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Record W3139475357 · doi:10.3389/fnins.2021.645255

Local Neuronal Synchronization in Frequent Nightmare Recallers and Healthy Controls: A Resting-State Functional Magnetic Resonance Imaging Study

2021· article· en· W3139475357 on OpenAlexafffund
Louis‐Philippe Marquis, Sarah-Hélène Julien, Véronique Daneault, Cloé Blanchette‐Carrière, Tyna Paquette, Michelle Carr, Jean‐Paul Soucy, Jacques Montplaisir, Toré Nielsen

Bibliographic record

VenueFrontiers in Neuroscience · 2021
Typearticle
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsMontreal Neurological Institute and HospitalCanadian Sleep & Circadian NetworkUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalHôpital du Sacré-Cœur de Montréal
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsNightmareFunctional magnetic resonance imagingUnconsciousnessPsychologyAnterior cingulate cortexResting state fMRINeural correlates of consciousnessNeuroscienceAudiologyPrefrontal cortexMagnetic resonance imagingMedicinePsychiatryCognitionRadiology

Abstract

fetched live from OpenAlex

Nightmares are highly dysphoric dreams that are well-remembered upon awakening. Frequent nightmares have been associated with psychopathology and emotional dysregulation, yet their neural mechanisms remain largely unknown. Our neurocognitive model posits that nightmares reflect dysfunction in a limbic-prefrontal circuit comprising medial prefrontal and anterior cingulate cortices, hippocampus, and amygdala. However, there is a paucity of studies that used brain imaging to directly test the neural correlates of nightmares. One such study compared the regional homogeneity (ReHo) of resting-state functional magnetic resonance imaging blood-oxygen level-dependent signals between frequent nightmare recallers and controls. The main results were greater regional homogeneity in the left anterior cingulate cortex and right inferior parietal lobule for the nightmare recallers than for the controls. In the present study, we aimed to document the ReHo correlates of frequent nightmares using several nightmare severity measures. We acquired resting-state functional magnetic resonance imaging data from 18 frequent nightmare recallers aged 18-35 (3 males and 15 females) and 18 age- and sex-matched controls, as well as retrospective and prospective disturbed dreaming frequency estimates and scores on the Nightmare Distress Questionnaire. While there were inconsistent results for our different analyses (group comparisons, correlational analyses for frequency estimates/Nightmare Distress scores), our results suggest that nightmares are associated with altered ReHo in frontal (medial prefrontal and inferior frontal), parietal, temporal and occipital regions, as well as some subcortical regions (thalamus). We also found a positive correlation between retrospective disturbed dreaming frequency estimates and ReHo values in the hippocampus. These findings are mostly in line with a recent SPECT study from our laboratory. Our results point to the possibility that a variety of regions, including but not limited to the limbic-prefrontal circuit of our neurocognitive model, contribute to nightmare formation.

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)0.001
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.029
GPT teacher head0.276
Teacher spread0.247 · 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".

Quick stats

Citations5
Published2021
Admission routes2
Has abstractyes

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