Language lateralization differences between left and right temporal lobe epilepsy as measured by overt word reading fMRI activation and DTI structural connectivity
Bibliographic record
Abstract
In cases of brain disease such as temporal lobe epilepsy (TLE), damage may lead to functional reorganization and a shift in language dominance to homolog regions in the other hemisphere. If the effects of TLE on language dominance are hemisphere-focused, then brain regions and connections involved in word reading should be less left-lateralized in left temporal lobe epilepsy (lTLE) than right temporal lobe epilepsy (rTLE) or healthy controls, and the opposite effect should be observed in patients with rTLE. In our study, functional magnetic resonance imaging (fMRI) showed that patients with rTLE had more strongly lateralized left hemisphere (LH) activation than patients with lTLE and healthy controls in language-related brain regions (pars opercularis and fusiform gyrus (FuG)). Corresponding with this difference, diffusion tensor imaging (DTI) found differences in connectivity indicative of patients with lTLE having greater tract integrity than patients with rTLE in the right hemisphere (RH) uncinate fasciculus (UF), inferior longitudinal fasciculus (ILF), and inferior fronto-occipital fasciculus (IFOF) using the network-based statistic analysis method. The UF, ILF, and IFOF tract integrity have previously been associated with lexical (whole-word) processing abilities. Multivariate distance matrix regression provided converging evidence for regions of the IFOF having different connectivity patterns between groups with lTLE and rTLE. This research demonstrates language lateralization differences between patient groups with lTLE and rTLE, and corresponding differences in the connectivity strength of the ILF, IFOF, and UF. This research provides a novel approach to measuring lateralization of language in general, and the fMRI and DTI findings were integral for guiding the neurosurgeons performing the TLE resections. This approach should inform future studies of language lateralization and language reorganization in patients such as those with TLE.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".