Minute changes in brain structure and function precede visible symptoms in brain diseases. These changes are observable by combining brain imaging and advanced computational data-analysis techniques, specifically within machine learning (ML). This project develops ML methods to facilitate detection of brain changes and to combine imaging information to information on risk-factors and cognitive assessments for personalized decision making concerning brain diseases, such as dementia. The idea of ML algorithms is to use existing data to make computers learn to automatically interpret new data. In this project, a series of brain images and long term follow-up information of the large subject pool form the input to ML algorithms. By utilizing this example data, the algorithms learn to predict disease progression. The use of longitudinal data for prediction is a specific focus area of the project. The developed methods are applied to solve new prediction problems concerning brain health.

2022

2026