Klinik für Psychiatrie und Psychotherapie
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  5. Predictive psychiatry

Predictive Psychiatry

Head: Prof. Dr. Nikolaos Koutsouleris

Staff:
Adyasha Khuntia, Alessa Grund, Anne Ruef, Caroline Plett M.D., Christopher Eberle, Clara Weyer, Clara Vetter, David Popovic M.D., John Fanning, Lisa Hahn, Lisa-Maria Neuner, Madalina Buciuman, Maria Fernanda Urquijo, Maureen Tanuadji, Nadia Bieler M.D., Ronja Stohmann, Susanne Miedl, Esther Rituerto-Gonzalez, Renata Falguera De Souza

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:

  • What is new in our research group?

    Key Publications:

    • 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
    • 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.
    • 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.

    • Antonucci, L. A., Penzel, N., Pergola, G., Kambeitz-Ilankovic, L., Dwyer, D., Kambeitz, J., . . . Koutsouleris, N. (2020). Multivariate classification of schizophrenia and its familial risk based on load-dependent attentional control brain functional connectivity. Neuropsychopharmacology, 45(4), 613-621. doi:10.1038/s41386-019-0532-3
    • Antonucci, L. A., Pergola, G., Pigoni, A., Dwyer, D., Kambeitz-Ilankovic, L., Penzel, N., . . . Bertolino, A. (2020). A Pattern of Cognitive Deficits Stratified for Genetic and Environmental Risk Reliably Classifies Patients With Schizophrenia From Healthy Control Subjects. Biol Psychiatry, 87(8), 697-707. doi:10.1016/j.biopsych.2019.11.007
    • Armio, R. L., Laurikainen, H., Ilonen, T., Walta, M., Salokangas, R. K. R., Koutsouleris, N., . . . Tuominen, L. (2020). Amygdala subnucleus volumes in psychosis high-risk state and first-episode psychosis. Schizophr Res, 215, 284-292. doi:10.1016/j.schres.2019.10.014
    • Avram, M., Brandl, F., Knolle, F., Cabello, J., Leucht, C., Scherr, M., . . . Sorg, C. (2020). Aberrant striatal dopamine links topographically with cortico-thalamic dysconnectivity in schizophrenia. Brain, 143(11), 3495-3505. doi:10.1093/brain/awaa296
    • Baldinger-Melich, P., Urquijo Castro, M. F., Seiger, R., Ruef, A., Dwyer, D. B., Kranz, G. S., . . . Koutsouleris, N. (2020). Sex Matters: A Multivariate Pattern Analysis of Sex- and Gender-Related Neuroanatomical Differences in Cis- and Transgender Individuals Using Structural Magnetic Resonance Imaging. Cereb Cortex, 30(3), 1345-1356. doi:10.1093/cercor/bhz170
    • Bashyam, V. M., Erus, G., Doshi, J., Habes, M., Nasralah, I., Truelove-Hill, M., . . . Davatzikos, C. (2020). MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide. Brain, 143(7), 2312-2324. doi:10.1093/brain/awaa160
    • Burkhardt, G., Adorjan, K., Kambeitz, J., Kambeitz-Ilankovic, L., Falkai, P., Eyer, F., . . . Dwyer, D. B. (2020). A machine learning approach to risk assessment for alcohol withdrawal syndrome. Eur Neuropsychopharmacol, 35, 61-70. doi:10.1016/j.euroneuro.2020.03.016
    • Chand, G. B., Dwyer, D. B., Erus, G., Sotiras, A., Varol, E., Srinivasan, D., . . . Davatzikos, C. (2020). Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. Brain, 143(3), 1027-1038. doi:10.1093/brain/awaa025
    • 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
    • Franzmeier, N., Koutsouleris, N., Benzinger, T., Goate, A., Karch, C. M., Fagan, A. M., . . . Ewers, M. (2020). Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease-informed machine-learning. Alzheimers Dement, 16(3), 501-511. doi:10.1002/alz.12032
