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Record W4284706782 · doi:10.1016/j.celrep.2022.111043

Enhanced motor cortex output and disinhibition in asymptomatic female mice with C9orf72 genetic expansion

2022· article· en· W4284706782 on OpenAlexafffund
Sona Amalyan, Suhel Tamboli, Ivan Lazarevich, Dimitry Topolnik, Leandra Harriet Bouman, Lisa Topolnik

Bibliographic record

VenueCell Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsCentre hospitalier universitaire de QuébecUniversité Laval
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaALS Society of Canada
KeywordsC9orf72NeuroscienceDisinhibitionAsymptomaticAmyotrophic lateral sclerosisMotor cortexPrimary motor cortexBiologyMedicineTrinucleotide repeat expansionDiseasePathologyGeneticsGeneAllele

Abstract

fetched live from OpenAlex

Information and action coding by cortical circuits relies on a balanced dialogue between excitation and inhibition. Circuit hyperexcitability is considered a potential pathophysiological mechanism in various brain disorders, but the underlying deficits, especially at early disease stages, remain largely unknown. We report that asymptomatic female mice carrying the chromosome 9 open reading frame 72 (C9orf72) repeat expansion, which represents a high-prevalence genetic abnormality for human amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) spectrum disorder, exhibit abnormal motor cortex output. The number of primary motor cortex (M1) layer 5 pyramidal neurons is reduced in asymptomatic mice, with the surviving neurons receiving a decreased inhibitory drive that results in a higher M1 output, specifically during high-speed animal locomotion. Importantly, using deep-learning algorithms revealed that speed-dependent M1 output predicts the likelihood of C9orf72 genetic expansion. Our data link early circuit abnormalities with a gene mutation in asymptomatic ALS/FTLD carriers.

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 score0.934
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

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

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 head0.015
GPT teacher head0.248
Teacher spread0.233 · 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

Citations21
Published2022
Admission routes2
Has abstractyes

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