Tonic GABA<sub>A</sub>Receptor-Mediated Currents of Human Cortical GABAergic Interneurons Vary Amongst Cell Types
Bibliographic record
Abstract
Persistent anion conductances through GABAAreceptors (GABAARs) are important modulators of neuronal excitability. However, it is currently unknown how the amplitudes of these currents vary among different cell types in the human neocortex, particularly among diverse GABAergic interneurons. We have recorded 101 interneurons in and near layer 1 from cortical tissue surgically resected from both male and female patients, visualized 84 of them and measured tonic GABAAR currents in 48 cells with an intracellular [Cl–] of 65 mmand in the presence of 5 μmGABA. We compare these tonic currents among five groups of interneurons divided by firing properties and four types of interneuron defined by axonal distributions; rosehip, neurogliaform, stalked-bouton, layer 2–3 innervating and a pool of other cells. Interestingly, the rosehip cell, a type of interneuron only described thus far in human tissue, and layer 2–3 innervating cells exhibit larger tonic currents than other layer 1 interneurons, such as neurogliaform and stalked-bouton cells; the latter two groups showing no difference. The positive allosteric modulators of GABAARs allopregnanolone and DS2 also induced larger current shifts in the rosehip and layer 2–3 innervating cells, consistent with higher expression of the δ subunit of the GABAAR in these neurons. We have also examined how patient parameters, such as age, seizures, type of cancer and anticonvulsant treatment may alter tonic inhibitory currents in human neurons. The cell type-specific differences in tonic inhibitory currents could potentially be used to selectively modulate cortical circuitry. SIGNIFICANCE STATEMENTTonic currents through GABAAreceptors (GABAARs) are a potential therapeutic target for a number of neurologic and psychiatric conditions. Here, we show that these currents in human cerebral cortical GABAergic neurons display cell type-specific differences in their amplitudes which implies differential modulation of their excitability. Additionally, we examine whether the amplitudes of the tonic currents measured in our study show any differences between patient populations, finding some evidence that age, seizures, type of cancer, and anticonvulsant treatment may alter tonic inhibition in human tissue. These results advance our understanding of how pathology affects neuronal excitability and could potentially be used to selectively modulate cortical circuitry.
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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) | 0.001 | 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".