Prof.Dr. Nikolaos Koutsouleris

Managing Consultant of the Clinic


Research Focus

My research aims at extracting predictive and diagnostic information from diverse neurobiological, neurocognitive and clinical data for an improved early recognition of affective and non-affective psychoses. For this purpose, I have been conducting cross-sectional and longitudinal studies of persons at an increased risk of developing psychotic illness and examined them with structural MRI, neuropsychological testing and clinical evaluations within the specialized Early Recognition Service of our Department. Using advanced pattern recognition methods, which I implemented during my postdoctoral work, I was able to identify and cross-validate MRI and neurocognitive biomarkers allowing for an accurate, single-subject prediction of psychoses. As Head of the Department’s Early Psychosis Studies and Section for Neurodiagnostic Applications, I am striving to develop and implement prognostic and diagnostic models that (1) enable an effective personalized management of high-risk individuals across different centers and healthcare settings, (2) facilitate the individualized stratification of risk for disease onset, chronicity and poor functional outcomes across different psychiatric disorders, and (3) improve our understanding of the diagnostic boundaries between and within these disease entities based on multivariate subgroup identification methods. Equally important, I train new investigators at the pre- and post-doctoral levels to comprehensively analyze complex, high-dimensional data using multivariate analysis methods. 


  • Koutsouleris, N., Dwyer, D. B., Degenhardt, F., Maj, C., Urquijo-Castro, M. F., Sanfelici, R., . . . Meisenzahl, E. (2020). Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression. JAMA Psychiatry. doi:10.1001/jamapsychiatry.2020.3604


  • Dwyer, D. B., Kalman, J. L., Budde, M., Kambeitz, J., Ruef, A., Antonucci, L. A., . . . Koutsouleris, N. (2020). An Investigation of Psychosis Subgroups With Prognostic Validation and Exploration of Genetic Underpinnings: The PsyCourse Study. JAMA Psychiatry, 77(5), 523-533. doi:10.1001/jamapsychiatry.2019.4910


  • Popovic, D., Ruef, A., Dwyer, D. B., Antonucci, L. A., Eder, J., Sanfelici, R., . . . Koutsouleris, N. (2020). Traces of Trauma: A Multivariate Pattern Analysis of Childhood Trauma, Brain Structure, and Clinical Phenotypes. Biol Psychiatry, 88(11), 829-842. doi:10.1016/j.biopsych.2020.05.020


  • Sanfelici, R., Dwyer, D. B., Antonucci, L. A., & Koutsouleris, N. (2020). Individualized Diagnostic and Prognostic Models for Patients With Psychosis Risk Syndromes: A Meta-analytic View on the State of the Art. Biol Psychiatry, 88(4), 349-360. doi:10.1016/j.biopsych.2020.02.009


  • Koutsouleris N, Upthegrove R, Wood SJ. Importance of Variable Selection in Multimodal Prediction Models in Patients at Clinical High Risk for Psychosis and Recent Onset Depression-Reply. JAMA Psychiatry. 2019 Mar 1;76(3):339-340. doi: 10.1001/jamapsychiatry.2018.4237.


  • Koutsouleris, N., Wobrock, T., Guse, B., Langguth, B., Landgrebe, M., Eichhammer, P., . . . Hasan, A. (2018). Predicting Response to Repetitive Transcranial Magnetic Stimulation in Patients With Schizophrenia Using Structural Magnetic Resonance Imaging: A Multisite Machine Learning Analysis. Schizophr Bull, 44(5), 1021-1034. doi:10.1093/schbul/sbx114


  • Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018). Machine Learning Approaches for Clinical Psychology and Psychiatry. Annu Rev Clin Psychol, 14, 91-118. doi:10.1146/annurev-clinpsy-032816-045037


  • Koutsouleris, N., Kambeitz-Ilankovic, L., Ruhrmann, S., Rosen, M., Ruef, A., Dwyer, D. B., . . . Borgwardt, S. (2018). Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression: A Multimodal, Multisite Machine Learning Analysis. JAMA Psychiatry, 75(11), 1156-1172. doi:10.1001/jamapsychiatry.2018.2165


  • Koutsouleris N, Kahn RS, Chekroud AM, Leucht S, Falkai P, Wobrock T, Derks EM, Fleischhacker WW, Hasan A. (2016). Multisite prediction of 4-week and 52-week treatment outcomes in patients with first-episode psychosis: a machine learning approach. Lancet Psychiatry, 3(10):935-946. doi: 10.1016/S2215-0366(16)30171-7.