    • Fusar-Poli, P., Salazar de Pablo, G., Correll, C. U., Meyer-Lindenberg, A., Millan, M. J., Borgwardt, S., . . . Arango, C. (2020). Prevention of Psychosis: Advances in Detection, Prognosis, and Intervention. JAMA Psychiatry, 77(7), 755-765. doi:10.1001/jamapsychiatry.2019.4779
    • Haas, S. S., Antonucci, L. A., Wenzel, J., Ruef, A., Biagianti, B., Paolini, M., . . . Kambeitz-Ilankovic, L. (2020). A multivariate neuromonitoring approach to neuroplasticity-based computerized cognitive training in recent onset psychosis. Neuropsychopharmacology. doi:10.1038/s41386-020-00877-4
    • Haidl, T. K., Schneider, N., Dickmann, K., Ruhrmann, S., Kaiser, N., Rosen, M., . . . Schultze-Lutter, F. (2020). Validation of the Bullying Scale for Adults - Results of the PRONIA-study. J Psychiatr Res, 129, 88-97. doi:10.1016/j.jpsychires.2020.04.004
    • Kambeitz, J., Goerigk, S., Gattaz, W., Falkai, P., Benseñor, I. M., Lotufo, P. A., . . . Brunoni, A. R. (2020). Clinical patterns differentially predict response to transcranial direct current stimulation (tDCS) and escitalopram in major depression: A machine learning analysis of the ELECT-TDCS study. J Affect Disord, 265, 460-467. doi:10.1016/j.jad.2020.01.118
    • Klein, P. C., Ettinger, U., Schirner, M., Ritter, P., Rujescu, D., Falkai, P., . . . Kambeitz, J. (2020). Brain Network Simulations Indicate Effects of Neuregulin-1 Genotype on Excitation-Inhibition Balance in Cortical Dynamics. Cereb Cortex. doi:10.1093/cercor/bhaa339
    • 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
    • Meisenzahl, E., Walger, P., Schmidt, S. J., Koutsouleris, N., & Schultze-Lutter, F. (2020). [Early recognition and prevention of schizophrenia and other psychoses]. Nervenarzt, 91(1), 10-17. doi:10.1007/s00115-019-00836-5
    • Pomponio, R., Erus, G., Habes, M., Doshi, J., Srinivasan, D., Mamourian, E., . . . Davatzikos, C. (2020). Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan. Neuroimage, 208, 116450. doi:10.1016/j.neuroimage.2019.116450
    • 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
    • Salokangas, R. K. R., Hietala, J., Armio, R. L., Laurikainen, H., From, T., Borgwardt, S., . . . Koutsouleris, N. (2020). Effect of childhood physical abuse on social anxiety is mediated via reduced frontal lobe and amygdala-hippocampus complex volume in adult clinical high-risk subjects. Schizophr Res. doi:10.1016/j.schres.2020.05.041
    • 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
    • Schwarz, E., Alnæs, D., Andreassen, O. A., Cao, H., Chen, J., Degenhardt, F., . . . Meyer-Lindenberg, A. (2020). Identifying multimodal signatures underlying the somatic comorbidity of psychosis: the COMMITMENT roadmap. Mol Psychiatry. doi:10.1038/s41380-020-00915-z
    • Truelove-Hill, M., Erus, G., Bashyam, V., Varol, E., Sako, C., Gur, R. C., . . . Davatzikos, C. (2020). A Multidimensional Neural Maturation Index Reveals Reproducible Developmental Patterns in Children and Adolescents. J Neurosci, 40(6), 1265-1275. doi:10.1523/jneurosci.2092-19.2019
    • Upthegrove, R., Lalousis, P., Mallikarjun, P., Chisholm, K., Griffiths, S. L., Iqbal, M., . . . Koutsouleris, N. (2020). The Psychopathology and Neuroanatomical Markers of Depression in Early Psychosis. Schizophr Bull. doi:10.1093/schbul/sbaa094
    • Woods, S. W., Bearden, C. E., Sabb, F. W., Stone, W. S., Torous, J., Cornblatt, B. A., . . . Anticevic, A. (2020). Counterpoint. Early intervention for psychosis risk syndromes: Minimizing risk and maximizing benefit. Schizophr Res. doi:10.1016/j.schres.2020.04.020

