A Longitudinal Multimodal Imaging Study in Patients with Temporo-Insular Diffuse Low-Grade Tumors: How the Inferior Fronto-Occipital Fasciculus Provides Information on Cognitive Outcomes
Bibliographic record
Abstract
Background: Tractography allows the in vivo study of subcortical white matter, and it is a potential tool for providing predictive indices on post-operative outcomes. We aim at establishing whether there is a relation between cognitive outcome and the status of the inferior fronto-occipital fasciculus’s (IFOF’s) microstructure. Methods: The longitudinal neuropsychological data of thirty young (median age: 35 years) patients operated on for DLGG in the left temporo-insular cortex along with pre-surgery tractography data were processed. Results: A degraded integrity of the left (vs. right) IFOF (lower fractional anisotropy and length, p < 0.001; higher mean and axial diffusivity, p < 0.01) was found, with lower microstructural variables in the infiltration (vs. dislocation) group. Significant decreases immediately post-surgery vs. pre-surgery mainly occurred in lexico-semantics (p < 0.001), with significant improvements at follow-up in all the tests (p < 0.01 to p < 0.001), despite values in the range of 44% to 47.82% of patients with below cut-off scores regarding naming verbs and making visual lexical decisions. The status of left and right IFOFs is predictive of a decrease in immediate post-surgery performance for several tests (p < 0.05); similarly, it is predictive of better recovery in the follow-up performance for naming nouns, naming verbs, making phonological fluency lexical decisions, and the token test (p < −0.05). For the ROC analysis, a significant result was obtained for the verb-naming test, with a cut-off of 79%. Conclusions: This study supports the role of the predictive value of pre-operative tractography for assessing the immediate post-operative result and at follow-up the risk of developing a cognitive deficit.
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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 | 0.001 |
| 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".