Machine Learning; Magnetic Resonance Imaging - methods; Male; Neural Pathways - physiopathology; Rest - psychology; Resting state fMRI; Schizophrenia; Schizophrenia - diagnosis; Support Vector Machine; Temporal Lobe - physiopathology; Thalamus; Thalamus - physiopathology; Schizophrenia; Resting state fMRI; Anticorrelated network; Thalamus; Support vector machine; Machine learning
• ISSN: 0925-4927; 1872-7506
• DOI: 10.1016/j.pscychresns.2018.12.013

•Altered resting state anticorrelated networks are present in schizophrenia.•Decreased anti-correlated connectivity is found in thalamus and basal ganglia.•Increased anticorrelation in medial temporal lobe regions and posterior cingulate gyri.•Support vector machines using AMA discriminated schizophrenia with accuracy of 74%. Converging evidences from different lines of research suggest abnormalities in functional brain connectivity in schizophrenia. While positively correlated brain networks have been well researched, anticorrelated functional connectivity remains under explored. Hence, in this study we examined (1) the resting-state anticorrelated networks in schizophrenia, and (2) the accuracy of support vector machines (SVMs) in differentiating healthy individuals from schizophrenia patients using these anticorrelated networks. The sample consisted of 56 patients with DSM-IV schizophrenia and 56 healthy controls. We computed functional connectivity matrices and used Anticorrelation after Mean of Antilog method (AMA) to select predominantly anticorrelated networks. The basal ganglia, thalamus, lingual gyrus, and cerebellar vermis showed significantly different, Type A (decreased anticorrelation) connections. The medial temporal lobe and posterior cingulate gyri showed significantly different, Type B (increased anticorrelation) connections. Use of SVM on AMA networks showed moderate accuracy in differentiating schizophrenia and healthy controls. Our results suggest that anticorrelated networks between the sub-cortical and cortical areas are abnormal in schizophrenia and this has potential to be a differential biomarker. These preliminary findings, if replicated in future studies with larger number of patients, and advanced machine learning techniques could have potential clinical applications.