A New Modular Brain Organization of the BOLD Signal during Natural Vision

• Published in: Cerebral cortex (New York, N.Y. 1991) Vol. 28; no. 9; pp. 3065 - 3081
• Main Authors: Kim, DoHyun, Kay, Kendrick, Shulman, Gordon L, Corbetta, Maurizio
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 01.09.2018
• Subjects: Original; fMRI; BOLD; resting-state functional connectivity (rs-FC); natural viewing; intersubject functional correlation (ISFC)
• ISSN: 1047-3211; 1460-2199
• DOI: 10.1093/cercor/bhx175

Abstract The resting blood oxygen level-dependent (BOLD) signal is synchronized in large-scale brain networks (resting-state networks, RSNs) defined by interregional temporal correlations (functional connectivity, FC). RSNs are thought to place strong constraints on task-evoked processing since they largely match the networks observed during task performance. However, this result may simply reflect the presence of spontaneous activity during both rest and task. Here, we examined the BOLD network structure of natural vision, as simulated by viewing of movies, using procedures that minimized the contribution of spontaneous activity. We found that the correlation between resting and movie-evoked FC (ρ = 0.60) was lower than previously reported. Hierarchical clustering and graph-theory analyses indicated a well-defined network structure during natural vision that differed from the resting structure, and emphasized functional groupings adaptive for natural vision. The visual network merged with a network for navigation, scene analysis, and scene memory. Conversely, the dorsal attention network was split and reintegrated into 2 groupings likely related to vision/scene and sound/action processing. Finally, higher order groupings from the clustering analysis combined internally directed and externally directed RSNs violating the large-scale distinction that governs resting-state organization. We conclude that the BOLD FC evoked by natural vision is only partly constrained by the resting network structure.