Combined Functional MRI and Diffusion Tensor Imaging Analysis of Visual Motion Pathways
Bibliographic record
Abstract
BACKGROUND: Motion perception may be preserved after damage to striate cortex (primary visual cortex, area V1). Awareness and normal discrimination of fast-moving stimuli have been observed even in the complete absence of V1. These facts suggest that motion-sensitive cortex (the V5/MT complex or V5/MT+) may be activated by direct thalamic or collicular inputs that bypass V1. Such projections have been identified previously in monkeys but have not been shown in humans using neuroimaging techniques. METHODS: We used diffusion tensor imaging (DTI) tractography to visualize white matter fiber tracts connecting with V5/MT+ in 10 healthy volunteers. V5/MT+ was localized for each subject using functional MRI (fMRI). Functional activity maps were overlaid on high-resolution anatomical images and registered with the diffusion-weighted images to define V5/MT+ as the region of interest (ROI) for DTI tractography analysis. Fibers connecting to V1 were excluded from the analysis. RESULTS: Using conservative tractography parameters, we found connections between the V5/MT+ region and the posterior thalamus and/or superior colliculus in 4 of 10 subjects. CONCLUSIONS: Connections between the V5/MT+ region and the posterior thalamus and/or superior colliculus may explain visual motion awareness in the absence of a functioning V1.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".