TY - JOUR
T1 - Disentangling Heterogeneity in Alzheimer's Disease and Related Dementias Using Data-Driven Methods
AU - Habes, Mohamad
AU - Grothe, Michel J.
AU - Tunc, Birkan
AU - McMillan, Corey
AU - Wolk, David A.
AU - Davatzikos, Christos
N1 - Funding Information:
This work was supported in part by the Allen H. and Selma W. Berkman Charitable Trust (Accelerating Research on Vascular Dementia) and National Institutes of Health Grant Nos. 1RF1AG054409 (to MH, CD, DW), R01 HL127659-04S1 (to MH), AG057832 (to CM), AG054519 (to CM), AG055005 (to DW, MH, CM), and P30-AG010124 (to DW, MH, CM). MJG is supported by the “Miguel Servet” program [CP19/00031] of the Spanish Instituto de Salud Carlos III (ISCIII-FEDER).
Funding Information:
CM has received compensation for consulting services from Axon Advisors. DAW reports grants from Eli Lilly /Avid, grants from Merck , grants from Biogen , personal fees from GE Healthcare , and personal fees from Neuronix Ltd., outside the current work. All other authors report no biomedical financial interests or potential conflicts of interest.
Publisher Copyright:
© 2020 Society of Biological Psychiatry
PY - 2020/7/1
Y1 - 2020/7/1
N2 - 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.
AB - 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.
KW - Alzheimer's disease
KW - Brain aging
KW - Clustering
KW - Frontotemporal dementia
KW - Heterogeneity
KW - Lewy body dementias
KW - MRI
KW - Machine learning
KW - Neuroimaging
KW - PET
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U2 - 10.1016/j.biopsych.2020.01.016
DO - 10.1016/j.biopsych.2020.01.016
M3 - Review article
C2 - 32201044
AN - SCOPUS:85081950579
VL - 88
SP - 70
EP - 82
JO - Biological Psychiatry
JF - Biological Psychiatry
SN - 0006-3223
IS - 1
ER -