Support Vector Machine

In this chapter, we explore Support Vector Machine (SVM)—a machine learning method that has become exceedingly popular for neuroimaging analysis in recent years. Because of their relative simplicity and flexibility for addressing a range of classification problems, SVMs distinctively afford balanced predictive performance, even in studies where sample sizes may be limited. In brain disorders research, SVMs are typically employed using multivoxel pattern analysis (MVPA) because their relative simplicity carries a lower risk of overfitting even using high-dimensional imaging data. More recently, SVMs have been used in the context of precision psychiatry, particularly for applications that involve predicting diagnosis and prognosis of brain diseases such as Alzheimer's disease, schizophrenia, and depression. In the last section of this chapter, we review a number of recent studies that use SVM for such applications.