BACKGROUND AND OBJECTIVES Disentangling brain aging from disease-related neurodegeneration in patients with multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window into this problem but may miss disease-specific effects. In this study, we investigated whether a disease-specific model might complement the brain-age gap (BAG) by capturing aspects unique to MS. METHODS In this retrospective study, we collected 3D T1-weighted brain MRI scans of PwMS to build (1) a cross-sectional multicentric cohort for age and disease duration (DD) modeling and (2) a longitudinal single-center cohort of patients with early MS as a clinical use case. We trained and evaluated a 3D DenseNet architecture to predict DD from minimally preprocessed images while age predictions were obtained with the DeepBrainNet model. The brain-predicted DD gap (the difference between predicted and actual duration) was proposed as a DD-adjusted global measure of MS-specific brain damage. Model predictions were scrutinized to assess the influence of lesions and brain volumes while the DD gap was biologically and clinically validated within a linear model framework assessing its relationship with BAG and physical disability measured with the Expanded Disability Status Scale (EDSS). RESULTS We gathered MRI scans of 4,392 PwMS (69.7% female, age: 42.8 ± 10.6 years, DD: 11.4 ± 9.3 years) from 15 centers while the early MS cohort included 749 sessions from 252 patients (64.7% female, age: 34.5 ± 8.3 years, DD: 0.7 ± 1.2 years). Our model predicted DD better than chance (mean absolute error = 5.63 years, R2 = 0.34) and was nearly orthogonal to the brain-age model (correlation between DD and BAGs: r = 0.06 [0.00-0.13], p = 0.07). Predictions were influenced by distributed variations in brain volume and, unlike brain-predicted age, were sensitive to MS lesions (difference between unfilled and filled scans: 0.55 years [0.51-0.59], p < 0.001). DD gap significantly explained EDSS changes (B = 0.060 [0.038-0.082], p < 0.001), adding to BAG (ΔR2 = 0.012, p < 0.001). Longitudinally, increasing DD gap was associated with greater annualized EDSS change (r = 0.50 [0.39-0.60], p < 0.001), with an incremental contribution in explaining disability worsening compared with changes in BAG alone (ΔR2 = 0.064, p < 0.001). DISCUSSION The brain-predicted DD gap is sensitive to MS-related lesions and brain atrophy, adds to the brain-age paradigm in explaining physical disability both cross-sectionally and longitudinally, and may be used as an MS-specific biomarker of disease severity and progression.

中文翻译：

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使用深度学习将神经退行性变与衰老分开：大脑预测的疾病持续时间差距。


背景和目标 将多发性硬化症 （PwMS） 患者的大脑衰老与疾病相关的神经变分开越来越成为话题。脑龄范式为这个问题提供了一个窗口，但可能会错过疾病特异性影响。在这项研究中，我们调查了疾病特异性模型是否可以通过捕获 MS 特有的方面来补充脑年龄差距 （BAG）。方法 在这项回顾性研究中，我们收集了 PwMS 的 3D T1 加权脑部 MRI 扫描，以构建 （1） 年龄和疾病持续时间 （DD） 建模的横断面多中心队列和 （2） 早期 MS 患者的纵向单中心队列作为临床用例。我们训练和评估了 3D DenseNet 架构，以从最低限度预处理的图像中预测 DD，同时使用 DeepBrainNet 模型获得年龄预测。大脑预测的 DD 差距（预测持续时间和实际持续时间之间的差异）被提议作为 DD 调整的 MS 特异性脑损伤的全局测量方法。仔细检查模型预测以评估病变和脑容量的影响，同时在线性模型框架内对 DD 差距进行生物学和临床验证，评估其与 BAG 的关系，并使用扩展残疾状况量表 （EDSS） 测量身体残疾。结果我们收集了来自 15 个中心的 4,392 名 PwMS （69.7% 女性，年龄：42.8 ± 10.6 岁，DD：11.4 ± 9.3 岁） 的 MRI 扫描，而早期 MS 队列包括来自 252 名患者 （64.7% 女性，年龄：34.5 ± 8.3 岁，DD：0.7 ± 1.2 岁）。我们的模型预测 DD 优于机会 （平均绝对误差 = 5.63 岁，R2 = 0.34），并且与脑龄模型几乎正交 （DD 和 BAGs 之间的相关性： r = 0.06 [0.00-0.13]，p = 0.07）。 预测受脑容量分布变化的影响，并且与大脑预测的年龄不同，对 MS 病变敏感 （未填充和填充扫描之间的差异：0.55 岁 [0.51-0.59]，p < 0.001）。DD 间隙显著解释了 EDSS 变化 （B = 0.060 [0.038-0.082]，p < 0.001），增加了 BAG （ΔR2 = 0.012，p < 0.001）。纵向上，DD 差距的增加与更大的年化 EDSS 变化相关 （r = 0.50 [0.39-0.60]，p < 0.001），与单独 BAG 的变化相比，在解释残疾恶化方面的贡献增加 （ΔR2 = 0.064，p < 0.001）。讨论大脑预测的 DD 差距对 MS 相关病变和脑萎缩敏感，在解释横断面和纵向身体残疾方面增加了脑龄范式，可用作疾病严重程度和进展的 MS 特异性生物标志物。