Functional Reorganization of the Visual Dorsal Stream as Probed by 3-D Visual Coherence in Williams Syndrome
Bibliographic record
Abstract
Object and depth perception from motion cues involves the recruitment of visual dorsal stream brain areas. In 3-D structure-from-motion (SFM) perception, motion and depth information are first extracted in this visual stream to allow object categorization, which is in turn mediated by the ventral visual stream. Such interplay justifies the use of SFM paradigms to understand dorsal-ventral integration of visual information. The nature of such processing is particularly interesting to be investigated in a neurological model of cognitive dissociation between dorsal (impaired) and ventral stream (relatively preserved) processing, Williams syndrome (WS). In the current fMRI study, we assessed dorsal versus ventral stream processing by using a performance-matched 3-D SFM object categorization task. We found evidence for substantial reorganization of the dorsal stream in WS as assessed by whole-brain ANOVA random effects analysis, with subtle differences in ventral activation. Dorsal reorganization was expressed by larger medial recruitment in WS (cuneus, precuneus, and retrosplenial cortex) in contrast with controls, which showed the expected dorsolateral pattern (caudal intraparietal sulcus and lateral occipital cortex). In summary, we found a substantial reorganization of dorsal stream regions in WS in response to simple visual categories and 3-D SFM perception, with less affected ventral stream. Our results corroborate the existence of a medial dorsal pathway that provides the substrate for information rerouting and reorganization in the presence of lateral dorsal stream vulnerability. This interpretation is consistent with recent findings suggesting parallel routing of information in medial and lateral parts of dorsal stream.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".