In Psychiatry research ; h5-index 64.0
OBJECTIVE : This paper aims to model the anatomical circuits underlying schizophrenia symptoms, and to explore patterns of abnormal connectivity among brain networks affected by psychopathology.
METHODS : T1 magnetic resonance imaging (MRI), diffusion weighted imaging (DWI), and resting-state functional MRI (rsfMRI) were obtained from a total of 126 patients with schizophrenia who were recruited for the study. The images were processed using the Omniscient software (https://www.o8t. com). We further apply the use of the Hollow-tree Super (HoTS) method to gain insights into what brain regions had abnormal connectivity that might be linked to the symptoms of schizophrenia.
RESULTS : The Positive and Negative Symptom Scale is characterised into 6 factors. Each symptom is mapped with specific anatomical abnormalities and circuits. Comparison between factors reveals co-occurrence in parcels in Factor 1 and Factor 2. Multiple large-scale networks are involved in SCZ symptomatology, with functional connectivity within Default Mode Network (DMN) and Central Executive Network (CEN) regions most frequently associated with measures of psychopathology.
CONCLUSION : We present a summary of the relevant anatomy for regions of the cortical areas as part of a larger effort to understand its contribution in schizophrenia. This unique machine learning-type approach maps symptoms to specific brain regions and circuits by bridging the diagnostic subtypes and analysing the features of the connectome.
Wang Yingchan, Wang Jijun, Su Wenjun, Hu Hao, Xia Mengqing, Zhang Tianhong, Xu Lihua, Zhang Xia, Taylor Hugh, Osipowicz Karol, Young Isabella M, Lin Yueh-Hsin, Nicholas Peter, Tanglay Onur, Sughrue Michael E, Tang Yingying, Doyen Stephane
2023-Feb-26
Abnormal connectivity, Anatomical circuits, Connectivity, Machine learning, Schizophrenia, Symptoms, Treatment response
