用新方法发现大脑结构复杂度随年龄下降，可预测生物年龄。
Structural Complexity of Brain MRI reveals age-associated patterns
- 提出滑动窗粗粒化法，更稳定地分析三维脑影像多尺度结构
- 在中晚年群体中发现结构复杂度随年龄系统性降低，粗尺度影响最显著
- 适用于脑影像多尺度分析，适合研究衰老与神经退行性疾病
我们将结构复杂度分析拓展至三维信号，重点关注脑部磁共振成像（MRI）。该框架通过逐级增大空间尺度对体数据进行粗粒化，并量化相邻分辨率间的信息损失。传统块状方法在粗尺度下因采样不足易不稳定，我们引入滑动窗口粗粒化方案，实现更平滑的估计和更高的大尺度鲁棒性。利用此改进方法，分析涵盖中年至晚年的大规模结构MRI数据集，发现结构复杂度随年龄系统性下降，且在较粗尺度上效应最强。结果表明，结构复杂度是三维影像多尺度分析的可靠工具，同时在基于脑MRI预测生物年龄方面具有应用价值。
原文摘要 · Abstract (English)
We adapt structural complexity analysis to three-dimensional signals, with an emphasis on brain magnetic resonance imaging (MRI). This framework captures the multiscale organization of volumetric data by coarse-graining the signal at progressively larger spatial scales and quantifying the information lost between successive resolutions. While the traditional block-based approach can become unstable at coarse resolutions due to limited sampling, we introduce a sliding-window coarse-graining scheme that provides smoother estimates and improved robustness at large scales. Using this refined method, we analyze large structural MRI datasets spanning mid- to late adulthood and find that structural complexity decreases systematically with age, with the strongest effects emerging at coarser scales. These findings highlight structural complexity as a reliable signal processing tool for multiscale analysis of 3D imaging data, while also demonstrating its utility in predicting biological age from brain MRI.
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