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

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.

Keywords: Alzheimer’s disease; Brain aging; Clustering; Frontotemporal dementia; Heterogeneity; Lewy body dementias; MRI; Machine learning; Neuroimaging; PET.

Figure 1:

Resemblance between gray matter loss associated with the neuropathological subtypes of differentially distributed neurofibrillary tangles (56) and data-driven subtypes captured with clustering MRI measures (33)

Figure 2:

Over the past decade data-driven clustering approaches have helped in making new discoveries of previously unappreciated subtypes for the ADRD conditions, offering novel frameworks for improving individualized diagnosis and prognosis. Often these approaches made use of either simplistic metrics from multiple modalities or high-dimensional data from one single modality. As more phenotypic information is being collected within large-scale observational cohort studies together with improvements in computational power and algorithmic solutions, future work will incorporate combination of high-dimensional information from multiple modalities (i.e., clinical, pathological, and imaging) that may achieve a more comprehensive definition of distinct disease subtypes in AD and related dementia. Towards that standardization in data acquisition and harmonization between various cohorts is key element for future success for such frameworks implementing data synthesis across the different domains.