Yan Weizheng, Yu Linzhen, Liu Dandan, Sui Jing, Calhoun Vince D, Lin Zheng
Department of Psychiatry, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.
Front Psychiatry. 2023 Jun 27;14:1202049. doi: 10.3389/fpsyt.2023.1202049. eCollection 2023.
Accurate classification based on affordable objective neuroimaging biomarkers are important steps toward designing individualized treatment.
In this work, we investigated a deep learning classification model, multi-scale convolutional recurrent neural network (MCRNN), to explore psychiatric disorder-related biomarkers by leveraging the spatiotemporal information of resting-state EEG (rsEEG) using a multiple psychiatric disorder database containing 327 individuals diagnosed with schizophrenia, bipolar, major depressive disorders, and healthy controls. All subjects were mapped to a shared low-dimensional subspace for intuitively interpreting the inter-relationship and separation of psychiatric disorders.
Psychiatric disorders were identified using rsEEG with high accuracy ranged from 78.6 to 91.3% in patient vs. controls two-class classification, and 68.2% in four-class classification. The control-to-schizophrenia trajectory interpretated by the model was consistent with the disease severity in clinical observation.
The MsRNN demonstrated a capability in extracting discriminative rsEEG biomarkers for psychiatric disorder classification, indicating its potential to facilitate our understanding of psychiatric disorders and monitoring interventions.
基于经济实惠的客观神经影像学生物标志物进行准确分类是设计个性化治疗的重要步骤。
在这项工作中，我们研究了一种深度学习分类模型——多尺度卷积循环神经网络（MCRNN），通过利用静息态脑电图（rsEEG）的时空信息，使用一个包含327名被诊断为精神分裂症、双相情感障碍、重度抑郁症患者以及健康对照的多精神疾病数据库，来探索与精神疾病相关的生物标志物。所有受试者都被映射到一个共享的低维子空间，以便直观地解释精神疾病之间的相互关系和区分。
使用rsEEG对精神疾病进行识别，在患者与对照的二分类中准确率高达78.6%至91.3%，在四分类中为68.2%。模型解释的从对照到精神分裂症的轨迹与临床观察中的疾病严重程度一致。
MsRNN展示了提取用于精神疾病分类的有区分力的rsEEG生物标志物的能力，表明其在促进我们对精神疾病的理解和监测干预方面的潜力。
