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Record W2795686876 · doi:10.3389/fneur.2018.00188

GABAA Receptor Availability Changes Underlie Symptoms in Isolated Cervical Dystonia

2018· article· en· W2795686876 on OpenAlexaboutno aff
Brian D. Berman, Rebecca Pollard, Erika Shelton, Ramesh Karki, Peter Smith‐Jones, Yubin Miao

Bibliographic record

VenueFrontiers in Neurology · 2018
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsnot available
FundersDana Foundation
KeywordsGABAA receptorCervical dystoniaNeuroscienceDystoniaMedicineReceptorPsychologyPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

GABAA receptor density changes within sensorimotor regions have been reported in some isolated forms of dystonia. Whether similar abnormalities underlie symptoms in cervical dystonia is not known. In the present study, a total of 15 cervical dystonia patients and 15 age- and sex-matched controls underwent 11C-flumazenil PET/CT scanning. The density of available GABAA receptors was estimated using a Simplified Reference Tissue Model 2. Group differences were evaluated using a two-sample T test, and correlations with dystonia severity, as measured by the Toronto Western Spasmodic Torticollis Rating Scale, and disease duration were evaluated using a regression analysis. Voxel-based analyses revealed increased GABAA availability within the right precentral gyrus in brain motor regions previously associated with head turning and the left parahippocampal gyrus. GABAA availability within the bilateral cerebellum was negatively correlated with dystonia severity, and GABAA availability within the right thalamus and a variety of cerebellar and cortical regions were negatively correlated with disease duration. While GABAA density changes within primary motor areas could represent a partial compensatory response to loss of inhibition within sensorimotor network, GABAergic signaling impairment within the cerebellum may be a key contributor to dystonia severity.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.012
GPT teacher head0.246
Teacher spread0.234 · 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

Citations32
Published2018
Admission routes1
Has abstractyes

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