Computational Psychology
Introduction to multivariate and neuroimaging methods: using R Studio to navigate through clinical research
In the last two decades of psychiatric research, we have witnessed a growing interest in machine-learning and neuroimaging approaches. This course equips students with foundational knowledge of multivariate methods (both supervised and unsupervised), structural and functional neuroimaging, and their application across psychiatric-research modalities. The readings concentrate chiefly on psychosis, depression, and the clinical high-risk state.
In the first part of the course, students analyze clinical data in R Studio with intensive, hands-on coaching from the tutors, establishing strong methodological and practical skills essential for understanding machine-learning principles. Important notice: no prior programming experience is required; the syllabus is adapted for all participants.
The second part of the course deepens theoretical understanding through lectures. Students are encouraged to consolidate their grasp of these topics by attending classes, reading state-of-the-art literature, and practicing presentation skills. By the end, they should be able to draw on a “novel platform” of modern psychiatric knowledge, gaining a deeper appreciation of the potential biomarkers for psychiatric disorders and the analytical methods used to identify them.