Application of deep learning to understand resilience to Alzheimer's disease pathology

• Published in: Brain pathology (Zurich, Switzerland) Switzerland), 2021-11, Vol.31 (6), p.e12974-n/a
• Main Authors: Lee, Cecilia S., Latimer, Caitlin S., Henriksen, Jonathan C., Blazes, Marian, Larson, Eric B., Crane, Paul K., Keene, C. Dirk, Lee, Aaron Y.
• Format: Article
• Language: English
• Subjects: Adult Changes in Thought (ACT); Aged; Aged, 80 and over; Algorithms; Alzheimer Disease - metabolism; Alzheimer Disease - pathology; Alzheimer's disease; Amyloid; Axons; Brain - metabolism; Brain - pathology; Cognition - physiology; Cognitive ability; Cognitive Dysfunction - metabolism; Cognitive Dysfunction - pathology; Deep Learning; Dementia; Dementia disorders; Disease Progression; DNA-Binding Proteins - metabolism; Encephalopathy; Female; Humans; Learning algorithms; Machine learning; Male; Neurodegenerative diseases; Neurofibrillary tangles; Neuroimaging; Neuropathology; Pathology; phosphorylated tau; Phosphorylation; Resilience; resistance; Tau protein; tau Proteins - metabolism; TDP‐43
• ISSN: 1015-6305; 1750-3639
• DOI: 10.1111/bpa.12974

People who have Alzheimer's disease neuropathologic change (ADNC) typically associated with dementia but not the associated cognitive decline can be considered to be “resilient” to the effects of ADNC. We have previously reported lower neocortical levels of hyperphosphorylated tau (pTau) and less limbic‐predominant age‐related TDP‐43 encephalopathy neuropathologic change (LATE‐NC) in the resilient participants compared to those with dementia and similar ADNC as determined by current NIA‐AA recommendations using traditional semi‐quantitative assessments of amyloid β and pathological tau burden. To better understand differences between AD‐dementia and resilient participants, we developed and applied a deep learning approach to analyze the neuropathology of 14 brain donors from the Adult Changes in Thought study, including seven stringently defined resilient participants and seven age‐matched AD‐dementia controls. We created two novel, fully automated deep learning algorithms to quantify the level of phosphorylated TDP‐43 (pTDP‐43) and pTau in whole slide imaging. The models performed better than traditional techniques for quantifying pTDP‐43 and pTau. The second model was able to segment lesions staining for pTau into neurofibrillary tangles (NFTs) and tau neurites (neuronal processes positive for pTau). Both groups had similar quantities of pTau localizing to neurites, but the pTau burden associated with NFTs in the resilient group was significantly lower compared to the group with dementia. These results validate use of deep learning approaches to quantify clinically relevant microscopic characteristics from neuropathology workups. These results also suggest that the burden of NFTs is more strongly associated with cognitive impairment than the more diffuse neuritic tau commonly seen with tangle pathology and suggest that additional factors may underlie resilience mechanisms defined by traditional means. To better understand the neuropathology of individuals with Alzheimer's disease (AD) dementia and "resilient" individuals who have AD pathology but are cognitively normal, we developed deep learning algorithms to quantify the level of phosphorylated TDP‐43 and pTau in whole slide imaging. The models performed better than traditional techniques for quantifying pTDP‐43 and pTau, and the pTau model was able to segment pTau lesions into neurofibrillary tangles (NFTs) and tau neurites (neuronal processes positive for pTau). While both groups had similar quantiti