Abstract: Discrepancies between the chronological age of an individual and the neuroimaging based data driven "brain age" have been shown to be feasible biomarkers associated to a wide range of neurological disorders such as Alzheimer's Disease, traumatic brain injuries or psychiatric disorders. We devised a framework based on Deep Gaussian Processes which achieves state-of-the-art results in terms of global brain age prediction. We also introduced the first ever attempt of predicting brain age at voxel-level using context-sensitive Random Forests. The resulting models provide feasible brain-predicted age estimates for younger to middle-aged subjects, with less reliable estimates for older subjects.
Keywords: gaussian processes, random forest, deep learning, neuroimaging, healthy ageing, neurodegenerative diseases
Author Affiliation: Imperial College London, King's College London