Cortical functional hyperconnectivity in a mouse model of depression and selective network effects of ketamine
Bibliographic record
Abstract
See Huang and Liston (doi:10.1093/awx166) for a scientific commentary on this article.Human depression is associated with glutamatergic dysfunction and alterations in resting state network activity. However, the indirect nature of human in vivo glutamate and activity assessments obscures mechanistic details. Using the chronic social defeat mouse model of depression, we determine how mesoscale glutamatergic networks are altered after chronic stress, and in response to the rapid acting antidepressant, ketamine. Transgenic mice (Ai85) expressing iGluSnFR (a recombinant protein sensor) permitted real-time in vivo selective characterization of extracellular glutamate and longitudinal imaging of mesoscale cortical glutamatergic functional circuits. Mice underwent chronic social defeat or a control condition, while spontaneous cortical activity was longitudinally sampled. After chronic social defeat, we observed network-wide glutamate functional hyperconnectivity in defeated animals, which was confirmed with voltage sensitive dye imaging in an independent cohort. Subanaesthetic ketamine has unique effects in defeated animals. Acutely, subanaesthetic ketamine induces large global cortical glutamate transients in defeated animals, and an elevated subanaesthetic dose resulted in sustained global increase in cortical glutamate. Local cortical inhibition of glutamate transporters in naïve mice given ketamine produced a similar extracellular glutamate phenotype, with both glutamate transients and a dose-dependent accumulation of glutamate. Twenty-four hours after ketamine, normalization of depressive-like behaviour in defeated animals was accompanied by reduced glutamate functional connectivity strength. Altered glutamate functional connectivity in this animal model confirms the central role of glutamate dynamics as well as network-wide changes after chronic stress and in response to ketamine.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".