Presurgical Language fMRI and Postsurgical Deficits: A Single Centre Experience
Bibliographic record
Abstract
BACKGROUND: In this study, we conducted a retrospective investigation of our initial single-centre experience with the clinical use of functional magnetic resonance imaging (fMRI) of hemisphere dominance for language processing (i.e., language lateralization). We demonstrated its association with surgical outcome and its potential impact on surgical planning and patient management. METHODS: Fifty-two cases were reviewed, covering the period from July 2007 to July 2010. Clinical fMRI reports were examined to determine the hemisphere dominance for language processing. Neurological reports were examined to determine if new language deficits were present post-surgery. Neurosurgeon notes were also reviewed to determine if fMRI had an impact on surgical planning. RESULTS: Of the cases reviewed, 49 (94%) generated conclusive fMRI. Eleven (22%) patients exhibited fMRI language lateralization contralateral to pathology; zero of nine of these patients that had surgery experienced post-surgical deficits. Twenty-two (44%) patients exhibited fMRI language lateralization ipsilateral to pathology; three of 13 of these patients that had surgery experienced post-surgical deficits. Sixteen (34%) patients exhibited bilateral lateralization of language; five of 13 of these patients that had surgery experienced post-surgery deficits. Several post-fMRI reports indicated that fMRI results had an impact on surgical planning. CONCLUSIONS: Our results suggest that fMRI demonstrations of language processing within the hemisphere ipsilateral to pathology (either ipsilateral alone or bilateral) is associated with a greater risk for post-surgical language deficits, and in these cases, fMRI results should be taken into consideration for pre-surgical planning. IRMf du langage avant la chirurgie et déficits après la chirurgie : expérience d'un centre.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".