Recruitment of the indirect pathway by subthalamic deep brain stimulation
Bibliographic record
Abstract
Abstract Background: Deep brain stimulation (DBS) of the subthalamic nucleus is a therapeutic neurocircuit intervention to treat symptoms of Parkinson’s disease (PD). Recently, evoked resonant neural activity (ERNA) has been described as a signature of subthalamic DBS that scales with therapeutic efficacy, but the single neuron and synaptic bases underlying ERNA remain unsubstantiated. Methods: We combine STN microelectrode recordings in PD patients undergoing DBS surgery with computational mesocircuit modelling to test different circuit montages and short-term synaptic dynamics necessary for the emergence of ERNA and use mapping of ERNA hotspots to test the relation of ERNA to clinical improvement achieved by DBS. Results: High frequency stimulation (HFS) of the STN resulted in ERNA waveforms predictive of patterned inhibition of action potential firing. At HFS, depression of the first peak of ERNA was coupled to the emergence of a second peak. Computational modelling revealed that this relationship could be explained by (i) distinct synaptic dynamics in the reciprocal STN- external-pllidum (GPe)-loop and (ii) self-inhibition within GPe via recruitment of axon-collaterals. Finally, electrophysiological mapping localized the highest ERNA amplitudes within the STN and was able to predict clinical improvement achieved by DBS. Interpretation: These multi-modal findings suggest that concurrent antidromic and orthodromic activations of the indirect pathway by subthalamic DBS produce a spatially defined neuronal circuit signature that is predictive of its therapeutic potential. Research Category and Technology and Methods Translational Research: 1. Deep Brain Stimulation (DBS) Keywords: DBS mechanism of action, Synpatic mechanisms, ERNA, Indirect pathway
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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".