    • Betz, L. T., Brambilla, P., Ilankovic, A., Premkumar, P., Kim, M. S., Raffard, S., . . . Kambeitz, J. (2019). Deciphering reward-based decision-making in schizophrenia: A meta-analysis and behavioral modeling of the Iowa Gambling Task. Schizophr Res, 204, 7-15. doi:10.1016/j.schres.2018.09.009
    • Brandl, F., Avram, M., Weise, B., Shang, J., Simões, B., Bertram, T., . . . Sorg, C. (2019). Specific Substantial Dysconnectivity in Schizophrenia: A Transdiagnostic Multimodal Meta-analysis of Resting-State Functional and Structural Magnetic Resonance Imaging Studies. Biol Psychiatry, 85(7), 573-583. doi:10.1016/j.biopsych.2018.12.003
    • Fusar-Poli, P., Bauer, M., Borgwardt, S., Bechdolf, A., Correll, C. U., Do, K. Q., . . . Arango, C. (2019). European college of neuropsychopharmacology network on the prevention of mental disorders and mental health promotion (ECNP PMD-MHP). Eur Neuropsychopharmacol, 29(12), 1301-1311. doi:10.1016/j.euroneuro.2019.09.006
    • Honnorat, N., Dong, A., Meisenzahl-Lechner, E., Koutsouleris, N., & Davatzikos, C. (2019). Neuroanatomical heterogeneity of schizophrenia revealed by semi-supervised machine learning methods. Schizophr Res, 214, 43-50. doi:10.1016/j.schres.2017.12.008
    • Kambeitz-Ilankovic, L., Betz, L. T., Dominke, C., Haas, S. S., Subramaniam, K., Fisher, M., . . . Kambeitz, J. (2019). Multi-outcome meta-analysis (MOMA) of cognitive remediation in schizophrenia: Revisiting the relevance of human coaching and elucidating interplay between multiple outcomes. Neurosci Biobehav Rev, 107, 828-845. doi:10.1016/j.neubiorev.2019.09.031
    • Kambeitz-Ilankovic, L., Haas, S. S., Meisenzahl, E., Dwyer, D. B., Weiske, J., Peters, H., . . . Koutsouleris, N. (2019). Neurocognitive and neuroanatomical maturation in the clinical high-risk states for psychosis: A pattern recognition study. Neuroimage Clin, 21, 101624. doi:10.1016/j.nicl.2018.101624
    • Kamp, F., Proebstl, L., Penzel, N., Adorjan, K., Ilankovic, A., Pogarell, O., . . . Kambeitz, J. (2019). Effects of sedative drug use on the dopamine system: a systematic review and meta-analysis of in vivo neuroimaging studies. Neuropsychopharmacology, 44(4), 660-667. doi:10.1038/s41386-018-0191-9
    • Karow, A., Holtmann, M., Koutsouleris, N., Pfennig, A., & Resch, F. (2019). [Psychotic disorders in the transition phase: early detection and early intervention]. Fortschr Neurol Psychiatr, 87(11), 629-633. doi:10.1055/a-1025-1994
    • Koutsouleris, N., Upthegrove, R., & Wood, S. J. (2019). Importance of Variable Selection in Multimodal Prediction Models in Patients at Clinical High Risk for Psychosis and Recent Onset Depression-Reply. JAMA Psychiatry, 76(3), 339-340. doi:10.1001/jamapsychiatry.2018.4237
    • Polner, B., Faiola, E., Urquijo, M. F., Meyhöfer, I., Steffens, M., Rónai, L., . . . Ettinger, U. (2019). The network structure of schizotypy in the general population. Eur Arch Psychiatry Clin Neurosci. doi:10.1007/s00406-019-01078-x
    • Popovic, D., Schmitt, A., Kaurani, L., Senner, F., Papiol, S., Malchow, B., . . . Falkai, P. (2019). Childhood Trauma in Schizophrenia: Current Findings and Research Perspectives. Front Neurosci, 13, 274. doi:10.3389/fnins.2019.00274
    • Schmidt, S. J., Hurlemann, R., Schultz, J., Wasserthal, S., Kloss, C., Maier, W., . . . Ruhrmann, S. (2019). Multimodal prevention of first psychotic episode through N-acetyl-l-cysteine and integrated preventive psychological intervention in individuals clinically at high risk for psychosis: Protocol of a randomized, placebo-controlled, parallel-group trial. Early Interv Psychiatry, 13(6), 1404-1415. doi:10.1111/eip.12781
    • Shang, J., Fisher, P., Bäuml, J. G., Daamen, M., Baumann, N., Zimmer, C., . . . Dwyer, D. B. (2019). A machine learning investigation of volumetric and functional MRI abnormalities in adults born preterm. Hum Brain Mapp, 40(14), 4239-4252. doi:10.1002/hbm.24698
    • Walter, M., Alizadeh, S., Jamalabadi, H., Lueken, U., Dannlowski, U., Walter, H., . . . Dwyer, D. B. (2019). Translational machine learning for psychiatric neuroimaging. Prog Neuropsychopharmacol Biol Psychiatry, 91, 113-121. doi:10.1016/j.pnpbp.2018.09.014

