论文标题
大脑的显着性
Brain Structural Saliency Over The Ages
论文作者
论文摘要
大脑年龄（BA）通过深度学习的估计已成为大脑健康的强大而可靠的生物标志物，但是神经网络的黑盒性质并不能轻易地深入了解大脑老龄化的特征。我们训练了重新连接模型作为BA重新制定的T1结构MRI，该结构MRI来自524个个人的小型横截面同类。使用层次相关性传播（LRP）和深度逐步映射技术，我们分析了训练有素的模型，以确定网络脑老化的最相关结构，并在显着映射技术之间进行比较。我们通过衰老过程显示了与不同大脑区域相关性的归因变化。出现了对大脑区域的相关性归因的三方模式。一些区域与年龄相关性（例如，右颞回）的相关性增加；与年龄相关性有所减少（例如，右四个心室）；其他人在各个年龄段都始终如一。我们还研究了大脑年龄间隙（BAG）对大脑体积中相关性分布的影响。希望这些发现将为正常的脑老化提供临床相关的区域轨迹，并提供比较脑老化轨迹的基线。
Brain Age (BA) estimation via Deep Learning has become a strong and reliable bio-marker for brain health, but the black-box nature of Neural Networks does not easily allow insight into the features of brain ageing.We trained a ResNet model as a BA regressor on T1 structural MRI volumes from a small cross-sectional cohort of 524 individuals. Using Layer-wise Relevance Propagation (LRP) and DeepLIFT saliency mapping techniques, we analysed the trained model to determine the most relevant structures for brain ageing for the network, and compare these between the saliency mapping techniques. We show the change in attribution of relevance to different brain regions through the course of ageing. A tripartite pattern of relevance attribution to brain regions emerges. Some regions increase in relevance with age (e.g. the right Transverse Temporal Gyrus); some decrease in relevance with age (e.g. the right Fourth Ventricle); and others are consistently relevant across ages. We also examine the effect of the Brain Age Gap (BAG) on the distribution of relevance within the brain volume. It is hoped that these findings will provide clinically relevant region-wise trajectories for normal brain ageing, and a baseline against which to compare brain ageing trajectories.
