Language network measures at rest indicate individual differences in naming decline after anterior temporal lobe resection
Bibliographic record
Abstract
While anterior temporal lobe (ATL) resection is an effective treatment for temporal lobe epilepsy, surgery on the dominant hemisphere is associated with variable decline in confrontation naming. Accurate prediction of naming impairment is critical to inform clinical decision making, and while there has been some degree of success using task-based functional MRI (fMRI) paradigms, there remains a growing interest in the predictive utility of resting-state connectivity as it allows for relatively shorter scans with low task demands. Our objective was to assess the relationship between measures of preoperative resting-state connectivity and postoperative naming change in patients following left ATL resection. We compared the resting language network connectivity of each patient to a normative healthy control template using a novel measure called "matrix similarity," and found that patients with more abnormal global language-network connectivity-particularly of regions spared from surgery-showed greater postoperative naming decline than those with normative patterns of connectivity. When we interrogated the degree centrality of to-be-resected regions in a more targeted approach of the pathological temporal lobe, we found that greater functional integration of those regions with the rest of the language network at rest was related to greater decline in naming following surgery. Finally, we found that matrix similarity was a better predictor of postoperative outcome than degree within to-be-resected regions, network clustering, modularity, and language task fMRI laterality. We provide some of the first evidence that using this novel measure, a relatively short preoperative resting scan can be exploited to inform naming ability following ATL resection.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".