NF1 dissociates cell type specific contributions toward reward valuation and motor control
Bibliographic record
Abstract
Neurofibromin 1 (NF1) is a large multidomain signaling molecule that is a major Ras regulator but also acts as a positive regulator of cAMP levels by mediating G protein‐coupled receptor (GPCR)‐dependent activation of adenylate cyclase. Mutations in the NF1 gene cause Neurofibromatosis type 1, a genetic disorder characterized by multiple benign and malignant tumors with prominent neuropsychiatric symptoms that include learning and attention deficits, as well as motor impairments. Recently, we found that NF1 is a direct effector of GPCR signaling via Gβγ subunits in the striatum. However, it remains unclear the signaling contribution of NF1 in striatal‐mediator behaviors. Here we demonstrate the impact of intracellular signaling pathways commonly targeted by neurotransmitter inputs that impact medium spiny neurons (MSN) activity can be dissociated in a cell‐specific manner with distinct contribution to motor learning and reward‐related behaviors. We genetically ablated NF1 with cellular resolution demonstrating the diverging behavioral and signaling specific manner in which NF1 contributes to the acquisition and consolidating of learning a motor skill, as well as its regulation of morphine reward. Support or Funding Information This work was supported by the NIH and by the Canadian Institutes of Health Research (CIHR) Fellowship. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".