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Peer-reviewedCC BY-NC-ND 4.0The Journal of Headache and Pain

Structural brain age gap and clinical determinants in migraine: a cross-sectional MRI study

  • Pin-Yi Chiang1,2
  • Chia-Lin Tsai3,4
  • Kun-Hsien Chou5,6
  • Pei-Lin Lee7
  • Chih-Sung Liang8
  • Yueh-Feng Sung3,4
  • Chia-Kuang Tsai3,4
  • Yu-Kai Lin3,4,9
  • Wei-Chou Chang10
  • Yi-Chia Kung10
  • Ching-Po Lin2,5,6,11
  • Fu-Chi Yang3,4,9
11 affiliations
  1. 1Department of Radiation Oncology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, 600056 Taiwan
  2. 2Ph.D. Program of Interdisciplinary Medicine, National Yang Ming Chiao Tung University, Taipei, 112304 Taiwan
  3. 3Department of Neurology, Tri-Service General Hospital, National Defense Medical University, Taipei, 114202 Taiwan
  4. 4Department of Neurology, School of Medicine, College of Medicine, National Defense Medical University, Taipei, 114824 Taiwan
  5. 5Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, 112304 Taiwan
  6. 6Brain Research Center, National Yang Ming Chiao Tung University, Taipei, 112304 Taiwan
  7. 7Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University, Taipei, 112304 Taiwan
  8. 8Department of Psychiatry, Beitou Branch, Tri-Service General Hospital, National Defense Medical University, Taipei, 112003 Taiwan
  9. 9Graduate Institute of Medical Sciences, National Defense Medical University, Taipei, 114824 Taiwan
  10. 10Department of Radiology, Tri-Service General Hospital, National Defense Medical University, Taipei, 114202 Taiwan
  11. 11Department of Education and Research, Taipei City Hospital, Taipei, 103212 Taiwan
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Abstract

Background: Migraine has been linked to structural brain alterations that may deviate from normative aging patterns. Recent brain-age modeling studies have reported elevated brain age gap (BAG) values in migraine populations, but the clinical factors associated with individual variation in BAG remain incompletely characterized. This study quantified BAG in patients with migraine and identified demographic, lifestyle, and clinical factors independently associated with structural brain-age deviation.

Methods: This cross-sectional study enrolled 128 patients with migraine (ICHD-3 criteria) and 35 non-migraine controls. Brain age was estimated using a support vector regression model trained on T1-weighted MRI scans from an independent multicenter dataset of 1,410 healthy adults. The primary outcome was the bias-corrected BAG. Multivariable linear regression analysis identified independent predictors of BAG within the migraine cohort across headache, psychiatric, sleep, and lifestyle domains. An exploratory extreme-group comparison contrasted migraine patients in the upper and lower BAG tertiles using binary logistic regression.

Results: Patients with migraine exhibited a significantly higher bias-corrected BAG than controls (9.76 ± 8.97 vs. 4.88 ± 8.25 years; ANCOVA adjusted for age, age², and total intracranial volume: F = 6.795, p = 0.010, partial η²=0.041). In multivariable linear regression analysis, depressive symptom severity (Beck Depression Inventory-II [BDI-II]: β = 0.39, 95% CI = 0.03–0.75, p = 0.033) was independently associated with a larger BAG. Current smoking was also independently associated with a larger BAG (β = 7.56, 95% CI = 0.63–14.48, p = 0.033), although this estimate was based on only 10 current smokers and should be interpreted with caution. In the exploratory extreme-group analysis, higher depressive symptoms (BDI-II, OR = 1.24, 95% CI = 1.04–1.49, p = 0.019) were associated with the high-aging phenotype. Headache frequency, disease duration, and aura status showed no significant association with BAG in any analysis.

Conclusions: Migraine was associated with a significantly higher structural BAG relative to non-migraine controls. Within the migraine cohort, depressive symptom burden was the most consistent clinical correlate of BAG, whereas current smoking emerged as a preliminary lifestyle signal requiring confirmation in cohorts with more current smokers. These findings suggest that structural brain-age deviation in migraine is associated with psychiatric and lifestyle factors beyond headache-specific characteristics, supporting further longitudinal research into potentially modifiable clinical correlates.

Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s10194-026-02480-2.

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Cite

BibTeXRIS
BibTeX
@article{chiang2026structural,
  title = {{Structural brain age gap and clinical determinants in migraine: a cross-sectional MRI study}},
  author = {Pin-Yi Chiang and Chia-Lin Tsai and Kun-Hsien Chou and Pei-Lin Lee and Chih-Sung Liang and Yueh-Feng Sung and Chia-Kuang Tsai and Yu-Kai Lin and Wei-Chou Chang and Yi-Chia Kung and Ching-Po Lin and Fu-Chi Yang},
  journal = {The Journal of Headache and Pain},
  year = {2026},
  month = aug,
  doi = {10.1186/s10194-026-02480-2},
  url = {https://doi.org/10.1186/s10194-026-02480-2},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Structural brain age gap and clinical determinants in migraine: a cross-sectional MRI study
AU  - Pin-Yi Chiang
AU  - Chia-Lin Tsai
AU  - Kun-Hsien Chou
AU  - Pei-Lin Lee
AU  - Chih-Sung Liang
AU  - Yueh-Feng Sung
AU  - Chia-Kuang Tsai
AU  - Yu-Kai Lin
AU  - Wei-Chou Chang
AU  - Yi-Chia Kung
AU  - Ching-Po Lin
AU  - Fu-Chi Yang
PY  - 2026
DA  - 2026/08/08
JO  - The Journal of Headache and Pain
DO  - 10.1186/s10194-026-02480-2
UR  - https://doi.org/10.1186/s10194-026-02480-2
AB  - Background: Migraine has been linked to structural brain alterations that may deviate from normative aging patterns. Recent brain-age modeling studies have reported elevated brain age gap (BAG) values in migraine populations, but the clinical factors associated with individual variation in BAG remain incompletely characterized. This study quantified BAG in patients with migraine and identified demographic, lifestyle, and clinical factors independently associated with structural brain-age deviation.  Methods: This cross-sectional study enrolled 128 patients with migraine (ICHD-3 criteria) and 35 non-migraine controls. Brain age was estimated using a support vector regression model trained on T1-weighted MRI scans from an independent multicenter dataset of 1,410 healthy adults. The primary outcome was the bias-corrected BAG. Multivariable linear regression analysis identified independent predictors of BAG within the migraine cohort across headache, psychiatric, sleep, and lifestyle domains. An exploratory extreme-group comparison contrasted migraine patients in the upper and lower BAG tertiles using binary logistic regression.  Results: Patients with migraine exhibited a significantly higher bias-corrected BAG than controls (9.76 ± 8.97 vs. 4.88 ± 8.25 years; ANCOVA adjusted for age, age², and total intracranial volume: F = 6.795, p = 0.010, partial η²=0.041). In multivariable linear regression analysis, depressive symptom severity (Beck Depression Inventory-II [BDI-II]: β = 0.39, 95% CI = 0.03–0.75, p = 0.033) was independently associated with a larger BAG. Current smoking was also independently associated with a larger BAG (β = 7.56, 95% CI = 0.63–14.48, p = 0.033), although this estimate was based on only 10 current smokers and should be interpreted with caution. In the exploratory extreme-group analysis, higher depressive symptoms (BDI-II, OR = 1.24, 95% CI = 1.04–1.49, p = 0.019) were associated with the high-aging phenotype. Headache frequency, disease duration, and aura status showed no significant association with BAG in any analysis.  Conclusions: Migraine was associated with a significantly higher structural BAG relative to non-migraine controls. Within the migraine cohort, depressive symptom burden was the most consistent clinical correlate of BAG, whereas current smoking emerged as a preliminary lifestyle signal requiring confirmation in cohorts with more current smokers. These findings suggest that structural brain-age deviation in migraine is associated with psychiatric and lifestyle factors beyond headache-specific characteristics, supporting further longitudinal research into potentially modifiable clinical correlates.  Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s10194-026-02480-2.
SN  - 1129-2369
SN  - 1129-2377
ER  -