Evidence map›Paper›PMID 39349057›Full record

ArticleeNeuro2024

Coding Dynamics of the Striatal Networks During Learning.

Maxime Villet, Patricia Reynaud-Bouret, Julien Poitreau, Jacopo Baldi, Sophie Jaffard, Ashwin James, Alexandre Muzy, Evgenia Kartsaki, Gilles Scarella, Francesca Sargolini and 1 more

Abstract read
In one paragraph

Article in eNeuro, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Maxime VilletUniversité Côte d'Azur, CNRS, INSERM, Institut de Pharmacologie Moléculaire et Cellulaire, Valbonne 06560, France.
Patricia Reynaud-BouretUniversité Côte d'Azur, CNRS, LJAD and NeuroMod, Nice 0600, France Patricia.Reynaud-Bouret@univ-cotedazur.fr bethus@ipmc.cnrs.fr.
Julien PoitreauCRPN, UMR 7077, Aix-Marseille University, CNRS, Marseille 13331, France.
Jacopo BaldiUniversité Côte d'Azur, CNRS, INSERM, Institut de Pharmacologie Moléculaire et Cellulaire, Valbonne 06560, France.
Sophie JaffardUniversité Côte d'Azur, CNRS, LJAD and NeuroMod, Nice 0600, France.
Ashwin JamesUniversité Côte d'Azur, CNRS, I3S, Valbonne 06560, France.
Alexandre MuzyUniversité Côte d'Azur, CNRS, I3S, Valbonne 06560, France.
Evgenia KartsakiUniversité Côte d'Azur, NeuroMod, Valbonne 06560, France.
Gilles ScarellaUniversité Côte d'Azur, CNRS, LJAD and NeuroMod, Nice 0600, France.
Francesca SargoliniCRPN, UMR 7077, Aix-Marseille University, CNRS, Marseille 13331, France.
Ingrid BethusUniversité Côte d'Azur, CNRS, INSERM, Institut de Pharmacologie Moléculaire et Cellulaire, Valbonne 06560, France Patricia.Reynaud-Bouret@univ-cotedazur.fr bethus@ipmc.cnrs.fr.ORCID https://orcid.org/0000-0001-8356-6963

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rat dorsomedial (DMS) and dorsolateral striatum (DLS), equivalent to caudate nucleus and putamen in primates, are required for goal-directed and habit behaviour, respectively. However, it is still unclear whether and how this functional dichotomy emerges in the course of learning. In this study, we investigated this issue by recording DMS and DLS single neuron activity in rats performing a continuous spatial alternation task, from the acquisition to optimized performance. We first applied a classical analytical approach to identify task-related activity based on the modifications of single neuron firing rate in relation to specific task events or maze trajectories. We then used an innovative approach based on Hawkes process to reconstruct a directed connectivity graph of simultaneously recorded neurons, that was used to decode animal behavior. This approach enabled us to better unravel the role of DMS and DLS neural networks across learning stages. We showed that DMS and DLS display different task-related activity throughout learning stages, and the proportion of coding neurons over time decreases in the DMS and increases in the DLS. Despite these major differences, the decoding power of both networks increases during learning. These results suggest that DMS and DLS neural networks gradually reorganize in different ways in order to progressively increase their control over the behavioral performance.

Indexed as

Action PotentialsCorpus StriatumNeuronsAnimalsLearningMaleMaze LearningNerve NetNeural PathwaysRatsRats, Long-Evans

Identifiers

PMID39349057
PMCPMC11521795

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.