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.
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.
Biol Psychiatry. 2020 Jul 1;88(1):70-82. doi: 10.1016/j.biopsych.2020.01.016. Epub 2020 Jan 31.
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.
大脑老化是一个复杂的过程，包括萎缩、血管损伤和各种与年龄相关的神经退行性病变，这些共同决定了个体认知能力下降的进程。虽然阿尔茨海默病和相关痴呆症导致了大脑老化的异质性，但这些疾病本身在临床表现、进展和神经损伤模式上也存在异质性。我们回顾了利用数据驱动方法研究阿尔茨海默病和相关痴呆症异质性的研究，主要关注探索区域神经退行性病变模式亚型的神经影像学研究。在过去的十年中，通过在大型观察性队列研究中不断积累的丰富的临床、神经影像学和分子生物标志物信息，人们对阿尔茨海默病和相关痴呆症连续体中疾病表现的可变性有了更深入的了解。此外，这些大型数据集的可用性支持了聚类技术的发展和日益广泛的应用，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新的发现，即以前未被认识到的疾病亚型，其特点是区域神经退行性病变的神经影像学模式不同，与之相对应的是病理、临床和分子生物标志物特征的异质模式。将这些发现纳入更具区分度的疾病分层的新框架，有望改善对预期临床进展的个体化诊断和预后，并为治疗干预的精准医学方法的发展提供机会。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的发展方向。
