Bishal Rabindev, Cherodath Sarika, Singh Nandini Chatterjee, Gupte Neelima
Department of Physics, Indian Institute of Technology Madras, Chennai, India.
National Center for Brain Research, Gurgaon, India.
Front Netw Physiol. 2022 Aug 23;2:924446. doi: 10.3389/fnetp.2022.924446. eCollection 2022.
The topological analysis of fMRI time series data has recently been used to characterize the identification of patterns of brain activity seen during specific tasks carried out under experimentally controlled conditions. This study uses the methods of algebraic topology to characterize time series networks constructed from fMRI data measured for adult and children populations carrying out differentiated reading tasks. Our pilot study shows that our methods turn out to be capable of identifying distinct differences between the activity of adult and children populations carrying out identical reading tasks. We also see differences between activity patterns seen when subjects recognize word and nonword patterns. The results generalize across different populations, different languages and different active and inactive brain regions.
功能磁共振成像（fMRI）时间序列数据的拓扑分析最近已被用于描述在实验控制条件下执行特定任务时所观察到的大脑活动模式的识别特征。本研究使用代数拓扑方法来描述从执行不同阅读任务的成人和儿童群体所测量的fMRI数据构建的时间序列网络。我们的初步研究表明，我们的方法能够识别执行相同阅读任务的成人和儿童群体活动之间的明显差异。我们还观察到受试者识别单词和非单词模式时所看到的活动模式之间的差异。这些结果在不同人群、不同语言以及不同的活跃和不活跃脑区中具有普遍性。
