Mental Health Mapping

In the laboratory for Mental Health Mapping (PI: Thomas Wolfers) we aim to make a difference for people with complex health challenges. Each person’s trajectory through life is a consequence of a complex developmental process, that involves biological, social, societal, and environmental factors. In our research we use large and diverse datasets in combination with machine learning to map this process. We collaborate with patients and clinicians building bridges between clinical practice and basic research in neuroscience and machine learning, with the ultimate objective to translate our work into clinical practice.

For more information visit the Home of the laboratory for Mental Health Mapping

Wolfers, Thomas; …; Marquand, Andre F; (2015). From estimating activation locality to predicting disorder: a review of pattern recognition for neuroimaging-based psychiatric diagnostics. Neuroscience & Biobehavioural Reviews. link 

• SUMMARY: In this seminal paper, we showed that with increasing sample size the accuracy of pattern classification approaches for the prediction of mental disorders decreased. We could attribute it to the high heterogeneity of mental disorders and a publication bias. This work contributed to a debate on cross-validation failures and provides important evidence for the development of methods to map heterogeneity of mental disorders.

Wolfers, Thomas; …; Westlye, Lars; Marquand, Andre F; (2018). Mapping the heterogeneous phenotype of schizophrenia and bipolar disorder using normative models. JAMA psychiatry. link 

• SUMMARY: We mapped the heterogeneity of schizophrenia and bipolar disorder showing an individualized profile of deviations from the norm. These results were very surprising and framed by the media as “Who is the average patient in psychiatry?”. The results have been frequently replicated by us and other groups using various algorithms and samples.

Wolfers, Thomas; …; Franke, Barbara; Marquand, Andre F; (2019) Individual differences v. the average patient: mapping the heterogeneity in ADHD using normative models. Psychological medicine. link 

• SUMMARY: We mapped the heterogeneity of ADHD at the level of the individual patient, showing that each patient had a distinct profile of deviations from the norm.

Rutherford, Saige; …; Wolfers, Thomas; …; Marquand, Andre F; (2022). Charting brain growth and aging at high spatial precision. Elife. link 

• SUMMARY: We charted normative models at high spatial precision for samples that are larger than 50k individuals. This is the first massive population scale neuroimaging based normative modelling approach that can be translated to new samples and is freely available online for other scientists to use.

Leonardsen, Esten; …; Westlye, Lars T #; Wolfers, Thomas #; Yunpeng, Wang # (2022). Deep neural networks learn general and clinically relevant representations of the ageing brain. NeuroImage. # shared last author link 

• SUMMARY: We trained a deep neural network on one of the largest data resources assembled today (N > 50k) to predict the aging brain. We transferred this model to make clinical predictions and share our model and the resource for others to use.