Mental Health Mapping
We use large, diverse datasets and machine learning to understand mental and neurological disorders in each person: how biology, society, and environment shape them. Together with patients and clinicians, we bring these insights into clinical care.
Foundations
What is healthy, and what is a disorder?
Structures
From biology to families and society.
Markers
The hidden factors behind disorders.
Models
Machine learning, from lab to clinic.
Recent Highlights
Paper · Oct 2026
npj Digital Medicine
Accepted: Alexander Frotscher's large-scale benchmark of anomaly detection in brain imaging.
Thesis · Oct 2026
Gokul Perumbayil Vijayakrishnan completes his master's thesis on self-supervised EEG learning in our lab, with Prof. Dr. Magda Gregorová (THWS). Well done, Gokul, and thank you.
Talk & paper · Oct 2026
MICCAI 2026 (Main Track)
Mariam Zabihi presents SIGMA-VAE, her multi-view autoencoder for normative modeling, with a talk and a paper in the main track.
Workshop · Oct 2026
We co-organize the Machine Learning in Clinical Neuroimaging (MLCN) workshop at MICCAI for the eighth time.
Lab life · Oct 2026
We enjoy a wonderful lab retreat with friends in Jena.
Conference · Oct 2026
39th ECNP Congress, Munich
Thomas Wolfers joins more than 6,300 colleagues from 10 to 13 October and is grateful to serve on the congress's Scientific Program Committee.
Paper · Aug 2026
Nature Neuroscience
Accepted: Yanwu Yang's normative reference for human brain dynamics across the lifespan.
Talk & preprint · Jun 2026
OHBM 2026
Nitin Sharma presents transferable conditional brain charts, which link brain structure to Alzheimer's disease progression, at OHBM 2026. The preprint is on arXiv.
Led by Thomas Wolfers at Friedrich Schiller University Jena.