中国科学院心理健康重点实验室知识库

Functional magnetic resonance imaging is capable of estimat- ing functional activation and connectivity in the human brain, and lately there has been increased interest in the use of these functional modalities combined with machine learning for identi cation of psychiatric traits. While these methods bear great potential for early diagnosis and better understanding of disease processes, there are wide ranges of processing choices and pitfalls that may severely hamper interpretation and gen- eralization performance unless carefully considered. In this perspective article, we aim to motivate the use of machine learning schizotypy research. To this end, we describe com- mon data processing steps while commenting on best prac- tices and procedures. First, we introduce the important role of schizotypy to motivate the importance of reliable classi-  cation, and summarize existing machine learning literature on schizotypy. Then, we describe procedures for extraction of features based on fMRI data, including statistical para- metric mapping, parcellation, complex network analysis, and decomposition methods, as well as classi cation with a spe- cial focus on support vector classi cation and deep learning. We provide more detailed descriptions and software as sup- plementary material. Finally, we present current challenges in machine learning for classi cation of schizotypy and com- ment on future trends and perspectives.