Discriminating the eye-specific layers of the human lateral geniculate nucleus using high-resolution fMRI
Bibliographic record
Abstract
Introduction The lateral geniculate nucleus (LGN) is a small thalamic nucleus receiving binocular input from the contralateral visual field. The LGN is organized into six interleaved monocular layers. The dorsal four main layers receive parvocellular (P) input and the dorsal two layers magnocellular (M). Our experimental objective was to directly image and discriminate these eye-specific layers of the LGN in human participants. Methods Participants were scanned using a Siemens Trio 3T MRI scanner and 32-channel head coil. Anatomical regions of interest (ROIs) were created for the LGN by manually tracing 1 h of registered and averaged proton density weighted images. Functional MRI scanning utilized an EPI sequence with a 256 matrix and 192 mm field of view, resulting in an in-plane resolution of 0.75 × 0.75 mm2. Stimuli were presented using an Avotec 7021 goggle system that allowed independent high-contrast stimulation of each eye. The stimuli consisted of a field of moving dots, a variable fraction of which moved coherently. The dots in the left and right visual fields periodically switched between the eyes. Subjects were required to detect changes in the direction of coherence. The coherence fraction was manipulated such that subjects were approximately 75% correct, maintaining the attentional demands of the stimulus. Results We were able to reliably activate the LGN using the high-resolution EPI sequence, and we compared these activations to the anatomically defined ROIs as well as to previous results acquired at lower resolutions. Using the high-resolution imaging, we were able to reliably assign voxels as being dominated by one eye or the other and discriminate the monocular layers. Conclusions We have demonstrated that it is possible to discriminate the eye-specific layers of the LGN by directly imaging their functional activation using high-resolution fMRI. Meeting abstract presented at VSS 2012
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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 | 0.001 |
| 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 | 0.001 | 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".