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[Modulation of transcutaneous auricular vagus nerve stimulation on default mode network in patients with primary insomnia].

2022· article· en· W4223489996 on OpenAlexaboutno aff
Shuai Zhang, Jiakai He, Ya-Nan Zhao, Yu Wang, Bin Zhao, Yu Chen, JiLiang Fang, Peijing Rong

Bibliographic record

VenuePubMed · 2022
Typearticle
Languageen
FieldNeuroscience
TopicVagus Nerve Stimulation Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVagus nerve stimulationDefault mode networkAnesthesiaPittsburgh Sleep Quality IndexSuperior frontal gyrusEveningMontreal Cognitive AssessmentGyrusInternal medicineStimulationFunctional magnetic resonance imagingCardiologyVagus nerveInsomniaRadiologyPsychiatrySleep quality

Abstract

fetched live from OpenAlex

OBJECTIVE: To explore the modulation of transcutaneous auricular vagus nerve stimulation (taVNS) on default mode network (DMN) in patients with primary insomnia (PI). METHODS: ) were selected and treated with disperse-dense wave at frequency of 4 Hz/20 Hz, the intensity was based on the patient's tolerance. taVNS was given once in the morning and once in the evening for 30 minutes each time. The treatment lasted for at least 5 days a week for 4 weeks. At the same time, 16 healthy subjects matched with gender and age were recruited. The Pittsburgh sleep quality index (PSQI) score was evaluated before and after treatment in PI patients. The resting-state functional magnetic resonance imaging (rs-fMRI) data of PI patients before and after treatment and healthy subjects at baseline period were collected to observe the effect of taVNS on the functional connection (FC) between posterior cingulate cortex (PCC) and whole brain. RESULTS: <0.01). CONCLUSION: taVNS can modulate the FC between anterior and posterior DMN, and between DMN and cognitive control network of PI patients, which may be one of the brain effect mechanisms of taVNS in the treatment of PI patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.235
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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