Réseaux moléculaires, structure, et fonction du striatum pendant l'apprentissage comportemental et l'automatisation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".