Structural Asymmetries in the Human Brain: a Voxel-based Statistical Analysis of 142 MRI Scans
Bibliographic record
Abstract
The use of computational approaches in the analysis of high resolution magnetic resonance images (MRI) of the human brain provides a powerful tool for in vivo studies of brain anatomy. Here, we report results obtained with a voxel-wise statistical analysis of hemispheric asymmetries in regional 'amounts' of gray matter, based on MRI scans obtained in 142 healthy young adults. Firstly, the voxel-wise analysis detected the well-known frontal (right > left) and occipital (left > right) petalias. Secondly, our analysis confirmed the presence of left-greater-than-right asymmetries in several posterior language areas, including the planum temporale and the angular gyrus; no significant asymmetry was detected in the anterior language regions. We also found previously described asymmetries in the cingulate sulcus (right > left) and the caudate nucleus (right > left). Finally, in some brain regions we observed highly significant asymmetries that were not reported before, such as in the anterior insular cortex (right > left). The above asymmetries were observed in men and women. Our results thus provide confirmation of the known structural asymmetries in the human brain as well as new findings that may stimulate further research of hemispheric specialization.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".