Resting-state fMRI detects alterations in whole brain connectivity related to tumor biology in glioma patients
Bibliographic record
Abstract
BACKGROUND: Systemic infiltration of the brain by tumor cells is a hallmark of glioma pathogenesis which may cause disturbances in functional connectivity. We hypothesized that aggressive high-grade tumors cause more damage to functional connectivity than low-grade tumors. METHODS: We designed an imaging tool based on resting-state functional (f)MRI to individually quantify abnormality of functional connectivity and tested it in a prospective cohort of patients with newly diagnosed glioma. RESULTS: Thirty-four patients were analyzed (World Health Organization [WHO] grade II, n = 13; grade III, n = 6; grade IV, n = 15; mean age, 48.7 y). Connectivity abnormality could be observed not only in the lesioned brain area but also in the contralateral hemisphere with a close correlation between connectivity abnormality and aggressiveness of the tumor as indicated by WHO grade. Isocitrate dehydrogenase 1 (IDH1) mutation status was also associated with abnormal connectivity, with more alterations in IDH1 wildtype tumors independent of tumor size. Finally, deficits in neuropsychological performance were correlated with connectivity abnormality. CONCLUSION: Here, we suggested an individually applicable resting-state fMRI marker in glioma patients. Analysis of the functional connectome using this marker revealed that abnormalities of functional connectivity could be detected not only adjacent to the visible lesion but also in distant brain tissue, even in the contralesional hemisphere. These changes were associated with tumor biology and cognitive function. The ability of our novel method to capture tumor effects in nonlesional brain suggests a potential clinical value for both individualizing and monitoring glioma therapy.
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".