Glutamatergic‐receptors blockade does not regularize the slow wave sleep bursty pattern of subthalamic neurons
Bibliographic record
Abstract
The subthalamic nucleus (STN) has been implicated in movement disorders observed in Parkinson's disease because of its pathological mixed burst firing mode and hyperactivity. In physiological conditions, STN bursty pattern has been shown to be dependent on slow wave cortical activity. Indeed, cortical ablation abolished STN bursting activity in urethane-anaesthetized intact or dopamine depleted rats. Thus, glutamate afferents might be involved in STN bursting activity during slow wave sleep (SWS) when thalamic and cortical cells oscillate in a low-frequency range. The present work was aimed to test, on non-anaesthetized rats, if it was possible to regularize the SWS STN bursty pattern by microiontophoresis of kynurenate, a broad-spectrum glutamate ionotropic receptors antagonist. As glutamatergic effects might be masked by GABAergic inputs arriving tonically and during the entire sleep-wake cycle on STN neurons, kynurenate was also co-iontophoresed with bicuculline, a GABA(A) receptors antagonist. Kynurenate iontophoretic applications had a weak inhibitory effect on the discharge rate of STN neurons whatever the vigilance state, and did not regularize the SWS STN bursty pattern. But, the robust bursty bicuculline-induced pattern was impaired by kynurenate, which elicited the emergence of single spikes between remaining bursts. These data indicate that the bursty pattern exhibited by STN neurons specifically in SWS, does not seem to exclusively depend on glutamatergic inputs to STN cells. Furthermore, GABA(A) receptors may play a critical role in regulating the influence of excitatory inputs on STN cells.
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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) | 0.001 | 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".