Effects of a Phonological Intervention on EEG Connectivity Dynamics in Dyslexic Children
Bibliographic record
Abstract
We examined the brain networks and oscillatory dynamics, inferred from EEG recordings during a word-reading task, of a group of children in grades 4 and 5 (ages 9–11), some of whom were dyslexic. We did this in order to characterize the differences in these dynamics between typical and dyslexic readers, and to begin to characterize the effect of a phonological intervention on those differences. Dyslexic readers were recorded both before and after they participated in a FastForWord (FFW) reading training program for approximately six months and typical readers were recorded once during this period. Before FFW dyslexic readers showed (i) a bottleneck in letter recognition areas, (ii) expansion in activity and connectivity into the right hemisphere not seen in typical readers, and (iii) greater engagement of higher-level language areas, even for consonant string stimuli. After FFW, dyslexic readers evinced a significant reduction in the engagement of language processing areas, and more activity and connectivity expanding to frontal areas, more resembling typical readers. Reduction of connectivity was negatively correlated with gains in reading performance, suggesting an increase in communication efficiency. Training appeared to improve the efficiency of the alternative (bilateral) pathways already used by the dyslexic readers, rather than inducing them to create new pathways more similar to those employed by typical readers.
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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".