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

Andrew T. McKenzie, Gabriel Marx, Daniel Koenigsberg, Mary Sawyer, Megan A. Iida, Jamie M. Walker, Timothy E. Richardson, Gabriele Campanella, Johannes Attems, Ann C. McKee, Thor D. Stein, Thomas J. Fuchs, Charles L. White III, The PART working group, Kurt Farrell, John F. Crary
doi: https://doi.org/10.1101/2022.06.06.495016
Andrew T. McKenzie
1Departments of Pathology, Neuroscience, and Artificial Intelligence & Human Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
2Neuropathology Brain Bank & Research Core, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
3Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
4Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Gabriel Marx
1Departments of Pathology, Neuroscience, and Artificial Intelligence & Human Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
2Neuropathology Brain Bank & Research Core, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
3Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
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Daniel Koenigsberg
1Departments of Pathology, Neuroscience, and Artificial Intelligence & Human Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
2Neuropathology Brain Bank & Research Core, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
3Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
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Mary Sawyer
1Departments of Pathology, Neuroscience, and Artificial Intelligence & Human Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
2Neuropathology Brain Bank & Research Core, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
3Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
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Megan A. Iida
1Departments of Pathology, Neuroscience, and Artificial Intelligence & Human Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
2Neuropathology Brain Bank & Research Core, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
3Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
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Jamie M. Walker
5Department of Pathology, University of Texas Health Science Center, San Antonio, Texas, USA
6Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, Texas, USA
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Timothy E. Richardson
1Departments of Pathology, Neuroscience, and Artificial Intelligence & Human Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
5Department of Pathology, University of Texas Health Science Center, San Antonio, Texas, USA
6Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, Texas, USA
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Gabriele Campanella
1Departments of Pathology, Neuroscience, and Artificial Intelligence & Human Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Johannes Attems
8Translation and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE4 5PL UK
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Ann C. McKee
9Department of Pathology, VA Medical Center & Boston University School of Medicine, Boston, Massachusetts, USA
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Thor D. Stein
9Department of Pathology, VA Medical Center & Boston University School of Medicine, Boston, Massachusetts, USA
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Thomas J. Fuchs
1Departments of Pathology, Neuroscience, and Artificial Intelligence & Human Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Charles L. White III
7Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
1Departments of Pathology, Neuroscience, and Artificial Intelligence & Human Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Kurt Farrell
1Departments of Pathology, Neuroscience, and Artificial Intelligence & Human Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
2Neuropathology Brain Bank & Research Core, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
3Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
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  • For correspondence: john.crary@mountsinai.org Kurt.farrell@mssm.edu
John F. Crary
1Departments of Pathology, Neuroscience, and Artificial Intelligence & Human Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
2Neuropathology Brain Bank & Research Core, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
3Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
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  • For correspondence: john.crary@mountsinai.org Kurt.farrell@mssm.edu
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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.

Competing Interest Statement

The authors have declared no competing interest.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Interpretable deep learning of myelin histopathology in age-related cognitive impairment
Andrew T. McKenzie, Gabriel Marx, Daniel Koenigsberg, Mary Sawyer, Megan A. Iida, Jamie M. Walker, Timothy E. Richardson, Gabriele Campanella, Johannes Attems, Ann C. McKee, Thor D. Stein, Thomas J. Fuchs, Charles L. White III, The PART working group, Kurt Farrell, John F. Crary
bioRxiv 2022.06.06.495016; doi: https://doi.org/10.1101/2022.06.06.495016
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Interpretable deep learning of myelin histopathology in age-related cognitive impairment
Andrew T. McKenzie, Gabriel Marx, Daniel Koenigsberg, Mary Sawyer, Megan A. Iida, Jamie M. Walker, Timothy E. Richardson, Gabriele Campanella, Johannes Attems, Ann C. McKee, Thor D. Stein, Thomas J. Fuchs, Charles L. White III, The PART working group, Kurt Farrell, John F. Crary
bioRxiv 2022.06.06.495016; doi: https://doi.org/10.1101/2022.06.06.495016

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