Resolving the individual layers of the human lateral geniculate nucleus using high-resolution structural MRI
Bibliographic record
Abstract
Introduction A large number of visual sensory pathways relay through the lateral geniculate nucleus (LGN), a visual relay and control nucleus in the thalamus that normally is organized into six interleaved monocular layers. Neuroimaging studies have not been able to reliably distinguish these layers due its small size— total maximal diameter of 4–6 mm and layer thicknesses of approximately 1 mm. It is a technical challenge optimizing the imaging parameters to provide a sufficient signal to noise ratio (SNR) and tissue contrast to be able to measure these layers in the least amount of time. We used a proton density pulse sequence and varied the acquisition resolution and other parameters to determine the number of images needed to be acquired and averaged to be able to reliably identify the LGN and measure its laminar structure. Methods Subjects were scanned using a Siemens Trio 3T MRI scanner and 32-channel head coil in York University’s Neuroimaging Laboratory. We compared images obtained from variety of scanning parameters and resolutions in terms of their intrinsic SNR as well as their utility in observing structure in the LGN. 40 high resolution proton density (PD) images from one session with a 256 matrix, 48 1 mm thick slices, 0.75x075x1 mm[sup]3[/sup]; 100 PD images from two sessions with a 320 matrix, 15 1mm thick slices, 0.5x05x1 mm[sup]3[/sup]; and 175 PD images from four sessions with a 512 matrix, 30 1mm thick slices, 0.35x0.35x1 mm[sup]3[/sup]. Results The 256 matrix image showed clear demarcation of the LGN borders with slight contrast detection in LGN layers, although not fully distinguishable. At the 320 and 512 matrix acquisitions, the differences between the LGN layers were visible. Conclusions We have demonstrated that it is possible to differentiate between the eye-specific layers of the LGN using multiple acquisitions of high-resolution structural MRI. Meeting abstract presented at VSS 2013
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".