Intrinsic functional connectivity of the humans lateral geniculate nucleus
Bibliographic record
Abstract
Introduction: The lateral geniculate nucleus (LGN) is the thalamic relay between the retina and visual cortex. We sought to measure the functional connectivity between the LGN and other nearby thalamic structures, notably the thalamic reticular nucleus (TRN). The TRN receives excitatory glutamatergic input from the LGN and visual cortices and sends inhibitory GABAergic projections back to the LGN. TRN output generates spindle oscillations in the LGN, disrupting output to V1. This inhibitory complex has been observed in many animal models, but has not yet been observed in the human thalamus. Methods: The present experiment sought to observe functional connectivity in resting humans using functional magnetic resonance imaging (fMRI). Participants were scanned using a Siemens Trio 3T MRI scanner and 32-channel head coil in York University's Neuroimaging Laboratory. Anatomical regions of interest (ROIs) were generated for the LGN and TRN by manually tracing 1 hour of interpolated proton density weighted images. fMRI scans utilized an EPI sequence with a 3 coronal slices, 2mm thick, with a 128 matrix and 192 mm field of view resulting in an in-plane resolution of 1.5 x 1.5 mm2, TR = 0.251, TE = 36 ms, and flip angle of 10°. Four scans of 1200 time points each were collected in which participants were instructed to lie in the scanner with their eyes closed. The anatomically-defined ROIs were coregistered with the EPI data, and the mean time series extracted from the defined LGN mask was correlated with the entire dataset using to find functionally related voxels. Results: We observed significant correlations with the LGN and vascular structures, but not between the left and right LGN, nor with the neighboring thalamic structures. Based on these negative findings we outline multiple future strategies to examine the functional connectivity of the human LGN. 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 | 0.000 | 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".