Abstract
The neurobiological heterogeneity in migraine is poorly studied, resulting in conflicting neuroimaging findings. This study used a newly proposed method based on gray matter volumes (GMVs) to investigate objective neuroanatomical subtypes of migraine. Structural MRI and clinical measures of 31 migraine patients without aura and 33 matched healthy controls (HCs) were explored. Firstly, we investigated whether migraine patients exhibited higher interindividual variability than HCs in terms of GMVs. Then, heterogeneity through discriminative analysis (HYDRA) was applied to categorize migraine patients into distinct subtypes by regional volumetric measures of GMVs. Voxel-wise volume and clinical characteristics among different subtypes were also explored. Migraine patients without aura exhibited higher interindividual GMVs variability. Two distinct and reproducible neuroanatomical subtypes of migraine were revealed. These two subtypes exhibited opposite neuroanatomical aberrances compared to HCs. Subtype 1 showed widespread decreased GMVs, while Subtype 2 showed increased GMVs in limited regions. The total intracranial volume was significantly positively correlated with cognitive function in Subtype 2. Subtype 1 showed significantly longer illness duration and less cognitive scores compared to Subtype 2. The present study shows that migraine patients without aura have high structural heterogeneity and uncovers two distinct and robust neuroanatomical subtypes, which provide a possible explanation for conflicting neuroimaging findings.
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The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
References
Ashburner, J. (2009). Computational anatomy with the SPM software. Magnetic Resonance Imaging, 27(8), 1163–1174. https://doi.org/10.1016/j.mri.2009.01.006
Ashina, M. (2020). Migraine. New England Journal of Medicine, 383(19), 1866–1876. https://doi.org/10.1056/NEJMra1915327
Ashina, M., Terwindt, G. M., Al-Karagholi, M. A., de Boer, I., Lee, M. J., Hay, D. L., et al. (2021). Migraine: Disease characterisation, biomarkers, and precision medicine. Lancet, 397(10283), 1496–1504. https://doi.org/10.1016/s0140-6736(20)32162-0
Bonanno, L., Lo Buono, V., De Salvo, S., Ruvolo, C., Torre, V., Bramanti, P., et al. (2020). Brain morphologic abnormalities in migraine patients: An observational study. The Journal of Headache and Pain, 21(1), 39. https://doi.org/10.1186/s10194-020-01109-2
Brennan, K. C., & Pietrobon, D. (2018). A Systems Neuroscience Approach to Migraine. Neuron, 97(5), 1004–1021. https://doi.org/10.1016/j.neuron.2018.01.029
Cao, Z., Yu, W., Zhang, Z., Xu, M., Lin, J., Zhang, L., et al. (2022). Decreased Gray Matter Volume in the Frontal Cortex of Migraine Patients with Associated Functional Connectivity Alterations: A VBM and rs-FC Study. Pain Research & Management, 2022, 2115956. https://doi.org/10.1155/2022/2115956
Chand, G. B., Dwyer, D. B., Erus, G., Sotiras, A., Varol, E., Srinivasan, D., et al. (2020). Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. Brain, 143(3), 1027–1038. https://doi.org/10.1093/brain/awaa025
Chong, C. D., & Schwedt, T. J. (2015). Migraine affects white-matter tract integrity: A diffusion-tensor imaging study. Cephalalgia, 35(13), 1162–1171. https://doi.org/10.1177/0333102415573513
Chong, C. D., Gaw, N., Fu, Y., Li, J., Wu, T., & Schwedt, T. J. (2017). Migraine classification using magnetic resonance imaging resting-state functional connectivity data. Cephalalgia, 37(9), 828–844. https://doi.org/10.1177/0333102416652091
Clementz, B. A., Sweeney, J. A., Hamm, J. P., Ivleva, E. I., Ethridge, L. E., Pearlson, G. D., et al. (2018). Identification of Distinct Psychosis Biotypes Using Brain-Based Biomarkers. Focus (am Psychiatr Publ), 16(2), 225–236. https://doi.org/10.1176/appi.focus.16207
Craddock, R. C., James, G. A., Holtzheimer, P. E., 3rd., Hu, X. P., & Mayberg, H. S. (2012). A whole brain fMRI atlas generated via spatially constrained spectral clustering. Human Brain Mapping, 33(8), 1914–1928. https://doi.org/10.1002/hbm.21333
Dai, W., Liu, R. H., Qiu, E., Liu, Y., Chen, Z., Chen, X., et al. (2021). Cortical mechanisms in migraine. Molecular Pain, 17, 17448069211050246. https://doi.org/10.1177/17448069211050246
Edes, A. E., McKie, S., Szabo, E., Kokonyei, G., Pap, D., Zsombok, T., et al. (2019). Increased activation of the pregenual anterior cingulate cortex to citalopram challenge in migraine: An fMRI study. BMC Neurology, 19(1), 237. https://doi.org/10.1186/s12883-019-1478-0
Han, S., Chen, Y., Zheng, R., Li, S., Jiang, Y., Wang, C., et al. (2021). The stage-specifically accelerated brain aging in never-treated first-episode patients with depression. Human Brain Mapping, 42(11), 3656–3666. https://doi.org/10.1002/hbm.25460
Han, S., Xu, Y., Guo, H. R., Fang, K., Wei, Y., Liu, L., et al. (2022). Two distinct subtypes of obsessive compulsive disorder revealed by heterogeneity through discriminative analysis. Human Brain Mapping, 43(10), 3037–3046. https://doi.org/10.1002/hbm.25833
Han, S., Zheng, R., Li, S., Liu, L., Wang, C., Jiang, Y., et al. (2023). Progressive brain structural abnormality in depression assessed with MR imaging by using causal network analysis. Psychol Med, 53(5), 2146–2155. https://doi.org/10.1017/s0033291721003986
Headache Classification Committee of the International Headache Society (IHS) The International Classification of Headache Disorders, 3rd edition. Cephalalgia 38(1), 1–211. https://doi.org/10.1177/0333102417738202.
