Fürtjes Anna E, Foote Isabelle F, Xia Charley, Davies Gail, Moodie Joanna, Taylor Adele, Liewald David C, Redmond Paul, Corley Janie, McIntosh Andrew M, Whalley Heather C, Maniega Susana Muñoz, Hernández Maria Valdés, Backhouse Ellen, Ferguson Karen, Bastin Mark E, Wardlaw Joanna, de la Fuente Javier, Grotzinger Andrew D, Luciano Michelle, Hill W David, Deary Ian J, Tucker-Drob Elliot M, Cox Simon R
School of Philosophy, Psychology & Language Sciences, The University of Edinburgh, Edinburgh, United Kingdom.
Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, Edinburgh, United Kingdom.
bioRxiv. 2024 Nov 7:2024.11.06.622274. doi: 10.1101/2024.11.06.622274.
A measure of lifetime brain atrophy (LBA) obtained from a single magnetic resonance imaging (MRI) scan could be an attractive candidate to boost statistical power in uncovering novel genetic signals and mechanisms of neurodegeneration. We analysed data from five young and old adult cohorts (MRi-Share, Human Connectome Project, UK Biobank, Generation Scotland Subsample, and Lothian Birth Cohort 1936 [LBC1936]) to test the validity and utility of LBA inferred from cross-sectional MRI data, i.e., a single MRI scan per participant. LBA was simply calculated based on the relationship between total brain volume (TBV) and intracranial volume (ICV), using three computationally distinct approaches: the difference (), ratio (/), and regression-residual method (TBV~ICV). LBA derived with all three methods were substantially correlated with well-validated neuroradiological atrophy rating scales ( = 0.37-0.44). Compared with the difference or ratio method, LBA computed with the residual method most strongly captured phenotypic variance associated with cognitive decline ( = 0.36), frailty ( = 0.24), age-moderated brain shrinkage ( = 0.45), and longitudinally-measured atrophic changes ( = 0.36). LBA computed using a difference score was strongly correlated with baseline (i.e., ICV; = 0.81) and yielded GWAS signal similar to ICV ( = 0.75). We performed the largest genetic study of LBA to date ( = 43,110), which was highly heritable ( SNP GCTA = 41% [95% CI = 38-43%]) and had strong polygenic signal (LDSC = 26%; mean = 1.23). The strongest association in our genome-wide association study (GWAS) implicated , a gene previously linked with neurodegenerative diseases such as Alzheimer, and Parkinson disease, and amyotrophic lateral sclerosis. This study is the first side-by-side evaluation of different computational approaches to estimate lifetime brain changes and their measurement characteristics. Careful assessment of methods for LBA computation had important implications for the interpretation of existing phenotypic and genetic results, and showed that relying on the residual method to estimate LBA from a single MRI scan captured brain shrinkage rather than current brain size. This makes this computationally-simple definition of LBA a strong candidate for more powerful analyses, promising accelerated genetic discoveries by maximising the use of available cross-sectional data.
通过单次磁共振成像（MRI）扫描获得的终生脑萎缩（LBA）指标，可能是提高发现新的遗传信号和神经退行性变机制统计功效的一个有吸引力的候选指标。我们分析了来自五个年轻和老年成人队列（MRi-Share、人类连接组计划、英国生物银行、苏格兰一代子样本和1936年洛锡安出生队列[LBC1936]）的数据，以测试从横断面MRI数据（即每位参与者一次MRI扫描）推断出的LBA的有效性和实用性。LBA是根据全脑体积（TBV）和颅内体积（ICV）之间的关系简单计算得出的，使用了三种计算方式不同的方法：差值法（）、比值法（/）和回归残差法（TBV~ICV）。用这三种方法得出的LBA与经过充分验证的神经放射学萎缩评级量表（=0.37 - 0.44）显著相关。与差值法或比值法相比，用残差法计算的LBA最能强烈捕捉与认知衰退（=0.36）、虚弱（=0.24）、年龄相关的脑萎缩（=0.45）以及纵向测量的萎缩变化（=0.36）相关的表型变异。用差值分数计算的LBA与基线（即ICV；=0.81）高度相关，并且产生了与ICV相似的全基因组关联研究（GWAS）信号（=0.75）。我们进行了迄今为止最大规模的LBA基因研究（=43,110），该研究具有高度遗传性（单核苷酸多态性基因组最佳线性无偏预测=41%[95%可信区间=38 - 43%]）且具有强烈的多基因信号（连锁不平衡分数检验=26%；平均值=1.23）。我们的全基因组关联研究（GWAS）中最强的关联涉及，一个先前与阿尔茨海默病、帕金森病和肌萎缩侧索硬化等神经退行性疾病相关的基因。这项研究是对估计终生脑变化的不同计算方法及其测量特征的首次并列评估。对LBA计算方法的仔细评估对解释现有的表型和基因结果具有重要意义，并表明依靠残差法从单次MRI扫描估计LBA捕捉的是脑萎缩而不是当前脑大小。这使得这种计算简单的LBA定义成为进行更强大分析的有力候选指标，有望通过最大限度地利用可用横断面数据加速基因发现。
