Non-invasive simultaneous recording of neuronal and vascular signals in subacute ischemic stroke
Bibliographic record
Abstract
In acute focal cerebral ischemia blood flow and neuronal activity change dramatically. A better understanding of the pathophysiological interactions of these two important parameters is limited owing to the lack of noninvasive techniques to simultaneously measure these parameters in humans. In this feasibility study, we used DC-magnetoencephalography and near-infrared spectroscopy to find out whether blood flow and neuronal activity as well as neurovascular coupling can be analyzed in patients suffering from subacute ischemic stroke. In a simple motor test condition, six patients with subacute ischemic stroke performed self-paced finger movements (30-s periods of movement, separated by 30-s periods of rest; for a total of 15 min). Combined DC-magnetoencephalography and near-infrared spectroscopy were recorded over the affected and unaffected hemispheres. As a control group, four healthy subjects were investigated. In four out of six patients, the time courses of both signals closely followed the motor task cycles revealing significant differences between movement and rest periods. The vascular signal reached a maximum 1-5 s later than the neuronal signals. This proof-of-principle study demonstrates that it has become feasible to simultaneously and noninvasively monitor neuronal and vascular signal changes in patients in the subacute state of ischemic stroke.
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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) | 0.000 | 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".