Individualized psychiatric imaging based on inter-subject neural synchronization in movie watching

NeuroImage

卷:216

Zhengzheng Deng¹  Chunbo Li²  Yingying Tang³  Yang Hu⁴  Jijun Wang⁴  Jinfeng Wu⁵  Lihua Xu⁶  Jiaqi Gao⁷  Shaozheng Qin⁸  Zhi Yang⁹  Yiwen Zhang⁹ 

[1] Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China;

[2] CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China;

[3] Corresponding author. Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.;

[4] Department of Psychology, University of Chinese Academy of Sciences, Beijing, China;

[5] Institute of Psychological and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China;

[6] Laboratory of Psychological Heath and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China;

[7] Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China;

关键词: Mental disorders;    fMRI;    Individualized imaging;    Synchronized brain activity;    Schizophrenia;    Machine learning;   

DOI  :  

来源: DOAJ

【 摘 要 】

The individual heterogeneity is a challenge to the prosperous promises of cutting-edge neuroimaging techniques for better diagnosis and early detection of psychiatric disorders. Individuals with similar clinical manifestations may result from very different pathophysiology. Conventional approaches based on comparing group-averages provide insufficient information to support the individualized diagnosis. Here we present an individualized imaging methodology that combines naturalistic imaging and the normative model. This paradigm adopts video clips with rich cognitive, social, and emotional contents to evoke synchronized brain dynamics of healthy participants and builds a spatiotemporal response norm. By comparing individual brain responses with the response norm, we could recognize patients using machine learning techniques. We applied this methodology to recognize first-episode drug-naïve schizophrenia patients in a dataset containing 72 patients and 54 healthy controls. Some segments of the video evoked more synchronized brain activity in the healthy controls than in the schizophrenia patients. We built a spatiotemporal response norm by averaging the brain responses of the healthy controls in a training set, and trained a classifier to recognize patients based on the differences between individual brain responses and the norm. The performance of the classifier was then evaluated using an independent test set. The mean accuracies from a 5-fold cross-validation were 0.71–0.78 depending on the parameters such as the number of features and the width of the sliding windows. These findings reflected the potential of this methodology towards a clinical tool for individualized diagnosis.

【 授权许可】

Unknown