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Migraine aura discrimination using machine learning: an fMRI study during ictal and interictal periods

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Abstract

Functional magnetic resonance imaging (fMRI) studies on migraine with aura are challenging due to the rarity of patients with triggered cases. This study optimized methodologies to explore differences in ictal and interictal spatiotemporal activation patterns based on visual stimuli using fMRI in two patients with unique aura triggers. Both patients underwent separate fMRI sessions during the ictal and interictal periods. The Gaussian Process Classifier (GPC) was used to differentiate these periods by employing a machine learning temporal embedding approach and spatiotemporal activation patterns based on visual stimuli. When restricted to visual and occipital regions, GPC had an improved performance, with accuracy rates for patients A and B of roughly 86–90% and 77–81%, respectively (p < 0.01). The algorithm effectively differentiated visual stimulation and rest periods and identified times when aura symptoms manifested, as evident from the varying predicted probabilities in the GPC models. These findings contribute to our understanding of the role of visual processing and brain activity patterns in migraine with aura and the significance of temporal embedding techniques in examining aura phenomena. This finding has implications for diagnostic tools and therapeutic techniques, especially for patients suffering from aura symptoms.

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References

  1. Hansen JM, Baca SM, VanValkenburgh P, Charles A (2013) Distinctive anatomical and physiological features of migraine aura revealed by 18 years of recording. Brain 136:3589–3595. https://doi.org/10.1093/brain/awt309

    Article  PubMed  Google Scholar 

  2. Viana M, Tronvik EA, Do TP et al (2019) Clinical features of visual migraine aura: a systematic review. J Headache Pain 20:64. https://doi.org/10.1186/s10194-019-1008-x

    Article  PubMed Central  PubMed  Google Scholar 

  3. (2018) Headache Classification Committee of the International Headache Society (IHS) The international classification of headache disorders, 3rd edition. Cephalalgia : an Intl J Headache 38:1–211. https://doi.org/10.1177/0333102417738202

  4. Lipton RB, Scher AI, Kolodner K et al (2002) Migraine in the United States: epidemiology and patterns of health care use. Neurology 58:885–894. https://doi.org/10.1212/WNL.58.6.885

    Article  CAS  PubMed  Google Scholar 

  5. Viana M, Sances G, Linde M et al (2017) Clinical features of migraine aura: results from a prospective diary-aided study. Cephalalgia: an Intl J Headache 37:979–989. https://doi.org/10.1177/0333102416657147

    Article  Google Scholar 

  6. Hadjikhani N, Sanchez del Rio M, Wu O et al (2001) Mechanisms of migraine aura revealed by functional MRI in human visual cortex. Proc Natl Acad Sci 98:4687–4692. https://doi.org/10.1073/pnas.071582498

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  7. Hougaard A, Amin FM, Amin F et al (2013) Provocation of migraine with aura using natural trigger factors. Neurology 80:428–431. https://doi.org/10.1212/WNL.0b013e31827f0f10

    Article  PubMed  Google Scholar 

  8. Lauritzen M (1994) Pathophysiology of the migraine aura. The spreading depression theory. Brain: a J Neurol 117(Pt 1):199–210. https://doi.org/10.1093/brain/117.1.199

    Article  Google Scholar 

  9. Messina R, Filippi M, Goadsby PJ (2018) Recent advances in headache neuroimaging. Curr Opin Neurol 31:379–385. https://doi.org/10.1097/WCO.0000000000000573

    Article  PubMed  Google Scholar 

  10. Tu Y, Zeng F, Lan L et al (2020) An fMRI-based neural marker for migraine without aura. Neurology 94:e741–e751. https://doi.org/10.1212/wnl.0000000000008962

    Article  PubMed Central  PubMed  Google Scholar 

  11. Messina R, Cetta I, Colombo B, Filippi M (2022) Tracking the evolution of non-headache symptoms through the migraine attack. J Headache Pain 23:149. https://doi.org/10.1186/s10194-022-01525-6

    Article  PubMed Central  PubMed  Google Scholar 

  12. Chong CD, Gaw N, Fu Y et al (2017) Migraine classification using magnetic resonance imaging resting-state functional connectivity data. Cephalalgia 37:828–844. https://doi.org/10.1177/0333102416652091

    Article  PubMed  Google Scholar 

  13. Jorge-Hernandez F, Chimeno YG, Garcia-Zapirain B et al (2014) Graph theory for feature extraction and classification: a migraine pathology case study. Bio-Med Mater Eng 24:2979–2986. https://doi.org/10.3233/BME-141118

    Article  Google Scholar 

  14. Rocca MA, Harrer JU, Filippi M (2020) Are machine learning approaches the future to study patients with migraine? Neurology 94:291–292. https://doi.org/10.1212/WNL.0000000000008956

    Article  PubMed  Google Scholar 

  15. Yang H, Zhang J, Liu Q, Wang Y (2018) Multimodal MRI-based classification of migraine: using deep learning convolutional neural network. Biomed Eng Online 17:1–14. https://doi.org/10.1186/s12938-018-0587-0

