Deep learning for risk-based stratification of cognitively impaired individuals
Michael F. Romano, Xiao Zhou, Akshara R. Balachandra, Michalina F. Jadick, Shangran Qiu, Diya A. Nijhawan, Prajakta Joshi, Mohammad Shariq, Peter H. Lee, Maximilian J. Smith, Aaron B. Paul, Asim Mian, Juan E. Small, Sang Chin, Rhoda Au, Vijaya B. Kolachalama
Abstract
Quantifying the risk of progression to Alzheimer's disease (AD) could help identify persons who could benefit from early interventions. We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 544, discovery cohort) and the National Alzheimer's Coordinating Center (NACC, n = 508, validation cohort), subdividing individuals with mild cognitive impairment (MCI) into risk groups based on cerebrospinal fluid amyloid-β levels and identifying differential gray matter patterns. We then created models that fused neural networks with survival analysis, trained using non-parcellated T1-weighted brain MRIs from ADNI data, to predict the trajectories of MCI to AD conversion within the NACC cohort (integrated Brier score: 0.192 [discovery], and 0.108 [validation]). Using modern interpretability techniques, we verified that regions important for model prediction are classically associated with AD. We confirmed AD diagnosis labels using postmortem data. We conclude that our framework provides a strategy for risk-based stratification of individuals with MCI and for identifying regions key for disease prognosis.