Hong, S. J., Vogelstein, J. T., Gozzi, A., Bernhardt, B. C., Yeo, B. T. T., Milham, M. P., et al. (2020). Toward Neurosubtypes in Autism. Biological Psychiatry, 88(1), 111–128. https://doi.org/10.1016/j.biopsych.2020.03.022
Insel, T. R., & Cuthbert, B. N. (2015). Medicine. Brain disorders? Precisely. Science, 348(6234), 499–500. https://doi.org/10.1126/science.aab2358
Jia, Z., & Yu, S. (2017). Grey matter alterations in migraine: A systematic review and meta-analysis. Neuroimage Clin, 14, 130–140. https://doi.org/10.1016/j.nicl.2017.01.019
Jin, C., Yuan, K., Zhao, L., Zhao, L., Yu, D., von Deneen, K. M., et al. (2013). Structural and functional abnormalities in migraine patients without aura. NMR in Biomedicine, 26(1), 58–64. https://doi.org/10.1002/nbm.2819
Li, Z., Zhou, J., Lan, L., Cheng, S., Sun, R., Gong, Q., et al. (2020). Concurrent brain structural and functional alterations in patients with migraine without aura: An fMRI study. The Journal of Headache and Pain, 21(1), 141. https://doi.org/10.1186/s10194-020-01203-5
Liu, G., Shi, L., Qiu, J., & Lu, W. (2022). Two neuroanatomical subtypes of males with autism spectrum disorder revealed using semi-supervised machine learning. Mol Autism, 13(1), 9. https://doi.org/10.1186/s13229-022-00489-3
Maleki, N., Becerra, L., Brawn, J., Bigal, M., Burstein, R., & Borsook, D. (2012). Concurrent functional and structural cortical alterations in migraine. Cephalalgia, 32(8), 607–620. https://doi.org/10.1177/0333102412445622
Maleki, N., Becerra, L., Brawn, J., McEwen, B., Burstein, R., & Borsook, D. (2013). Common hippocampal structural and functional changes in migraine. Brain Structure & Function, 218(4), 903–912. https://doi.org/10.1007/s00429-012-0437-y
May, A. (2009). New insights into headache: An update on functional and structural imaging findings. Nature Reviews. Neurology, 5(4), 199–209. https://doi.org/10.1038/nrneurol.2009.28
May, A., & Gaser, C. (2006). Magnetic resonance-based morphometry: A window into structural plasticity of the brain. Current Opinion in Neurology, 19(4), 407–411. https://doi.org/10.1097/01.wco.0000236622.91495.21
Mehnert, J., Schulte, L., & May, A. (2020). No grey matter alterations in longitudinal data of migraine patients. Brain, 143(11), e93. https://doi.org/10.1093/brain/awaa300
Messina, R., Rocca, M. A., Colombo, B., Pagani, E., Falini, A., Goadsby, P. J., et al. (2018). Gray matter volume modifications in migraine: A cross-sectional and longitudinal study. Neurology, 91(3), e280–e292. https://doi.org/10.1212/wnl.0000000000005819
Oschwald, J., Guye, S., Liem, F., Rast, P., Willis, S., Röcke, C., et al. (2019). Brain structure and cognitive ability in healthy aging: A review on longitudinal correlated change. Reviews in the Neurosciences, 31(1), 1–57. https://doi.org/10.1515/revneuro-2018-0096
Palm-Meinders, I. H., Arkink, E. B., Koppen, H., Amlal, S., Terwindt, G. M., Launer, L. J., et al. (2017). Volumetric brain changes in migraineurs from the general population. Neurology, 89(20), 2066–2074. https://doi.org/10.1212/wnl.0000000000004640
Qin, Z., He, X. W., Zhang, J., Xu, S., Li, G. F., Su, J., et al. (2019). Structural changes of cerebellum and brainstem in migraine without aura. The Journal of Headache and Pain, 20(1), 93. https://doi.org/10.1186/s10194-019-1045-5
Schwedt, T. J., Berisha, V., & Chong, C. D. (2015). Temporal lobe cortical thickness correlations differentiate the migraine brain from the healthy brain. PLoS One, 10(2), e0116687. https://doi.org/10.1371/journal.pone.0116687
Schwedt, T. J., Chong, C. D., Wu, T., Gaw, N., Fu, Y., & Li, J. (2015b). Accurate Classification of Chronic Migraine via Brain Magnetic Resonance Imaging. Headache, 55(6), 762–777. https://doi.org/10.1111/head.12584
