Romme Catharina J A, Stanley Emma A M, Mouches Pauline, Wilms Matthias, Pike G Bruce, Metz Luanne M, Forkert Nils D
Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Front Neurol. 2024 Oct 10;15:1423485. doi: 10.3389/fneur.2024.1423485. eCollection 2024.
The rate of neurodegeneration in multiple sclerosis (MS) is an important biomarker for disease progression but can be challenging to quantify. The brain age gap, which quantifies the difference between a patient's chronological and their estimated biological brain age, might be a valuable biomarker of neurodegeneration in patients with MS. Thus, the aim of this study was to investigate the value of an image-based prediction of the brain age gap using a deep learning model and compare brain age gap values between healthy individuals and patients with MS.
A multi-center dataset consisting of 5,294 T1-weighted magnetic resonance images of the brain from healthy individuals aged between 19 and 89 years was used to train a convolutional neural network (CNN) for biological brain age prediction. The trained model was then used to calculate the brain age gap in 195 patients with relapsing remitting MS (20-60 years). Additionally, saliency maps were generated for healthy subjects and patients with MS to identify brain regions that were deemed important for the brain age prediction task by the CNN.
Overall, the application of the CNN revealed accelerated brain aging with a larger brain age gap for patients with MS with a mean of 6.98 ± 7.18 years in comparison to healthy test set subjects (0.23 ± 4.64 years). The brain age gap for MS patients was weakly to moderately correlated with age at disease onset (ρ = -0.299, < 0.0001), EDSS score (ρ = 0.206, = 0.004), disease duration (ρ = 0.162, = 0.024), lesion volume (ρ = 0.630, < 0.0001), and brain parenchymal fraction (ρ = -0.718, < 0.0001). The saliency maps indicated significant differences in the lateral ventricle ( < 0.0001), insula ( < 0.0001), third ventricle ( < 0.0001), and fourth ventricle ( = 0.0001) in the right hemisphere. In the left hemisphere, the inferior lateral ventricle ( < 0.0001) and the third ventricle ( < 0.0001) showed significant differences. Furthermore, the Dice similarity coefficient showed the highest overlap of salient regions between the MS patients and the oldest healthy subjects, indicating that neurodegeneration is accelerated in this patient cohort.
In conclusion, the results of this study show that the brain age gap is a valuable surrogate biomarker to measure disease progression in patients with multiple sclerosis.
多发性硬化症（MS）中的神经退行性变率是疾病进展的重要生物标志物，但量化可能具有挑战性。脑年龄差距量化了患者实际年龄与估计的生物学脑年龄之间的差异，可能是MS患者神经退行性变的有价值生物标志物。因此，本研究的目的是使用深度学习模型研究基于图像预测脑年龄差距的价值，并比较健康个体和MS患者的脑年龄差距值。
使用一个多中心数据集，该数据集由5294例年龄在19至89岁之间的健康个体的脑部T1加权磁共振图像组成，用于训练用于生物脑年龄预测的卷积神经网络（CNN）。然后使用训练好的模型计算195例复发缓解型MS患者（20 - 60岁）的脑年龄差距。此外，还为健康受试者和MS患者生成了显著性图，以识别CNN认为对脑年龄预测任务重要的脑区。
总体而言，CNN的应用显示MS患者脑老化加速，脑年龄差距更大，平均为6.98±7.18岁，而健康测试组受试者为0.23±4.64岁。MS患者的脑年龄差距与疾病发病年龄（ρ = -0.299，<0.0001）、扩展残疾状态量表（EDSS）评分（ρ = 0.206，=0.004）、疾病持续时间（ρ = 0.162，=0.024）、病变体积（ρ = 0.630，<0.0001）和脑实质分数（ρ = -0.718，<0.0001）呈弱至中度相关。显著性图表明右半球的侧脑室（<0.0001）、岛叶（<0.0001）、第三脑室（<0.0001）和第四脑室（=0.0001）存在显著差异。在左半球，下外侧脑室（<0.0001）和第三脑室（<0.0001）显示出显著差异。此外，骰子相似系数显示MS患者与年龄最大的健康受试者之间显著区域的重叠最高，表明该患者队列中的神经退行性变加速。
总之，本研究结果表明，脑年龄差距是测量多发性硬化症患者疾病进展的有价值替代生物标志物。
