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Dopaminergic Modulation of Spinal Neuronal Excitability

2007· article· en· W2039829972 on OpenAlexafffund
Pengcheng Han, Stan T. Nakanishi, Michelle Tran, Patrick J. Whelan

Bibliographic record

VenueJournal of Neuroscience · 2007
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersCanadian Institutes of Health ResearchFondation pour la Recherche MédicaleHeart and Stroke Foundation of Canada
KeywordsNeuroscienceDopaminergicModulation (music)PsychologyDopaminePhysics

Abstract

fetched live from OpenAlex

It is well recognized that dopamine (DA) can modulate spinal networks and reflexes. DA fibers and receptors are present in the spinal cord, and evidence for DA release within the spinal cord has been published. A critical gap is the lack of data regarding dopaminergic modulation of intrinsic and synaptic properties of motoneurons and ventral interneurons in the mammalian spinal cord. In this paper, we address this issue by examining the cellular mechanisms underlying the excitatory effect of DA on motor systems. We examine the effects of DA on two classes of cells important for motor control, motoneurons and Hb9 interneurons, located in lamina VIII. We show that DA can boost excitability in spinal motoneurons by decreasing the first spike latency and the afterhyperpolarization. Collectively, this leads to an increase in the frequency-current slope likely attributable to modulation of I(A) and SK(Ca) (small-conductance calcium-activated K+ channel) currents. We also demonstrate that DA increases glutamatergic transmission onto motoneurons. Our data also suggest that DA stabilizes the rhythmic output of conditionally bursting interneurons. Collectively, these data indicate that DA has widespread actions on intrinsic and synaptic properties of ventral spinal neurons.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.091
GPT teacher head0.389
Teacher spread0.299 · 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 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

Citations110
Published2007
Admission routes2
Has abstractyes

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