Chronic lithium treatment alters the excitatory/inhibitory balance of synaptic networks and reduces mGluR5-PKC signaling
Bibliographic record
Abstract
ABSTRACT Bipolar disorder (BD) is characterized by cyclical alternations between mania and depression, often comorbid with psychosis, and suicide. The mood stabilizer lithium, compared to other medications, is the most efficient treatment for prevention of manic and depressive episodes. The pathophysiology of BD, and lithium’s mode of action, are yet to be fully understood. Evidence suggests a change in the balance of excitatory/inhibitory activity, favouring excitation in BD. Here, we sought to establish a holistic appreciation of the neuronal consequences of lithium exposure in mouse cortical neurons and identify underlying mechanisms. We found that chronic (but not acute) lithium treatment significantly reduced intracellular calcium flux, specifically through the activation of the metabotropic glutamatergic receptor mGluR5. This was associated with altered phosphorylation of PKC and GSK3 kinases, reduced neuronal excitability, and several alterations to synapse function. Consequently, lithium treatment shifts the excitatory/inhibitory balance in the network toward inhibition. Together, the results revealed how lithium dampens neuronal excitability and glutamatergic network activity, which are predicted to be overactive in the manic phase of BD. Our working model of lithium action enables the development of targeted strategies to restore the balance of overactive networks, mimicking the therapeutic benefits of lithium, but with reduced toxicity.
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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) | 0.004 | 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".