EEG‐fMRI using z‐shimming in patients with temporal lobe epilepsy
Bibliographic record
Abstract
PURPOSE: To use z-shimming, a technique that reduces signal loss due to susceptibility artifacts that can result in reduced or absent activation in electroencephalography (EEG) functional MRI (fMRI) sessions in patients with temporal lobe epilepsy (TLE), to determine whether it would result in an increased ability to detect significant regions of blood oxygenation level-dependent (BOLD) signal change. MATERIALS AND METHODS: Eight patients with TL EEG spikes underwent an EEG-fMRI scanning session using z-shimming. The signal intensities in the z-shimmed images were compared with those in the standard images. BOLD activation maps were created from the two sets of images using the timings of the spikes observed on the EEG. RESULTS: The mean signal increase in the TLs as a result of z-shimming was 45.9%+/-4.5%. The percentage of TL voxels above a brain intensity threshold rose from 66.1%+/-7.6% to 77.6%+/-5.7%. This appreciable increase in signal did not lead to any significant differences in the statistical maps created with the two sets of functional images. CONCLUSION: The results suggest that loss of signal is not the limiting factor for the detection of spike-related BOLD signal changes in patients with TLE activity.
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 | 0.002 |
| 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".