Premises of Computational Neuroscience: Machine Learning Tools and Multivariate Analyses

Abstract

This chapter provides an overview of multivariate methods and several advanced demonstrations of how these methods can be applied to machine learning applications in mental health. Multivariate methods can help identify relationships between different variables (symptoms, behavior, brain anatomy, brain function, genetics, etc.) that may influence mental health. The methods provide valuable insights into how best to target interventions for a particular brain disorder. When using these methods, it is important to know the advantages, but also the limitations, of each method and how they can be applied in different contexts. In this chapter, we define and explain the concept of multivariate analyses from the theoretical basis to practical issues related to data preparation and interpretation of results. The methods described here include factor analysis, principal component analysis, path analysis, partial least squares, and linear multivariate methods. The chapter includes examples of how multivariate methods have been used, for example, to predict treatment outcomes or identify risk factors for mental disorders. We not only discuss the many challenges of using these methods in computational neuroscience research, such as the need for large and diverse datasets, but also introduce new approaches, such as guided model-based approaches and advanced AI-based approaches, to enrich these mostly data-driven methods and obtain better insight that integrates a priori information.