Prof. Dr. Nikolaos Koutsouleris

Keywords: Psychiatry, Predictive modelling, Machine Learning, Psychotic disorders, Affective disorders, Early recognition, Youth mental health

Research methods: Machine Learning, Neuroimaging, Neurocognition, Omics, Multimodal data fusion

Brief research description: The Precision Psychiatry Section was initially conceived as the Neurodiagnostic Applications Section founded in 2013 by Prof. Koutsouleris with the aim of developing machine learning methods for diagnostics and prediction in psychiatry. The team is a multidisciplinary group from different fields of medicine, psychology, and computer science. Promising research results from multiple international projects have been published in more than 50 scientific articles imaging, predictive affective disorders (topics). The team led the EU-funded (FP7) project PRONIA (Prognostic Tools for Early Psychosis Management; www.pronia.eu ) that recruited a clinical cohort of more than 1974 individuals across European countries who were extensively clinically and biologically characterized. In addition, decisive progress has been made in establishing translational tools to deploy machine learning models (www.proniapredictors.eu) and software (https://github.com/neurominer-git; www.proniapredictors.eu)

Current GSN students: Clara Weyer

Selected publications:

1. Koutsouleris N, Pantelis C, Velakoulis D, …, Schroeter M. Exploring links between Psychosis and Frontotemporal Dementia using Multi-Modal Machine Learning: ‘Dementia praecox‘ revisited. JAMA Psychiatry. 2022; 79(9):907-919. doi:10.1001/jamapsychiatry.2022.2075

2. Koutsouleris N, Dwyer D, Degenhardt F, …, Meisenzahl E, and the PRONIA Consortium. Multi-modal Workflows for Psychosis Prediction in Clinical High-Risk Syndromes and Recent-Onset Depression: A Multi-Site Machine Learning Analysis. JAMA Psychiatry. 2021; 78(2):195-209. doi: 10.1001/jamapsychiatry.2020.3604

3. Koutsouleris N, Kambeitz-Ilankovic L, Ruhrmann S, …, Borgwardt S, and the PRONIA Consortium. Individualized Prediction of Functional Outcomes in the Clinical High-Risk State for Psycho-sis and in Recent-Onset Depression: A Multi-modal, Multi-Site Machine Learning Analysis. JA-MA Psychiatry. 2018; 75(11):1156-1172. doi: 10.1001/jamapsychiatry.2018.2165.

4. Dwyer D, Buciuman M, Ruef A, …, Koutsouleris N. Clinical, Brain, and Multilevel Clustering in Early Psychosis and Affective Stages. JAMA Psychiatry. 2022; e221163. doi: 10.1001/jamapsychiatry.2022.1163

5. Koutsouleris N, Kahn RS, Chekroud AM, …, Hasan A. Multisite prediction of 4-week and 52-week treatment outcomes in patients with first-episode psychosis: a machine learning ap-proach. Lancet Psychiatry, 2016; 3(10):935-946. doi: 10.1016/S2215-0366(16)30171-7.