Across Time and Space: Machine Learning Reveals Paths to Dementia

Developing computational algorithms & machine-learning techniques, to transform cross-sectional imaging data to a longitudinal picture and reconstruct the sequence of disease progression.

Researchers face a challenge in understanding the brain changes during the long course of Alzheimer’s disease. It’s not possible to track neurodegeneration continuously in individual people for up to 30 years, so instead scientists collect snapshots of the disease from different people in all stages of the disease. Now, using advanced computational approaches and a massive trove of MRI brain volume data, scientists have stitched together a series of these snapshots. This way, they identified disease subtypes with distinct progression patterns in people with Alzheimer’s disease or with mutations that cause frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). They dubbed their method SuStaIn, for Subtype and Stage Inference.

To read the full article visit the Alz Forum – https://www.alzforum.org/news/research-news/across-time-and-space-machine-learning-reveals-paths-dementia