Review| Volume 88, ISSUE 1, P70-82, July 01, 2020
Disentangling Heterogeneity in Alzheimer's Disease and Related Dementias Using Data-Driven Methods
Mohamad Habes 1
1 MH and MJG contributed equally to this work as joint first authors.
Mohamad Habes
Address correspondence to Mohamad Habes, Ph.D., Center for Biomedical Image Computing Analytics, 3700 Hamilton Walk, 7th Floor, University of Pennsylvania, Philadelphia, PA 19104.
Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania
Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania
Penn Memory Center, Perelman Center for Advanced Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, Texas
Michel J. Grothe 1
Michel J. Grothe
Michel Grothe, Ph.D., German Center for Neurodegenerative Diseases (DZNE), Gehlsheimer Straße 20, 18147 Rostock, Germany.
German Center for Neurodegenerative Diseases, Rostock, Germany
Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain
Wallenberg Center for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
Birkan Tunc
Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
Center for Autism Research, The Children's Hospital of Philadelphia, Pennsylvania
Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Pennsylvania
Corey McMillan
Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
David A. Wolk 2
2 DAW and CD contributed equally to this work as joint senior authors.
David A. Wolk
Christos Davatzikos 2
Christos Davatzikos
Published:January 30, 2020DOI:https://doi.org/10.1016/j.biopsych.2020.01.016
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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 data-driven heterogeneity analyses and outline avenues for future developments in the field.
Lewy body dementias