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 Peer reviewed DOI: 10.1162/netn.a.537 via PubMed
Summary
Metastability, a measure of brain signal variability, is often used as a marker of brain dynamics. Analyzing a single person's brain scans from 84 sessions over 18 months, the study found that the reliability of metastability estimates depends on how much data is used, similar to standard functional connectivity measures. Different brain networks required different amounts of data for stable estimates. Combining network metrics into a single feature vector improved reliability tenfold.
Study at a glance
| Design | observational cohort |
|---|---|
| Sample size | 11 |
| Population | highly sampled individual and 10 subjects from the Midnight Scan Club dataset |
| Key finding | A combined network metastability feature vector exhibits an order of magnitude improvement in within-subject reliability over 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.