Preliminary Experience with Visualization of Intracortical Fibers by Focused High-Resolution Diffusion Tensor Imaging
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The inherent low anisotropy of gray matter and the lack of adequate imaging sensitivity and resolution has, so far, impeded depiction of axonal fibers to their intracortical origin or termination. We tested the hypothesis that an experimental approach with high-resolution diffusion tensor imaging (DTI) provides anisotropic data for fiber tractography with sufficient sensitivity to visualize in vivo the fine distribution of white matter bundles at the intracortical level. MATERIALS AND METHODS: We conducted phantom measurements of signal-to-noise ratio (SNR) and obtained diffusion tensor maps of the occipital lobe in 6 healthy volunteers using a dedicated miniature phased array detector at 3T. We reconstructed virtual fibers using a standard tracking algorithm. RESULTS: The coil array provided a SNR of 8.0 times higher at the head surface compared with a standard quadrature whole head coil. Diffusion tensor maps could be obtained with an in-plane resolution of 0.58 x 0.58 mm(2). The axonal trajectories reconstructed from the diffusion data penetrate into the cortical ribbon perpendicular to the pial surface. This is the expected pattern for the terminations of thalamocortical afferent fibers to the middle layers of the occipital cortex and is consistent with the known microstructural organization of the mammalian cerebral cortex. CONCLUSION: High-resolution DTI reveals intracortical anisotropy with a distinct parallel geometrical order, perpendicular to the pial surface, consistent with structures that may be identified as the terminal afferents in cortical gray matter.
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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 | 0.001 |
| 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".