The cingulo-opercular network is composed of two distinct sub-systems

Gratton, Caterina; Dworetsky, Ally; Adeyemo, Babatunde; Seitzman, Benjamin A.; Smith, Douglas B.; Petersen, Steven E.; Neta, Maital

doi:10.1101/2022.09.16.508254

Cited by 4 publications

(1 citation statement)

References 102 publications

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“…This analysis identified the CON as consisting of two core regionsthe dACC and dAIC/frontal operculum-along with other prefrontal and parietal regions implicated in various control functions [131,132]. Functionally, the CON is active in tasks requiring executive control, particularly when establishing and maintaining task sets and tonic alertness [132][133][134][135][136].…”

Section: Relationship With the Cingulo-opercular Networkmentioning

confidence: 99%

Connecting Circuits with Networks in Addiction Neuroscience: A Salience Network Perspective

Cushnie

Tang

Heilbronner

2023

IJMS

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Human neuroimaging has demonstrated the existence of large-scale functional networks in the cerebral cortex consisting of topographically distant brain regions with functionally correlated activity. The salience network (SN), which is involved in detecting salient stimuli and mediating inter-network communication, is a crucial functional network that is disrupted in addiction. Individuals with addiction display dysfunctional structural and functional connectivity of the SN. Furthermore, while there is a growing body of evidence regarding the SN, addiction, and the relationship between the two, there are still many unknowns, and there are fundamental limitations to human neuroimaging studies. At the same time, advances in molecular and systems neuroscience techniques allow researchers to manipulate neural circuits in nonhuman animals with increasing precision. Here, we describe attempts to translate human functional networks to nonhuman animals to uncover circuit-level mechanisms. To do this, we review the structural and functional connections of the salience network and its homology across species. We then describe the existing literature in which circuit-specific perturbation of the SN sheds light on how functional cortical networks operate, both within and outside the context of addiction. Finally, we highlight key outstanding opportunities for mechanistic studies of the SN.

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Section: Relationship With the Cingulo-opercular Networkmentioning

confidence: 99%

Connecting Circuits with Networks in Addiction Neuroscience: A Salience Network Perspective

Cushnie

Tang

Heilbronner

2023

IJMS

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Network biomarkers in recovered psychosis patients who discontinued antipsychotics

Odkhuu,

Kim,

Tsogt

et al. 2023

Mol Psychiatry

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There are no studies investigating topological properties of resting-state fMRI (rs-fMRI) in patients who have recovered from psychosis and discontinued medication (hereafter, recovered patients [RP]). This study aimed to explore topological organization of the functional brain connectome in the RP using graph theory approach. We recruited 30 RP and 50 age and sex-matched healthy controls (HC). The RP were further divided into the subjects who were relapsed after discontinuation of antipsychotics (RP-R) and who maintained recovered state without relapse (RP-M). Using graph-based network analysis of rs-fMRI signals, global and local metrics and hub information were obtained. The robustness of the network was tested with random failure and targeted attack. As an ancillary analysis, Network-Based Statistic (NBS) was performed. Association of significant findings with psychopathology and cognitive functioning was also explored. The RP showed intact network properties in terms of global and local metrics. However, higher global functional connectivity strength and hyperconnectivity in the interconnected component were observed in the RP compared to HC. In the subgroup analysis, the RP-R were found to have lower global efficiency, longer characteristic path length and lower robustness whereas no such abnormalities were identified in the RP-M. Associations of the degree centrality of some hubs with cognitive functioning were identified in the RP-M. Even though network properties of the RP were intact, subgroup analysis revealed more altered topological organizations in the RP-R. The findings in the RP-R and RP-M may serve as network biomarkers for predicting relapse or maintained recovery after the discontinuation of antipsychotics.

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Situating the parietal memory network in the context of multiple parallel distributed networks using high-resolution functional connectivity

Kwon,

Salvo,

Anderson

et al. 2023

Preprint

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A principle of brain organization is that networks serving higher cognitive functions are widely distributed across the brain. One exception has been the parietal memory network (PMN), which plays a role in recognition memory but is often defined as being restricted to posteromedial association cortex. We hypothesized that high-resolution estimates of the PMN would reveal small regions that had been missed by prior approaches. High-field 7T functional magnetic resonance imaging (fMRI) data from extensively sampled participants was used to define the PMN within individuals. The PMN consistently extended beyond the core posteromedial set to include regions in the inferior parietal lobule; rostral, dorsal, medial, and ventromedial prefrontal cortex; the anterior insula; and ramus marginalis of the cingulate sulcus. The results suggest that, when fine-scale anatomy is considered, the PMN matches the expected distributed architecture of other association networks, reinforcing that parallel distributed networks are an organizing principle of association cortex.

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The cingulo-opercular network is composed of two distinct sub-systems

Cited by 4 publications

References 102 publications

Connecting Circuits with Networks in Addiction Neuroscience: A Salience Network Perspective

Connecting Circuits with Networks in Addiction Neuroscience: A Salience Network Perspective

Network biomarkers in recovered psychosis patients who discontinued antipsychotics

Situating the parietal memory network in the context of multiple parallel distributed networks using high-resolution functional connectivity

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