Using AI to assess brain health bridges the gap between rapid technology advances and the need for precise care in neurology and psychiatry.

Prof. Dr. Kerstin Ritter

Prof. Dr. Kerstin Ritter is a Full Professor of Machine Learning for Clinical Neuroscience at the University of Tübingen and is a Director at the Hertie Institute for AI in Brain Health. She is PI in the Excellence Cluster “Machine Learning – New Perspectives for Science” and the Tübingen AI Center as well as multiple interdisciplinary research consortia focusing on innovative methods at the intersection of machine learning, statistics and medical applications in neurology and psychiatry. Her research focuses on using advanced AI methods to assess brain health through diverse data types, including neuroimaging, clinical, genetic, and behavioral data. Her contributions to the field have been recognized with awards such as the NARSAD Young Investigator Grant and the Deutsche Multiple Sklerose Gesellschaft Research Prize.

Using AI to assess brain health bridges the gap between rapid technology advances and the need for precise care in neurology and psychiatry.

Gijsen, S., Schulz, M. A., & Ritter, K.

Brain-Semantoks: Learning Semantic Tokens of Brain Dynamics with a Self-Distilled Foundation Model.

Ritter, K., Brandt, L., & Walter, H.

KI in der Psychiatrie

Heinrichs, B., Diegelmann, D., Friedrich, O., Heinrichs, J. H., Kellmeyer, P., Madai, V. I., ... & Schleidgen, S.

Neuroethik–eine Bestandsaufnahme und ein Blick in die Zukunft.

Serin, E., Ritter, K., Schumann, G., Banaschewski, T., Marquand, A., & Walter, H

Generating synthetic task-based brain fingerprints for population neuroscience using deep learning

Schulz, M. A., Siegel, N. T., & Ritter, K.

Brain-age models with lower age prediction accuracy have higher sensitivity for disease detection

Gijsen, S., & Ritter, K.

EEG-Language Modeling for Pathology Detection

Reinhardt, P., Zacharias, N., Fislage, M., Böhmer, J., Hollunder, B., Reppmann, Z., ... & Winterer, G.

Machine Learning Classification of Smoking Behaviours-From Social Environment to the Prefrontal Cortex

Siegel, N. T., Kainmueller, D., Deniz, F., Ritter, K., & Schulz, M. A.

Do Transformers and CNNs Learn Different Concepts of Brain Age?

Seiler, M., & Ritter, K.

Pioneering new paths: the role of generative modelling in neurological disease research

Gijsen, S., & Ritter, K

Self-supervised Learning for Encoding Between-Subject Information in Clinical EEG

Langhammer T, Unterfeld C, Blankenburg F, ..., Ritter K, et al

Design and methods of the research unit 5187 PREACT (towards precision psychotherapy for non-respondent patients: from signatures to predictions to clinical utility) – a study protocol for a multicentre observational study in outpatient clinics

Rane, R.P., Kim, J., Umesha, A., Stark, D., Schulz, MA., Ritter, K.

DeepRepViz: Identifying Potential Confounders in Deep Learning Model Predictions

Hilbert, K., Weller, P., Ritter, K., Haynes, J.D., Walter, H., Lueken, U.

Design studies for clinical prediction

Spanagel R., Bach P., Banaschewski T., et al.

The ReCoDe addiction research consortium: Losing and regaining control over drug intake—Findings and future perspectives

Noteboom, S., Seiler, M., Chien, C., Rane, R. P., Barkhof, F., Strijbis, E.M.M,...& Ritter, K.

Evaluation of machine learning-based classification of clinical impairment and prediction of clinical worsening in multiple sclerosis

Schulz, M.A., Albrecht, J.P., Yilmaz, A., Koch, A., Kainmüller, D., Leser, U. & Ritter, K.

TLIMB-a transfer learning framework for image analysis of the brain

Mitrovska, A., Safari, P., Ritter, K., Shariati, B., Fischer, J. K.

Secure federated learning for Alzheimer's disease detection

Oliveira, M., Wilming, R., Clark, B., Budding, C., Eitel, F., Ritter, K., Haufe, S.

Benchmarking the influence of pre-training on explanation performance in MR image classification

Schulz, M.A., Bzdok, D., Haufe, S., Haynes, J.D., Ritter, K.

Performance reserves in brain-imaging-based phenotype prediction

Schulz, M.A., Hetzer, S., Eitel, F., Asseyer, S., Meyer-Arndt, L., Schmitz-Hübsch, T., et al

Artificial Intelligence Bias in Health Care: Web-Based Survey

Klingenberg, M., Stark, D., Eitel, F. et al

Higher performance for women than men in MRI-based Alzheimer’s disease detection

Wang, D., Honnorat, N., Fox, P. T., Ritter, K., Eickhoff, S. B., Seshadri, S., ... & Alzheimer’s Disease Neuroimaging Initiative

Deep neural network heatmaps capture Alzheimer’s disease patterns reported in a large meta-analysis of neuroimaging studies

Brandt, L., Ritter, K., Schneider-Thoma, J., Siafis, S., Montag, C., Ayrilmaz, H. et al

Predicting psychotic relapse following randomised discontinuation of paliperidone in individuals with schizophrenia or schizoaffective disorder: an individual participant data analysis

Fast, L., Temuulen, U., Villringer, K., Kufner, A., Ali, H.F., Siebert, E., Huo, S et al

Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke

Rane, R. P., Musial, M. P. M., Beck, A., Rapp, M., Schlagenhauf, F., Banaschewski, T., ... & IMAGEN consortium

Uncontrolled eating and sensation-seeking partially explain the prediction of future binge drinking from adolescent brain structure

Schulz, M. A., Koch, A., Guarino, V. E., Kainmueller, D., & Ritter, K

Data augmentation via partial nonlinear registration for brain-age prediction

Chien, C., Seiler, M., Eitel, F., Schmitz-Hübsch, T., Paul, F., & Ritter, K.

Prediction of high and low disease activity in early MS patients using multiple kernel learning identifies importance of lateral ventricle intensity

Rane, R. P., de Man, E. F., Kim, J., Görgen, K., Tschorn, M., Rapp, M. A., ... & IMAGEN consortium.

Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer’s disease classification

Srivastava, S., Eitel, F., & Ritter, K.

Predicting fluid intelligence in adolescent brain MRI data: An ensemble approach

Eitel, F., Soehler, E., Bellmann-Strobl, J., Brandt, A. U., Ruprecht, K., Giess, R. M., ... & Ritter, K.

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

Weygandt, M., Behrens, J., Brasanac, J., Söder, E., Meyer-Arndt, L., Wakonig, K., ... & Paul, F.

Neural mechanisms of perceptual decision-making and their link to neuropsychiatric symptoms in multiple sclerosis

Böhle, M., Eitel, F., Weygandt, M., & Ritter, K.

Layer-wise relevance propagation for explaining deep neural network decisions in MRI-based Alzheimer's disease classification

Hornstein, S., Seiler, M., Hoffman, V., Nelson, B., Aschbacher, K., Ritter, K., & Hilbert, K.

Association of Depressive Symptoms with Resting Heart Rate Variability recorded from a Wearable Device under Naturalistic Conditions: A Machine Learning Study