用AI生成的血容量图增强脑龄预测，提前发现血管老化迹象。
Enhancing Brain Age Estimation with a Multimodal 3D CNN Approach Combining Structural MRI and AI-Synthesized Cerebral Blood Volume Measures
- 融合结构MRI与AI合成血容量图，提升脑龄预测精度。
- 联合模型对健康人预测误差仅3.95年，优于单一模态。
- 能灵敏捕捉早期血管变化，对轻度认知障碍进展有强区分力。
脑龄差（BrainAGE）是神经生物学老化与疾病风险的影像生物标志物，但现有方法主要依赖T1加权结构MRI，忽略了可能早于组织损伤出现的功能性血管变化。通过非对比MRI生成的人工智能脑血容量（AICBV）图可替代增强灌注成像，反映早期神经退行性变的血管信息。本研究构建了一个多模态脑龄框架，使用两个独立的3D VGG网络分别训练：一个基于结构T1w扫描，另一个基于预训练3D块深度学习模型生成的AICBV图。两模型均在13个公开数据集共2,851例扫描上训练验证，并在ADNI队列（n=1,233）中评估与轻度认知障碍（MCI）和阿尔茨海默病（AD）的关联。联合模型在认知正常者中的脑龄差预测误差最低（平均绝对误差MAE=3.95年，R²=0.943），优于仅用T1w（MAE=4.10）或仅用AICBV（MAE=4.49）的模型。显著性图显示：T1w关注白质和皮层萎缩，而AICBV突出血管丰富及脑室周围区域，与正常衰老一致。进一步发现，脑龄差随诊断阶段递增（CN < MCI < AD），并与认知功能下降相关（CDRSB r=0.403；MMSE r=-0.310）。尤其值得注意的是，基于AICBV的脑龄差在稳定型与进展型MCI间差异极显著（p=1.47×10⁻⁸），提示其对前驱期血管改变具有高敏感性。
原文摘要 · Abstract (English)
Brain age gap estimation (BrainAGE) is a promising imaging-derived biomarker of neurobiological aging and disease risk, yet current approaches rely predominantly on T1-weighted structural MRI (T1w), overlooking functional vascular changes that may precede tissue damage and cognitive decline. Artificial intelligence-generated cerebral blood volume (AICBV) maps, synthesized from non-contrast MRI, offer an alternative to contrast-enhanced perfusion imaging by capturing vascular information relevant to early neurodegeneration. We developed a multimodal BrainAGE framework that integrates brain age predictions using linear regression from two separate 3D VGG-based networks, one model trained on only structural T1w scans and one trained on only AICBV maps generated from a pre-trained 3D patch-based deep learning model. Each model was trained and validated on 2,851 scans from 13 open-source datasets and was evaluated for concordance with mild cognitive impairment (MCI) and Alzheimer's disease (AD) using ADNI subjects (n=1,233). The combined model achieved the most accurate brain age gap for cognitively normal (CN) controls, with a mean absolute error (MAE) of 3.95 years ($R^2$=0.943), outperforming models trained on T1w (MAE=4.10) or AICBV alone (MAE=4.49). Saliency maps revealed complementary modality contributions: T1w emphasized white matter and cortical atrophy, while AICBV highlighted vascular-rich and periventricular regions implicated in hypoperfusion and early cerebrovascular dysfunction, consistent with normal aging. Next, we observed that BrainAGE increased stepwise across diagnostic strata (CN < MCI < AD) and correlated with cognitive impairment (CDRSB r=0.403; MMSE r=-0.310). AICBV-based BrainAGE showed particularly strong separation between stable vs. progressive MCI (p=$1.47 \times 10^{-8}$), suggesting sensitivity to prodromal vascular changes that precede overt atrophy.
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