The effects of intracranial stimulation on local neurovascular responses in humans
Bibliographic record
Abstract
Abstract Background Deep brain stimulation (DBS) is used to treat neurological and psychiatric disorders by modulating neuronal circuits. However, other mechanisms of DBS, such as the effects of electrical stimulation on the neurovascular unit remain poorly understood due to limitations of capturing microvascular changes near implanted leads. This motivated us to investigate electrophysiological surrogates of vascular dynamics in response to intraoperative microstimulation. Methods Microelectrode recordings (n = 193; 108 patients) were obtained during DBS implantation surgery from two electrodes (∼600 µm apart) before and after microstimulation through one electrode. Linear mixed models assessed cardioballistic waveform (CBW) amplitude changes following low-frequency (LFS, 1 Hz) or high-frequency stimulation (HFS, 100 Hz), with comparisons across basal ganglia regions and in white matter. An analytical model interpreted CBW amplitudes as pressure-driven vessel wall expansion, enabling estimation of stimulation-evoked vasodilation and cerebral blood flow. Results CBW amplitudes increased significantly after HFS at the stimulating electrode, but not at the non-stimulating electrode or after LFS. Significant region-specific effects were observed in the ventral intermediate nucleus (Vim; 107 ± 13%), subthalamic nucleus (STN; 79 ± 7%), and globus pallidus internus (GPi; 78 ± 8%), but not in white matter (50 ± 14%) or substantia nigra (45 ± 8%). Modelling showed that the mean 88% CBW increase across Vim, STN, and GPi corresponds to an acute increase in cerebral blood flow. Conclusion Intracranial CBW recordings reveal that high-frequency DBS evokes region-specific vascular responses which can be modelled as substantial increases in local blood flow, establishing CBW amplitude as a novel biomarker of subcortical hemodynamics, and a potential therapeutic modality.
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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 | 0.001 |
| 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".