Zhang Chenxiao, Nan Pengzhi, Song Limei, Wang Yuhao, Su Kaile, Zheng Qiang
School of Computer and Control Engineering, Yantai University, No. 30, Qingquan Road, Laishan District, Yantai City, 264005, Shandong Province, China.
School of Medical Imaging, Shandong Second Medical University, Weifang, 261053, China.
MAGMA. 2025 Sep 18. doi: 10.1007/s10334-025-01294-8.
Brain age estimation plays a significant role in understanding the aging process and its relationship with neurodegenerative diseases. The aim of the study is to devise a unified multi-dimensional feature fusion model (MDFNet) to enhance the brain age estimation solely on structural MRI but with a diverse representation of whole brain, tissue segmentation of gray matter volume, node message passing of brain network, edge-based graph path convolution of brain connectivity, and demographic data.
The MDFNet was developed by devising and integrating a whole-brain-level Euclidean-Convolution channel (WBEC-channel), a tissue-level Euclidean-convolution channel (TEC-channel), a Graph-convolution channel based on node message passing (nodeGCN-channel) and an edge-based graph path convolution channel on brain connectivity (edgeGCN-channel), and a multilayer perceptron (MLP) channel for demographic data (MLP-channel) to enhance the multi-dimensional feature fusion. The MDFNet was validated on 1872 healthy subjects from four public datasets, and applied to an independent cohort of Alzheimer's Disease (AD) patients. The interpretability analysis and normative modeling of the MDFNet in brain age estimation were also performed.
The MDFNet achieved a superior performance of Mean Absolute Error (MAE) of 4.396 ± 0.244 years, a Pearson Correlation Coefficient (PCC) of 0.912 ± 0.002, and a Spearman's Rank Correlation (SRCC) of 0.819 ± 0.015 when comparing with the state-of-the-art deep learning models. The AD group exhibited a significantly greater brain age gap (BAG) than health group (P < 0.05), and the normative modeling also exhibited a significantly higher mean Z-scores of AD patients than healthy subjects (P < 0.05). The interpretability was also visualized at both the group and individual level, enhancing the reliability of the MDFNet.
The MDFNet enhanced the brain age estimation solely on structural MRI by employing a multi-dimensional feature integration strategy.
脑龄估计在理解衰老过程及其与神经退行性疾病的关系中起着重要作用。本研究的目的是设计一种统一的多维度特征融合模型（MDFNet），仅基于结构磁共振成像（MRI）来提高脑龄估计，该模型具有全脑的多样化表示、灰质体积的组织分割、脑网络的节点信息传递、基于脑连接性的边图路径卷积以及人口统计学数据。
通过设计并整合全脑水平的欧几里得卷积通道（WBEC通道）、组织水平的欧几里得卷积通道（TEC通道）、基于节点信息传递的图卷积通道（节点GCN通道）和基于脑连接性的边图路径卷积通道（边GCN通道）以及用于人口统计学数据的多层感知器（MLP）通道（MLP通道）来开发MDFNet，以增强多维度特征融合。MDFNet在来自四个公共数据集的1872名健康受试者上进行了验证，并应用于一个独立的阿尔茨海默病（AD）患者队列。还对MDFNet在脑龄估计中的可解释性分析和规范建模进行了研究。
与最先进的深度学习模型相比，MDFNet实现了卓越的性能，平均绝对误差（MAE）为4.396±0.244岁，皮尔逊相关系数（PCC）为0.912±0.002，斯皮尔曼等级相关（SRCC）为0.819±0.015。AD组的脑龄差距（BAG）显著大于健康组（P<0.05），规范建模也显示AD患者的平均Z分数显著高于健康受试者（P<0.05）。可解释性在组和个体水平上也进行了可视化，提高了MDFNet的可靠性。
MDFNet通过采用多维度特征整合策略，仅基于结构MRI增强了脑龄估计。
