The small world networks property and cognitive function in the frontal low-grade glioma patients: a pre-and-postoperative fMRI study
Bibliographic record
Abstract
Objective To explore the property of brain functional networks and cognitive function changes in patients with frontal lobe low-grade gliomas ( LGG ). Methods 8 cases of suspected frontal lobe LGG patients were undergone with resting-fMRI scanning to analyze the small-world property of the LGG , meanwhile the LGG groups had Montreal (MoCA) cognitive score exam compared with the control group. Results The value of MoCA was 22.5±1.5 , 21.8±2.0, and 27.9±2.1 respectively with statistical significance (P<0.05) in the LGG groups and the control groups.The LGG group cognitive score was significantly lower than that in the control group with statistical significance (P<0.05 ). As to threshold, the two groups were consistent with the small world property.The LGG local efficiency was smaller than that of the controls, the postoperative small world properties(σ=2.49)were lower than that the pre-operative (σ=2.68), the largest brain function areas of preoperative information transmission were respectively the supramarginal gyrus, posterior cingulate, insula, and the postoperative being the precuneus, calcarine sulcus and superior frontal gyrus. The maximum cluster coefficient of the preoperative functional network were respectively the entorhinal cortex, transverse temporal gyrus and the calcarine sulcus, and postoperative were Wilson, transverse temporal gyri and occipital gyrus.Preoperative information transmission path was less than the postoperative, and the small world properties were positively correlated with MoCA. Conclusion LGG accompany by the changes of cognitive function, and with the small world network property pre-and post-operation. Key words: Low-grade gliomas; Small-world networks; Cognitive
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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 | 0.000 |
| 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".