Functional reorganization during the recovery of contralesional target selection deficits after prefrontal cortex lesions in macaque monkeys
Bibliographic record
Abstract
Visual extinction has been characterized by the failure to respond to a visual stimulus in the contralesional hemifield when presented simultaneously with an ipsilesional stimulus (Corbetta and Shulman, 2011). Unilateral damage to the macaque frontoparietal cortex commonly leads to deficits in contralesional target selection that resemble visual extinction. Recently, we showed that macaque monkeys with unilateral lesions in the caudal prefrontal cortex (PFC) exhibited contralesional target selection deficits that recovered over 2-4 months (Adam et al., 2019). Here, we investigated the longitudinal changes in functional connectivity (FC) of the frontoparietal network after a small or large right caudal PFC lesion in four macaque monkeys. We collected ultra-high field resting-state fMRI at 7-T before the lesion and at weeks 1-16 post-lesion and compared the functional data with behavioural performance on a free-choice saccade task. We found that the pattern of frontoparietal network FC changes depended on lesion size, such that the recovery of contralesional extinction was associated with an initial increase in network FC that returned to baseline in the two small lesion monkeys, whereas FC continued to increase throughout recovery in the two monkeys with a larger lesion. We also found that the FC between contralesional dorsolateral PFC and ipsilesional parietal cortex correlated with behavioural recovery and that the contralesional dorsolateral PFC showed increasing degree centrality with the frontoparietal network. These findings suggest that both the contralesional and ipsilesional hemispheres play an important role in the recovery of function. Importantly, optimal compensation after large PFC lesions may require greater recruitment of distant and intact areas of the frontoparietal network, whereas recovery from smaller lesions was supported by a normalization of the functional 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".