Head: Univ.-Prof. Dr. Nikolaos Koutsouleris
Team members: 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
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 published 25 papers (list) (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)
NeuroMiner is a free machine learning software written by Prof. Nikolaos Koutsouleris in MATLAB, developed since 2009, that facilitates research into better tools for precision medicine. It provides a wealth of state-of-the-art supervised Machine Learning techniques, such as linear and non-linear support-vector machines, relevance vector machines, random forests, and gradient-boosting algorithms. It also comes with numerous dimensionality reduction methods and feature selection strategies that allow finding optimal combinations of predictive features for the user’s given prediction problem.
It can be operated with little coding experience as it is fully menu-driven which allows using NeuroMiner in server/remote environments with no or limited graphical interfaces. Furthermore, the NeuroMiner interface facilitates standardized parameter setup, storage, and dissemination across research labs, which is an essential requirement for more robust and generalizable predictive models.
NeuroMiner is constantly updated by the Section for Neurodiagnostic Applications (SNAP) in Psychiatry at the Department of Psychiatry and Psychiatry of Ludwig-Maximilian-University and can analyze all tabular data format that are stored in numeric MATLAB format, or alternatively in CSV or Microsoft Excel spreadsheets. It supports the in-depth analysis of 3D voxel-based neuroimaging data. These different data sources can be analyzed separately or be combined using a variety of data fusion approaches ranging from data concatenation, over bagging to more advanced stacking, or sequential data integration techniques.
In high-performance computing environments, researchers can exploit the SGE-/SLURM-based parallelization functionality of NeuroMiner to tremendously speed up the model training, cross-validation and visualization procedures.
The NeuroMiner Machine Learning School was first conducted in 2018. NeuroMiner is a machine learning software written by Prof. Nikolaos Koutsouleris in MATLAB that can be operated with little coding experience with a text-based menu system. This gives clinical researchers access to cutting-edge tools that can be used for predictive medicine and has been used for multiple publications.
Goal: The aim of this school is to introduce the participant to psychiatric machine learning with a specific focus on diagnostic and prognostic neuroimaging.
Structure: Fundamental machine learning concepts are introduced in lectures and the participant is guided through practical tutorials using software that does not require programming experience. By the end of the course, the participant will be able to plan and implement his own machine learning analyses with multiple types of data (e.g., clinical, biological, and neuroimaging).
Highlights of the school:
- Progression from data entry to advanced techniques (e.g., fusion and stacking)
- Hands-on tutorials using own computers in small groups guided by a tutor (n=10)
- Guest lecture from a machine learning expert
- A machine learning competition on the last day
- At the end of the machine learning school, the participant will be able to plan and conduct its own NeuroMiner analyses using varied data
Previous Machine Learning Schools experiences
Online Machine Learning School September 2022
- A total of 254 international participants joined the school with 10 tutors organization for the participants that attended specific tutorials.
- It was conducted by the ECNP Neuroimaging Network, the Section for Precision Psychiatry Applications, and the Max Planck Fellow Group for Precision Psychiatry.
Next Machine Learning School:
Please check the following link for more information about our upcoming events: Online Machine Learning School
Psychiatric Residents:Dr. med. Nadia Bieler, Caroline Plett M.D., Dr. med David Popovic
ProNET `The Psychosis-Risk Outcomes Network`, is a research project founded by the US-based public-private program AMP-SCZ (Accelerating Medicines Partnership in Schizophrenia). The project proposes a standardized large-scale international effort to deconstruct CHR (Clinical High Risk) heterogeneity with state-of-the-art multimodal methods and map clinically relevant trajectories and outcomes of the CHR syndrome. The stratification calculators will allow future clinical trial designers to select optimal samples for determining whether a novel compound improves the particular CHR outcome of interest and pave the way for phase-specific and safe new interventions to benefit patients and their families and communities.
