Ultra-High Field 7-Tesla Magnetic Resonance Imaging and Electroencephalography Findings in Epilepsy
Bibliographic record
Abstract
PURPOSE: Assessment of patients for temporal lobe epilepsy (TLE) surgery requires multimodality input, including EEG recordings to ensure optimal surgical planning. Often EEG demonstrates abnormal foci not detected on 1.5T MRI. Ultra-high field MRI at 7T provides improved resolution of the brain. We investigated the utility of 7T MRI to detect potential anatomical abnormalities associated with EEG changes. METHODS: Ultra-high field data were acquired on a 7T MRI scanner for 13 patients with history of drug resistant TLE who had had EEG telemetry recordings. Qualitative evaluation of 7T imaging for presence of focal abnormalities detected on EEG was performed. Correlation of 7T MRI findings with EEG recordings of focal slowing or interictal epileptic spikes (IEDs), and seizures was performed. RESULTS: Assessment of 7T MRI demonstrated concordance with TLE as determined by the multidisciplinary team in 61.5% of cases (n = 8). Among these, 3 patients exhibited supportive abnormal 7T MRI abnormalities not detected by 1.5T MRI. In patients who underwent surgery, 72.7% had concordant histopathology findings with 7T MRI findings (n = 8). However, qualitative assessment of 7T images revealed focal anatomical abnormalities to account for EEG findings in only 15.4% of patients (n = 2). Other regions that were found to have localized IEDs in addition to the lesional temporal lobe, included the contralateral temporal lobe (n = 5), frontal lobe (n = 3), and parieto-occipital lobe (n = 2). CONCLUSION: Ultra-high field 7T MRI findings show concordance with clinical data. However, 7T MRI did not reveal anatomical findings to account for abnormalities detected by EEG.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".