Estimating dynamic individual coactivation patterns based on densely sampled resting-state fMRI data and utilizing it for better subject identification

Abstract

Brain states can be characterized by recurring coactivation patterns (CAPs). Traditional CAP analysis is performed at the group-level, while the human brain is individualized and the functional connectome has shown the uniqueness as fingerprint. Whether stable individual CAPs could be obtained from a single fMRI scan and could individual CAPs improve the identification is unclear. An open dataset, the midnight scan club was used in this study to answer these questions. Four CAP states were identified at three distinct levels (group-, subject- and scan-level) separately, and the CAPs were then reconstructed for each scan. Identification rate and differential identifiability were used to evaluate the identification performance. Our results demonstrated that the individual CAPs were unstable when using a single scan. By maintaining high intra-subject similarity and inter-subject differences, subject-level CAPs achieved the best identification performance. Brain regions that contributed to the identifiability were mainly located in higher-order networks (e.g., frontal-parietal network). Besides, head motion reduced the intra-subject similarity, while its impact on identification rate was non-significant. Finally, a pipeline was developed to depict brain-behavior associations in dataset with few samples but dense sampling, and individualized CAP dynamics showed an above-chance level correlation with IQ.

Precision functional magnetic resonance imaging reveals individual brain functional network organization

“…, 而 传统 fMRI 研究或是未采用此方法对头动噪声进 行进一步的控制, 或是虽然运用了此方法但采用 了更加宽松的阈值(一般为删除 FD > 0.5 mm 的图像) (Duda et al, 2021;Fan et al, 2021;Power et al, 2012;Sripada et al, 2020;Tarchi et al, 2022) (Bergmann et al, 2020;Dworetsky, Seitzman, Adeyemo, Smith, et al, 2021;Kong et al, 2019;Seitzman et al, 2019;Yang et al, 2022) 的 (Glasser et al, 2013;Miller et al, 2016)…”