We analyze neuroimaging, clinical, psychometric, smartphone, and neurobiological data within a clinical framework to derive meaningful insights and improve patient outcomes in neurology and psychiatry.

01

Seiler, M., Ritter, K.

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

02

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

DeepRepViz: Identifying Potential Confounders in Deep Learning Model Predictions

03

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

Design studies for clinical prediction

04

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

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

05

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

06

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

07

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

Secure federated learning for Alzheimer's disease detection

08

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

09

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

Performance reserves in brain-imaging-based phenotype prediction

10

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

12

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

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

13

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

14

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

15

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

16

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

17

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

Data augmentation via partial nonlinear registration for brain-age prediction

18

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

19

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

Structural differences in adolescent brains can predict alcohol misuse

20

Subramaniam, P., Kossen, T., Ritter, K., Hennemuth, A., Hildebrand, K., Hilbert, A., ... & Madai, V. I.

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

21

Rane, R. P., Heinz, A., & Ritter, K

AIM in Alcohol and Drug Dependence

22

Brasanac, J., Ramien, C., Gamradt, S., Taenzer, A., Glau, L., Ritter, K. et al

Immune signature of multiple sclerosis-associated depression

23

Kübler, D., Wellmann, S. K., Kaminski, J., Skowronek, C., Schneider, G. H., Neumann, W. J., ... & Kühn, A.

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

24

Eitel, F., Albrecht, J. P., Weygandt, M., Paul, F., & Ritter, K.

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

25

Klingenberg, M., Stark, D., Eitel, F., Ritter, K., & Alzheimer’s Disease Neuroimaging Initiative

MRI Image Registration Considerably Improves CNN-Based Disease Classification

26

Eitel, F., Schulz, M. A., Seiler, M., Walter, H., & Ritter, K.

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

27

Ritter, M., Ott, D. V., Paul, F., Haynes, J. D., & Ritter, K.

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

28

Wakonig, K., Eitel, F., Ritter, K., Hetzer, S., Schmitz-Hübsch, T., Bellmann-Strobl, J., ... & Weygandt, M.

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

29

Stark, D., & Ritter, K.

AIM and Gender Aspects

30

Eitel, F., Ritter, K., & Alzheimer’s Disease Neuroimaging Initiative (ADNI).

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

31

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

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

32

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

33

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

34

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

35

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

Machine Learning / Precision Brain Science