Leveraging the resting brain to predict memory decline after temporal lobectomy
Bibliographic record
Abstract
OBJECTIVE: Predicting memory morbidity after temporal lobectomy in patients with temporal lobe epilepsy (TLE) relies on indices of preoperative temporal lobe structural and functional integrity. However, epilepsy is increasingly considered a network disorder, and memory a network phenomenon. We assessed the utility of functional network measures to predict postoperative memory changes. METHODS: Seventy-two adults with TLE (37 left/35 right) underwent preoperative resting-state functional magnetic resonance imaging and pre- and postoperative neuropsychological assessment. We compared functional connectivity throughout the memory network of each patient to a healthy control template (n = 19) to identify differences in global organization. A second metric indicated the degree of integration of the to-be-resected temporal lobe with the rest of the memory network. We included these measures in a linear regression model alongside standard clinical variables as predictors of memory change after surgery. RESULTS: Left TLE patients with more atypical memory networks, and with greater functional integration of the to-be-resected region with the rest of the memory network preoperatively, experienced the greatest decline in verbal memory after surgery. Together, these two measures explained 44% of variance in verbal memory change, outperforming standard clinical and demographic variables. None of the variables examined was associated with visuospatial memory change in patients with right TLE. SIGNIFICANCE: Resting-state connectivity provides valuable information concerning both the integrity of to-be-resected tissue and functional reserve across memory-relevant regions outside of the to-be-resected tissue. Intrinsic functional connectivity has the potential to be useful for clinical decision-making regarding memory outcomes in left TLE, and more work is needed to identify the factors responsible for differences seen in right 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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".