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Cooperating yet distinct brain networks engaged during naturalistic paradigms: A meta-analysis of functional MRI results.

Authors :
Bottenhorn KL
Flannery JS
Boeving ER
Riedel MC
Eickhoff SB
Sutherland MT
Laird AR
Source :
Network neuroscience (Cambridge, Mass.) [Netw Neurosci] 2018 Oct 01; Vol. 3 (1), pp. 27-48. Date of Electronic Publication: 2018 Oct 01 (Print Publication: 2019).
Publication Year :
2018

Abstract

Cognitive processes do not occur by pure insertion and instead depend on the full complement of co-occurring mental processes, including perceptual and motor functions. As such, there is limited ecological validity to human neuroimaging experiments that use highly controlled tasks to isolate mental processes of interest. However, a growing literature shows how dynamic, interactive tasks have allowed researchers to study cognition as it more naturally occurs. Collective analysis across such neuroimaging experiments may answer broader questions regarding how naturalistic cognition is biologically distributed throughout the brain. We applied an unbiased, data-driven, meta-analytic approach that uses k -means clustering to identify core brain networks engaged across the naturalistic functional neuroimaging literature. Functional decoding allowed us to, then, delineate how information is distributed between these networks throughout the execution of dynamical cognition in realistic settings. This analysis revealed six recurrent patterns of brain activation, representing sensory, domain-specific, and attentional neural networks that support the cognitive demands of naturalistic paradigms. Although gaps in the literature remain, these results suggest that naturalistic fMRI paradigms recruit a common set of networks that allow both separate processing of different streams of information and integration of relevant information to enable flexible cognition and complex behavior.<br />Competing Interests: Competing Interests: The authors have declared that no competing interests exist.

Details

Language :
English
ISSN :
2472-1751
Volume :
3
Issue :
1
Database :
MEDLINE
Journal :
Network neuroscience (Cambridge, Mass.)
Publication Type :
Academic Journal
Accession number :
30793072
Full Text :
https://doi.org/10.1162/netn_a_00050