Abstract
As a complex dynamic system, the brain exhibits spatially organized recurring patterns of activity over time. Coactivation patterns (CAPs), which analyzes data from each single frame, have been utilized to detect transient brain activity states recently. However, previous CAP analyses have been conducted at the group level, which might neglect meaningful individual differences. Here, we estimated individual CAP states at both subject- and scan-level based on a densely sampled dataset: Midnight Scan Club. We used differential identifiability, which measures the gap between intra- and inter-subject similarity, to evaluate individual differences. We found individual CAPs at the subject-level achieved the best fingerprinting ability by maintaining high intra-subject similarity and enlarging inter-subject differences, and brain regions of association networks mainly contributed to the identifiability. On the other hand, scan-level CAP states were unstable across scans for the same participant. Expectedly, we found subject-specific CAPs became more reliable and discriminative with more data (i.e., longer duration). As the acquisition time of each participant is limited in practice, our results recommend a data collection strategy that collects more scans with appropriate duration (e.g., 12 ~ 15 min/scan) to obtain more reliable subject-specific CAPs, when total acquisition time is fixed (e.g., 150 min). In summary, this work has constructed reliable subject-specific CAP states with meaningful individual differences, and recommended an appropriate data collection strategy, which can guide subsequent investigations into individualized brain dynamics.
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Data availability
The preprocessed Midnight Scan Club (MSC) data used in this study are available in the OpenfMRI data repository at https://openneuro.org/datasets/ds000224. The code used for CAP state can be found in https://github.com/davidyoung1994/CoactivationPattern.
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Acknowledgements
We thank Evan M. Gordon and his team for collecting and sharing the midnight scan club data. This work was supported by the National Natural Science Foundation of China (NSFC) grant (No. 61871420 and 62071109).
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This work was supported by the National Natural Science Foundation of China (NSFC) grant (No. 61871420 and 62071109).
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HY: conceptualization, methodology, software, data analysis, writing original draft, revision, and editing. XY: data analysis, and reviewing. HZ: data analysis, and reviewing. CM: reviewing, methodology and editing. BB: conceptualization, methodology, software, revision, and editing.
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Yang, H., Yao, X., Zhang, H. et al. Estimating dynamic individual coactivation patterns based on densely sampled resting-state fMRI data and utilizing it for better subject identification. Brain Struct Funct 228, 1755–1769 (2023). https://doi.org/10.1007/s00429-023-02689-w
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DOI: https://doi.org/10.1007/s00429-023-02689-w