Genius Patricia, Calle M Luz, Rodríguez-Fernández Blanca, Minguillon Carolina, Cacciaglia Raffaele, Garrido-Martin Diego, Esteller Manel, Navarro Arcadi, Gispert Juan Domingo, Vilor-Tejedor Natalia
Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.
Hospital del Mar Research Institute, Barcelona, Spain., Barcelona, Spain.
Alzheimers Dement. 2025 Feb;21(2):e14490. doi: 10.1002/alz.14490. Epub 2025 Jan 27.
Traditional multivariate methods for neuroimaging studies overlook the interdependent relationship between brain features. This study addresses this gap by analyzing relative brain volumetric patterns to capture how Alzheimer's disease (AD) and genetics influence brain structure along the disease continuum.
This study analyzed data from participants across the AD continuum from the Alzheimer's and Families (ALFA) and Alzheimer's Disease Neuroimaging Initiative (ADNI) studies. Compositional data analysis (CoDA) was exploited to examine relative brain volumetric variations that (1) were linked to different AD stages compared to cognitively unimpaired amyloid-β-negative (CU A-) individuals and (2) varied by AD genetic risk.
Disease stage-specific compositional brain scores were identified, differentiating CU A- individuals from those in more advanced stages. Genetic risk-stratified models revealed a broader genetic landscape affecting brain morphology in AD, beyond the well-known apolipoprotein E ε4 allele.
CoDA emerges as an alternative multivariate framework to deepen understanding of AD-related structural changes and support targeted interventions for those at higher genetic risk.
Compositional data analysis (CoDA) revealed the relative variation of brain region volumes, captured in compositional brain scores, capable of discerning between cognitively unimpaired amyloid-β-negative individuals and subjects within other disease-stage groups along the Alzheimer's disease (AD) continuum. CoDA also uncovered the genetic vulnerability of specific brain regions at each stage of the disease along the continuum. CoDA is capable of integrating magnetic resonance imaging data from two different cohorts without stringent requirements for harmonization. This translates as an advantage, compared to traditional methods, and strengthens the reliability of cross-study comparisons by standardizing the data despite different labeling agreements, facilitating collaborative and large-scale research. The algorithm is sensitive to AD-specific effects, as the main compositional brain scores display little overlap with the age-specific compositional brain score. CoDA provides a more accurate analysis of brain imaging data addressing its compositional nature, which can influence the development of targeted approaches, opening new avenues for enhancing brain health.
用于神经影像学研究的传统多变量方法忽略了大脑特征之间的相互依存关系。本研究通过分析相对脑容量模式来填补这一空白，以了解阿尔茨海默病（AD）和基因如何在疾病连续体中影响脑结构。
本研究分析了来自阿尔茨海默病与家族（ALFA）研究和阿尔茨海默病神经影像学倡议（ADNI）研究中AD连续体参与者的数据。利用成分数据分析（CoDA）来检查相对脑容量变化，这些变化（1）与认知未受损的淀粉样β蛋白阴性（CU A-）个体相比，与不同的AD阶段相关；（2）因AD遗传风险而异。
确定了疾病阶段特异性的成分脑分数，将CU A-个体与更晚期阶段的个体区分开来。遗传风险分层模型揭示了除众所周知的载脂蛋白E ε4等位基因外，影响AD脑形态的更广泛的遗传图谱。
CoDA成为一种替代多变量框架，可加深对AD相关结构变化的理解，并为遗传风险较高者提供有针对性的干预措施。
成分数据分析（CoDA）揭示了脑区体积的相对变化，以成分脑分数表示，能够区分认知未受损的淀粉样β蛋白阴性个体与阿尔茨海默病（AD）连续体中其他疾病阶段组的受试者。CoDA还揭示了疾病连续体中每个阶段特定脑区的遗传易感性。CoDA能够整合来自两个不同队列的磁共振成像数据，而无需严格的协调要求。与传统方法相比，这是一个优势，通过标准化数据加强了跨研究比较的可靠性，尽管有不同的标记协议，促进了协作和大规模研究。该算法对AD特异性效应敏感，因为主要的成分脑分数与年龄特异性成分脑分数几乎没有重叠。CoDA针对脑成像数据的成分性质提供了更准确的分析，这可能影响有针对性方法的开发，为促进脑健康开辟新途径。
