Probabilistic topography of human corpus callosum using cytoarchitectural parcellation and high angular resolution diffusion imaging tractography
Bibliographic record
Abstract
The function of the corpus callosum (CC) is to distribute perceptual, motor, cognitive, learned, and voluntary information between the two hemispheres of the brain. Accurate parcellation of the CC according to fiber composition and fiber connection is of upmost important. In this work, population-based probabilistic connection topographies of the CC, in the standard Montreal Neurological Institute (MNI) space, are estimated by incorporating anatomical cytoarchitectural parcellation with high angular resolution diffusion imaging (HARDI) tractography. First, callosal fibers are extracted using multiple fiber assignment by continuous tracking algorithm based on q-ball imaging (QBI), on 12 healthy and young subjects. Then, the fiber tracts are aligned in the standard MNI coordinate system based on a tract-based transformation scheme. Next, twenty-eight Brodmann's areas on the surface of cortical cortex are registered to the MNI space to parcellate the aligned callosal fibers. Finally, the population-based topological subdivisions of the midsagittal CC to each cortical target are then mapped. And the resulting subdivisions of the CC that connect to the frontal and somatosensory associated cortex are also showed. To our knowledge, it is the first topographic subdivisions of the CC done using HARDI tractography and cytoarchitectonic information. In conclusion, this sophisticated topography of the CC may serve as a landmark to further understand the correlations between the CC, brain intercommunication, and functional cytoarchitectures.
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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 | 0.001 | 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".