    Article  Google Scholar 

  16. Fu T, Liu L, Huang X et al (2022) Cerebral blood flow alterations in migraine patients with and without aura: an arterial spin labeling study. J Headache Pain 23:131. https://doi.org/10.1186/s10194-022-01501-0

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  17. Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press

    Book  Google Scholar 

  18. Mitrović K, Petrušić I, Radojičić A et al (2023) Migraine with aura detection and subtype classification using machine learning algorithms and morphometric magnetic resonance imaging data. Front Neurol 14:1106612. https://doi.org/10.3389/fneur.2023.1106612

    Article  PubMed Central  PubMed  Google Scholar 

  19. Gou C, Yang S, Hou Q et al (2023) Functional connectivity of the language area in migraine: a preliminary classification model. BMC Neurol 23:142. https://doi.org/10.1186/s12883-023-03183-w

    Article  PubMed Central  PubMed  Google Scholar 

  20. Dumkrieger G, Chong CD, Ross K et al (2023) The value of brain MRI functional connectivity data in a machine learning classifier for distinguishing migraine from persistent post-traumatic headache. Front Pain Res 3:1012831. https://doi.org/10.3389/fpain.2022.1012831

    Article  Google Scholar 

  21. Hong J, Sun J, Zhang L et al (2022) Neurological mechanism and treatment effects prediction of acupuncture on migraine without aura: study protocol for a randomized controlled trial. Front Neurol 13:981752. https://doi.org/10.3389/fneur.2022.981752

    Article  PubMed Central  PubMed  Google Scholar 

  22. Wei H-L, Xu C-H, Wang J-J et al (2022) Disrupted functional connectivity of the amygdala predicts the efficacy of non-steroidal anti-inflammatory drugs in migraineurs without aura. Front Mol Neurosci 15:819507. https://doi.org/10.3389/fnmol.2022.819507

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  23. Cheng S, Zhang X, Zheng H et al (2022) Efficacy prediction of acupuncture treatment for migraine without aura based on multimodal MRI: A study protocol. Front Neurol 13:953921. https://doi.org/10.3389/fneur.2022.953921

    Article  PubMed Central  PubMed  Google Scholar 

  24. Lee CH, Park H, Lee MJ, Park B (2023) Whole-brain functional gradients reveal cortical and subcortical alterations in patients with episodic migraine. Hum Brain Mapp 44:2224–2233. https://doi.org/10.1002/hbm.26204

    Article  PubMed Central  PubMed  Google Scholar 

  25. Mu J, Chen T, Quan S et al (2020) Neuroimaging features of whole-brain functional connectivity predict attack frequency of migraine. Hum Brain Mapp 41:984–993. https://doi.org/10.1002/hbm.24854

    Article  PubMed  Google Scholar 

  26. Mitchell TM, Hutchinson R, Niculescu RS et al (2004) Learning to decode cognitive states from brain images. Mach Learn 57:145–175. https://doi.org/10.1023/B:MACH.0000035475.85309.1b

    Article  Google Scholar 

  27. Mourão-Miranda J, Friston KJ, Brammer M (2007) Dynamic discrimination analysis: a spatial-temporal SVM. Neuroimage 36:88–99. https://doi.org/10.1016/j.neuroimage.2007.02.020

    Article  PubMed  Google Scholar 

  28. Chu C, Mourão-Miranda J, Chiu YC et al (2011) Utilizing temporal information in fMRI decoding: classifier using kernel regression methods. Neuroimage 58:560–571. https://doi.org/10.1016/j.neuroimage.2011.06.053

    Article  PubMed  Google Scholar 

  29. Guidotti R, Del Gratta C, Baldassarre A et al (2015) Visual learning induces changes in resting-state fMRI multivariate pattern of information. J Neurosci : official J Soc Neurosci 35:9786–9798. https://doi.org/10.1523/JNEUROSCI.3920-14.2015

    Article  CAS  Google Scholar 

  30. Janoos F, Machiraju R, Singh S, Morocz IÁ (2011) Spatio-temporal models of mental processes from fMRI. Neuroimage 57:362–377. https://doi.org/10.1016/j.neuroimage.2011.03.047

    Article  PubMed  Google Scholar 

  31. Venkatesh M, Jaja J, Pessoa L (2019) Brain dynamics and temporal trajectories during task and naturalistic processing. Neuroimage 186:410–423. https://doi.org/10.1016/j.neuroimage.2018.11.016

    Article  PubMed  Google Scholar 

  32. Hagenbeek RE, Rombouts SARB, Van Dijk BW, Barkhof F (2002) Determination of individual stimulus-response curves in the visual cortex. Hum Brain Mapp 17:244–250. https://doi.org/10.1002/hbm.10067

    Article  PubMed Central  PubMed  Google Scholar 

  33. Evans ACC, Collins DLL, Mills SRR, Brown ED, Kelly RL, Peters TM (1993) 3D statistical neuroanatomical models from 305 MRI volumes. In: 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference, San Francisco, CA, USA. IEEE 3:1813–1817. https://doi.org/10.1109/NSSMIC.1993.373602

  34. Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. The MIT Press. Gambridge, Massachusetts

