A parcellation-based model of the auditory network

Kuiper, Joseph J.; Lin, Yueh‐Hsin; Young, Isabella M.; Bai, Michael Y.; Briggs, Robert G.; Tanglay, Onur; Fonseka, R. Dineth; Hormovas, Jorge; Dhanaraj, Vukshitha; Conner, Andrew K.; O’Neal, Christen M.; Sughrue, Michael E.

doi:10.1016/j.heares.2020.108078

Cited by 14 publications

(7 citation statements)

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“…The STG is primarily responsible for processing sound and is also involved in processing the emotional meaning derived from facial expressions (39). The auditory network, consisting of the IFG, STG, and adjacent insula, plays an important role in language and interaction with the environment (40). However, there are limited reports on the relationships between acupuncture at ST36 and the auditory network, and the clinical applications of acupuncture in language disorders should be explored.…”

Section: Discussionmentioning

confidence: 99%

Brain Activities Responding to Acupuncture at ST36 (zusanli) in Healthy Subjects: A Systematic Review and Meta-Analysis of Task-Based fMRI Studies

et al. 2022

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PurposeStomach 36 (ST36, zusanli) is one of the important acupoints in acupuncture. Despite clinical functional magnetic resonance imaging (fMRI) studies of ST36 acupuncture, the brain activities and the neural mechanism following acupuncture at ST36 remain unclear.MethodsLiterature searches were conducted on online databases, including MEDLINE, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure, Wanfang database, WeiPu database, and China Biology Medicine, for task-based fMRI studies of acupuncture at ST36 in healthy subjects. Brain regions activated by ST36 acupuncture were systematically evaluated and subjected to seed-based d mapping meta-analysis. Subgroup analysis was conducted on control procedures, manual acupuncture, electrical acupuncture (EA), and acupuncture-specific activations. Meta-regression analysis was performed to explore the effects of needle retention time on brain activities following ST36 acupuncture stimulation. The activated brain regions were further decoded and mapped on large-scale functional networks to further decipher the clinical relevance of acupuncturing at ST36.ResultsA total of sixteen studies, involving a total of 401 right-handed healthy participants, that satisfied the inclusion criteria were included in the present meta-analysis. Meta-analysis showed that acupuncturing on ST36 positively activates the opercular part of the right inferior frontal gyrus (IFG.R), left superior temporal gyrus (STG.L), and right median cingulate/paracingulate gyri (MCG.R) regions. Needle retention time in an acupuncture session positively correlates with the activation of the left olfactory cortex, as shown in meta-regression analysis. Subgroup analysis revealed that EA stimulation may be a source of heterogeneity in the pooled results. Functional network mappings showed that the activated areas were mapped to the auditory network and salience network. Further functional decoding analysis showed that acupuncture on ST36 was associated with pain, secondary somatosensory, sound and language processing, and mood regulation.ConclusionAcupuncture at ST36 in healthy individuals positively activates the opercular part of IFG.R, STG.L, and MCG.R. The left olfactory cortex may exhibit positive needle retention time-dependent activities. Our findings may have clinical implications for acupuncture in analgesia, language processing, and mood disorders.Systematic Review Registrationhttps://inplasy.com/inplasy-2021-12-0035.

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Section: Discussionmentioning

confidence: 99%

Brain Activities Responding to Acupuncture at ST36 (zusanli) in Healthy Subjects: A Systematic Review and Meta-Analysis of Task-Based fMRI Studies

et al. 2022

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“…By including reported stereotactic coordinates from previous studies, more anatomically precise results can be obtained. In this context, we have been attempting to characterize all of our current findings within the HCP nomenclature given its more precise parcellation scheme (Kuiper et al., 2020 ; Milton et al., 2021 ; Sandhu et al., 2021 ; Sheets, Briggs, Dadario, et al., 2021 ). For instance, the anterior cingulate cortex and insula are known to be functionally divided within numerous subdivisions (Glasser et al., 2016 ), yet previous studies on the SN network continue to generally reference these regions as just general nodes in the SN.…”

Section: Discussionmentioning

confidence: 99%

“…Possible relevant cortical regions in the SN were identified in the literature with methodology reiteratively applied and refined by our team in other regions with great reproducibility (Baker, Burks, Briggs, Conner, Glenn, Sali, et al., 2018 ; Kuiper et al., 2020 ; Sandhu et al., 2021 ; Sheets, Briggs, Dadario, et al., 2021 ). With software publicly available through BrainMap ( http://www.brainmap.org ), we used the meta‐analytic software Ginger ALE 2.3.6 to extract the relevant fMRI data for the creation of an ALE (Eickhoff et al., 2012 ; Turkeltaub et al., 2012 ).…”

