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Record W2989465201 · doi:10.3389/fnsys.2019.00069

Activation of Brainstem Neurons During Mesencephalic Locomotor Region-Evoked Locomotion in the Cat

2019· article· en· W2989465201 on OpenAlexaff
Ioan Opriş, X Dai, Dawn M. G. Johnson, Francisco J. Sánchez, Luz M. Villamil, Songtao Xie, Cecelia R. Lee-Hauser, Stephano J. Chang, Larry M. Jordan, Brian R. Noga

Bibliographic record

VenueFrontiers in Systems Neuroscience · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience of respiration and sleep
Canadian institutionsUniversity of Manitoba
FundersNational Institute of Neurological Disorders and StrokeNational Institutes of HealthNeurosurgery Research and Education FoundationMedical Research CouncilFoundation for the National Institutes of Health
KeywordsNeuroscienceReticular formationBrainstemDorsal raphe nucleusParabrachial NucleusPonsRaphe nucleiPeriaqueductal grayNucleusLateral parabrachial nucleusSerotonergic cell groupsNucleus raphe magnusTegmentumMidbrainPedunculopontine nucleusChemistryLocus coeruleusBiologySerotonergicCentral nervous systemSerotoninDeep brain stimulationInternal medicineParkinson's diseaseMedicine

Abstract

fetched live from OpenAlex

The distribution of locomotor-activated neurons in the brainstem of the cat was studied by c-Fos immunohistochemistry in combination with antibody-based cellular phenotyping following electrical stimulation of the mesencephalic locomotor region (MLR) – the anatomical constituents of which remain debated today, primarily between the cuneiform (CnF) and the pedunculopontine tegmental nuclei (PPT). Effective MLR sites were co-extensive with the CnF nucleus. Animals subject to the locomotor task showed abundant Fos labeling in the CnF, parabrachial nuclei of the subcuneiform region, periaqueductal grey, locus ceruleus/subceruleus, Kölliker-Fuse, magnocellular and lateral tegmental fields, raphe, and the parapyramidal region. Labeled neurons were more abundant on the side of stimulation. In some animals, Fos labeled cells were also observed in the ventral tegmental area, medial and intermediate vestibular nuclei, dorsal motor nucleus of the vagus, n. tractus solitarii and retrofacial nucleus in the ventrolateral medulla. Many neurons in the reticular formation were innervated by serotonergic fibers. Numerous locomotor-activated neurons in the parabrachial nuclei and locus ceruleus/subceruleus/Kölliker-Fuse were noradrenergic. Few cholinergic neurons within the PPT stained for Fos. In the medulla, serotonergic neurons within the parapyramidal region and the nucleus raphe magnus were positive for Fos. Control animals, not subject to locomotion, showed few Fos labeled neurons in these areas. The current study provides positive evidence for a role for the CnF in the initiation of locomotion while providing little evidence for the participation of the PPT. The results also show that MLR-evoked locomotion involves the parallel activation of reticular and monoaminergic neurons in the pons/medulla, and provides the anatomical and functional basis for spinal monoamine release during evoked locomotion. Lastly, the results indicate that vestibular, cardiovascular and respiratory centers are centrally activated during MLR evoked locomotion. Altogether, the results show a complex pattern of neuromodulatory influences of brainstem neurons by electrical activation of the MLR.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.250
Teacher spread0.221 · 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 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

Citations50
Published2019
Admission routes1
Has abstractyes

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