CARE `Computer-assistierte Risiko-Evaluation in der Früherkennung psychotischer Erkrankungen´, is a project funded by the Innovationsausschuss at the Gemeinsamen Bundesausschuss (G-BA).
The new form of CARE is an innovative and efficient treatment model for high risk patients, in which AI-supported algorithms based on clinical data of the patients (psychometry, neuropsychology, cMRI) are used to make fine-grained individual predictions of the psycho-risk and functional losses. On this basis, a risk-stratified therapy in the sense of an indicated prevention is carried out for each patient. The goal is to prevent the disease or to significantly mitigate its course.
PRONIA "Personalized Prognostic Tools for Early Psychosis Management" is a research project funded by the European Union to significantly improving the early detection of psychotic illness. The project aims to produce an easily accessible prognostic service for early illness detection. This forecasting system will use clinical information, cognitive data, neuroimaging, and DNA, RNA, and metabolomics.
PRONIA is comprised by a professional, motivated, and international team who develop computer models that can assess the risk of psychosis development, illness outcomes, and a range of other critical diagnostic and prognostic endpoints. In the future, these procedures will help doctors and psychologists to better assess and treat the course of the disease. For more information on this project, see PRONIA.
PNKT "Personalized neuro-cognitive training in depression and psychosis (high risk)" is a project in which researchers are working on the development of personalized neuro-cognitive therapies based on an individualized stratification of the risk for psychosis and a reduced functional level.
In addition to the typical symptoms of affective and non-affective psychosis, affected patients regularly suffer from cognitive impairments. Deficits in cognitive abilities in patients with psychosis are closely related to their quality of life and therefore represent a special focus in the rehabilitation of those affected. To date, it has been shown that computer-assisted neuro-cognitive training can be successfully used to reduce these cognitive deficits and at the same time improve the psychosocial function level of patients. However, previous studies show that only subpopulation of these patients offers a satisfactory response to cognitive training. One aim of the study is, therefore, to predict the positive response to this measure in order to offer cognitive training to those patients who will most likely benefit from it.
In the PNKT study, patients participate in a detailed clinical, neuro-cognitive, and imaging survey, followed by several weeks of computer-assisted cognitive training. Participation in the study ends with a follow-up examination. On the basis of multivariate pattern analyses, it is hoped that it will be possible to determine which data patterns are best for predicting whether and under what conditions a patient will benefit from cognitive training.
CIP - Cannabis-induced psychosis - is a DFG-funded study aiming to identifying individuals with cannabis-induced psychosis at an early stage and, in the context of a longitudinal imaging study, to find out in which affected individuals the psychotic symptoms remit spontaneously after abstinence and which patients are likely to develop a permanent form of psychosis.
Recent studies have shown that up to 50% of all patients with cannabis-induced psychosis develop a form of psychosis in the long term. Thus, patients with cannabis-induced psychosis represent the population with the highest known risk of permanent forms of psychosis. However, the neurobiological fundamentals of the connection between psychosis and cannabis use have hardly been understood so far. Nevertheless, this is urgently needed in order to identify the individuals with an increased risk of psychosis already at an early stage and to prevent the development of the disease and to positively influence the course of the disease.
In the CIP study, patients with cannabis-induced psychosis are tested for baseline and follow-up after 9 months by clinical and neuropsychological tests, as well as by using structural, functional and molecular imaging. The data is then evaluated by multivariate pattern analysis to identify brain patterns associated with clinical and neuropsychological variables.
TYPIA is a DFG-funded bicentric study conducted at the University of Bonn and the LMU Hospital in Munich. The aim of the study is to find out why not all people with highly pronounced schizotypic personality traits develop psychosis and the factors that protect against the development of psychosis. The project is based on observations of that suggest a continuum of psychosis symptoms between the individuals with no symptoms and populations of patients with psychosis.
