Neurovascular coupling during deep brain stimulation
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation (DBS) is an effective treatment for movement disorders, yet its mechanisms of action remain unclear. One method used to study its circuit-wide neuromodulatory effects is functional magnetic resonance imaging (fMRI) which measures hemodynamics as a proxy of neural activity. To interpret functional imaging data, we must understand the relationship between neural and vascular responses, which has never been studied with the high frequencies used for DBS. OBJECTIVE: To measure neurovascular coupling in the rat motor cortex during thalamic DBS. METHOD: Simultaneous intrinsic optical imaging and extracellular electrophysiology was performed in the motor cortex of urethane-anesthetized rats during thalamic DBS at 7 different frequencies. We related Maximum Change in Reflectance (MCR) from the imaging data to Integrated Evoked Potential (IEP) and change in broadband power of multi-unit (MU) activity, computing Spearman's correlation to determine the strength of these relationships. To determine the source of these effects, we studied the contributions of antidromic versus orthodromic activation in motor cortex perfusion using synaptic blockers. RESULTS: MCR, IEP and change in MU power increased linearly to 60 Hz and saturated at higher frequencies of stimulation. Blocking orthodromic transmission only reduced the DBS-induced change in optical signal by ∼25%, suggesting that activation of corticofugal fibers have a major contribution in thalamic-induced cortical activation. CONCLUSION: DBS-evoked vascular response is related to both evoked field potentials as well as multi-unit activity.
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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".