Centre de recherche CERVO, Québec, Canada.
Département de radiologie et de médecine nucléaire, Faculté de médecine, Université Laval, Québec, Canada.
Hum Brain Mapp. 2021 Feb 15;42(3):690-698. doi: 10.1002/hbm.25253. Epub 2020 Nov 18.
We recently introduced a patch-wise technique to estimate brain age from anatomical T1-weighted magnetic resonance imaging (T1w MRI) data. Here, we sought to assess its longitudinal reliability by leveraging a unique dataset of 99 longitudinal MRI scans from a single, cognitively healthy volunteer acquired over a period of 17 years (aged 29-46 years) at multiple sites. We built a robust patch-wise brain age estimation framework on the basis of 100 cognitively healthy individuals from the MindBoggle dataset (aged 19-61 years) using the Desikan-Killiany-Tourville atlas, then applied the model to the volunteer dataset. The results show a high prediction accuracy on the independent test set (R = .94, mean absolute error of 0.63 years) and no statistically significant difference between manufacturers, suggesting that the patch-wise technique has high reliability and can be used for longitudinal multi-centric studies.
我们最近提出了一种基于补丁的技术，可从解剖 T1 加权磁共振成像 (T1w MRI) 数据估算大脑年龄。在这里，我们利用来自单个认知健康志愿者的独特数据集，该数据集由 99 个纵向 MRI 扫描组成，在多个地点采集，历时 17 年（年龄 29-46 岁），旨在评估其纵向可靠性。我们在 MindBoggle 数据集的 100 名认知健康个体（年龄 19-61 岁）的基础上，使用 Desikan-Killiany-Tourville 图谱构建了一个强大的基于补丁的大脑年龄估算框架，然后将模型应用于志愿者数据集。结果表明，在独立测试集中具有较高的预测准确性（R =.94，平均绝对误差为 0.63 岁），并且与制造商之间没有统计学上的显著差异，这表明基于补丁的技术具有较高的可靠性，可用于纵向多中心研究。
