Literature DB >> 35284947

Interaction of the salience network, ventral attention network, dorsal attention network and default mode network in neonates and early development of the bottom-up attention system.

Valeria Onofrj1,2,3, Antonio Maria Chiarelli4, Richard Wise4, Cesare Colosimo5, Massimo Caulo4.   

Abstract

The salience network (SN), ventral attention network (VAN), dorsal attention network (DAN) and default mode network (DMN) have shown significant interactions and overlapping functions in bottom-up and top-down mechanisms of attention. In the present study, we tested if the SN, VAN, DAN and DMN connectivity can infer the gestational age (GA) at birth in a study group of 88 healthy neonates, scanned at 40 weeks of post-menstrual age, and with GA at birth ranging from 28 to 40 weeks. We also ascertained whether the connectivity within each of the SN, VAN, DAN and DMN was able to infer the average functional connectivity of the others. The ability to infer GA at birth or another network's connectivity was evaluated using a multivariate data-driven framework. The VAN, DAN and the DMN inferred the GA at birth (p < 0.05). The SN, DMN and VAN were able to infer the average connectivity of the other networks (p < 0.05). Mediation analysis between VAN's and DAN's inference on GA at birth found reciprocal transmittance of change with GA at birth of VAN's and DAN's connectivity (p < 0.05). Our findings suggest that the VAN has a prominent role in bottom-up salience detection in early infancy and that the role of the VAN and the SN may overlap in the bottom-up control of attention.
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Entities:  

Keywords:  Bottom-up salience detection; Data-driven analysis; Default mode network; Dorsal attention network; Mediation analysis; Salience network; Ventral attention network

Mesh:

Year:  2022        PMID: 35284947     DOI: 10.1007/s00429-022-02477-y

Source DB:  PubMed          Journal:  Brain Struct Funct        ISSN: 1863-2653            Impact factor:   3.270


  54 in total

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Review 4.  The brain's default network: anatomy, function, and relevance to disease.

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Journal:  Neuroimage       Date:  2010-09-17       Impact factor: 6.556

6.  The optimal template effect in hippocampus studies of diseased populations.

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Journal:  Neuroimage       Date:  2009-10-08       Impact factor: 6.556

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Authors:  Alan Anticevic; Grega Repovs; Gordon L Shulman; Deanna M Barch
Journal:  Neuroimage       Date:  2009-11-12       Impact factor: 6.556

Review 8.  The brain's default network: updated anatomy, physiology and evolving insights.

Authors:  Randy L Buckner; Lauren M DiNicola
Journal:  Nat Rev Neurosci       Date:  2019-09-06       Impact factor: 34.870

9.  Potential pitfalls when denoising resting state fMRI data using nuisance regression.

Authors:  Molly G Bright; Christopher R Tench; Kevin Murphy
Journal:  Neuroimage       Date:  2016-12-23       Impact factor: 6.556

10.  Machine-learning to characterise neonatal functional connectivity in the preterm brain.

Authors:  G Ball; P Aljabar; T Arichi; N Tusor; D Cox; N Merchant; P Nongena; J V Hajnal; A D Edwards; S J Counsell
Journal:  Neuroimage       Date:  2015-09-02       Impact factor: 6.556

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  1 in total

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Journal:  Front Neuroanat       Date:  2022-08-23       Impact factor: 3.543

  1 in total

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