A brain signature highly predictive of future progression to Alzheimer's dementia

2017/12/21 by Dansereau, Christian, Tam, Angela, Badhwar, AmanPreet +4
#FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (stat.ML) #Quantitative Methods (q-bio.QM)

paper · doi:10.48550/arxiv.1712.08058

Abstract

Early prognosis of Alzheimer's dementia is hard. Mild cognitive impairment (MCI) typically precedes Alzheimer's dementia, yet only a fraction of MCI individuals will progress to dementia, even when screened using biomarkers. We propose here to identify a subset of individuals who share a common brain signature highly predictive of oncoming dementia. This signature was composed of brain atrophy and functional dysconnectivity and discovered using a machine learning model in patients suffering from dementia. The model recognized the same brain signature in MCI individuals, 90% of which progressed to dementia within three years. This result is a marked improvement on the state-of-the-art in prognostic precision, while the brain signature still identified 47% of all MCI progressors. We thus discovered a sizable MCI subpopulation which represents an excellent recruitment target for clinical trials at the prodromal stage of Alzheimer's disease.

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