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Parallel cortical-brainstem pathways to attentional analgesia

2020· article· en· W3099030518 on OpenAlexfundno aff
Valeria Oliva, Rob Gregory, Wendy-Elizabeth Davies, Lee Harrison, Rosalyn Moran, Anthony E. Pickering, J.C. Brooks

Bibliographic record

VenueNeuroImage · 2020
Typearticle
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsnot available
FundersMedical Research CouncilElizabeth Blackwell Institute for Health Research, University of BristolAlberta Environment and ParksWellcome Trust
KeywordsBrainstemNeurosciencePsychologyCognitive psychology

Abstract

fetched live from OpenAlex

Pain demands attention, yet pain can be reduced by focusing attention elsewhere. The neural processes involved in this robust psychophysical phenomenon, attentional analgesia, are still being defined. Our previous fMRI study linked activity in the brainstem triad of locus coeruleus (LC), rostral ventromedial medulla (RVM) and periaqueductal grey (PAG) with attentional analgesia. Here we identify and model the functional interactions between these regions and the cortex in healthy human subjects (n = 57), who received painful thermal stimuli whilst simultaneously performing a visual attention task. RVM activity encoded pain intensity while contralateral LC activity correlated with attentional analgesia. Psycho-Physiological Interaction analysis and Dynamic Causal Modelling identified two parallel paths between forebrain and brainstem. These connections are modulated by attentional demand: a bidirectional anterior cingulate cortex (ACC) - right-LC loop, and a top-down influence of task on ACC-PAG-RVM. By recruiting discrete brainstem circuits, the ACC is able to modulate nociceptive input to reduce pain in situations of conflicting attentional demand.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.857
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.003

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 head0.077
GPT teacher head0.273
Teacher spread0.195 · 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.

Study designBench or experimental
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

Citations49
Published2020
Admission routes1
Has abstractyes

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