Differential effects of natural rewards and pain on vesicular glutamate transporter expression in the nucleus accumbens
Bibliographic record
Abstract
BACKGROUND: Pain and natural rewards such as food elicit different behavioral effects. Both pain and rewards, however, have been shown to alter synaptic activities in the nucleus accumbens (NAc), a key component of the brain reward system. Mechanisms by which external stimuli regulate plasticity at NAc synapses are largely unexplored. Medium spiny neurons (MSNs) from the NAc receive excitatory glutamatergic inputs and modulatory dopaminergic and cholinergic inputs from a variety of cortical and subcortical structures. Glutamate inputs to the NAc arise primarily from prefrontal cortex, thalamus, amygdala, and hippocampus, and different glutamate projections provide distinct synaptic and ultimately behavioral functions. The family of vesicular glutamate transporters (VGLUTs 1-3) plays a key role in the uploading of glutamate into synaptic vesicles. VGLUT1-3 isoforms have distinct expression patterns in the brain, but the effects of external stimuli on their expression patterns have not been studied. RESULTS: In this study, we use a sucrose self-administration paradigm for natural rewards, and spared nerve injury (SNI) model for chronic pain. We examine the levels of VGLUTs (1-3) in synaptoneurosomes of the NAc in these two behavioral models. We find that chronic pain leads to a decrease of VGLUT1, likely reflecting decreased projections from the cortex. Pain also decreases VGLUT3 levels, likely representing a decrease in projections from GABAergic, serotonergic, and/or cholinergic interneurons. In contrast, chronic consumption of sucrose increases VGLUT3 in the NAc, possibly reflecting an increase from these interneuron projections. CONCLUSION: Our study shows that natural rewards and pain have distinct effects on the VGLUT expression pattern in the NAc, indicating that glutamate inputs to the NAc are differentially modulated by rewards and pain.
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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".