NIMG-25. LESION-NETWORK ANALYSIS TO IDENTIFY PREFERENTIALLY-ENGAGED NETWORKS IN EPILEPTOGENIC TUMORS
Bibliographic record
Abstract
Abstract BACKGROUND Lesion network mapping (LNM) is a method used to identify potential networks that can be ascribed to particular neurological functions/ deficits. LNM has yet to be implemented for large brain lesions such as tumors. OBJECTIVES: To apply LNM for potential identification of vulnerable epileptogenic networks in tumors causing medically-refractory epilepsy (MRE), compared with non-epileptogenic tumors. METHODS MRE and non-epileptogenic lesions were normalized to standard space for group analysis. These were used as a seed in high-resolution normative resting state fMRI, which was then transformed to t-maps and thresholded by t = 5.1; this corrected for multiple comparisons (Bonferroni corrections) across the whole brain at pcor < 0.05. The statistically-significant thresholded maps were binarized and summed connectivity maps were generated for both groups. This allowed computation of voxel-wise odds ratios (VORs) in order to identify voxels that were more likely associated with tumors that either did or did not result in MRE. RESULTS Twenty-seven patients were included. Eleven brain metastases with no history of seizures, M/F: 5/6, mean age 68.4+/-8.4 years, and 16 had MRE (10 low-grade glioma, 2 cavernoma, 3 “other”), M/F: 7:9, mean age 33.7 +/-12.2 years. Lesions causing MRE were preferentially located in the cingulate gyrus, calcarine fissure, parahippocampal gyrus and lateral temporal neocortex. The resting-state networks that were >1.5x likely to be connected with MRE lesions were the salience, executive control, and dorsal default mode networks. CONCLUSION In this proof of concept study, we have demonstrated that (1) in addition to stroke, tumors may also be amenable to LNM and (2) the underlying normative neural circuitry may in part explain the propensity of particular lesions toward development of MRE. This has ramifications in patient counseling and surgical management planning, as earlier surgery could be applied for lesions thought to be more prone to development of MRE.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".