Moon SungHwan, Lee Junhyeok, Lee Won Hee
Department of Software Convergence, Kyung Hee University, Yongin, Republic of Korea.
Department of Software Convergence, Kyung Hee University, Yongin, Republic of Korea.
Comput Biol Med. 2025 Jan;184:109411. doi: 10.1016/j.compbiomed.2024.109411. Epub 2024 Nov 17.
Brain age, an emerging biomarker for brain diseases and aging, is typically predicted using single-modality T1-weighted structural MRI data. This study investigates the benefits of integrating structural MRI with diffusion MRI to enhance brain age prediction. We propose an attention-based deep learning model that fuses global-context information from structural MRI with local details from diffusion metrics. The model was evaluated using two large datasets: the Human Connectome Project (HCP, n = 1064, age 22-37) and the Cambridge Center for Aging and Neuroscience (Cam-CAN, n = 639, age 18-88). It was tested for generalizability and robustness on three independent datasets (n = 546, age 20-86), reproducibility on a test-retest dataset (n = 44, age 22-35), and longitudinal consistency (n = 129, age 46-92). We also examined the relationship between predicted brain age and behavioral measures. Results showed that the multimodal model improved prediction accuracy, achieving mean absolute errors (MAEs) of 2.44 years in the HCP dataset (sagittal plane) and 4.36 years in the Cam-CAN dataset (axial plane). The corresponding R values were 0.258 and 0.914, respectively, reflecting the model's ability to explain variance in the predictions across both datasets. Compared to single-modality models, the multimodal approach showed better generalization, reducing MAEs by 10-76 % and enhancing robustness by 22-82 %. While the multimodal model exhibited superior reproducibility, the sMRI model showed slightly better longitudinal consistency. Importantly, the multimodal model revealed unique associations between predicted brain age and behavioral measures, such as walking endurance and loneliness in the HCP dataset, which were not detected with chronological age alone. In the Cam-CAN dataset, brain age and chronological age exhibited similar correlations with behavioral measures. By integrating sMRI and dMRI through an attention-based model, our proposed approach enhances predictive accuracy and provides deeper insights into the relationship between brain aging and behavior.
脑龄是一种新兴的脑疾病和衰老生物标志物，通常使用单模态T1加权结构MRI数据进行预测。本研究探讨了将结构MRI与扩散MRI相结合以提高脑龄预测的益处。我们提出了一种基于注意力的深度学习模型，该模型将结构MRI的全局上下文信息与扩散指标的局部细节相融合。该模型使用两个大型数据集进行评估：人类连接体计划（HCP，n = 1064，年龄22 - 37岁）和剑桥衰老与神经科学中心（Cam - CAN，n = 639，年龄18 - 88岁）。在三个独立数据集（n = 546，年龄20 - 86岁）上测试了其泛化性和稳健性，在重测数据集（n = 44，年龄22 - 35岁）上测试了其可重复性，并在（n = 129，年龄46 - 92岁）上测试了纵向一致性。我们还研究了预测脑龄与行为指标之间的关系。结果表明，多模态模型提高了预测准确性，在HCP数据集（矢状面）中平均绝对误差（MAE）为2.44岁，在Cam - CAN数据集（横断面）中为4.36岁。相应的R值分别为0.258和0.914，反映了该模型解释两个数据集预测方差的能力。与单模态模型相比，多模态方法表现出更好的泛化性，将MAE降低了10 - 76%，并将稳健性提高了22 - 82%。虽然多模态模型表现出卓越的可重复性，但sMRI模型在纵向一致性方面表现稍好。重要的是，多模态模型揭示了预测脑龄与行为指标之间独特的关联，例如在HCP数据集中的步行耐力和孤独感，这些关联仅按实际年龄无法检测到。在Cam - CAN数据集中，脑龄和实际年龄与行为指标表现出相似的相关性。通过基于注意力的模型整合sMRI和dMRI，我们提出的方法提高了预测准确性，并为脑衰老与行为之间的关系提供了更深入的见解。
