Spatiotemporal Evolution of the Functional Magnetic Resonance Imaging Response to Ultrashort Stimuli
Bibliographic record
Abstract
The specificity of the hemodynamic response function (HRF) is determined spatially by the vascular architecture and temporally by the evolution of hemodynamic changes. The stimulus duration has additional influence on the spatiotemporal evolution of the HRF, as brief stimuli elicit responses that engage only the local vasculature, whereas long stimuli lead to the involvement of remote vascular supply and drainage. Here, we used functional magnetic resonance imaging to investigate the spatiotemporal evolution of the blood oxygenation level-dependent (BOLD), cerebral blood flow (CBF), and cerebral blood volume (CBV) HRF to ultrashort forelimb stimulation in an anesthetized rodent model. The HRFs to a single 333-μs-long stimulus were robustly detected and consisted of a rapid response in both CBF and CBV, with an onset time (OT) of 350 ms and a full width at half-maximum of 1 s. In contrast, longer stimuli elicited a dispersive transit of oxygenated blood across the cortical microvasculature that significantly prolonged the evolution of the CBV HRF, but not the CBF. The CBF and CBV OTs suggest that vasoactive messengers are synthesized, released, and effective within 350 ms. However, the difference between the BOLD and CBV OT (∼100 ms) was significantly smaller than the arteriolar-venular transit time (∼500 ms), indicating an arterial contribution to the BOLD HRF. Finally, the rapid rate of growth of the active region with stimulus elongation suggests that functional hyperemia is an integrative process that involves the entire functional cortical depth. These findings offer a new view into the spatiotemporal dynamics of functional hemodynamic regulation in the brain.
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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 | 0.000 |
| 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".