A Functional Magnetic Resonance Imaging Study on Activation of Anterior Cingulate Cortex at Episode and Interictal Phases in Migraine
Bibliographic record
Abstract
Objective: By using amplitude of low-frequency fluctuations (ALFF) we have analyzed activationsin brain regions at different phases in migraineurs. Methods: Participants included 41 patients with migraine, 19 in episode and 22 in interictal phase, and 22 controls in the healthy condition. To analyze the brain function of patients and controls, ALFF was used for performing the post-processing in the resting state by scores of Montreal Cognitive Assessment (MoCA) scale, Mini-Mental State Examination (MMSE), Hamilton Anxiety Rating Scale (HAM-A) and Hamilton Depression Rating Scale (HAM-D). Results: The comparison between groups of patients with migraine in the episode or interictal phases, and healthy controls showed that both episode and interictal migraine groups had the similar HAM-A and HAM-D scores (P> 0.05), but higher than that in controls (P< 0.01). For ALFF values of Episode and Interictal groups, the Montreal Neurological Institute (MNI) coordinates of the decreased ALFF were (−9, 42, 9), the voxel size = 215, including the bilateral anterior cingulate cortex (ACC),T=−4.15, without significant differences. Patients in Interictal group were with a stronger activation at MNI coordinates (12, 51, 12), in the bilateral ACC, voxel size = 90,T=3.87. Conclusion: ACC plays an adaptive, regulatory role in migraine and is related to multiple brain regions, which may mediate activation through descending anti-nociceptive pathways. ACC is related to opioid receptor and glutamate excitatory regulation.
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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 | 0.000 |
| 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".