Response of the human LGN to different temporal frequencies for achromatic, L/M opponent and S-cone opponent stimuli measured with high field fMRI
Bibliographic record
Abstract
Aims. We compare the responsiveness of the human LGN to different temporal frequencies (2–16Hz) for stimulation of each of the two cone opponent systems (L/M and S-cone) and the achromatic system. For comparison, measurements were also obtained from cortical area V1. Methods. MR images were acquired on a 4T Bruker MedSpec scanner. Stimuli were sinewave counter-phasing rings (0.5cpd, 2, 4, 8 Hz for chromatic stimuli and 2, 8 & 16 Hz for achromatic stimuli) that selectively activated the L/M cone opponent (RG), S cone opponent (BY) or achromatic (Ach) systems. Stimuli were presented in a temporal Gaussian envelope (s=125ms) at high cone contrasts (11% Ach; 4% RG; 30% BY). The LGNs of 6 subjects were localized in separate scans using broadband stimuli. V1s were mapped previously (Mullen et al., Euro. J. Neuroscience, 2007). We performed region of interest analyses on all LGNs and V1s and results are based on average time courses. Results. for achromatic stimuli, the human LGN shows a declining response with increasing TF (2–16Hz), which also occurs but is less marked in V1. For RG stimuli, the LGN shows some loss of response across TF from 2 to 8Hz, whereas the V1 response is unaffected by TF. For BY stimuli, the response shows some peaking at 4Hz in the LGN, but has no dependence on TF in V1 (2–8Hz). Conclusions. We find that for our stimuli the BOLD response of the LGN is optimal at low temporal frequencies (2–4Hz) for both achromatic and chromatic stimuli. This contrasts with previous results for achromatic stimuli which show a rise in response with increasing temporal frequency (Kastner et al., J. Neurophysiol., 2004).
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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".