Alcohol dependence modifies brain networks activated during abstinence and reaccess: a c-fos-based analysis in mice
Bibliographic record
Abstract
Abstract High-level alcohol consumption causes neuroplastic changes in the brain that lead to negative affective and somatic symptoms when alcohol is withdrawn, promoting relapse drinking. We have some understanding of these plastic changes in defined brain circuits and cell types, but unbiased approaches are needed to explore broader patterns of adaptations. Here, we employed whole-brain c-fos mapping and network analysis to assess how brain-wide patterns of neuronal activity are altered during acute alcohol abstinence and reaccess in a well-characterized model of alcohol dependence. Mice underwent four cycles of chronic intermittent ethanol vapor exposure (CIE) with alternating weeks of voluntary alcohol drinking, and a subset of mice underwent forced swim stress (FSS) prior to drinking sessions to further escalate alcohol consumption. After four CIE cycles, brains were collected from mice in each group either 24 hours (abstinence) or immediately following a one-hour period of alcohol reaccess. Brains from CIE mice during acute abstinence displayed widespread neuronal activation relative to those from AIR mice, independent of FSS, and this increase in c-fos was reversed by reaccess drinking. For network analysis, mice were then classified as high or low drinkers (HD or LD). We computed Pearson correlations for all pairs of brain regions and used graph theoretical methods to identify changes in network properties associated with high-drinking behavior. Network modularity, a measure of network segregation into communities, was increased in HD mice after alcohol reaccess relative to abstinence. Within-community strength and diversity measures were computed for each region and condition, and highly coactive regions were identified. One high-diversity region, the cortical amygdala (COA), was further interrogated using a chemogenetic approach. COA silencing in CIE mice reduced voluntary drinking, validating our network analysis and indicating that this region may play an important but underappreciated role in alcohol dependence.
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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 | 0.001 | 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) | 0.001 | 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".