Haemodynamic Responses to Radial Motion in the Visual Cortex
Bibliographic record
Abstract
Functional near-infrared spectroscopy (fNIRS) is an optical imaging technique that relies on emitting near-infrared light into cortical tissue to measure changes in haemoglobin concentrations as a result of stimulation. The purpose of this study was to observe haemodynamic changes in response to moving stimuli over the primary visual cortex. The test stimuli were radially expanding and contracting concentric gratings and the control stimulus was a matched stationary pattern. A two-channel oximeter (Oxiplex TS) was used to record changes in oxyhaemoglobin (HbO), de-oxyhaemoglobin (Hb) and total haemoglobin concentrations (THb). An increase in haemodynamic activation was observed 20s after the onset of motion and maintained for up to 20s following motion offset (30s time window). This was compared to a time window of 10s before the offset of the stationary presentation. Differences between expanding and contracting motion did not achieve significance at any of the locations. However, a significantly larger HbO response was observed across the 30s time window when compared to the baseline window at both occipital locations. Preliminary results from more temporal locations also showed a similar trend. It is suggested that the delayed rise in HbO levels could be due to an inconsistent maintenance of luminance and contrast-related features of the moving stimuli in V1 receptive fields. Furthermore, motion-after effects could have contributed to delaying the drop to baseline levels. The current study has shown some evidence that fNIRS can be used to record haemodynamic responses to moving stimuli from the visual cortex. We suggest that longer durations of randomised moving, stationary and grey screen presentations would be useful in teasing apart pure motion responses and after-effects. fNIRS could also potentially be used to investigate haemodynamic changes in disorders such as amblyopia that present with motion processing deficits.
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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".