Functional Connectivity Graph Theory Analysis of Spoken Word Processing Efficiency in Prefrontal Cortical Activation
Bibliographic record
Abstract
ABSTRACT Purpose Network science models of real-time spoken word processing predict that word frequency and sublexical phonotactic probability directly influence verbal working memory. Behavioral accuracy and speed of processing suggest that high frequency words differ in cognitive processing effort as compared to low frequency words. Cognitive processing effort, assessed indirectly through changes in prefrontal oxygenated (HbO) and deoxygenated (HbR) hemoglobin concentrations, also suggest that high and low frequency words differ in cognitive effort. This novel study examines potential differences in linguistic processing effort and efficiency directly, using measures of functional connectivity (FC) strength, modularity, and global efficiency, derived from the listener’s prefrontal cortex hemodynamic responses using a paradigmatic verbal working memory paradigm. Method A total of 20 neurologically typical participants (age 18-21 years) completed an auditory n -back working memory task comparing performance for words differing in whole-word frequency and sub lexical phonotactic probability. Changes in HbO and HbR hemoglobin concentration were recorded with a continuous-wave, multi-channel fNIRS system (TechEn, Inc., Milford, MA) using a 20-channel optode montage across the prefrontal cortex. Partial correlation coefficients were calculated between each channel pair to produce 20 x 20 FC matrices for each participant. Prefrontal networks were constructed as a graph where nodes in the graph are the fNIRS measurements and edges are partial correlations between pairs of measurements. Results Word frequency effects were evident in both in the behavioral and functional connectivity measures. Task performance, regardless of word frequency, was related to brain measures, such that higher prefrontal FC strength was negatively correlated with accuracy ( d’ ) and high modularity was negatively correlated with response times for low frequency words. In keeping with the direction of the brain-behavior correlations, the highest performing participant had lower FC strength and higher efficiency whereas the lowest performing participant had higher modularity and lower global efficiency. Conclusions We identified network properties that are linked to lexical indices of cognitive processing effort in typical participants. These preliminary results point to strategies that could assess language function in atypical populations in future studies.
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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 | 0.001 | 0.001 |
| 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".