Structural connectivity predicts functional activation during lexical and sublexical reading
Bibliographic record
Abstract
A critical question in neuroscience is the extent to which structural connectivity of the brain predicts localization of brain function. Recent research has suggested that anatomical connectivity can predict functional magnetic resonance imaging (fMRI) responses in several cognitive domains, including face, object, scene, and body processing, and development of word recognition skills (Osher et al., 2016; Saygin et al., 2016). However, this technique has not yet been extended to skilled word reading. Thus, we developed a computational model that relates anatomical connectivity (measured using probabilistic tractography) of individual cortical voxels to fMRI responses of the same voxels during lexical and sublexical reading tasks. Our results showed that the model built from structural connectivity was able to accurately predict functional responses of individual subjects based on their structural connectivity alone. This finding was apparent across the cortex, as well as to specific regions of interest associated with reading, language, and spatial attention. Further, we identified the structural connectivity networks associated with different aspects of skilled reading using connectivity analyses, and showed that interconnectivity between left hemisphere language and right hemisphere attentional areas underlies both lexical and sublexical reading. This work has important implications for understanding how structural connectivity contributes to reading and suggests that there is a relationship between skilled reading and neuroanatomical brain connectivity that future research should continue to explore.
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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 | 0.004 |
| 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 | 0.000 |
| 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".