PS2 - 196 Investigating the Spatial Agreement Between Pre-Operative Functional MRI and Intra-Operative Direct Cortical Stimulation
Bibliographic record
Abstract
Pre-operative functional magnetic resonance imaging (fMRI) has emerged as valuable clinical tool to help surgically manage patients diagnosed with brain tumours. Surgical decision-making may be significantly improved through the provision of fMRI, however its clinical usage is contingent on the level of agreement with direct cortical stimulation (DCS). While previous studies have been undertaken to investigate the spatial agreement between fMRI and DCS, the influence that various factors may have on fMRI sensitivity and specificity is not fully clear. Thus, in a group of eight brain tumour patients who underwent pre-operative fMRI followed intra-operative DCS during an awake craniotomy procedure, we measured the agreement between the two brain mapping techniques looking at the influence of behavioural task, statistical threshold, and task standardization. Results: There were significant differences between motor and language mapping, where agreement was better for the former. Sensitivity and specificity shared an inverse relationship with increasing fMRI threshold, and were significantly reduced in the case where tasks were not standardized. Lastly, false positive occurrences were identified as the dominate source of error in comparison to false negative occurrences. Conclusion: Thus, the results from this work suggest that fMRI can predict intraoperative findings with good accuracy, however, sources of variability may significantly reduce the quality of fMRI data at the single-subject level. Neurosurgeons should carefully evaluate fMRI data with these considerations prior to its inclusion in the surgical-decision making process.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".