Negative BOLD responses to epileptic spikes
Bibliographic record
Abstract
Simultaneous electroencephalogram/functional magnetic resonance imaging (EEG-fMRI) during interictal epileptiform discharges can result in positive (activation) and negative (deactivation) changes in the blood oxygenation level-dependent (BOLD) signal. Activation probably reflects increased neuronal activity and energy demand, but deactivation is more difficult to explain. Our objective was to evaluate the occurrence and significance of deactivations related to epileptiform discharges in epilepsy. We reviewed all EEG-fMRI studies from our database, identified those with robust responses (P = 0.01, with > or =5 contiguous voxels with a |t| > 3.1, including > or =1 voxel at |t| > 5.0), and divided them into three groups: activation (A = 8), deactivation (D = 9), and both responses (AD = 43). We correlated responses with discharge type and location and evaluated their spatial relationship with regions involved in the "default" brain state (Raichle et al. [2001]: Proc Natl Acad Sci 98:676-682]. Deactivations were seen in 52/60 studies (AD + D): 26 related to focal discharges, 12 bilateral, and 14 generalized. Deactivations were usually distant from anatomical areas related to the discharges and more frequently related to polyspike- and spike-and-slow waves than to spikes. The "default" pattern occurred in 10/43 AD studies, often associated with bursts of generalized discharges. In conclusion, deactivations are frequent, mostly with concomitant activation, for focal and generalized discharges. Discharges followed by a slow wave are more likely to result in deactivation, suggesting neuronal inhibition as the underlying phenomenon. Involvement of the "default" areas, related to bursts of generalized discharges, provides evidence of a subclinical effect of the discharges, temporarily suspending normal brain function in the resting state.
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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 | 0.001 | 0.004 |
| 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) | 0.002 | 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".