ArticleActa neuropathologica communications2022
Interpretable deep learning of myelin histopathology in age-related cognitive impairment.
Article in Acta neuropathologica communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed, 35 citations in OpenAlex.
- Autoencoders for unsupervised analysis of rat myeloarchitecture.Brain structure & function · 2026Article
- Digital neuropathology of neurodegenerative disorders: Foundations, research advances, and future directions.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Review
- Primary age-related tauopathy.Acta neuropathologica · 2025Review
- Age-informed, attention-based weakly supervised learning for neuropathological image assessment.Brain informatics · 2025Article
- Celebrating neuropathology's contributions to Alzheimer's Disease Research Centers.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Comparison of multiple quantitative strategies for neuropathologic image analyses.Journal of neuropathology and experimental neurology · 2025Article
- Article
- A machine learning approach to automate microinfarct and microhemorrhage screening in hematoxylin and eosin-stained human brain tissues.Journal of neuropathology and experimental neurology · 2025Article
- Correlation between mild cognitive impairment and flourishing among Chinese residents: a cross-sectional study.Frontiers in psychology · 2025Article
- SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2024Article
- Disentangling and quantifying the relative cognitive impact of concurrent mixed neurodegenerative pathologies.Acta neuropathologica · 2024Article
- Spatial proteomics of hippocampal subfield-specific pathology in Alzheimer's disease and primary age-related tauopathy.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Histopathologic brain age estimation via multiple instance learning.Acta neuropathologica · 2023Article
- High-throughput digital quantification of Alzheimer disease pathology and associated infrastructure in large autopsy studies.Journal of neuropathology and experimental neurology · 2023Article
- Artificial Intelligence: Deciphering the Links between Psychiatric Disorders and Neurodegenerative Disease.Brain sciences · 2023Article
- The relationship between hippocampal amyloid beta burden and spatial distribution of neurofibrillary degeneration.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
- Artificial Intelligence as a Tool for the Diagnosis and Treatment of Neurodegenerative Diseases.Brain sciences · 2023Article
- The status of digital pathology and associated infrastructure within Alzheimer's Disease Centers.Journal of neuropathology and experimental neurology · 2023Article
- Preanalytic variable effects on segmentation and quantification machine learning algorithms for amyloid-β analyses on digitized human brain slides.Journal of neuropathology and experimental neurology · 2023Article
- Postmortem changes in brain cell structure: a review.Free neuropathology · 2023Article
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Authors and funding
16 authors at 6 institutions in 2 countries.
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Abstract
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.
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