Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, 710 West 168th Street, 3rd floor, New York, NY 10032, United States.
Mental Health Data Science, New York State Psychiatric Institute, New York, NY, United States; Department of Biostatistics, Mailman School of Public Health, New York, NY, United States; Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States.
Neuroimage. 2023 Aug 15;277:120237. doi: 10.1016/j.neuroimage.2023.120237. Epub 2023 Jun 19.
Recent attention has been given to topological data analysis (TDA), and more specifically persistent homology (PH), to identify the underlying shape of brain network connectivity beyond simple edge pairings by computing connective components across different connectivity thresholds (see Sizemore et al., 2019). In the present study, we applied PH to task-based functional connectivity, computing 0-dimension Betti (B) curves and calculating the area under these curves (AUC); AUC indicates how quickly a single connected component is formed across correlation filtration thresholds, with lower values interpreted as potentially analogous to lower whole-brain system segregation (e.g., Gracia-Tabuenca et al., 2020). One hundred sixty-three participants from the Reference Ability Neural Network (RANN) longitudinal lifespan cohort (age 20-80 years) were tested in-scanner at baseline and five-year follow-up on a battery of tests comprising four domains of cognition (i.e., Stern et al., 2014). We tested for 1.) age-related change in the AUC of the B curve over time, 2.) the predictive utility of AUC in accounting for longitudinal change in behavioral performance and 3.) compared system segregation to the PH approach. Results demonstrated longitudinal age-related decreases in AUC for Fluid Reasoning, with these decreases predicting longitudinal declines in cognition, even after controlling for demographic and brain integrity factors; moreover, change in AUC partially mediated the effect of age on change in cognitive performance. System segregation also significantly decreased with age in three of the four cognitive domains but did not predict change in cognition. These results argue for greater application of TDA to the study of aging.
最近人们对拓扑数据分析（TDA），特别是持久同调（PH）给予了关注，通过在不同连接性阈值下计算连通分量，来识别大脑网络连接的潜在形状，超越简单的边缘配对（参见 Sizemore 等人，2019 年）。在本研究中，我们将 PH 应用于基于任务的功能连接，计算 0 维贝蒂（B）曲线，并计算这些曲线下的面积（AUC）；AUC 表示在相关滤波阈值下单个连通分量形成的速度，较低的值表示可能类似于整个大脑系统的分割程度较低（例如，Gracia-Tabuenca 等人，2020 年）。163 名来自参考能力神经网络（RANN）纵向寿命队列（年龄 20-80 岁）的参与者在基线和五年随访时在扫描仪中接受了一系列认知测试（即 Stern 等人，2014 年）的测试。我们测试了 1.）B 曲线 AUC 随时间的年龄相关变化，2.）AUC 在解释行为表现的纵向变化方面的预测效用，3.）将系统分割与 PH 方法进行比较。结果表明，流体推理的 AUC 随着年龄的纵向下降，即使在控制人口统计学和大脑完整性因素后，这些下降也预测了认知的纵向下降；此外，AUC 的变化部分介导了年龄对认知表现变化的影响。在四个认知领域中的三个领域中，系统分割也随着年龄的增长而显著下降，但不能预测认知的变化。这些结果表明，TDA 在衰老研究中的应用应该更加广泛。
