GP.02 Resting state functional connectivity: a biomarker for pre-operative cognitive function and cognitive outcome following surgery in patients with diffuse glioma
Bibliographic record
Abstract
Background: Resting state functional connectivity (RSFC) in the fronto-parietal network (FPN) has been associated with cognitive ability. For this reason, it was hypothesized that RSFC connectivity of the FPN would be related to cognition in patients with diffuse glioma. To assess this relationship, pre-operative cognitive status was correlated to patient specific connectivity within the FPN. Further, we assessed whether RSFC could predict neuropsychological outcome following surgery Methods: Sixteen patients with diffuse glioma underwent neuropsychological assessment and pre-operative task and resting state fMRI. Thirteen had post-operative cognitive assessment at one-month post-surgery. RSFC in a subject-specific FPN was correlated with pre- and post-operative cognitive scores. Results: Higher connectivity within the FPN was associated with lower composite cognitive scores, while higher connectivity of the parietal node of the tumor-affected hemisphere was associated with lower fluid cognition but not crystallized cognition. Higher connectivity values between the parietal node of the healthy hemisphere and the rest of the FPN was associated with better neuropsychological outcome one month after surgery. Conclusions: RSFC between key nodes of the FPN is associated with cognitive performance in patients with diffuse glioma and is a promising biomarker for cognitive outcome following surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".