Chronic Chemogenetic Manipulation of Ventral Pallidum Targeted Neurons in Rats Fed an Obesogenic Diet
Bibliographic record
Abstract
Abstract Current treatments for obesity are unable to reliably reduce weight over time and alternative treatments need to address this challenge. New interventions that target the nervous system could manipulate brain networks underlying reward, metabolic rate and behavior. Here, the ventral pallidum was evaluated as a target of manipulation due to its established role in the networks underlying motivation, pleasure and behavioral output. Chronic inhibitory or excitatory chemogenetic activation was used to modulate the activity of ventral pallidum (VP) targeted neurons in rats on an obesogenic diet. We hypothesized that inhibition of VP activity would decrease the salience of the rats’ high-sugar, high-fat diet and lead to reduced food consumption and weight gain over time. Paradoxically, measurements of weight, water and food consumption revealed significantly increased weight gain in both groups receiving VP targeted manipulation that was not readily explained by food or water consumption. We theorize that the complex reciprocal feedback between ventral striatal structures (e.g., VP) and metabolic centers of the hypothalamus and brainstem, demonstrated by prior research, likely underpin our findings. This study suggests that the treatment of appetitive disorders (e.g., obesity) with chronic neuromodulation-based interventions could be burdened by the delayed onset of outcomes that are difficult to predict from related prior studies that used acute interventions.
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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 | 0.001 |
| 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".