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Amyloid beta (A\u03b2) plays a major role in Alzheimer\u2019s type age-related cognitive impairment, in addition to other etiopathologies such as A\u03b2-independent hyperphosphorylated tau, cerebrovascular disease, and myelin damage, which also warrant further investigation. Classical methods, even in the setting of the gold standard of postmortem brain assessment, involve semi-quantitative ordinal staging systems that often correlate poorly with clinical outcomes, due to imperfect cognitive measurements and preconceived notions regarding the neuropathologic features that should be chosen for study. Improved approaches are needed to identify histopathological changes correlated with cognition in an unbiased way. We used a weakly supervised multiple instance learning algorithm on whole slide images of human brain autopsy tissue sections from a group of elderly donors to predict the presence or absence of cognitive impairment (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009367 with cognitive impairment,\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009349 without). Attention analysis allowed us to pinpoint the underlying subregional architecture and cellular features that the models used for the prediction in both brain regions studied, the medial temporal lobe and frontal cortex. Despite noisy labels of cognition, our trained models were able to predict the presence of cognitive impairment with a modest accuracy that was significantly greater than chance. Attention-based interpretation studies of the features most associated with cognitive impairment in the top performing models suggest that they identified myelin pallor in the white matter. Our results demonstrate a scalable platform with interpretable deep learning to identify unexpected aspects of pathology in cognitive impairment that can be translated to the study of other neurobiological disorders.\n                  <\/jats:p>","DOI":"10.1186\/s40478-022-01425-5","type":"journal-article","created":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T19:03:00Z","timestamp":1663700580000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Interpretable deep learning of myelin histopathology in age-related cognitive impairment"],"prefix":"10.1186","volume":"10","author":[{"given":"Andrew T.","family":"McKenzie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriel 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