Whole Brain Profile of Connector Hub Alteration Patterns in Focal Epilepsy When Compared to Healthy Controls
Bibliographic record
Abstract
Using resting-state functional Magnetic Resonance Imaging (rs-fMRI), we can non-invasively measure functional connectivity (FC) between brain regions using the blood-oxygen-level-dependent (BOLD) signal which characterizes slow fluctuations of hemodynamic processes.Through various FC analysis techniques, we can extract the resting state networks (RSNs) of the brain, and identify highly connected regions called hubs, which are important for long-range and efficient communication within the brain. In this thesis, we applied, adapted, and carefully investigated the method entitled SParsity-based Analysis of Reliable k-hubness (SPARK), \nintroduced by our group, to identify and quantify in a reliable manner the reorganization of connector hubs for patients with frontal lobe epilepsy (FLE) and temporal lobe epilepsy (TLE), \nwhen compared to healthy controls (HC). In this work, we used several metrics to characterize reorganization of connector hubs, aiming at generating whole-brain fingerprint models to \ncharacterize patients with epilepsy. We considered the following metrics, estimated on different parcellations of the brain in either nineteen anatomical regions or eleven functional networks: the \nhub disruption index (HDI), the hub emergence index (HEI), the hierarchical segregation index (HSI) and regional k-hubness. Our results are suggesting that we found more significant \nreorganization of hubness assessed using HDI and HEI when considering a segmentation in functional networks, as opposed to a segmentation in anatomical regions. We also reported \nsignificant decreases in regional k in epilepsy patients when compared to controls. In addition, we reported a significant decrease in HSI between epilepsy and controls as well. These preliminary results should be confirmed when applied on larger epilepsy cohorts, where we can use our proposed methodology and metrics combined with other non-invasive imaging modalities to \ndiscover potential biomarkers that could predict the postsurgical outcome of these patients.
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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 | 0.001 | 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) | 0.002 | 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".