The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
The salience network is responsible for switching between the default mode network and the central executive network: Replication from DCM
Open accessAbstract
With the advent of new analysis methods in neuroimaging that involve independent component analysis (ICA) and dynamic causal modelling (DCM), investigations have focused on measuring both the activity and connectivity of specific brain networks. In this study we combined DCM with spatial ICA to investigate network switching in the brain. Using time courses determined by ICA in our dynamic causal models, we focused on the dynamics of switching between the default mode network (DMN), the network which is active when the brain is not engaging in a specific task, and the central executive network (CEN), which is active when the brain is engaging in a task requiring attention. Previous work using Granger causality methods has shown that regions of the brain which respond to the degree of subjective salience of a stimulus, the salience network, are responsible for switching between the DMN and the CEN (Sridharan et al., 2008). In this work we apply DCM to ICA time courses representing these networks in resting state data. In order to test the repeatability of our work we applied this to two independent datasets. This work confirms that the salience network drives the switching between default mode and central executive networks and that our novel technique is repeatable.
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“The SN has been implicated in switching between these networks and in processing self-relevant emotional information (Goulden et al., 2014; Kaiser et al., 2015b).”
From Understanding and predicting future relapse in depression from resting state functional connectivity and self-referential processing (2023) · cites this paper as Goulden et al., 2014
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Together, the VLPFC-anterior insula have been shown to form a fronto-insular functional node implicated in switching between salience and executive control networks (Goulden et al., 2014; Sridharan et al., 2008).”
From Neural correlates of emotion acceptance vs worry or suppression in generalized anxiety disorder (2017) · cites this paper as Goulden et al., 2014
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A default mode of brain function
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The Brain's Default Network: Anatomy, Function, and Relevance to Disease
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An Information-Maximization Approach to Blind Separation and Blind Deconvolution
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A fast diffeomorphic image registration algorithm
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Dissociable intrinsic connectivity networks for salience processing and executive control
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Saliency, switching, attention and control: a network model of insula function
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Cited by
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Salience processing and insular cortical function and dysfunction
Uddin, L. Q. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn3857 · Cited by 2,083
Resting-state functional connectivity in major depressive disorder: A review
Mulders, P. C., van Eijndhoven, P. F., Schene, A. H., Beckmann, C. F., & Tendolkar, I. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.07.014 · Cited by 884
Zaccaro, A., Piarulli, A., Laurino, M., Garbella, E., Menicucci, D., Neri, B., & Gemignani, A. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00353 · Cited by 820
Schurz, M., Raduà, J., Tholen, M. G., Maliske, L., Margulies, D. S., Mars, R. B., Sallet, J., & Kanske, P. (2020) · Psychological Bulletin · doi:10.1037/bul0000303 · Cited by 702
“…found between the ventral attention network and the DMN, which is considered to reflect the former down-regulating the latter to reduce interference from task-unrelated processes (Goulden et al., 2014; Trautwein, Singer, & Kanske, 2016; Wen, Liu, Yao, & Ding, 2013; see also Anticevic et al., 2012).”
BackgroundA guide to group effective connectivity analysis, part 1: First level analysis with DCM for fMRI
Zeidman, P., Jafarian, A., Corbin, N., Seghier, M. L., Razi, A., Price, C. J., & Friston, K. J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.06.031 · Cited by 369
“This means that stochastic DCM can be used to model resting state (e.g. Bastos-Leite et al., 2014; Goulden et al., 2014; Dirkx et al., 2016) as well as task-based fMRI studies where endogenous fluctuations are important (e.”
Method
Metadata
- Authors
- Goulden, N., Khusnulina, A., Davis, N. J., Bracewell, R. M., Bokde, A. L., McNulty, J. P., & Mullins, P. G.
- Affiliations
- Bangor University (GB)
- Trinity College Dublin (IE)
- University College Dublin (IE)
- Corresponding author
- Goulden, Nia
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-10
- Volume
- 99
- Pages
- 180-190
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2066208486
- Cited by
- 810
- References
- 47
- Citations by year
- 2026: 65 · 2025: 101 · 2024: 83 · 2023: 85 · 2022: 79 · 2021: 112 · 2020: 67 · 2019: 58 · 2018: 62 · 2017: 34 · 2016: 37 · 2015: 20 · 2014: 6
- FWCI
- 18.03
- Type
- journal article
- Language
- en
- Open access
- green
- e-space.mmu.ac.uk
- Licence
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- e-space (Manchester Metropolitan University) (submittedVersion, other-oa)
- Funders
- European Regional Development Fund
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- salience network, default mode network, frontoparietal network, network switching, dynamic causal modeling, independent component analysis, resting state, Granger causality, reproducibility, brain connectivity
- MeSH terms
- Adult, Bayes Theorem, Brain Mapping, Data Interpretation, Statistical, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Models, Neurological, Nerve Net, Rest, Stochastic Processes, Executive Function
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