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.

Seiler, M., Ritter, K.

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

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

Prof. Dr. Kerstin Ritter

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

DeepRepViz: Identifying Potential Confounders in Deep Learning Model Predictions

Design studies for clinical prediction

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

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

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

Secure federated learning for Alzheimer's disease detection

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

Performance reserves in brain-imaging-based phenotype prediction

Similar neural pathways link psychological stress and brain-age in health and multiple sclerosis

Artificial Intelligence Bias in Health Care: Web-Based Survey

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

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

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

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

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

Data augmentation via partial nonlinear registration for brain-age prediction

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

Structural differences in adolescent brains can predict alcohol misuse

Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks

AIM in Alcohol and Drug Dependence

Immune signature of multiple sclerosis-associated depression

Nucleus basalis of Meynert predicts cognition after deep brain stimulation in Parkinson's disease

Patch individual filter layers in CNNs to harness the spatial homogeneity of neuroimaging data

MRI Image Registration Considerably Improves CNN-Based Disease Classification

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research

COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease

Altered coupling of psychological relaxation and regional volume of brain reward areas in multiple sclerosis

AIM and Gender Aspects