    • Chekroud, A. M., & Koutsouleris, N. (2018). The perilous path from publication to practice. Mol Psychiatry, 23(1), 24-25. doi:10.1038/mp.2017.227
    • Dwyer, D. B., Cabral, C., Kambeitz-Ilankovic, L., Sanfelici, R., Kambeitz, J., Calhoun, V., . . . Koutsouleris, N. (2018). Brain Subtyping Enhances The Neuroanatomical Discrimination of Schizophrenia. Schizophr Bull, 44(5), 1060-1069. doi:10.1093/schbul/sby008
    • 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
    • Falkai, P., Schmitt, A., & Koutsouleris, N. (2018). Impaired recovery in affective disorders and schizophrenia: sharing a common pathophysiology? Eur Arch Psychiatry Clin Neurosci, 268(8), 739-740. doi:10.1007/s00406-018-0951-x
    • Kambeitz, J., Cabral, C., Sacchet, M. D., Gotlib, I. H., Zahn, R., Serpa, M. H., . . . Koutsouleris, N. (2018). Reply to: Sample Size, Model Robustness, and Classification Accuracy in Diagnostic Multivariate Neuroimaging Analyses. Biol Psychiatry, 84(11), e83-e84. doi:10.1016/j.biopsych.2018.01.023
    • 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
    • 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
    • Rozycki, M., Satterthwaite, T. D., Koutsouleris, N., Erus, G., Doshi, J., Wolf, D. H., . . . Davatzikos, C. (2018). Multisite Machine Learning Analysis Provides a Robust Structural Imaging Signature of Schizophrenia Detectable Across Diverse Patient Populations and Within Individuals. Schizophr Bull, 44(5), 1035-1044. doi:10.1093/schbul/sbx137
    • Shang, J., Bäuml, J. G., Koutsouleris, N., Daamen, M., Baumann, N., Zimmer, C., . . . Sorg, C. (2018). Decreased BOLD fluctuations in lateral temporal cortices of premature born adults. Hum Brain Mapp, 39(12), 4903-4912. doi:10.1002/hbm.24332