Normal brain development and aging are accompanied by patterns of neuroanatomical change that can be captured by machine learning methods applied to imaging data. MRI-derived brain age has been widely adopted by the neuroscience community as an informative biomarker of brain health at the individual level. Individuals displaying pathologic or atypical brain development and aging patterns can be identified through positive or negative deviations from typical Brain Age trajectories. For example, Schizophrenia, Mild Cognitive Impairment, Alzheimer’s Disease, Type 2 Diabetes, and mortality have all been linked to accelerated brain aging.
Deep learning has emerged as a powerful tool in medical image analysis, allowing for highly complex relationships to be modeled with minimal feature engineering. Since deep learning methods require large amounts of data in order achieve good results these methods have been slow to enter into neuroimaging research, where data is relatively limited. Recently, through large multi-study pools of imaging data, we are able to apply these methods.
Our large consortium of neuroimaging data represents a diverse set of individuals spanning different ages, geographic locations, and scanners. This allows us to train highly accurate and reliable models for predicting brain age.
In 2020, we published a paper titled “MRI signatures of brain age and disease over lifespan from a Deep Brain Network and 14,468 people”, in Brain. This paper represents the culmination of our findings related to the application of deep learning in identifying brain age and other neuroanatomical biomarkers from raw brain scans. In this paper, we present a deep learning model that has learned highly generalizable neuroimaging features. We further demonstrate that these features can be used to construct various predictive models for other related tasks such as Alzheimer’s Disease classification, via transfer learning.