Transient Hypertension Concurrent with Forepaw Stimulation Enhances Functional MRI Responsiveness in Infarct and Peri-Infarct Regions
Bibliographic record
Abstract
Although functional magnetic resonance imaging (fMRI) is gaining use as a tool to assess cerebral recovery following various insults, the effects of potential confounders such as hypertension are poorly defined. We hypothesized that after stroke, transient hypertension during an fMRI study could produce a detected activation unrelated to neuronal activity within the infarct. Thus, the effect of norepinephrine induced increases in blood pressure (BP) on the fMRI response to forepaw stimulation were investigated in controls or 1 week after transient middle cerebral artery occlusion in rats. Images were smoothed spatially and voxels correlating to either forepaw stimulation or the change in BP time courses were analyzed. Transient hypertension increased the signal intensity and numbers of voxels correlating to the BP time courses within and adjacent to the ischemic infarct and these exceeded the response in the contralateral hemisphere or in controls. With left paw stimulation at normotension, there was a loss of activation in right sensory-motor cortex -- a region with necrosis and disruption of cerebral vessels. As BP increased left paw stimulation also resulted in the detection of activation in the infarcted sensory-motor cortex and peri-infarct regions. Thus, BP changes synchronous with tasks in fMRI studies can result in MR signal changes consistent with a loss of cerebral blood flow (CBF) autoregulation rather than neuronal activation in necrotic brain. After stroke, the use of stressful tasks associated with BP changes in fMRI studies should be limited or the BP change should be considered as a potential source of MR signal changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".