Leveraging the resting brain to predict memory decline after temporal lobectomy
Bibliographic record
Abstract
Abstract Objectives Anterior temporal lobectomy as a treatment for temporal lobe epilepsy is associated with a variable degree of postoperative memory decline, and estimating this decline for individual patients is a critical step of preoperative planning. Presently, predicting memory morbidity relies on indices of preoperative temporal lobe structural and functional integrity. However, epilepsy is increasingly understood as a network disorder, and memory a network phenomenon. We aimed to assess the utility of functional network measures to predict postoperative memory changes. Methods Patients with left and right temporal lobe epilepsy (TLE) were recruited from an epilepsy clinic. Patients underwent preoperative resting-state fMRI (rs-fMRI) and pre- and postoperative neuropsychological assessment approximately one year after surgery. We compared functional connectivity throughout the memory network of each patient to a healthy control template based on 19 individuals 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 and demographic variables as predictors of memory change after surgery. Results Seventy-two adults with TLE were included in this study (37 left/35 right). Left TLE patients with more abnormal 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 (F(2,31)=12.01, p=0.0001), outperforming standard clinical and demographic variables. None of the variables examined in this study were associated with visuospatial memory change in patients with right TLE. Conclusion Resting-state connectivity provides valuable information concerning both the integrity of to-be-resected tissue as well as 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".