Section: Methodsmentioning

confidence: 99%

Parcellation‐based tractographic modeling of the salience network through meta‐analysis

et al. 2022

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Background:The salience network (SN) is a transitory mediator between active and passive states of mind. Multiple cortical areas, including the opercular, insular, and cingulate cortices have been linked in this processing, though knowledge of network connectivity has been devoid of structural specificity. Objective:The current study sought to create an anatomically specific connectivity model of the neural substrates involved in the salience network.Methods: A literature search of PubMed and BrainMap Sleuth was conducted for resting-state and task-based fMRI studies relevant to the salience network according to PRISMA guidelines. Publicly available meta-analytic software was utilized to extract relevant fMRI data for the creation of an activation likelihood estimation (ALE) map and relevant parcellations from the human connectome project overlapping with the ALE data were identified for inclusion in our SN model. DSI-based fiber tractography was then performed on publicaly available data from healthy subjects to determine the structural connections between cortical parcellations comprising the network.Results: Nine cortical regions were found to comprise the salience network: areas AVI (anterior ventral insula), MI (middle insula), FOP4 (frontal operculum 4), FOP5 (frontal operculum 5), a24pr (anterior 24 prime), a32pr (anterior 32 prime), p32pr (posterior 32 prime), and SCEF (supplementary and cingulate eye field), and 46. The frontal aslant tract was found to connect the opercular-insular cluster to the middle cingulate clusters of the network, while mostly short U-fibers connected adjacent nodes of the network. Conclusion:Here we provide an anatomically specific connectivity model of the neural substrates involved in the salience network. These results may serve as an empiric basis for clinical translation in this region and for future study which seeks to expand ourThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The AUN contains the lateral superior temporal gyrus, Heschl's gyri, planum temporale, and posterior insula (40,41). These brain areas play roles in cognitive tasks such as language processing and speech (42).…”

Section: Discussionmentioning

confidence: 99%

Alterations of static and dynamic functional network connectivity in acute ischemic brainstem stroke

Zhang

Chang

2022

Acta Radiol

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Background Prior studies have shown abnormal brain functional network changes in patients with acute ischemic stroke. However, the alterations of dynamic functional network connectivity (FNC) in brainstem strokes have not been elucidated. Purpose To assess alterations of static and dynamic FNCs and determine the relationships between these and upper limb movement performance in patients with acute brainstem ischemic stroke. Material and Methods In total, 50 patients with acute brainstem ischemic stroke and 50 age- and sex-matched healthy controls were enrolled in the present study and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Independent component analysis was conducted to assess static and dynamic FNC patterns based on seven resting-state networks, namely, the default mode network (DMN), executive control network (ECN), attention network (AN), somatomotor network (SMN), visual network (VN), auditory network (AUN), and cerebellum network (CN). Results Compared with controls, patients with acute brainstem ischemic stroke exhibited wide aberrations of static FNC, including increased FNC in DMN–ECN, DMN–VN, ECN–VN, ECN–AN and AN–AUN pairs. Patients with acute brainstem ischemic stroke showed aberrant dynamic FNC in State 1, involving increased FNC aberrance in the DMN with AN, DMN with ECN, and reduced FNC in SMN–VN pairs. In State 5, patients with acute brainstem ischemic stroke showed increased FNC in DMN–VN and AN–AUN, and decreased FNC in AN–SMN pairs. Conclusion This study suggests that static and dynamic FNC impairment and aberrant connections exist in acute brainstem ischemic stroke, which expands what is known regarding the relationship between stroke and FNC from static and dynamic perspectives.

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A parcellation-based model of the auditory network

Cited by 14 publications

References 151 publications

Brain Activities Responding to Acupuncture at ST36 (zusanli) in Healthy Subjects: A Systematic Review and Meta-Analysis of Task-Based fMRI Studies

Brain Activities Responding to Acupuncture at ST36 (zusanli) in Healthy Subjects: A Systematic Review and Meta-Analysis of Task-Based fMRI Studies

Parcellation‐based tractographic modeling of the salience network through meta‐analysis

Alterations of static and dynamic functional network connectivity in acute ischemic brainstem stroke

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