Machine Learning for Precision Neuropsychiatry
ML4PNP
How does each person’s brain differ, and what can those differences tell us about brain health? At ML4PNP, we combine machine learning with EEG, MEG, and MRI to study individual variation in brain structure and function. We build normative models and open-source tools to support research toward more personalized mental health care. We are based at Tilburg University, bringing together expertise in machine learning, neuroimaging, and clinical neuroscience.
We map how brain structure and function vary across people and throughout life. These reference models help us study individual differences that can be missed by comparisons between patient and control groups.
We develop reproducible tools for studying brain rhythms and responses to events. Our work spans large-scale MEG analysis and comparisons of stationary and portable EEG recordings.
We develop and study machine learning methods for personalized care and clinical decision support. Our projects include lifestyle recommendations in psychosis and predicting nurses’ perceptions of staffing adequacy.
Our Projects
Our projects map individual differences in brain activity and explore machine learning for healthcare. MEGaNorm studies brain rhythms across the lifespan, while P600Norm investigates language-related brain responses using stationary and portable EEG.
Our Team
Meet the researchers and students behind our models, software, and experiments. Our team brings together expertise in machine learning, neuroimaging, and clinical neuroscience at Tilburg University.
Our Software
Use our open-source tools for reproducible neuroimaging research. MEGaNorm connects EEG and MEG processing with normative modeling, and our contributions to PCNtoolkit support the study of individual variation in brain data.
Our Publications
Read our work on normative brain modeling, EEG and MEG, and machine learning for neuropsychiatry. Explore findings on lifespan brain dynamics alongside methods and tools for studying individual differences.
We are excited to announce that data collection has started for our NWA-funded P600Norm project, “Normative Range of P600 Morphology as a Diagnostic and Prognostic Tool in Neuropsychiatric Disorders.”
We are happy to welcome Roos as a new Research Assistant in the Machine Learning for Precision Neuropsychiatry (ML4PNP) Lab at Tilburg University.
We were delighted to welcome Julio E. Villalón-Reina from the University of Southern California for a research visit and presentation at the ML4PNP Lab.
Interested in collaborating on normative modeling, neuroimaging, or machine learning for healthcare? Get in touch to discuss a research question or potential collaboration.
Students interested in thesis projects or research experience are welcome to contact us about possible opportunities. Tell us about your interests, background, and the period you have in mind.
ML4PNP is part of the Computational Models of Brain and Behavior research unit within the Research Center for Cognitive Science and Artificial Intelligence, Tilburg School of Humanities and Digital Sciences, Tilburg University.
