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Descending Dopaminergic Inputs to Reticulospinal Neurons Promote Locomotor Movements

2020· article· en· W3089980773 on OpenAlexafffund
Dimitri Ryczko, Swantje Grätsch, Michael H. Alpert, Jackson J. Cone, Jacquelin Kasemir, Angelina Ruthe, Philippe‐Antoine Beauséjour, François Auclair, Mitchell F. Roitman, Simon Alford, Réjean Dubuc

Bibliographic record

VenueJournal of Neuroscience · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsUniversité du Québec à MontréalUniversité de MontréalCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
FundersFaculté de médecine et des sciences de la santé, Université de SherbrookeNational Institute on Drug AbuseFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchCanada Foundation for InnovationGovernment of CanadaNational Institutes of HealthNatural Sciences and Engineering Research Council of CanadaUniversité de SherbrookeGreat Lakes Fishery Commission
KeywordsDopaminergicReticular formationNeuroscienceDopamineSpinal cordBasal gangliaDopaminergic pathwaysBiologyAnatomyStimulationCentral nervous system

Abstract

fetched live from OpenAlex

Meso-diencephalic dopaminergic neurons are known to modulate locomotor behaviors through their ascending projections to the basal ganglia, which in turn project to the mesencephalic locomotor region, known to control locomotion in vertebrates. In addition to their ascending projections, dopaminergic neurons were found to increase locomotor movements through direct descending projections to the mesencephalic locomotor region and spinal cord. Intriguingly, fibers expressing tyrosine hydroxylase (TH), the rate-limiting enzyme of dopamine synthesis, were also observed around reticulospinal neurons of lampreys. We now examined the origin and the role of this innervation. Using immunofluorescence and tracing experiments, we found that fibers positive for dopamine innervate reticulospinal neurons in the four reticular nuclei of lampreys. We identified the dopaminergic source using tracer injections in reticular nuclei, which retrogradely labeled dopaminergic neurons in a caudal diencephalic nucleus (posterior tuberculum [PT]). Using voltammetry in brain preparations isolated in vitro , we found that PT stimulation evoked dopamine release in all four reticular nuclei, but not in the spinal cord. In semi-intact preparations where the brain is accessible and the body moves, PT stimulation evoked swimming, and injection of a D 1 receptor antagonist within the middle rhombencephalic reticular nucleus was sufficient to decrease reticulospinal activity and PT-evoked swimming. Our study reveals that dopaminergic neurons have access to command neurons that integrate sensory and descending inputs to activate spinal locomotor neurons. As such, our findings strengthen the idea that dopamine can modulate locomotor behavior both via ascending projections to the basal ganglia and through descending projections to brainstem motor circuits. SIGNIFICANCE STATEMENT Meso-diencephalic dopaminergic neurons play a key role in modulating locomotion by releasing dopamine in the basal ganglia, spinal networks, and the mesencephalic locomotor region, a brainstem region that controls locomotion in a graded fashion. Here, we report in lampreys that dopaminergic neurons release dopamine in the four reticular nuclei where reticulospinal neurons are located. Reticulospinal neurons integrate sensory and descending suprareticular inputs to control spinal interneurons and motoneurons. By directly modulating the activity of reticulospinal neurons, meso-diencephalic dopaminergic neurons control the very last instructions sent by the brain to spinal locomotor circuits. Our study reports on a new direct descending dopaminergic projection to reticulospinal neurons that modulates locomotor behavior.

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 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 score0.263
Threshold uncertainty score0.319

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 science0.0010.000
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 head0.030
GPT teacher head0.315
Teacher spread0.284 · 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

Citations28
Published2020
Admission routes2
Has abstractyes

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