@inproceedings{4655f28fea97492bbe4712ba85bf67e0,
title = "Neural effect induced by exercise intervention can be categorized by altered functional connectivity in early psychotic patients",
abstract = "Schizophrenia is associated with cognitive impairments. Exercise interventions including yoga and aerobic exercise have been shown to improve cognitive functioning in schizophrenic patients. Yet the underlying neural basis is not clear for this cognitive improvement. This work aimed to investigate the brain functional effect caused by exercise interventon in female patients with early psychosis. Resting-state fMRI was collected longitudinally from 71 patients who were randomized into three programs: yoga, aerobic exercise and waitlist control. Functional connectivity matrices of each individual at baseline and 12-week follow-up timepoint were estimated. Then the connectivity changes were calculated and used as potential predictors to classify the three groups. A machine learning method gcForest was used to train classification models on a subset and tested on the rest of the data. Classification performance was evaluated using multiple n-fold cross-validation to ensure a robust estimate of the accuracy. The classification accuracy ranges from 86.31 to 94.00. The most predictive features were examined in the brain, which include connectivity changes along several major pathways in high order functional networks including default mode and executive control networks. This is the first study showing that the connectivity alterations can successfully distinguish intervention from control groups, and also detect the two different types of intervention: yoga and aerobic exercise. Findings suggest that the altered functional connectivity may contribute to the cognitive improvement after intervention. Our work sheds light on the use of advanced neuroimaging and machine learning approaches to explore potential biomarkers for predicting outcomes of exercise intervention in psychosis.",
keywords = "Early psychosis, Exercise intervention, FMRI, Functional connectivity, GcForest classification, Yoga",
author = "Xiujuan Geng and Peilun Song and Chen, {Eric H.Y.} and Yaping Wang and Jingxia Lin",
note = "Funding Information: This work was supported by the Young Scientists Fund of the National Natural Science Foundation of China (NSFC) (Grant No: 31701002), NSFC (Grant No: U1504606), RGC funding (C00240/762412) by the Authority of Research, Hong Kong, Science and Technology Development Plan of Henan Province China 172102310270 and Key Research Projects of Henan Higher Education Institutions 20A510009. Publisher Copyright: {\textcopyright} 2020 SPIE. All rights reserved. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; Medical Imaging 2020: Image Processing ; Conference date: 17-02-2020 Through 20-02-2020",
year = "2020",
month = mar,
day = "10",
doi = "10.1117/12.2549908",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Ivana Isgum and Landman, {Bennett A.}",
booktitle = "Medical Imaging 2020",
address = "United States",
}