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.

IF 3.1 3区 医学 Q2 NEUROSCIENCES
Network Neuroscience Pub Date : 2026-04-22 eCollection Date: 2026-01-01 DOI:10.1162/NETN.a.537
Hiba Sheheitli, Robert Hermosillo, Gracie Grimsrud, Thomas Madison, Oscar Miranda Dominguez, Steven Nelson, Damien Fair, Ziad Nahas
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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.

静息状态大胆fMRI的亚稳态作为个体大脑动力学的可靠生物标志物:对主体内变异性作为总获取时间函数的质疑。
BOLD fMRI信号的亚稳态是行为和临床研究中常用的脑动力学指标。迄今为止,很少有人评估我们是否有信心将亚稳态估计作为个体大脑状态的可靠生物标志物。我们分析了一个高度采样的个体大脑的全脑和网络特异性亚稳态(超过18个月的84次会话),并将指标的主体内可靠性量化为所使用数据量的函数,我们发现这与静态功能连接的结果相当。由于在所需的数据量中观察到跨网络的相当大的可变性,我们将网络的度量组合在一个新的特征向量中,该特征向量在可靠性方面表现出数量级的提高。然后,我们通过分析Midnight Scan Club数据集(连续10天对10名受试者进行成像)来测试再现性。最后,我们在另一个研究裸盖菇素作用下脑动力学的数据集中研究了亚稳态测量对变化的易感性。我们的结论是,网络的亚稳态特征向量显示出很强的主体内可靠性,使其成为研究个体特异性脑动力学生物标志物和精确神经调节潜在靶点的有希望的候选者。
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来源期刊
Network Neuroscience
Network Neuroscience NEUROSCIENCES-
CiteScore
6.40
自引率
6.40%
发文量
68
审稿时长
16 weeks
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