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Cited by 96Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsArtificial Intelligence in Healthcare

Using machine learning to quantify structural MRI neurodegeneration patterns of Alzheimer's disease into dementia score: Independent validation on 8,834 images from ADNI, AIBL, OASIS, and MIRIAD databases

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Popuri, Karteek, Ma, Da, Wang, Lei, Beg, Mirza Faisal·2020·Human Brain Mapping
DOI 10.1002/hbm.25115PMC PMC7469784
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Abstract

Biomarkers for dementia of Alzheimer's type (DAT) are sought to facilitate accurate prediction of the disease onset, ideally predating the onset of cognitive deterioration. T1‐weighted magnetic resonance imaging (MRI) is a commonly used neuroimaging modality for measuring brain structure in vivo, potentially providing information enabling the design of biomarkers for DAT. We propose a novel biomarker using structural MRI volume‐based features to compute a similarity score for the individual's structural patterns relative to those observed in the DAT group. We employed ensemble‐learning framework that combines structural features in most discriminative ROIs to create an aggregate measure of neurodegeneration in the brain. This classifier is trained on 423 stable normal control (NC) and 330 DAT subjects, where clinical diagnosis is likely to have the highest certainty. Independent validation on 8,834 unseen images from ADNI, AIBL, OASIS, and MIRIAD Alzheimer's disease (AD) databases showed promising potential to predict the development of DAT depending on the time‐to‐conversion (TTC). Classification performance on stable versus progressive mild cognitive impairment (MCI) groups achieved an AUC of 0.81 for TTC of 6 months and 0.73 for TTC of up to 7 years, achieving state‐of‐the‐art results. The output score, indicating similarity to patterns seen in DAT, provides an intuitive measure of how closely the individual's brain features resemble the DAT group. This score can be used for assessing the presence of AD structural atrophy patterns in normal aging and MCI stages, as well as monitoring the progression of the individual's brain along with the disease course.

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  • The GLM-based harmonization approach has been shown to be effective in increasing classification power when predicting risk of future dementia onset and differentiating dementia subtypes.

    “This harmonization approach has been shown to be effective in increasing the classification power when predicting the risk of future dementia onset (Popuri et al., 2020) and differentiating dementia subtypes (Ma et al., 2020).”

    From Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI (2024) · cites this paper as Popuri et al., 2020

    The Methods describe GLM-based harmonization, and Results > W-score harmonization reports reduced within-group variation and says signals useful for discrimination are likely enhanced. The paper also reports performance in predicting future Alzheimer's-type dementia, but does not show that harmonization itself increased classification accuracy relative to an unharmonized model. The dementia-subtype claim is attached to a different citation and is not assessed here; page numbers were not provided in our copy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Using machine learning to quantify neurodegeneration patterns in structural MRI, studies have predicted MCI conversion to AD with modest accuracy ranging from 63 to 85% depending on the cohort, imaging modality and models used.

    “Using machine learning to quantify neurodegeneration patterns in structural MRI, studies have managed to predict MCI conversion to AD with modest accuracy ranging from 63 to 85% depending on the cohort, imaging modality and models used [25–27].”

    From The use of neuroimaging techniques in the early and differential diagnosis of dementia (2023) · cites this paper as [26]

    The Results section, Table 6 (page unavailable), compares MRI-based methods for predicting MCI conversion and reports varying performance across features and prediction windows. It includes a balanced accuracy of 0.625 (approximately 63%) for one MRDATS comparison, but does not establish the stated upper endpoint of 85% accuracy. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Popuri, K., Ma, D., Wang, L., & Beg, M. F.
Affiliations
  • Simon Fraser University (CA)
  • Northwestern University (US)
Corresponding author
Beg, Mirza Faisal
ORCID
Venue
Human Brain Mapping
Publisher
Wiley
Published
2020-07-02
Volume
41
Issue
14
Pages
4127-4147
ISSN
  • 1065-9471 (ISSN-L)
  • 1097-0193
DOI
10.1002/hbm.25115
PMC
PMC7469784
OpenAlex
W3040008283
Cited by
96
References
14
Citations by year
2026: 10 · 2025: 21 · 2024: 20 · 2023: 13 · 2022: 17 · 2021: 10 · 2020: 5
FWCI
8.21
Type
journal article
Language
en
Open access
Licence
Locations
  • Human Brain Mapping (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
  • National Institute on Aging
  • Michael Smith Foundation for Health Research
  • Canadian Institutes of Health Research
  • Natural Sciences and Engineering Research Council of Canada
  • Alzheimer Society Research Program (ASRP 19‐09)
  • Compute Canada
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
Alzheimer's disease, structural MRI, neurodegeneration biomarkers, brain atrophy patterns, mild cognitive impairment, ensemble learning, normal aging, disease progression monitoring
MeSH terms
Supervised Machine Learning, Aged, Alzheimer Disease, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Prognosis, Time Factors, Biomarkers, Disease Progression, Neuroimaging, Cognitive Dysfunction, Datasets as Topic

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