Structural organization of the praxis network predicts gesture production: Evidence from healthy subjects and patients with schizophrenia
Bibliographic record
Abstract
Hand gestures are an integral part of social interactions and communication. Several imaging studies in healthy subjects and lesion studies in patients with apraxia suggest the praxis network for gesture production, involving mainly left inferior frontal, posterior parietal and temporal regions. However, little is known about the structural connectivity underlying gesture production. We recruited 41 healthy participants and 39 patients with schizophrenia. All participants performed a gesture production test, the Test of Upper Limb Apraxia, and underwent diffusion tensor imaging. We hypothesized that gesture production is associated with structural network connectivity as well as with tract integrity. We defined the praxis network as an undirected graph comprised of 13 bilateral regions of interest and derived measures of local and global structural connectivity and tract integrity from Finsler geometry. We found an association of gesture deficit with reduced global and local efficiency of the praxis network. Furthermore, reduced tract integrity, for example in the superior longitudinal fascicle, arcuate fascicle or corpus callosum were related to gesture deficits. Our findings contribute to the understanding of structural correlates of gesture production as they first present diffusion tensor imaging data in a combined sample of healthy subjects and a patient cohort with gestural deficits.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".