Pendulum Study: Active Visual Tracking Elicits Non‐Selective Elevations in Cerebral Blood Flow
Bibliographic record
Abstract
Neurovascular coupling (NVC) describes the effective matching of cerebral blood flow (CBF) to regions of neuro‐metabolic demand. There is increasing interest to assess human NVC for both basic research and its potential role in vascular‐cognitive impairment. The clinical utility of NVC relies on a standardized protocol for which the driving metabolic demands are highly‐selective. Various research groups deploy divergent strategies to elicit visual NVC responses, including inactive processes (visual grating), passive visual tracking (target with predictable motion) and active visual tracking (target with unpredictable motion). These strategies differ in degree of cognitive and metabolic demand and may elicit different NVC responses, thus precluding study comparison. The present NVC assessment evaluated temporal and regional responsiveness of blood flow (transcranial Doppler) to the visual cortex [via the posterior cerebral artery (PCA)] and blood pressure (Finapres NOVA) during visual stimulation in 19 healthy subjects while also measuring middle cerebral artery (MCA) blood flow. Visual stimulation included 10 cycles of 30‐seconds with eyes‐closed, followed by 30‐seconds with eyes‐open tracking a moving computerized target. Each subject completed three trials of passive tracking and three trials of active tracking (114 NVC protocols, 1140 individual hyperemias). A custom eye‐scanning apparatus followed eye motion to quantify visual target‐tracking vigilance. Additional custom software was used to quantify NVC. The data demonstrated that active tracking elicited greater NVC responses compared to passive tracking. Specifically, there was 26% greater change in the mean elevation of PCA blood velocity (p=<0.000) and 13% greater peak NVC response (p<0.01). The MCA response was also greater during active tracking (mean response 111% greater, peak response 41% greater; both p<0.001). Visual target‐tracking vigilance was linearly correlated to the degree of hyperemia in the MCA and PCA, as well blood pressure during NVC. It was observed that active and passive visual tracking elicit different NVC responses and cannot be reliably compared. That PCA and MCA responses were greater with active tracking suggests an elevated global CBF (i.e. not selective to regions perfused by the PCA) that may result from recruitment of brain centres responsible for sustained attention and executive function. In other words, active tracking leads to non‐selective elevations in global CBF and greater target‐tracking vigilance impacts the NVC response. These findings are a critical step to better understand and standardize the evaluation of NVC in humans and for potential clinical deployment of NVC assessments. Support or Funding Information Natural Sciences and Engineering Research Council; Alberta Innovates Research Studentship; NSERC; CIHR; Libin Cardiovascular Institute; Hotchkiss Brain Institute; Compute Canada This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".