Speaker
Description
Synchronization- and metastability-related measures have been proposed as complementary descriptors of large-scale brain dynamics beyond conventional static functional connectivity. However, their reliability under realistic resting-state acquisition protocols remains insufficiently characterized, limiting their application as neuroimaging biomarkers, particularly in multimodal studies with constrained scan durations.
Here, we investigated how acquisition duration influences the reliability of dynamic synchronization- and metastability-related measures derived from resting-state fMRI. Six measures were evaluated: the mean, standard deviation, and coefficient of variation of the Kuramoto Order Parameter (meanKOP, stdKOP, and cvKOP), standard deviation of intrinsic ignition (stdIGNITE), standard deviation of spectral radius (stdSPECT), and mean temporal variability of phase alignment (meanVAR). We analyzed 367 HCP Aging participants, each undergoing four resting-state acquisitions across two sessions. Reliability was assessed using Intraclass Correlation Coefficients (ICC), Lin's Concordance Correlation Coefficient (CCC), bootstrap confidence intervals, Spearman–Brown prediction, and linear mixed-effects models.
Reliability varied across measures. MeanKOP and meanVAR showed moderate reliability (ICC ≈ 0.53–0.58), whereas stdIGNITE exhibited the highest reproducibility (ICC ≈ 0.71). Concatenating two runs improved reliability across all measures, increasing ICC by approximately 0.10–0.14 (bootstrap p < 0.001). Improvements were slightly lower than Spearman–Brown predictions but showed strong agreement. Concordance analyses indicated that disagreement was mainly driven by random variability rather than systematic biases. Linear mixed-effects models showed modest run and session effects, with stable inter-individual differences accounting for most variance.
These findings demonstrate that synchronization- and metastability-related measures capture reliable individual differences even from relatively short acquisitions, although reliability varies across metrics, and that concatenating short (~5 min) resting-state runs provides a simple strategy to obtain more robust estimates. More broadly, our results highlight that metric selection and acquisition duration should be guided by the reliability requirements of the specific research question, providing practical recommendations for designing resting-state and multimodal neuroimaging studies employing dynamic brain biomarkers.
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