Teaching and Seminars

We are committed contributing to the teaching portfolio at Friedrich Schiller University (FSU) by introducing a coherent, thoughtfully designed set of courses, modules and seminar formats. This teaching program is currently under development and will continue to mature over the coming years. The objective is to establish a high-quality, interdisciplinary instructional teaching stack, spanning Cognitive Neuroscience/Psychology, Brain Imaging, Machine Learning, and related domains, relevant not only for FSU students but also for national and international participants.

Module 1: Parsing Individual Differences in Brain Disorders

This module introduces the conceptual backbone of modern individual-level inference in psychiatry, grounding students in the shift from traditional case–control comparisons toward frameworks that treat inter-individual variability as structured, biologically meaningful signal. We trace the development of normative modeling from its early formulations for population reference distributions (Marquand et al., 2016, 2019) to contemporary applications for deviation mapping in large-scale clinical cohorts (Rutherford et al., 2022; Wolfers et al., 2018, 2021). Participants critically assess the promises and limitations of proposed “biotypes,” drawing on methodological evaluations such as Dinga et al. (2019), and learn why robust heterogeneity modeling requires precise statistical specification, rigorous uncertainty quantification, and strong out-of-sample generalization. By the end of the module, students will understand how individual-level deviation scores are derived and why they represent a central analytic currency in computational psychiatry.

Seminar 1: Machine Learning and Neuroscience - From Methods to Clinical Applications

This seminar series establishes a structured, interdisciplinary forum where state-of-the-art machine learning converges with contemporary neuroscience and clinical research. Alternating monthly between ML-focused and neuroscience-focused sessions, the format cultivates a shared methodological foundation across participants while maintaining a strong translational emphasis on mental and neurological health. Discussions cover the full analytical spectrum—from deep learning, causal inference, and interpretability to systems neuroscience, multimodal neuroimaging, and clinical phenotyping—prioritizing methodological rigor, transparency, and reproducibility. As a student-led series, regular attendees are expected to contribute by presenting at least once, progressively shaping the seminar into a collaboratively developed curriculum. Through this continuous exchange, participants learn to align computational innovation with empirical constraints, evaluate clinical relevance, and identify feasible pathways toward individualized diagnostics and precision-health applications. The hybrid format further broadens participation, enabling collaboration across groups and institutions and fostering an inclusive intellectual community at the interface of machine learning and neuroscience.