Effects of Visual Deprivation on Regional Cerebral Blood Flow Velocity and Neurovascular Coupling
Bibliographic record
Abstract
Recently, short-term visual deprivation has been shown to affect a variety of non-visual processes and regional cortical activity (Sathian & Zangaladze, 2001). Surprisingly, very little is known about how such visual deprivation impacts regional cerebral blood flow velocity (CBFv) or its adaptation with the underlying neuronal activity (i.e., neurovascular coupling). The current study sought to investigate the effects of short-term (two-hour) visual deprivation on regional CBFv and neurovascular coupling. CBFv (transcranial Doppler ultrasound) was measured concurrently in the posterior cerebral artery (PCA) and the middle cerebral artery (MCA). Neurovascular coupling was assessed using established methods, consisting of two minutes of baseline (eyes closed and reading), five cycles of 40 seconds reading - 20 seconds eyes-closed (primary protocol), and five cycles of 40 seconds eyes-moving - 20 seconds eyes-closed (secondary protocol). Neurovascular coupling, using both protocols, was collected before and following a two-hour visual deprivation (black out) protocol whilst both regional CBFv and secondary neurovascular coupling protocol was measured at thirty-minute intervals throughout deprivation. Baseline measures indicated mean MCAv decreased 7.36% as a function of visual deprivation while PCAv showed the reciprocal effect, increasing 8.2% with differences arising via systolic peaks on both arteries. The primary (reading/closed) neurovascular-coupling protocol revealed post-deprivation decreases in MCAv of 9.32% while the secondary (moving/closed) protocol also decreased MCAv 12.5% as a function of deprivation. In addition, the secondary (moving/closed) protocol elicited systematic decreases in both systolic and diastolic peaks of PCAv across the 30, 60, and 90-minute intervals of visual deprivation. Short-term visual deprivation has been shown to differentially affect regional CBFv. Moreover, variations in blood flow provide further insight regarding visual processing, attenuation, and neurovascular coupling.
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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 | 0.001 |
| 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".