Structural alterations in the macaque frontoparietal white matter network after recovery from prefrontal cortex lesions
Bibliographic record
Abstract
Abstract Unilateral damage to the frontoparietal network typically impairs saccade target selection within the contralesional visual hemifield. Severity of deficits and the degree of recovery have been associated with widespread network dysfunction, yet it is not clear how these behavioural and functional changes relate with the underlying structural white matter pathways. Here, we investigated whether recovery after unilateral prefrontal cortex (PFC) lesions was associated with structural white matter remodeling in the distributed frontoparietal network. Diffusion-weighted MRI was acquired in four macaque monkeys before the lesions and at 2-4 months post-lesion, after recovery of deficits in saccade selection of contralesional targets. Probabilistic tractography was used to reconstruct inter- and intra-hemispheric frontoparietal fiber tracts: bilateral superior longitudinal fasciculus (SLF) and transcallosal fibers connecting bilateral PFC or bilateral posterior parietal cortex (PPC). After behavioural recovery, tract-specific fractional anisotropy in contralesional SLF and transcallosal PPC increased after small lesions and decreased after larger lesions compared to pre-lesion. These findings indicate that remote fiber tracts may provide optimal compensation after small PFC lesions. However, larger lesions may have induced widespread structural damage and hindered compensatory remodeling in the structural frontoparietal network.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".