Anterior cingulate cortex and ventral tegmental area activity during cost-benefit decision-making following maternal immune activation
Bibliographic record
Abstract
Abstract Schizophrenia is associated with deficits in memory, behavioural flexibility, and motivation, which can result in difficulties in decision-making. The anterior cingulate cortex (ACC) and ventral tegmental area (VTA) are two brain regions that are involved in decision-making, and display dysfunction in schizophrenia. We investigated ACC and VTA activity in the maternal immune activation (MIA) model of a schizophrenia risk factor. Control and MIA rats completed a cost-benefit decision-making task in a continuous T-maze, choosing between a high cost and high reward (HCHR), and a low cost and low reward (LCLR), option. A choice reversal occurred halfway through each session. Single unit activity in the ACC and local field potentials (LFPs) in the VTA were monitored. Overall, MIA and control rats made a similar proportion of HCHR and LCLR choices across the whole recording session, suggesting similar levels of motivation. However, MIA rats made different decisions than controls during periods of increased uncertainty. This appeared to reflect memory deficits and behavioural inflexibility. MIA animals displayed an increase in ACC activity associated with cost, an increase in synchrony of ACC neurons to the VTA theta oscillation, and a decrease in coherence in the delta frequency between the ACC and VTA. These changes suggest that MIA animals may be biased towards focussing on the cost rather than the benefits of the task, a change also seen in schizophrenia. Here, however, the MIA animals may be able to increase motivation to maintain behaviour despite this change.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".