Dr. rer. nat. Nils R. Winter, M. Sc. 
Postdoc
nils.r.winter@uni-muenster.de
+49 (0)251 83-51847

Projects: FOR2107, MACS, Medical Machine Learning Lab, PHOTONAI

 

 

 

  • CV

    Research Profiles

    Twitter x.com/NilsRWinter
    Open Science Framework osf.io/rcua8
    ORCID id orcid.org/0000-0002-6241-1492
    Research Gate www.researchgate.net/profile/Nils-Winter-2
    Google scholar scholar.google.com/citations
    Personal website www.nilsrwinter.com

    Academic Career

    2023 present Postdoctoral Researcher at Medical Machine Learning Lab, University of Münster
    2019 2023 PhD student at the Otto-Creutzfeldt-Center for Cognitive and Behavioral Neuroscience (OCC), University of Münster, Germany www.uni-muenster.de/OCCMuenster/phd-students/nils-winter.html
    2018 2023 PhD student in psychology (summa cum laude); dissertation title: „Towards Precision Psychiatry – From Univariate to Multivariate Biomarkers of Major Depressive Disorder“ supervised by Prof. Dr. Tim Hahn / Prof. Dr. Dr. Udo Dannlowski / Prof. Dr. Niko Busch at the University of Münster, Germany
    01/2017 12/2017 Research assistant at the Dept. of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt - Goethe University Project: „Genome-based First-line Treatment Response Prediction for Bipolar Disorder“ supervised by Prof. Dr. Tim Hahn and Prof. Dr. Andreas Reif and funded by the LOEWE-Center for Translational Medicine and Pharmacology and the Fraunhofer Institute IME
    10/2014 12/2016 Master of science psychology (specialization in cognitive neuroscience and clinical psychology) at the University of Frankfurt, Germany
    10/2011 09/2014 Bachelor of science psychology at the University of Frankfurt, Germany
    07/2010   A-levels at the Prälat-Diehl-Schule, Groß-Gerau, Germany

    Awards, scholarships, and competitive funds

    2022 Merit Award of the Organization for Human Brain Mapping for an exceptional abstract submitted at the Annual Meeting of the OHBM 2022 in Glasgow, UK
    2019 Congress travel scholarship, DAAD financing the congress participation of the OHBM 2019, Rome, Italy
    2018 Congress travel scholarship, FAZIT financing the congress participation of the OHBM 2018, Singapore, Singapore
    2016 ERASMUS travel stipend financing the research stay at the Centre for Cognitive Neuroimaging, University of Glasgow, UK

  • Research

    Research Interests

    My research lies at the intersection of neuroscience, machine learning, and clinical psychiatry. With a background in cognitive neuroscience and clinical psychology, I am particularly interested in how computational methods can help us better understand, predict, and eventually intervene in mental disorders. I approach psychiatric illnesses as complex, dynamical systems and combine data-driven modeling with theoretical perspectives to uncover the mechanisms underlying individual symptom trajectories.

    As the head of the Precision Psychiatry research group, my work is organized around three main areas. First, I develop and apply machine learning models for individualized clinical prediction, aiming to support decision-making in psychiatry—for example, by predicting relapse risk or treatment response. Second, I use neuroimaging data, particularly structural and resting-state MRI, to identify biomarkers and network-level alterations associated with psychiatric conditions such as major depressive disorder. A core focus here lies in connectome-based modeling and the integration of biological information with behavioral phenotypes. Third, I explore formal models of psychopathology, drawing from computational neuroscience to better characterize psychiatric disorders as dynamical systems.

    Across all of these areas, I advocate for open, transparent science and contribute to the development of open-source research software, including PHOTONAI, a machine learning framework tailored to the needs of biomedical researchers.

    I am also an active member of the Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience (OCC), where I engage in interdisciplinary exchange on topics at the intersection of brain, behavior, and computation.

    Abroad residence

    01-04/2020 Research stay at the Machine Learning and Neuroimaging Lab, University College of London, UK Project: „Uncovering brain-behavior associations in Major Depressive Disorder using Canonical Correlation Analysis and Partial Least Squares Regression“ supervised by Prof. Dr. Janaina Mourao-Miranda funded by the DAAD
    02-04/2016 Research stay at the Centre for Cognitive Neuroimaging, University of Glasgow, UK Project: „Robust statistics and bayesian estimation for group comparisons in EEG data“ supervised by Dr. Guillaume Rousselet

  • Publications

    Notable publications

    • Winter, N. R., Leenings, R., Ernsting, J., Sarink, K., Fisch, L., Emden, D., ..., T., Dannlowski, U. & Hahn, T. Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder Across Neuroimaging Modalities. JAMA Psychiatry 79, 879–888 (2022).
    • Leenings, R., Winter, N. R., Dannlowski, U. & Hahn, T. Recommendations for machine learning benchmarks in neuroimaging. Neuroimage257, 119298 (2022).
    • Winter, N. R., Cearns, M., Clark, S. R., Leenings, R., Dannlowski, U., Baune, B. T. & Hahn, T. From multivariate methods to an AI ecosystem. Molecular Psychiatry 1–5 (2021). doi:10.1038/s41380-021-01116-y
    • Leenings, R., Winter, N. R., Plagwitz, L., Holstein, V., Ernsting, J., Sarink, K., ..., Dannlowski, U. & Hahn, T. PHOTONAI—A Python API for rapid machine learning model development. Plos One16, e0254062 (2021).
    • Mihalik, A., Chapman, J., Adams, R. A., Winter, N. R., Ferreira, F. S., Shawe-Taylor, J., Mourão-Miranda, J. & Initiative, A. D. N. Canonical Correlation Analysis and Partial Least Squares for identifying brain-behaviour associations: a tutorial and a comparative study. Biological Psychiatry Cognitive Neurosci Neuroimaging (2022). doi:10.1016/j.bpsc.2022.07.012