PC3 - 191 Assessment of Preoperative Functional MRI Measurement of Language Lateralisation in Brain Tumour Patients
Bibliographic record
Abstract
This study aims to evaluate reliability of clinical functional magnetic resonance imaging (fMRI) in identifying language lateralisation index (LI), verified with Edinburgh handedness inventory (EHI), in brain tumour patients. Methods In this retrospective study, 31 of a single surgeon’s brain tumour patients over a 12 year period have been selected. Lesion type varied, 12 (39 percent) were high grade gliomas, 10 (32 percent) low grade gliomas, 3 (10 percent) meningiomas, and 6 (19 percent) other types. Patients underwent language fMRI paradigms for preoperative assessment, and a neuroimaging analyst was able to identify an LI value for at least one Brodmann area (BA). For each paradigm, a neuroimaging analyst attempted to calculate LI for Wernicke’s area (BA 22) and Broca’s area (BA 44 and 45). Results Of 113 total LI values, 66 (58 percent) were concordant to EHI-predicted hemispheric dominance. Reliability of language LI appears dependent upon the type of language task performed. Verb generation correctly identified Broca’s area in 18 patients (64 percent) and Wernicke’s area in 11 patients (61 percent), sentence completion correctly identified Broca’s area in 18 patients (72 percent) and Wernicke’s area in 9 patients (60 percent), and naming correctly identified Broca’s area in 7 patients (47 percent) and Wernicke’s area in 3 patients (27 percent). Conclusions Results show limited correlation between language LI determined by fMRI and EHI. The main limitation of this study is that language LI is being compared to EHI, rather than gold standard measure of hemispheric dominance (e.g. Wada).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".