神经精神疾病自动分类与预测研究进展

神经精神疾病的神经病理机制仍有许多未知,客观临床诊断标准也十分欠缺,其诊断与预后面临巨大挑战.随着神经影像技术的快速发展,神经影像数据被广泛应用于神经精神疾病神经病理机制的探索和潜在生物标志物的发掘.相比于实现群体水平分析的传统单变量分析方法,机器学习模型基于神经影像数据,实现神经精神 疾病的个体化、智能化预测.综述近...

Advances in Automatic Classification and Prediction Study of Neuropsychiatric Diseases

There are still many unknown neuropathological mechanisms of neuropsychiatric diseases, and objective clinical diagnostic criteria are lacking, which brings great challenges to the diagnosis and prognosis of neuropsychiatric diseases. With the rapid development of neuroimaging technology, neuroimaging data have been widely used to explore the neuropathological mechanism and potential biomarkers of neuropsychiatric diseases. Compared with traditional univariate analysis methods, that can only perform population-level analyses, neuroimaging-data-driven machine learning models can realize individualized and automated prediction of neuropsychiatric diseases. In this paper, we reviewed recent research progress of automated classification and prediction of neuropsychiatric diseases based on machine learning technology, and summarized and analyzed the basic principles of machine learning technology and the latest research achievements of four typical neuropsychiatric diseases, including schizophrenia, depression, Alzheimer‘s disease and Parkinson's disease. It was shown that current studies still face the challenge of small sample size and low reproducibility. Nonetheless, the sample size can be increased through collaborative analysis of multi-site data in the future. Meanwhile, deep learning and cross-disease diagnosis and prediction are also important directions of future research.