The right amygdala and migraine: Analyzing volume reduction and its relationship with symptom severity
Bibliographic record
Abstract
This study aimed to explore the relationship between gray matter volume changes and various clinical parameters in patients with migraine, focusing on symptom severity, quality of life, and states of depression and anxiety. Using a case-control design, we examined 33 patients with migraine, with or without aura, and 27 age-matched healthy subjects. We used magnetic resonance imaging to assess the volumes of 140 bilateral brain regions. Clinical evaluations included the Migraine Disability Assessment, the Migraine Specific Quality of Life Questionnaire, the Center for Epidemiologic Studies Depression scale, Spielberger's State and Trait Anxiety scales, and the Japanese version of the Montreal Cognitive Assessment. We compared the scores of these measures between migraine patients and healthy controls to examine the interplay between brain structure and clinical symptoms. Significant volumetric differences were observed in the pallidum and amygdala between migraine patients and healthy individuals. The reduction in the right amygdala volume correlated significantly with migraine severity as measured by the Migraine Disability Assessment. Path analysis revealed a model where Migraine Disability Assessment scores were influenced by Migraine Specific Quality of Life Questionnaire outcomes, which were further affected by depression, anxiety, and a low right pallidum volume. Our findings suggest that the chronicity and severity of migraine headaches specifically affect the right amygdala. Our path model suggests a complex relationship whereby migraine disability is strongly influenced by quality of life, which is, in turn, affected by psychological states, such as anxiety and depression.
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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 | 0.001 |
| 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 | 0.000 |
| 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".