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Record W7105795550

Réseaux moléculaires, structure, et fonction du striatum pendant l'apprentissage comportemental et l'automatisation

2023· article· en· W7105795550 on OpenAlexfundno aff

Bibliographic record

Venuetheses.fr (ABES) · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaInstitut de Valorisation des DonnéesFondation pour la Recherche MédicaleInstitut National de la Santé et de la Recherche MédicaleCanada First Research Excellence FundAgence Nationale de la RechercheL'Oreal USA
KeywordsStriatumDopaminergicAssociative propertyBasal gangliaContext (archaeology)Ventral striatumVentral tegmental areaDorsolateral
DOInot available

Abstract

fetched live from OpenAlex

Efficient every day skills, such as tying shoelaces, rely on automatized behaviours. As behaviours are learnt and repeated, the link between the action and its context incrementally increases until automaticity. Behavioural automatization is essential to for smooth and effortless execution of tasks, freeing up cognitive resources for more complex activities. This process of acquisition, consolidation and automatization depends on cortico-basal ganglia circuits. These circuits are topographically organized into parallel limbic, associative and sensorimotor loops coursing through the ventromedial, dorsomedial, and dorsolateral striatum (DMS), respectively. These circuits dynamically interact and are recruited to different extents during learning. The limbic cortico‐striatal loop is especially important during initial acquisition, when behaviour is highly exploratory and reward-dependent. The associative loop is particularly recruited during the early, goal‐directed phase of learning, when associations are established. Finally, the sensorimotor loop is crucial when the learned behaviour becomes automatized. Some genes have been identified as being involved in certain phases of learning, however, subregion-specific genome-wide expression profiles of the striatum are lacking. To address this, the first part of my PhD project consisted in the assessment of the molecular signatures in the different striatal areas (ventromedial, dorsomedial, and dorsolateral striatum) during behavioural acquisition and automatization by creating a RNA expression map at each stage of learning. Furthermore, the seemingly segregated nature of the cortico-basal ganglia loops raises the question on how the information is transferred from one circuit to another. One strong candidate for such transversal flow are dopaminergic neurons that project across these loops. While dopaminergic afferents from ventral tegmental area (VTA) mainly target the ventral striatum, recent studies described a proportion of dopaminergic VTA neurons projecting to the dorsal striatum, hinting for its role in information transfer across distinct CBG loops, potentially consolidating the ongoing action into automatization. Therefore, my second part of my PhD project involved the investigation of the role of the dopaminergic VTA-DMS projections during behavioural automatization by projection-specific recording and modulation of its neurons. Finally, the third part of my PhD project focused on studying a mouse model of repetitive behaviours that presents aberrant habit formation, the Sapap3-KO mouse. I have concluded a detailed structural analysis of the neurons in the striatum of these animals, showing a reduced axon calibre in a subgroup of neurons in the DMS of the Sapap3-KO animals, when compared with their wild-type littermates. Overall, this thesis expands our knowledge of the neural circuits and molecular pathways involved in both normal and pathological regulation of habitual behaviors.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score0.916

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 head0.134
GPT teacher head0.383
Teacher spread0.249 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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