Long-Term Treatment with Low Doses of Methamphetamine Promotes Neuronal Differentiation and Strengthens Long-Term Potentiation of Glutamatergic Synapses onto Dentate Granule Neurons
Bibliographic record
Abstract
Methamphetamine (METH) is a psychostimulant, affecting hippocampal function with disparate cognitive effects, which depends on the dose and time of administration, ranging from improvement to impairment of memory. Importantly, in the United States, METH is approved for the treatment of attention deficit hyperactivity disorder. Modifications of long-term plasticity of synapses originating from the entorhinal cortex onto dentate granule cells (DGCs) have been proposed to underlie cognitive alterations similar to those seen in METH users. However, the effects of METH on synaptic plasticity of the dentate gyrus are unknown. Here, we investigated the impact of long-term administration of METH (2 mg/kg/d) on neurogenesis and synaptic plasticity of immature and mature DGCs of juvenile mice. We used a mouse model of neurogenesis (the G42 line of GAD67-GFP), in which GFP is expressed by differentiating young DGCs. METH treatment enhanced the differentiation of GFP(+) cells, as it increased the fraction of GFP(+) cells expressing the neuronal marker NeuN, and decreased the amount of immature DGCs coexpressing doublecortin. Interestingly, METH did not change the magnitude of long-term potentiation (LTP) in more immature neurons, but facilitated LTP induction in more differentiated GFP(+) and strengthened plasticity in mature GFP(-) DGCs. The METH-induced facilitation of LTP in GFP(+) neurons was accompanied with spine enlargement. Our results reveal a specific action of long-term use of METH in the long-term plasticity of excitatory synapses onto differentiating DGCs and might have important implications toward the understanding of the synaptic basis of METH-induced cognitive alterations.
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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 | 0.001 |
| 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".