Within the framework of TYPIA, test persons with high positive or negative schizotypic characteristics, control subjects with low schizotypic characteristics, and patients suffering from psychosis for the first time in the last three years are examined. The age range for all groups is between 18 and 40 years. The participation includes examinations of personality, cognition, DNA, a clinical interview, as well as images of brain structure and function using magnetic resonance imaging (MRI).
CARE and ProNET projects:
We started the recruitment of Clinical High Risk (CHR) for psychosis patients within our two new projects (CARE and ProNET). For further information of how to apply or to have more details about the projects please see the description stated in the Projects section of this website and contact: PSY-Pronia@med.uni-muenchen.de
Next online Machine Learning School:
We are happy to announce that the registration process for our next Online Machine Learning School from September 25th-29th, 2023, is open! For more information visit our website Online Machine Learning School 2023 - Central European — NeuroMiner Manual
9th Kraepelin Symposium
The 9th Kraepelin Symposium `Precision Psychiatry – Transforming the Landscape of Mental Health Care` conducted at the LMU Klinikum on Jan 19th-20th, 2023, was a successful international event with more than 200 participants around the world. The symposium was focused on the mechanistic view of basic neurosciences with translational approaches, and discussed the real-world implementation of precision psychiatry methods from commercial, regulatory and funder‘s perspectives.
● Koutsouleris N, Pantelis C, Velakoulis D, et al. Exploring Links Between Psychosis and Frontotemporal Dementia Using Multimodal Machine Learning: Dementia Praecox Revisited (2022). JAMA Psychiatry. 2022;79(9):907–919. doi:10.1001/jamapsychiatry.2022.2075
● Dwyer DB, Buciuman M, Ruef A,...Koutsouleris, N. (2022) Clinical, Brain, and Multilevel Clustering in Early Psychosis and Affective Stages. JAMA Psychiatry. doi:10.1001/jamapsychiatry.2022.1163
● Koutsouleris N, Dwyer DB, Degenhardt F, Maj C, Urquijo-Castro MF, Sanfelici R, Popovic D, Oeztuerk O,....; PRONIA Consortium (2022). Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression. JAMA Psychiatry. 2021 Feb 1;78(2):195-209. doi: 10.1001/jamapsychiatry.2020.3604. PMID: 33263726; PMCID: PMC7711566.
● Koutsouleris N, Worthington M, Dwyer DB, Kambeitz-Ilankovic L, Sanfelici R, Fusar-Poli P,…Cannon TD (2021). Toward Generalizable and Transdiagnostic Tools for Psychosis Prediction: An Independent Validation and Improvement of the NAPLS-2 Risk Calculator in the Multisite PRONIA Cohort. Biol Psychiatry. 2021 Nov 1;90(9):632-642. doi: 10.1016/j.biopsych.2021.06.023. Epub 2021 Jul 6.
As the leader of the PRONIA consortium, the neurodiagnostics laboratory closely collaborates with the following groups:
- The University of Basel, Department of Psychiatry, Prof. Stefan Borgwardt and Prof. Anita Riecher-Rössler
- The University of Cologne, Department of Psychiatry, Prof. Joachim Klosterkötter
- The University of Birmingham, Department of Psychology, Prof. Stephen Wood
- The University of Turku, Department of Psychiatry, Prof. Raimo Salokangas
- The University of Udine, Department of Psychiatry, Prof. Paolo Brambilla
- The University of Melbourne, Department of Psychiatry, Prof. Christos Pantelis
- General Electric Deutschland & GE Healthcare
As part of the HARMONY consortium, the neurodiagnostics laboratory closely collaborates with the following groups:
- Yale University, Department of Psychology, Prof. Tyrone Cannon
- King's College London, Institute of Psychiatry, Prof. Philip McGuire
- The University of Pennsylvania, Department of Psychiatry, Prof. Raquel Gur
An ongoing collaboration is also with:
- The University of Pennsylvania, Center for Biomedical Image Computing and Analytics, Prof. Christos Davatzikos