Schwedt, T. J., Si, B., Li, J., Wu, T., & Chong, C. D. (2017). Migraine Subclassification via a Data-Driven Automated Approach Using Multimodality Factor Mixture Modeling of Brain Structure Measurements. Headache, 57(7), 1051–1064. https://doi.org/10.1111/head.13121
Shen, X., Tokoglu, F., Papademetris, X., & Constable, R. T. (2013). Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. NeuroImage, 82, 403–415. https://doi.org/10.1016/j.neuroimage.2013.05.081
Sheng, L., Ma, H., Shi, Y., Dai, Z., Zhong, J., Chen, F., et al. (2020a). Cortical Thickness in Migraine: A Coordinate-Based Meta-Analysis. Front Neurosci, 14, 600423. https://doi.org/10.3389/fnins.2020.600423
Sheng, L., Zhao, P., Ma, H., Yuan, C., Zhong, J., Dai, Z., et al. (2020b). A lack of consistent brain grey matter alterations in migraine. Brain, 143(6), e45. https://doi.org/10.1093/brain/awaa123
Tolner, E. A., Chen, S. P., & Eikermann-Haerter, K. (2019). Current understanding of cortical structure and function in migraine. Cephalalgia, 39(13), 1683–1699. https://doi.org/10.1177/0333102419840643
Varol, E., Sotiras, A., & Davatzikos, C. (2017). HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework. NeuroImage, 145(Pt B), 346–364. https://doi.org/10.1016/j.neuroimage.2016.02.041
Wang, H. Z., Wang, W. H., Shi, H. C., & Yuan, C. H. (2020). Is there a reliable brain morphological signature for migraine? The Journal of Headache and Pain, 21(1), 89. https://doi.org/10.1186/s10194-020-01158-7
Wei, H. L., Tian, T., Zhou, G. P., Wang, J. J., Guo, X., Chen, Y. C., et al. (2022). Disrupted Dynamic Functional Connectivity of the Visual Network in Episodic Patients with Migraine without Aura. Neural Plasticity, 2022, 9941832. https://doi.org/10.1155/2022/9941832
Zhang, J., Wu, Y. L., Su, J., Yao, Q., Wang, M., Li, G. F., et al. (2017). Assessment of gray and white matter structural alterations in migraineurs without aura. The Journal of Headache and Pain, 18(1), 74. https://doi.org/10.1186/s10194-017-0783-5
Zhang, X., Zhou, J., Guo, M., Cheng, S., Chen, Y., Jiang, N., et al. (2022). A systematic review and meta-analysis of voxel-based morphometric studies of migraine. Journal of Neurology. https://doi.org/10.1007/s00415-022-11363-w
Zhao, L., Liu, J., Dong, X., Peng, Y., Yuan, K., Wu, F., et al. (2013). Alterations in regional homogeneity assessed by fMRI in patients with migraine without aura stratified by disease duration. The Journal of Headache and Pain, 14(1), 85. https://doi.org/10.1186/1129-2377-14-85
Zhao, L., Liu, J., Yan, X., Dun, W., Yang, J., Huang, L., et al. (2014). Abnormal brain activity changes in patients with migraine: A short-term longitudinal study. Journal of Clinical Neurology, 10(3), 229–235. https://doi.org/10.3988/jcn.2014.10.3.229
Acknowledgements
We are grateful to all the participants in this study.
Funding
This study was supported by the Natural Science Foundation of China (81601467, 81871327, 62106229) and Medical Science and Technology Research Project of Henan Province (201701011, SBGJ202102103, SBGJ202101013).
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Author contributions included conception and study design (HL, JC, and SH), data collection or acquisition (HL, RZ, and YZ), statistical analysis (HL, BZ), interpretation of results (HL, HH, YZ, and SH), drafting the manuscript work or revising it critically for important intellectual content (HL, RZ, JC, BZ, and SH) and approval of final version to be published and agreement to be accountable for the integrity and accuracy of all aspects of the work (All authors).
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Liu, H., Zheng, R., Zhang, Y. et al. Two distinct neuroanatomical subtypes of migraine without aura revealed by heterogeneity through discriminative analysis. Brain Imaging and Behavior 17, 715–724 (2023). https://doi.org/10.1007/s11682-023-00802-5
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DOI: https://doi.org/10.1007/s11682-023-00802-5