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Record W4302032963

Aberrant spontaneous low-frequency brain activity in male patients with severe obstructive sleep apnea revealed by resting-state functional MRI

2015· article· en· W4302032963 on OpenAlexaboutno aff
Li Hj, Dai XJ, Gong HH, W Zhang, Peng DC

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsnot available
Fundersnot available
KeywordsResting state fMRIObstructive sleep apneaFunctional connectivityMedicineSleep (system call)CardiologyInternal medicineBrain activity and meditationNeurosciencePsychologyRadiologyElectroencephalographyComputer science
DOInot available

Abstract

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Hai-Jun Li,1,* Xi-Jian Dai,1,2,* Hong-Han Gong,1 Xiao Nie,1 Wei Zhang,3 De-Chang Peng1 1Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China; 2Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong Special Administrative Region, People’s Republic of China; 3Department of Pneumology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China *These authors contributed equally to this work Background: The majority of previous neuroimaging studies have demonstrated both structural and functional abnormalities in obstructive sleep apnea (OSA). However, few studies have focused on the regional intensity of spontaneous fluctuations during the resting state and the relationship between the abnormal properties and the behavioral performances. In the present study, we employed the amplitude of low-frequency fluctuation (ALFF) method to explore the local features of spontaneous brain activity in OSA patients (OSAs).Methods: Twenty-five untreated male severe OSAs and 25 age-matched and years-of-education-matched male good sleepers (GSs) were included in this study. The ALFF method was used to assess the local features of spontaneous brain activity. The mean signal values of the altered ALFF areas were analyzed with receiver operating characteristic curve. Partial correlation analysis was used to explore the relationship between the observed mean ALFF values of the different areas and the behavioral performances.Results: Compared with GSs, OSAs had significantly higher scores for body mass index, apnea–hypopnea index, arterial oxygen saturation <90%, arousal index, and Epworth Sleepiness Scale (ESS) score; furthermore, OSAs had significantly lower scores for rapid eye movement sleep and in the Montreal Cognitive Assessment (MoCA). Compared with GSs, OSAs showed significant lower-ALFF areas in the cluster of the right precuneus and bilateral posterior cingulate gyrus, as well as a higher-ALFF area in the left inferior frontal gyrus. The area under the curve values of the lower- and higher-ALFF areas were 0.90 and 0.93, respectively. Further diagnostic analysis exhibited that the sensibility and specificity of the two clusters were 80% and 92%, respectively. The mean signal value of the lower-ALFF cluster displayed significant positive correlations with lowest oxygen saturation (r=0.447, P=0.025) and MoCA score (r=0.405, P=0.045).Conclusion: OSAs may involve in a dysfunction in the default mode network and an adaptive compensatory response in the frontal lobe, which reflect the underlying pathophysiology of cognitive impairment. Keywords: obstructive sleep apnea, amplitude of low-frequency fluctuation, functional magnetic resonance imaging, resting state, spontaneous activity, blood oxygen-level-dependent

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.089
GPT teacher head0.428
Teacher spread0.339 · 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 source (direct Gemma or distilled Codex), 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".

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Citations22
Published2015
Admission routes1
Has abstractyes

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