脑白质连接的形状能预测认知能力，比传统指标更有效。
The shape of the brain's connections is predictive of cognitive performance: an explainable machine learning study
- 用15种形状特征分析脑白质纤维簇，发现其形状可预测认知表现。
- 形状特征中‘不规则度’表现最佳，效果优于微结构和连接性指标。
- 关键纤维簇分布广泛，涵盖浅层、深层及小脑等多类通路，适合神经科学与临床研究。
脑白质连接的形状在扩散MRI追踪分析中尚属未充分探索领域。尽管已知纤维形状随人群和生命周期变化，但其与个体功能差异的关系仍不明确。本研究利用纤维簇形状测量值预测个体认知表现。基于HCP-YA大规模数据集，采用基于图谱的纤维簇分割方法，为每个个体的纤维簇计算15项形状、微结构与连接特征。以这些特征为输入，训练210个模型预测7项NIH Toolbox认知评估。通过可解释AI技术SHAP分析各纤维簇重要性。结果表明，形状特征可有效预测认知能力，其中不规则度（描述纤维簇与理想圆柱体的偏离程度）表现最优，效果与微结构及连接性特征相当。SHAP分析显示，高预测力的纤维簇遍布全脑，包括浅层联合、深层联合、小脑、纹状体及投射通路。该研究证明形状描述符对理解白质与认知功能关系具有巨大潜力。
原文摘要 · Abstract (English)
The shape of the brain's white matter connections is relatively unexplored in diffusion MRI tractography analysis. While it is known that tract shape varies in populations and across the human lifespan, it is unknown if the variability in dMRI tractography-derived shape may relate to the brain's functional variability across individuals. This work explores the potential of leveraging tractography fiber cluster shape measures to predict subject-specific cognitive performance. We implement machine learning models to predict individual cognitive performance scores. We study a large-scale database from the HCP-YA study. We apply an atlas-based fiber cluster parcellation to the dMRI tractography of each individual. We compute 15 shape, microstructure, and connectivity features for each fiber cluster. Using these features as input, we train a total of 210 models to predict 7 different NIH Toolbox cognitive performance assessments. We apply an explainable AI technique, SHAP, to assess the importance of each fiber cluster for prediction. Our results demonstrate that shape measures are predictive of individual cognitive performance. The studied shape measures, such as irregularity, diameter, total surface area, volume, and branch volume, are as effective for prediction as microstructure and connectivity measures. The overall best-performing feature is a shape feature, irregularity, which describes how different a cluster's shape is from an idealized cylinder. Further interpretation using SHAP values suggest that fiber clusters with features highly predictive of cognitive ability are widespread throughout the brain, including fiber clusters from the superficial association, deep association, cerebellar, striatal, and projection pathways. This study demonstrates the strong potential of shape descriptors to enhance the study of the brain's white matter and its relationship to cognitive function.
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