Interactions of the Salience Network and Its Subsystems with the Default-Mode and the Central-Executive Networks in Normal Aging and Mild Cognitive Impairment

Abstract

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.

Cerebellar and Prefrontal-Cortical Engagement During Higher-Order Rule Learning in Older Adulthood

“…Increased activation in OA compared to YA may suggest reduced suppression of the DMN in OA, consistent with past work predicting increased DMN activity in OA relative to YA (Andrews-Hanna et al, 2007;Damoiseaux et al, 2008;Grady et al, 2010;Grady, Sarraf, Saverino, & Campbell, 2016). Taken together, the results presented here are compatible with previous work suggesting OA show an inability to correctly suppress the DMN during cognitive processing (Chand, Wu, Hajjar, & Qiu, 2017;Grady et al, 2010Grady et al, , 2016Raichle, 2015) and a seemingly reduced capacity to efficiently bring task-positive, error-correcting and attention-controlling brain regions online to update future responses (Langenecker & Nielson, 2003;Zhu, Zacks, & Slade, 2010). In line with these results, accuracy analyses showed OA are on par with YA by the second control block (a purely firstorder rule task), yet do not ever meet YA performance in second-order learning blocks.…”

Functional segregation loss over time is moderated by APOE genotype in healthy elderly

“…While as distinct functional networks, the ECN and the SN tended to share positive FC (He et al, ), consistent with their increased activation during cognitive tasks compared to the deactivated DMN. Past studies on healthy ageing have demonstrated the importance of the connectivity profile with the SN (Chand, Wu, Hajjar, & Qiu, ; Onoda, Ishihara, & Yamaguchi, ), for instance, by improving the classification success on episodic memory performance and executive function between young and old participants (La Corte et al, ). Lower ECN‐SN FC may suggest that APOE ‐ɛ4 carriers had aberrant communication between networks critical for externally oriented cognition compared to noncarriers.…”

Enhanced prediction of cognitive function using aging-sensitive networks within the human structural connectome

“…It is potentially nontrivial to compare brain indices across scanners of different magnetic strength, and future research would benefit from assessing the extent to which these differences bias results in cross-cohort MRI studies. Finally, the neurostructural perspective is inherently limited in that it does not include functional information; since previous studies have found functional connections between several of the networks studied here 44 , our investigation, which treated networks separately, may have missed these links, which might explain additional cognitive variance over and above the properties of the individual networks. Integrating the structural and functional perspectives is a critical future task for network-focused cognitive neuroscience.…”

“…It is notable that elements within the Salience network were found to be relatively spared in late-life, implying that its trajectory across middle to older adulthood is flatter than other NOIs. Previous functional research has suggested that the Salience network acts as a control on other networks, such as the Central Executive and Default Mode networks, and that disruption of this control, or loss of functional connections between networks, is among the causes of cognitive decline 44,45,46 . Though we did not examine between-network connections here, our analyses did highlight the Central Executive network as being of particular interest for cognitive aging, despite it containing the fewest number of elements of any network.…”