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.

中文翻译：

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使用数据驱动方法解开阿尔茨海默病和相关痴呆症的异质性

大脑衰老是一个复杂的过程，包括萎缩、血管损伤和各种与年龄相关的神经退行性疾病，共同决定了个体认知能力下降的过程。虽然阿尔茨海默病和相关痴呆症导致脑衰老的异质性，但这些疾病本身在临床表现、进展和神经损伤模式方面也具有异质性。我们回顾了利用数据驱动方法来检查阿尔茨海默病和相关痴呆症的异质性的研究，主要关注探索区域神经变性模式亚型的神经影像学研究。在过去的十年中，临床、神经影像学、在大规模观察性队列研究中收集的分子生物标志物信息有助于更深入地了解衰老和阿尔茨海默病及相关痴呆症中疾病表达的变异性。此外，这些大规模数据集的可用性支持了聚类技术的发展和应用，以数据驱动的方式研究疾病异质性。特别是，数据驱动的研究导致了以前未被重视的疾病亚型的新发现，这些亚型的特征是区域神经变性的不同神经影像学模式，同时伴随着病理、临床和分子生物标志物特征的异质性特征。将这些发现纳入新的框架以进行更分化的疾病分层，对于改善预期临床进展的个体化诊断和预后具有广阔的前景，并为开发用于治疗干预的精准医学方法提供了机会。我们总结了与数据驱动的异质性分析相关的主要挑战，并概述了该领域未来发展的途径。