High-resolution diffusion MRI of the cortico-cortical connections between lower visual areas reveals divergence of connections and enhanced connectivity of the central visual field representation
Bibliographic record
Abstract
Studies of the dense structural connections between human lower visual areas with diffusion MRI (dMRI) are sparse. To investigate this fine connectivity, a dMRI pulse sequence is required to operate at both high-resolution and high signal-to-noise ratio (SNR). gSldier-SMS (Setsompop et al., 2018) makes acquiring diffusion MRI data with high SNR and high resolution possible. Using gSlider-SMS, we aim to image the white matter connections between the densely connected lower visual areas V1, V2, and V3. Data were obtained from 10 healthy subjects. Using gSlider-SMS, we acquired dMRI with isotropic 1 mm voxels. We used Benson’s atlas (Benson & Winawer, 2018) to generate the retinotopic polar angle and eccentricity maps of human visual areas V1, V2, and V3. Our diffusion data demonstrated a retinotopically organized connectivity pattern between V1-V2, V2-V3, and V1-V3. Moreover, our results also revealed differential density in connectivity patterns between the central and peripheral visual fields. We found that for each pair of connections between V1, V2, and V3, the central visual field regions were more densely connected than the regions in the periphery. This underscores the notion that the central visual field, responsible for detailed and sharp vision, might have evolved to have stronger connectivity to facilitate visual processing at high acuity. In addition, we evaluated the divergence of connections emerging from an eccentricity range in one visual area and connecting to a second visual area. A Gaussian curve showed a good fit to the divergence distribution. Our study provides novel insight into the visual cortex's intricate connectivity patterns, underlining the importance of central visual field representation, retinotopic organization, and hierarchical processing. Our findings also pave the way for investigating the structural-functional relationship of connections between lower visual areas, potentially leading to a deeper understanding of vision-related disorders.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".