Saccades alter cortical network modularity and decrease lateralization in a visual perception task
Bibliographic record
Abstract
We recently showed that the functional brain network (FBN) for saccades shows increased integration, segregation, and synchronization (Ghaderi et al. Cerebral Cortex 2022). However, the influence of saccades on FBN modularity, remains unclear. We hypothesized that when saccades reverse the visual field of an attended object, processes related to spatial updating would increase communication between FBN modules in two hemispheres. 64-channel EEG was recorded in two conditions (N=18). In the fixation condition, participants fixated to the left/right of centre while a reference stimulus (three horizontal/vertical lines, 10°×10° located 5° below the fixation-point, repeated 1-3 times) appeared, followed by a target stimulus (same type/location, opposite orientation). Participants judged the duration of the reference and target stimuli, requiring them to retain information from the stimulus train (Ghaderi et al. Heliyon 2021). The saccade condition was the same, except that 100ms before target presentation, participants were cued to re-fixate the opposite horizontal side, reversing the visual field of the presaccadic stimulus train. We extracted 250ms EEGs in the perisaccadic and corresponding fixation intervals. After preprocessing, we calculated lagged coherence between EEG source localized current densities in 84 Brodmann areas. Unsupervised extraction, followed by a supervised modularity analysis revealed four FBN modules in fixation: a bilateral fronto-parietal network (likely corresponding to the dorsal attention network) and three more lateralized networks (likely corresponding to visual, default mode, and cognitive control networks). In the saccade condition, the dorsal network extended bilaterally from occipital to frontal cortex, subsuming more ventral cortical nodes, but otherwise retained the same degree of modularity. Conversely, FDR showed a significant decrease in visual and control networks modularity (alpha band) and increase in the default network modularity (beta band). These results suggest saccades have a widespread impact on FBN modularity and increase bilateral communication of correlated signals, likely supporting trans-saccadic perception and integration.
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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) | 0.000 | 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) | 0.002 | 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".