  • Koutsouleris N, Meisenzahl EM, Borgwardt S, Riecher-Rössler A, Frodl T, Kambeitz J, Köhler Y, Falkai P, Möller HJ, Reiser M, Davatzikos C. (2015). Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers. Brain. 138(Pt 7):2059-73. doi: 10.1093/brain/awv111.


  • Kambeitz J, Kambeitz-Ilankovic L, Leucht S, Wood S, Davatzikos C, Malchow B, Falkai P, Koutsouleris N. (2015). Detecting neuroimaging biomarkers for schizophrenia: a meta-analysis of multivariate pattern recognition studies. Neuropsychopharmacology. 40(7):1742-51. (IF: 7.5)


  • Koutsouleris N, Riecher-Rössler A, Meisenzahl E, Smieskova R, Studerus E, Kambeitz-Ilankovic L, von Saldern S, Cabral C, Reiser M, Falkai P, Borgwardt S. (2014). Detecting the psychosis prodrome across high-risk populations using neuroanatomical biomarkers. Schizophrenia Bulletin. doi: 10.1093/schbul/sbu078


  • Koutsouleris N, Davatzikos C, Borgwardt S, Gaser C, Bottlender R, Frodl T, Falkai P, Riecher-Rössler A, Möller HJ, Reiser M, Pantelis C, Meisenzahl E. (2014). Accelerated Brain Aging in Schizophrenia and Beyond: A Neuroanatomical Marker of Psychiatric Disorders. Schizophrenia Bulletin. 40(5):1140-53


  • Koutsouleris N, Borgwardt S, Meisenzahl EM, Bottlender R, Möller HJ, Riecher-Rössler A. (2012). Disease prediction in the at-risk mental state for psychosis using neuroanatomical biomarkers: results from the FePsy-study. Schizophrenia Bulletin. 38(6):1234-46 (IF: 8.8)


  • Koutsouleris N, Davatzikos C, Bottlender R, Patschurek-Kliche K, Scheuerecker J, Decker Petra, Gaser C, Möller HJ; Meisenzahl E. (2012). Early recognition and disease prediction in the at-risk mental states for psychosis using neurocognitive pattern classification. Schizophrenia Bulletin. 38(6):1200-15 (IF: 8.8)


  • Koutsouleris N, Gaser C, Patschurek-Kliche K, Scheuerecker J, Bottlender R, Decker P, Schmitt G, Reiser M, Möller HJ and Meisenzahl EM. (2012). Multivariate patterns of brain–cognition associations relating to vulnerability and clinical outcome in the at-risk mental states for psychosis. Human Brain Mapping. 33(9):2104-2124.(IF: 5.9)


  • Koutsouleris N, Gaser C, Bottlender R, Davatzikos C, Decker P, Jäger M, Schmitt G, Reiser M, Möller HJ, Meisenzahl EM. (2010). Use of Neuroanatomical Pattern Regression to Predict the Structural Brain Dynamics of Vulnerability and Transition to Psychosis. Schizophrenia Research. 123(2-3):175-187 (IF: 4.5).

  • Koutsouleris N, Meisenzahl EM, Davatzikos C, Bottlender R, Frodl T, Scheuerecker J, Schmitt G, Zetzsche T, Decker P, Reiser M, Möller HJ, Gaser C. (2009). Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. Arch Gen Psychiatry. 66(7):700-12. doi: 10.1001/archgenpsychiatry.2009.62.


Telephone: 089/440055885


PRONIA-Early Diagnosis

Hospital of the University of Munich
Clinic for Psychiatry and Psychotherapy

Nussbaumstraße 7
80336 München