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Interpretable deep learning of myelin histopathology in age-related cognitive impairment

Lookup NU author(s): Professor Johannes Attems



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2022. The Author(s). Age-related cognitive impairment is multifactorial, with numerous underlying and frequently co-morbid pathological correlates. Amyloid beta (Aβ) plays a major role in Alzheimer's type age-related cognitive impairment, in addition to other etiopathologies such as Aβ-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 = 367 with cognitive impairment, n = 349 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.

Publication metadata

Author(s): McKenzie AT, Marx GA, Koenigsberg D, Sawyer M, Iida MA, Walker JM, Richardson TE, Campanella G, Attems J, McKee AC, Stein TD, Fuchs TJ, White CL, Farrell K, Crary JF

Publication type: Article

Publication status: Published

Journal: Acta Neuropathologica Communications

Year: 2022

Volume: 10

Issue: 1

Online publication date: 21/09/2022

Acceptance date: 09/08/2022

Date deposited: 07/10/2022

ISSN (electronic): 2051-5960

Publisher: BioMed Central Ltd


DOI: 10.1186/s40478-022-01425-5

PubMed id: 36127723


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Funder referenceFunder name
P30 AG066514