Spatial task instructions and global activation trends influence functional modularity in the cortical reach network
Bibliographic record
Abstract
Humans can be instructed to ignore visual cues or use them as landmarks for aiming movements (Musa et al. 2024), but it is not known how such allocentric cues interact with egocentric target codes and general planning activity to influence cortical network properties. To answer these questions, we applied graph theory analysis (GTA) to a previously described fMRI dataset (Chen et al. 2014). Participants were instructed to reach toward targets defined in egocentric or landmark-centered (allocentric) coordinates. During Egocentric pointing, cortical nodes clustered into four bilateral modules with correlated BOLD signals: a superior occipital-parietal / somatomotor module, an inferior parietal / lateral frontal module, a superior temporal / inferior frontal module, and an inferior occipital-temporal / prefrontal module. The Allocentric task showed only three modules, in part because inferior occipital nodes were incorporated into the superior occipital-parietal / somatomotor module. Both tasks engaged local (within module) and global (between module) cortical hubs, but the Allocentric task recruited additional hubs associated with allocentric visual codes and ego-allocentric integration. Removing reach-related activation trends reduced global synchrony and increased clustering, specifically diminishing dorsoventral coupling in the allocentric task. Cross-validated decoding and network parameter - reach error correlations confirmed that modularity was the best predictor of both task and specific behavioral measures. These results demonstrate that activation trends related to motor plans influence global network integration, whereas task instructions influence intermediate / local network properties, specifically the increased modular integration and hub recruitment observed in our Allocentric task.
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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 | 0.003 |
| 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 | 0.001 |
| 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".