Test–retest reliability of meta analytic networks during naturalistic viewing

Jean-Philippe Kröll, Patrick Friedrich, Xuan Li, Yulia Nurislamova, Nevena Kraljevic, Anna Geiger, Julia Mans, Laura Waite, Julian Caspers, Xing Qian, Michael W L Chee, Juan Helen Zhou, Simon Eickhoff and Susanne Weis

PLOS ONE, 2026, vol. 21, issue 5, 1-17

Abstract: Functional connectivity analyses have given considerable insights into human brain function and organization. As research moves towards clinical application, test-retest reliability has become a main focus of the field. So far, the majority of studies have relied on resting-state paradigms to examine brain connectivity, based on its low demand and ease of implementation. However, the reliability of resting-state measures is mostly poor to fair, potentially due to its unconstrained nature. Recently, naturalistic viewing paradigms have gained popularity because they probe the human brain under more ecologically valid conditions, possibly increasing reliability. We here compared the reliability of graph metrics extracted from resting-state and naturalistic viewing in functional networks, across two sessions. We show that naturalistic viewing can increase reliability over resting-state, but that its effect varies between stimuli and networks. Furthermore, we demonstrate that the effect of naturalistic viewing differs between two cohorts with Asian and European cultural backgrounds. Taken together, our study encourages the use of naturalistic viewing to increase reliability, but emphasizes the need to carefully select the appropriate stimulus for the network at hand.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0346967

DOI: 10.1371/journal.pone.0346967

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