Repeatability and reproducibility of brain age estimates in multiple sclerosis for three publicly available models

Q4 Neuroscience

Neuroimage. Reports Pub Date : 2025-03-21 DOI:10.1016/j.ynirp.2025.100252

Lonneke Bos , David R. van Nederpelt , J.H. Cole , E.M.M. Strijbis , B. Moraal , J.P.A. Kuijer , B.M.J. Uitdehaag , F. Barkhof , A.M. Wink , H. Vrenken , B. Jasperse

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Abstract

Accelerated brain aging is a marker of disease-related neurodegeneration in multiple sclerosis (MS). Artificial intelligence models, trained on healthy individuals, can estimate age from brain MRI scans, but the effects of technical variations between MR scanners and conditions on these estimates are currently insufficiently investigated. This study aims to determine the within-scanner repeatability and between-scanner reproducibility of the brain-predicted age difference (brain-PAD) across three brain age models.

30 people with multiple sclerosis and 10 healthy controls (mean age 44.2 ± 11.7 years and 39.2 ± 12.9 years, respectively), underwent six scans in a single day; a scan and immediate on a 3 T GE, 1.5 T Siemens and a 3 T Siemens MRI-scanner. Brain-PAD was determined using brainageR, DeepBrainNet and the MIDI-model from 3D T1w brain MRI-scans. Intraclass correlation coefficient (ICC) was used to quantify absolute agreement within-scanner (ICC-AA) and between-scanner consistency (ICC-C). Variance component analyses were used to determine the standard error of measurement (SEM) and the smallest detectable change (SDC).

Brain-PAD was higher for pwMS compared to HC when predicted with brainageR and DeepBrainNet, not when predicted with the MIDI-model. Within-scanner repeatability was excellent (ICC-AA>0.93) for all models. Between-scanner reproducibility was good to excellent (ICC-C>0.85) for brainageR and the MIDI-model, while DeepBrainNet, showed excellent between-scanner reproducibility for Sola vs. VIDA (ICC-C:0.97), but moderate for GE vs. Sola and for GE vs. Vida (ICC-C:0.63 and 0.61). Between-scanner SDC was 6.56 years for brainageR, 5.57 years for the MIDI-model and 22.65 years for DeepBrainNet.

Our findings demonstrated high repeatability of brain age estimates from the same scanner, but variable reproducibility across different scanners, irrespective of the brain age prediction model.

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三种公开可用模型对多发性硬化症脑年龄估计的可重复性和再现性

加速脑老化是多发性硬化症（MS）疾病相关神经变性的标志。对健康个体进行训练的人工智能模型可以从脑MRI扫描中估计年龄，但磁共振扫描仪和条件之间的技术差异对这些估计的影响目前尚未得到充分研究。本研究旨在确定三种脑年龄模型中脑预测年龄差异（brain- pad）的扫描内可重复性和扫描间可重复性。30名多发性硬化症患者和10名健康对照者（平均年龄分别为44.2±11.7岁和39.2±12.9岁）在一天内进行了6次扫描；立即在3t GE， 1.5 T Siemens和3t Siemens mri扫描仪上进行扫描。使用brainageR， DeepBrainNet和3D T1w脑mri扫描的midi模型确定脑- pad。用类内相关系数（ICC）量化扫描仪内绝对一致性（ICC- aa）和扫描仪间一致性（ICC- c）。方差成分分析用于确定测量的标准误差（SEM）和最小可检测变化（SDC）。用brainageR和DeepBrainNet预测时，pwMS的Brain-PAD比HC高，而用midi模型预测时则没有。所有型号的扫描仪内重复性都很好（ICC-AA>0.93）。brainageR和midi -模型的扫描仪间再现性为良好至优异（ICC-C>0.85），而DeepBrainNet对Sola与VIDA的扫描仪间再现性为优异（ICC-C:0.97），但对GE与Sola和GE与VIDA的扫描仪间再现性为中等（ICC-C:0.63和0.61）。brainageR的扫描间隔SDC为6.56年，MIDI-model为5.57年，DeepBrainNet为22.65年。我们的研究结果表明，从同一台扫描仪估计的脑年龄具有很高的可重复性，但与脑年龄预测模型无关，不同扫描仪的可重复性存在差异。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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