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Cited by 167Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsFunctional Brain Connectivity Studies

Disentangling Heterogeneity in Alzheimer’s Disease and Related Dementias Using Data-Driven Methods

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Habes, Mohamad, Grothe, Michel J., Tunc, Birkan, McMillan, Corey, Wolk, David A., Davatzikos, Christos·2020·Biological Psychiatry
DOI 10.1016/j.biopsych.2020.01.016PMC PMC7305953
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Abstract

Brain aging is a complex process that includes atrophy, vascular injury, and a variety of age-associated neurodegenerative pathologies, together determining an individual's course of cognitive decline. While Alzheimer's disease and related dementias contribute to the heterogeneity of brain aging, these conditions themselves are also heterogeneous in their clinical presentation, progression, and pattern of neural injury. We reviewed studies that leveraged data-driven approaches to examining heterogeneity in Alzheimer's disease and related dementias, with a principal focus on neuroimaging studies exploring subtypes of regional neurodegeneration patterns. Over the past decade, the steadily increasing wealth of clinical, neuroimaging, and molecular biomarker information collected within large-scale observational cohort studies has allowed for a richer understanding of the variability of disease expression within the aging and Alzheimer's disease and related dementias continuum. Moreover, the availability of these large-scale datasets has supported the development and increasing application of clustering techniques for studying disease heterogeneity in a data-driven manner. In particular, data-driven studies have led to new discoveries of previously unappreciated disease subtypes characterized by distinct neuroimaging patterns of regional neurodegeneration, which are paralleled by heterogeneous profiles of pathological, clinical, and molecular biomarker characteristics. Incorporating these findings into novel frameworks for more differentiated disease stratification holds great promise for improving individualized diagnosis and prognosis of expected clinical progression, and provides opportunities for development of precision medicine approaches for therapeutic intervention. We conclude with an account of the principal challenges associated with data-driven heterogeneity analyses and outline avenues for future developments in the field.

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  • Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies.

    “Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies”

    From AI-based differential diagnosis of dementia etiologies on multimodal data (2024) · cites this paper as [5]

    The Alzheimer’s disease dementia section describes atypical clinical presentations, and the Mild cognitive impairment section says similar clinical impairment can precede different dementia types. These passages support diagnostic complexity, but they do not specifically say that differential diagnosis of dementia is difficult because symptoms overlap across etiologies. No page numbers are available in our copy. (LLM, conf=0.76)

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Authors
Habes, M., Grothe, M. J., Tunc, B., McMillan, C., Wolk, D. A., & Davatzikos, C.
Affiliations
  • The University of Texas at San Antonio Health Science Center (US)
  • German Center for Neurodegenerative Diseases (DE)
  • University of Pennsylvania (US)
Corresponding author
  • Habes, Mohamad
  • Grothe, Michel J.
ORCID
Venue
Biological Psychiatry
Publisher
Elsevier BV
Published
2020-07
Volume
88
Issue
1
Pages
70-82
ISSN
0006-3223 (ISSN-L)
DOI
10.1016/j.biopsych.2020.01.016
PMC
PMC7305953
OpenAlex
W3004274611
Cited by
167
References
10
Citations by year
2026: 20 · 2025: 29 · 2024: 40 · 2023: 22 · 2022: 30 · 2021: 16 · 2020: 9 · 2019: 1
FWCI
3.19
Type
journal article
Language
en
Open access
Licence
Locations
  • Biological Psychiatry (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • RISalud Institutional Health Repository of Andalucía (submittedVersion, other-oa)
  • DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion)
  • DZNE Pub (submittedVersion)
Funders
  • Biogen
  • Eli Lilly and Company
  • National Institutes of Health (R01 HL127659-04S1, AG054519, 1RF1AG054409, P30-AG010124, AG057832, CP19/00031, AG055005)
  • Merck
  • Instituto de Salud Carlos III
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords
Alzheimer's disease, related dementias, disease heterogeneity, disease subtypes, brain aging, clustering algorithms, neuroimaging patterns, biomarkers, disease stratification, precision medicine, cognitive decline
MeSH terms
Alzheimer Disease, Atrophy, Brain, Humans, Neuroimaging, Cognitive Dysfunction

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