Welcome to the Koutsouleris lab

The Koutsouleris lab was founded in 2013 by Professor Koutsouleris with the aim of developing machine learning methods for diagnosis and prediction in psychiatry. The team is a multidisciplinary group from various fields of medicine, psychology and computer science.

Since then, promising research results from several international projects have already been published in more than 50 scientific articles. In addition, significant progress has been made in the development of translational tools for the use of machine learning models (www.proniapredictors.eu) and software (https://neurominer-git.github.io/NeuroMiner_1.3/intro.html ).

The Early Recognition Service at the LMU Clinic is another part of the Koutsouleris Lab and comprises an experienced team of doctors and psychologist for the early detection of mental illnesses in adolescents and young adults. It offers comprehensive assessments, in particular for patients with psychosis-related symptoms, but also in the case of non-specific symptoms.

The aim of the laboratory is to develop specific computer models that can be used to estimate the risk of transition to a full-blown disorder. In addition, new prognostic models and biomarkers are being investigated to predict the prognosis of mental illnesses and the individual response to treatment.

The aim is that these methods will help doctors and psychologists to better recognize the course of the illness and treat it accordingly.

Research Focus

Redefining Psychiatric Classification

We apply machine learning to integrate diverse data sources—clinical histories, genetic variations, neuroimaging, and behavioral patterns—building models that reveal complex relationships in psychiatric disorders. By analyzing the biological and environmental underpinnings of mental illness, we aim to move beyond broad, symptom-based diagnostic categories toward a more precise, biologically grounded classification system.

Early Detection & Risk Prediction

Our research focuses on identifying subtle changes in brain structure, cognitive function, and behavior that signal the early stages of psychiatric disorders. By breaking down the heterogeneity of symptoms, we develop predictive models that detect these conditions before they fully manifest, enabling timely interventions that can improve long-term outcomes and prevent disease progression.

Personalized Treatment Strategies

We are advancing a personalized approach to psychiatric care by tailoring treatments based on an individual’s unique biological, psychological, and environmental profile. Moving beyond traditional trial-and-error methods, our data-driven strategies enhance treatment precision, optimize patient response, and improve long-term recovery.

Institutional Affliations

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