In the Department of Machine Learning, we develop models to improve decision making in clinical brain research.

01
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

Apr 01, 2023 | NeuroImage
02
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

Mar 01, 2023 | The Lancet Psychiatry
03
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

Feb 21, 2023 | Frontiers in neurology
04
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

Jan 01, 2023 | NeuroImage: Clinical
05
Schulz, M. A., Koch, A., Guarino, V. E., Kainmueller, D., & Ritter, K

Data augmentation via partial nonlinear registration for brain-age prediction

Oct 06, 2022 | International Workshop on Machine Learning in Clinical Neuroimaging
06
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

Jul 03, 2022 | Multiple Sclerosis Journal–Experimental, Translational and Clinical
07
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

May 26, 2022 | Elife
08
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

May 01, 2022 | Medical image analysis
09
Rane, R. P., Heinz, A., & Ritter, K

AIM in Alcohol and Drug Dependence

Feb 18, 2022 | Artificial Intelligence in Medicine
10
Brasanac, J., Ramien, C., Gamradt, S., Taenzer, A., Glau, L., Ritter, K. et al

Immune signature of multiple sclerosis-associated depression

Feb 01, 2022 | Brain, Behavior, and Immunity
11
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

Jan 01, 2022 | Parkinsonism & Related Disorders
12
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

Dec 27, 2021 | scientific reports
13
Schulz, M. A., Baier, S., Timmermann, B., Bzdok, D., & Witt, K.

A cognitive fingerprint in human random number generation

Oct 12, 2021 | Scientific reports
14
Klingenberg, M., Stark, D., Eitel, F., Ritter, K., & Alzheimer’s Disease Neuroimaging Initiative

MRI Image Registration Considerably Improves CNN-Based Disease Classification

Sep 21, 2021 | Machine Learning in Clinical Neuroimaging 2021
15
Chapman-Rounds, M., Bhatt, U., Pazos, E., Schulz, M. A., & Georgatzis, K.

FIMAP: Feature Importance by Minimal Adversarial Perturbation

May 18, 2021 | AAAI Conference on Artificial Intelligence
16
Eitel, F., Schulz, M. A., Seiler, M., Walter, H., & Ritter, K.

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

May 01, 2021 | Experimental Neurology