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

• Published in: NeuroImage (Orlando, Fla.) Vol. 216; p. 116227
• Main Authors: Yang, Zhi, Wu, Jinfeng, Xu, Lihua, Deng, Zhengzheng, Tang, Yingying, Gao, Jiaqi, Hu, Yang, Zhang, Yiwen, Qin, Shaozheng, Li, Chunbo, Wang, Jijun
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.08.2020; Elsevier Limited; Elsevier
• Subjects: Adolescent; Adult; Age; Brain - diagnostic imaging; Brain - physiology; Brain research; Cognitive ability; Contraindications; Datasets; Diagnosis; Drug abuse; Female; fMRI; Humans; Individuality; Individualized imaging; Learning algorithms; Machine Learning; Magnetic Resonance Imaging - methods; Male; Mental disorders; Mental health; Motion Pictures; Nerve Net - diagnostic imaging; Nerve Net - physiology; Neuroimaging; Photic Stimulation - methods; Population; Pregnancy; Psychotropic drugs; Schizophrenia; Schizophrenia - diagnostic imaging; Schizophrenic Psychology; Studies; Synchronization; Synchronized brain activity; Time series; Visual Perception - physiology; Young Adult; Synchronized brain activity; Schizophrenia; fMRI; Mental disorders; Individualized imaging; Machine learning
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2019.116227

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. •An individualized psychiatric imaging methodology based on naturalistic stimuli.•A normative model that evokes synchronized brain responses in healthy individuals.•Comparing individual responses to the healthy norm avoids averaging across patients.•The performance to recognize schizophrenia patients shows its clinical potential.