Startseite » Research » Research fields » Predictive Psychiatry


Prof. Dr. Nikolaos Koutsouleris  


Dr. Lana Kambeitz-Ilankovic, Dr. Joseph KambeitzDr. David PopovicÖmer Faruk Öztürk MSc, Susanna Muckenhuber Sternbauer, Dr. Dominic DwyerDr. Anne Ruef

Maria Fernanda Urquijo MScNora Penzel MScRachele Sanfelici MScShalaila Haas MSc,

Pedro Costa Klein MScMark Sen MScJohanna Weiske MSc, cand. med., Julian Wenzel MSc, Susanne Miedl

The recent years have witnessed the rapid development of computer-assisted diagnostic and prognostic procedures in medicine. Machine learning and AI algorithms are increasingly being used to extract patterns from large and complex databases which, unlike in the past, no longer only describe differences between patient groups or associations between different clinically relevant characteristics but can be used for the prediction of individual disease courses and outcomes. This opens completely new possibilities for personalization in medicine, which allow the individual risk and resource profile of the individual patient to be better accommodated in therapy planning than ever before. 

In psychiatry in particular, these developments have led to new biomarker-based research approaches that aim to correctly assess the individual risk of developing psychiatric illnesses in the early disease stages and to initiate preventive interventions on the basis of these more precise and earlier predictions. In addition to improving the early detection of mental illnesses, predictive psychiatry endeavours to develop clinical and biological models for a better prediction of individual and differential therapeutic response. Pattern recognition is used to obtain signatures from clinical, neuropsychological, imaging-based and, where appropriate, genetic data that can be applied to individual patients for a quantitative prediction of desired and undesired drug effects. Should these experimental methods prove to be robust and replicable in the next few years, it would be possible to assemble a combination of therapeutic methods that is maximally effective and least burdensome for the individual patient.

The Section of Neurodiagnostic Applications at the Clinic for Psychiatry and Psychotherapy of the LMU has been pursuing these research approaches since 2008 with a growing track record of important scientific contributions in the field of early detection of psychotic diseases, differential diagnostics of affective and non-effective psychoses and the modelling of the response to antipsychotics and brain stimulation procedures.

Under the following links you can find out more about our current projects:

Neurodiagnostic Applications


  1. Koutsouleris N, Meisenzahl EM, Davatzikos C, Bottlender R, Frodl T, Scheuerecker J, Schmitt G, Zetzsche T, Decker P, Reiser M, Möller HJ, Gaser C. Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. Archives of General Psychiatry. 2009; 66(7):700-12
  2. Koutsouleris N, Meisenzahl EM, Borgwardt S, Riecher-Rössler A, Frodl T, Kambeitz J, Köhler Y, Falkai P, Möller H.-J., Reiser M, Davatzikos C. Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers. Brain. 2015 Jul;138(Pt 7):2059-73.
  3. Hasan A, Wobrock T, Guse B, Langguth B, Landgrebe M, Eichhammer P, Frank E,Cordes J, Wölwer W, Musso F, Winterer G, Gaebel W, Hajak G, Ohmann C, Verde PE, Rietschel M, Ahmed R, Honer WG, Dechent P, Malchow B, Castro MF, Dwyer D, Cabral C, Kreuzer PM, Poeppl TB, Schneider-Axmann T, Falkai P, Koutsouleris N. Structural brain changes are associated with response of negative symptoms to prefrontal repetitive transcranial magnetic stimulation in patients with schizophrenia. Molecular Psychiatry. 2016 doi: 10.1038/mp.2016.161.
  4. Koutsouleris N, Kahn RS, Chekroud AM, Leucht S, Falkai P, Wobrock T, Derks EM,Fleischhacker WW, Hasan A. Multisite prediction of 4-week and 52-week treatment outcomes in patients with first-episode psychosis: a machine learning approach. Lancet Psychiatry. 2016 Oct;3(10):935-946. doi: 10.1016/S2215-0366(16)30171-7.
  5. Chekroud A and Koutsouleris N. The perilous path from publication to practice. Molecular Psychiatry. 2018 (23), 24-25
  6. Dwyer D and Koutsouleris N.  Machine Learning Approaches for Clinical Psychology and Psychiatry. Annual Reviews of Clinical Psychology, 2018, in press,
  7. Dwyer D, Cabral C, Sanfelici R, Kambeitz-Ilankovic L, Kambeitz J, Calhoun V, Falkai P, Pantelis C, Meisenzahl EM, Koutsouleris N. Brain subtyping enhances the Neuroanatomical Discrimination of Schizophrenia. Schizophrenia Bulletin, 2018, in press,
  8. Koutsouleris N, Wobrock T, Guse B, …, Hasan A. Predicting Response to Repetitive Transcranial Magnetic Stimulation in Patients With Schizophrenia Using Structural Magnetic Resonance Imaging: A Multisite Machine Learning Analysis, Schizophrenia Bulletin 2017, article in press,
  9. 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. Detecting the psychosis prodrome across high-risk populations using neuroanatomical biomarkers. Schizophrenia Bulletin. 2014, 41(2):471-82.
  10. 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. Accelerated Brain Aging in Schizophrenia and Beyond: A Neuroanatomical Marker of Psychiatric Disorders. Schizophrenia Bulletin. 2014 Sep;40(5):1140-53
  11. Kambeitz J, Kambeitz-Ilankovic L, Leucht S, Wood S, Davatzikos C, Malchow B, Falkai P, Koutsouleris N. Detecting neuroimaging biomarkers for schizophrenia: a meta-analysis of multivariate pattern recognition studies. Neuropsychopharmacology, 2015; 40(7):1742-51