Han Laura K M, Dinga Richard, Leenings Ramona, Hahn Tim, Cole James H, Aftanas Lyubomir I, Amod Alyssa R, Besteher Bianca, Colle Romain, Corruble Emmanuelle, Couvy-Duchesne Baptiste, Danilenko Konstantin V, Fuentes-Claramonte Paola, Gonul Ali Saffet, Gotlib Ian H, Goya-Maldonado Roberto, Groenewold Nynke A, Hamilton Paul, Ichikawa Naho, Ipser Jonathan C, Itai Eri, Koopowitz Sheri-Michelle, Li Meng, Okada Go, Okamoto Yasumasa, Churikova Olga S, Osipov Evgeny A, Penninx Brenda W J H, Pomarol-Clotet Edith, Rodríguez-Cano Elena, Sacchet Matthew D, Shinzato Hotaka, Sim Kang, Stein Dan J, Uyar-Demir Aslihan, Veltman Dick J, Schmaal Lianne
Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia.
Orygen, Parkville, VIC, Australia.
Neuroimage Rep. 2022 Nov 29;2(4):100149. doi: 10.1016/j.ynirp.2022.100149. eCollection 2022 Dec.
Several studies have evaluated whether depressed persons have older appearing brains than their nondepressed peers. However, the estimated neuroimaging-derived "brain age gap" has varied from study to study, likely driven by differences in training and testing sample (size), age range, and used modality/features. To validate our previously developed ENIGMA brain age model and the identified brain age gap, we aim to replicate the presence and effect size estimate previously found in the largest study in depression to date (N = 2126 controls & N = 2675 cases; +1.08 years [SE 0.22], Cohen's d = 0.14, 95% CI: 0.08-0.20), in independent cohorts that were not part of the original study.
A previously trained brain age model (www.photon-ai.com/enigma_brainage) based on 77 FreeSurfer brain regions of interest was used to obtain unbiased brain age predictions in 751 controls and 766 persons with depression (18-75 years) from 13 new cohorts collected from 20 different scanners. Meta-regressions were used to examine potential moderating effects of basic cohort characteristics (e.g., clinical and scan technical) on the brain age gap.
Our ENIGMA MDD brain age model generalized reasonably well to controls from the new cohorts (predicted age vs. age: = 0.73, = 0.47, MAE = 7.50 years), although the performance varied from cohort to cohort. In these new cohorts, on average, depressed persons showed a significantly higher brain age gap of +1 year (SE 0.35) (Cohen's d = 0.15, 95% CI: 0.05-0.25) compared with controls, highly similar to our previous finding. Significant moderating effects of FreeSurfer version 6.0 (d = 0.41, p = 0.007) and Philips scanner vendor (d = 0.50, p < 0.0001) were found, leading to more positive effect size estimates.
This study further validates our previously developed ENIGMA brain age algorithm. Importantly, we replicated the brain age gap in depression with a comparable effect size. Thus, two large-scale independent mega-analyses across in total 32 cohorts and >3400 patients and >2800 controls worldwide show reliable but subtle effects of brain aging in adult depression. Future studies are needed to identify factors that may further explain the brain age gap variance between cohorts.
多项研究评估了抑郁症患者的大脑外观是否比非抑郁症同龄人更显衰老。然而,根据神经影像学估计的“脑龄差距”在不同研究中有所不同,这可能是由训练和测试样本(大小)、年龄范围以及使用的模态/特征的差异所导致的。为了验证我们之前开发的ENIGMA脑龄模型以及所确定的脑龄差距,我们旨在在独立队列中复制此前在抑郁症领域最大规模研究(N = 2126名对照者和N = 2675名病例;+1.08岁[标准误0.22],科恩d值 = 0.14,95%置信区间:0.08 - 0.20)中发现的结果,这些独立队列并非原始研究的一部分。
使用基于77个FreeSurfer脑感兴趣区域的先前训练的脑龄模型(www.photon - ai.com/enigma_brainage),从20台不同扫描仪收集的13个新队列中,对751名对照者和766名抑郁症患者(18 - 75岁)进行无偏脑龄预测。使用元回归分析来检验基本队列特征(如临床和扫描技术)对脑龄差距的潜在调节作用。
我们的ENIGMA重度抑郁症脑龄模型在新队列的对照者中具有较好的泛化能力(预测年龄与实际年龄:R = 0.73,R² = 0.47,平均绝对误差 = 7.50岁),尽管不同队列的表现有所差异。在这些新队列中,与对照者相比,抑郁症患者平均表现出显著更高的脑龄差距,为+1岁(标准误0.35)(科恩d值 = 0.15,95%置信区间:0.05 - 0.25),与我们之前的发现高度相似。发现FreeSurfer 6.0版本(d = 0.41,p = 0.007)和飞利浦扫描仪供应商(d = 0.50,p < 0.0001)具有显著的调节作用,导致效应量估计更积极。
本研究进一步验证了我们之前开发的ENIGMA脑龄算法。重要的是,我们在抑郁症中复制了脑龄差距,且效应量相当。因此,两项跨越全球总共32个队列以及超过3400名患者和超过2800名对照者的大规模独立荟萃分析表明,成人抑郁症中脑老化存在可靠但细微的影响。未来需要开展研究以确定可能进一步解释队列间脑龄差距差异的因素。