Home > Publications Database > Disentangling Heterogeneity in Alzheimer's Disease and Related Dementias Using Data-Driven Methods. > print

001     153378
005     20240321221043.0
024 7 _ |a 10.1016/j.biopsych.2020.01.016
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024 7 _ |a 0006-3223
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024 7 _ |a 1873-2402
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037 _ _ |a DZNE-2020-01375
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Habes, Mohamad
|0 P:(DE-HGF)0
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245 _ _ |a Disentangling Heterogeneity in Alzheimer's Disease and Related Dementias Using Data-Driven Methods.
260 _ _ |a Amsterdam [u.a.]
|c 2020
|b Elsevier Science
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520 _ _ |a 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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650 _ 2 |a Alzheimer Disease: diagnostic imaging
|2 MeSH
650 _ 2 |a Alzheimer Disease: pathology
|2 MeSH
650 _ 2 |a Atrophy: pathology
|2 MeSH
650 _ 2 |a Brain: diagnostic imaging
|2 MeSH
650 _ 2 |a Brain: pathology
|2 MeSH
650 _ 2 |a Cognitive Dysfunction: pathology
|2 MeSH
650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a Neuroimaging
|2 MeSH
700 1 _ |a Grothe, Michel J
|0 P:(DE-2719)2810708
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700 1 _ |a Tunc, Birkan
|b 2
700 1 _ |a McMillan, Corey
|b 3
700 1 _ |a Wolk, David A
|b 4
700 1 _ |a Davatzikos, Christos
|b 5
773 _ _ |a 10.1016/j.biopsych.2020.01.016
|g Vol. 88, no. 1, p. 70 - 82
|0 PERI:(DE-600)1499907-9
|n 1
|p 70 - 82
|t Biological psychiatry
|v 88
|y 2020
|x 0006-3223
856 4 _ |u https://pub.dzne.de/record/153378/files/DZNE-2020-01375_Restricted.pdf
856 4 _ |u https://pub.dzne.de/record/153378/files/DZNE-2020-01375_Restricted.pdf?subformat=pdfa
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910 1 _ |a Deutsches Zentrum für Neurodegenerative Erkrankungen
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