Machine learning is a powerful tool for early detection, prediction, and treatment of brain diseases in neuroimaging. We focus on methods for robust extraction of quantitative features from medical images. This includes the use of radiomics-based models for disease diagnosis and prognosis.
Large population studies such as UK Biobank provide an opportunity to train machine learning models on large data and account for physiological variation in the data through normative modeling.
In the context of psychiatric disorders, machine learning can be used to predict treatment outcomes and identify patients who are likely to benefit from specific interventions. Machine learning thus has the potential to revolutionize neuroimaging research and clinical practice by providing non-invasive, reliable indicators of brain health, resilience, and vulnerability before clinical manifestations of disease.