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

Mohamad Habes, Michel J. Grothe, Birkan Tunc, Corey McMillan, David A. Wolk, Christos Davatzikos

Research output: Contribution to journalReview articlepeer-review

6 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)70-82
Number of pages13
JournalBiological Psychiatry
Volume88
Issue number1
DOIs
StatePublished - Jul 1 2020

Keywords

  • Alzheimer's disease
  • Brain aging
  • Clustering
  • Frontotemporal dementia
  • Heterogeneity
  • Lewy body dementias
  • MRI
  • Machine learning
  • Neuroimaging
  • PET

ASJC Scopus subject areas

  • Biological Psychiatry

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