Disruptions in the left frontoparietal network underlie resting state endophenotypic markers in schizophrenia

Advances in functional brain imaging have improved the search for potential endophenotypic markers in schizophrenia. Here, we employed independent component analysis (ICA) and dynamic causal modeling (DCM) in resting state fMRI on a sample of 35 schizophrenia patients, 20 first‐degree relatives and...

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Veröffentlicht in:	Human brain mapping 2017-04, Vol.38 (4), p.1741-1750
Hauptverfasser:	Chahine, George, Richter, Anja, Wolter, Sarah, Goya‐Maldonado, Roberto, Gruber, Oliver
Format:	Artikel
Sprache:	eng
Schlagworte:	Abnormalities Adult biomarker Brain Brain mapping Endophenotypes Female Frontal Lobe - diagnostic imaging Frontal Lobe - physiopathology Functional Laterality - physiology Functional magnetic resonance imaging Hallucinations Humans Image Processing, Computer-Assisted Independent component analysis Magnetic Resonance Imaging Male Markers Mental disorders Models, Neurological Nerve Net - diagnostic imaging Nerve Net - physiopathology Neural networks Neuroimaging Nonlinear Dynamics Oxygen - blood Parietal Lobe - diagnostic imaging Parietal Lobe - physiopathology Patients Psychiatric Status Rating Scales Rest Schizophrenia Schizophrenia - diagnostic imaging Schizophrenia - pathology Schizophrenia - physiopathology Sensory evaluation Short term memory
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Zusammenfassung:	Advances in functional brain imaging have improved the search for potential endophenotypic markers in schizophrenia. Here, we employed independent component analysis (ICA) and dynamic causal modeling (DCM) in resting state fMRI on a sample of 35 schizophrenia patients, 20 first‐degree relatives and 35 control subjects. Analysis on ICA‐derived networks revealed increased functional connectivity between the left frontoparietal network (FPN) and left temporal and parietal regions in schizophrenia patients (P
ISSN:	1065-9471 1097-0193
DOI:	10.1002/hbm.23477