用拓扑方法分析脑影像，区分正常与病理性衰老。
sMRI-based Brain Age Estimation in MCI using Persistent Homology
- 通过贝蒂曲线捕捉脑结构的拓扑特征用于年龄预测。
- 在ADNI数据集上100例样本中准确区分健康与病态老化。
- 适合神经退行性疾病早期预警研究者使用。
本研究提出利用持久同调中的贝蒂曲线进行基于sMRI的脑龄估计，以区分健康与病理衰老。方法应用于公开ADNI数据集中的100例结构性MRI扫描。结果表明，一维（连通成分）和二维（一维洞）贝蒂曲线特征能有效捕捉与衰老相关的脑结构变化。临床特征根据其与预测脑龄及实际年龄的相关性分为三类。该方法成功实现正常与病态衰老的区分，为理解结构脑改变与认知障碍的关系提供了新框架，有望发展为认知衰退早期检测与监测的潜在生物标志物。
原文摘要 · Abstract (English)
In this study, we propose the use of persistent homology -- specifically Betti curves for brain age prediction and for distinguishing between healthy and pathological aging. The proposed framework is applied to 100 structural MRI scans from the publicly available ADNI dataset. Our results indicate that Betti curve features, particularly those from dimension-1 (connected components) and dimension-2 (1D holes), effectively capture structural brain alterations associated with aging. Furthermore, clinical features are grouped into three categories based on their correlation, or lack thereof, with (i) predicted brain age and (ii) chronological age. The findings demonstrate that this approach successfully differentiates normal from pathological aging and provides a novel framework for understanding how structural brain changes relate to cognitive impairment. The proposed method serves as a foundation for developing potential biomarkers for early detection and monitoring of cognitive decline.
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