The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Interactions of the Salience Network and Its Subsystems with the Default-Mode and the Central-Executive Networks in Normal Aging and Mild Cognitive Impairment
Open accessAbstract
Previous functional magnetic resonance imaging (fMRI) investigations suggest that the intrinsically organized large-scale networks and the interaction between them might be crucial for cognitive activities. A triple network model, which consists of the default-mode network, salience network, and central-executive network, has been recently used to understand the connectivity patterns of the cognitively normal brains versus the brains with disorders. This model suggests that the salience network dynamically controls the default-mode and central-executive networks in healthy young individuals. However, the patterns of interactions have remained largely unknown in healthy aging or those with cognitive decline. In this study, we assess the patterns of interactions between the three networks using dynamical causal modeling in resting state fMRI data and compare them between subjects with normal cognition and mild cognitive impairment (MCI). In healthy elderly subjects, our analysis showed that the salience network, especially its dorsal subnetwork, modulates the interaction between the default-mode network and the central-executive network (Mann-Whitney U test; p < 0.05), which was consistent with the pattern of interaction reported in young adults. In contrast, this pattern of modulation by salience network was disrupted in MCI (p < 0.05). Furthermore, the degree of disruption in salience network control correlated significantly with lower overall cognitive performance measured by Montreal Cognitive Assessment (r = 0.295; p < 0.05). This study suggests that a disruption of the salience network control, especially the dorsal salience network, over other networks provides a neuronal basis for cognitive decline and may be a candidate neuroimaging biomarker of cognitive impairment.
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Cited by
McCutcheon, R. A., Nour, M. M., Dahoun, T., Jauhar, S., Pepper, F., Expert, P., Veronese, M., Adams, R. A., Turkheimer, F., Mehta, M. A., et al. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2018.09.010 · Cited by 104
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Resting‐state connectivity within and across neural circuits in anorexia nervosa
Uniacke, B., Wang, Y., Biezonski, D., Sussman, T., Lee, S., Posner, J., & Steinglass, J. (2019) · Brain and Behavior · doi:10.1002/brb3.1205 · Cited by 31
Fang, X. T., Volpi, T., Holmes, S. E., Esterlis, I., Carson, R. E., & Worhunsky, P. D. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1124254 · Cited by 18
Healey, K., Fang, Z., Smith, A., Zemek, R., & Ledoux, A.-A. (2022) · NeuroImage: Clinical · doi:10.1016/j.nicl.2022.103211 · Cited by 19
Metadata
- Authors
- Chand, G. B., Wu, J., Hajjar, I., & Qiu, D.
- Venue
- Brain Connectivity
- Publisher
- SAGE Publications
- Published
- 2017-09
- Volume
- 7
- Issue
- 7
- Pages
- 401-412
- ISSN
- 2158-0014 (ISSN-L)
- 2158-0022
- OpenAlex
- W2734757979
- Cited by
- 196
- References
- 20
- FWCI
- 6.44
- Type
- journal article
- Language
- en
- Open access
- green
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