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

Habes, M. (author)
Grothe, Michel J., 1981 (author)
Gothenburg University,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi,Wallenberg Centre for Molecular and Translational Medicine,Institute of Neuroscience and Physiology
Tunc, B. (author)
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McMillan, C. (author)
Wolk, D. A. (author)
Davatzikos, C. (author)
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 (creator_code:org_t)
Elsevier BV, 2020
2020
English.
In: Biological Psychiatry. - : Elsevier BV. - 0006-3223. ; 88:1, s. 70-82
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • 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 datadriven heterogeneity analyses and outline avenues for future developments in the field.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Psykiatri (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Psychiatry (hsv//eng)

Keyword

Alzheimer's disease
Brain aging
Clustering
Frontotemporal dementia
Heterogeneity
Lewy body dementias
Machine learning
MRI
Neuroimaging
PET
mild cognitive impairment
parkinsons-disease
lewy bodies
behavioral
variant
frontotemporal dementia
brain atrophy
anatomical subtypes
cerebrospinal-fluid
distinct subtypes
defined subtypes
Neurosciences & Neurology
Psychiatry

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By the author/editor
Habes, M.
Grothe, Michel J ...
Tunc, B.
McMillan, C.
Wolk, D. A.
Davatzikos, C.
About the subject
MEDICAL AND HEALTH SCIENCES
MEDICAL AND HEAL ...
and Clinical Medicin ...
and Psychiatry
Articles in the publication
Biological Psych ...
By the university
University of Gothenburg

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