Metastability of resting-state bold fMRI as a reliable biomarker of individual brain dynamics: An interrogation of within-subject variability as a function of total acquisition time.
Hiba Sheheitli, Robert Hermosillo, Gracie Grimsrud, Thomas Madison, Oscar Miranda Dominguez, Steven Nelson, Damien Fair, Ziad Nahas
Network neuroscience (Cambridge, Mass.) January 1, 2026 DOI: 10.1162/netn.a.537 via PubMed
Summary
AI-generated from the abstractMetastability of BOLD fMRI signals, a proxy for brain dynamics, shows within-subject reliability comparable to static functional connectivity when enough data are used, but the amount needed varies across brain networks. Combining network-specific metastability metrics into a single feature vector improves reliability by an order of magnitude. This finding was reproduced in the Midnight Scan Club dataset (10 subjects over 10 days). The measure also proved sensitive to change in brain dynamics under psilocybin. The authors conclude the combined feature vector is a promising candidate for individual-specific biomarkers and precision neuromodulation targets.
Study at a glance
| Characteristics | Observational cohort Peer reviewed |
|---|---|
| Population | Highly sampled individual (84 sessions over 18 months); Midnight Scan Club dataset (10 subjects over 10 consecutive days); another dataset examining psilocybin effects |
| Keywords | Brain dynamics biomarker Dynamic functional connectivity Metastability Phase synchrony Precision functional mapping |
| Key finding | Combining network-specific metastability metrics into a single feature vector yields an order of magnitude improvement in within-subject reliability compared to individual network metrics. |
Abstract
Metastability of BOLD fMRI signals is a commonly used proxy of brain dynamics in behavioral and clinical studies. To date, little has been done to assess the confidence with which we can use estimates of metastability as reliable biomarkers of individual brain state. We analyze whole-brain and network-specific metastability for a highly sampled individual brain (84 sessions taken over 18 months) and quantify the within-subject reliability for the metrics as a function of the amount of data used, which we find to be comparable to that seen for static functional connectivity. As considerable variability is observed across networks in the required amount of data, we combine the networks' metrics in one novel feature vector that exhibits an order of magnitude improvement in reliability. We then test reproducibility by analyzing the Midnight Scan Club dataset (10 subjects imaged over 10 consecutive days). Finally, we examine the susceptibility to change of the proposed metastability measure in another dataset examining brain dynamics under the effect of psilocybin. We conclude that the networks' metastability feature vector exhibits strong within-subject reliability that renders it a promising candidate for the study of individual-specific biomarkers of brain dynamics and potential targets for precision neuromodulation.