  35. Schrouff J, Rosa MJ, Rondina JM et al (2013) PRoNTo: pattern recognition for neuroimaging toolbox. Neuroinformatics 11:319–337. https://doi.org/10.1007/s12021-013-9178-1

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  36. Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2:121–167. https://doi.org/10.1023/A:1009715923555

    Article  Google Scholar 

  37. SB Eickhoff, KE Stephan, H Mohlberg, et al (2005) A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data 25 1325-1335 https://doi.org/10.1016/j.neuroimage.2004.12.034

  38. Amunts K, Malikovic A, Mohlberg H et al (2000) Brodmann’s areas 17 and 18 brought into stereotaxic space—where and how variable? Neuroimage 11:66–84. https://doi.org/10.1006/nimg.1999.0516

    Article  CAS  PubMed  Google Scholar 

  39. Rottschy C, Eickhoff SB, Schleicher A et al (2007) Ventral visual cortex in humans: cytoarchitectonic map** of two extrastriate areas. Hum Brain Mapp 28:1045–1059. https://doi.org/10.1002/hbm.20348

    Article  PubMed Central  PubMed  Google Scholar 

  40. Kujovic M, Zilles K, Malikovic A et al (2013) Cytoarchitectonic map** of the human dorsal extrastriate cortex. Brain Struct Funct 218:157–172. https://doi.org/10.1007/s00429-012-0390-9

    Article  PubMed  Google Scholar 

  41. Malikovic A, Amunts K, Schleicher A et al (2006) Cytoarchitectonic analysis of the human extrastriate cortex in the region of V5/MT+: a probabilistic, stereotaxic map of area hOc5. Cereb Cortex 17:562–574. https://doi.org/10.1093/cercor/bhj181

    Article  PubMed  Google Scholar 

  42. Maldjian JA, Laurienti PJ, Kraft RA, Burdette JH (2003) An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage 19:1233–1239. https://doi.org/10.1016/S1053-8119(03)00169-1

    Article  PubMed  Google Scholar 

  43. Portugal LCL, Rosa MJ, Rao A et al (2016) Can emotional and behavioral dysregulation in youth be decoded from functional neuroimaging? PLoS ONE 11:e0117603. https://doi.org/10.1371/journal.pone.0117603

    Article  PubMed Central  PubMed  Google Scholar 

  44. Schrouff J, Monteiro JM, Portugal L et al (2018) Embedding anatomical or functional knowledge in whole-brain multiple kernel learning models. Neuroinformatics 16:117–143. https://doi.org/10.1007/s12021-017-9347-8

    Article  PubMed Central  PubMed  Google Scholar 

  45. Cao Y, Aurora SK, Nagesh V et al (2002) Functional MRI-BOLD of brainstem structures during visually triggered migraine. Neurology 59:72–78. https://doi.org/10.1212/wnl.59.1.72

    Article  CAS  PubMed  Google Scholar 

  46. Hougaard A, Amin FM, Hoffmann MB et al (2014) Interhemispheric differences of fMRI responses to visual stimuli in patients with side-fixed migraine aura. Hum Brain Mapp 35:2714–2723. https://doi.org/10.1002/hbm.22361

    Article  PubMed  Google Scholar 

  47. Silvestro M, Tessitore A, Di Nardo F, et al (2021) Functional connectivity changes in complex migraine aura: beyond the visual network. Euro J Neurol 15061. https://doi.org/10.1111/ene.15061

  48. Tedeschi G, Russo A, Conte F et al (2016) Increased interictal visual network connectivity in patients with migraine with aura. Cephalalgia an Intl J Headache 36:139–147. https://doi.org/10.1177/0333102415584360

    Article  Google Scholar 

  49. Tessitore A, Russo A, Conte F et al (2015) Abnormal connectivity within executive resting-state network in migraine with aura. Headache 55:794–805. https://doi.org/10.1111/head.12587

    Article  PubMed  Google Scholar 

  50. Zhang D, Huang X, Su W, et al (2020) Altered lateral geniculate nucleus functional connectivity in migraine without aura: a resting-state functional MRI study. J Headache Pain 21 https://doi.org/10.1186/s10194-020-01086-6

  51. Arngrim N, Hougaard A, Ahmadi K et al (2017) Heterogenous migraine aura symptoms correlate with visual cortex functional magnetic resonance imaging responses. Ann Neurol 82:925–939. https://doi.org/10.1002/ana.25096

    Article  PubMed  Google Scholar 

  52. Rasmussen AH, Kogelman LJA, Kristensen DM et al (2020) Functional gene networks reveal distinct mechanisms segregating in migraine families. Brain: J Neurol 143:2945–2956. https://doi.org/10.1093/brain/awaa242

    Article  Google Scholar 

  53. Russell MB, Olesen J (1996) A nosographic analysis of the migraine aura in a general population. Brain: J Neurol 119(Pt 2):355–361. https://doi.org/10.1093/brain/119.2.355

    Article  Google Scholar 

  54. Marquand AF, De Simoni S, O’Daly OG et al (2011) Pattern classification of working memory networks reveals differential effects of methylphenidate, atomoxetine, and placebo in healthy volunteers. Neuropsychopharmacol 36:1237–1247. https://doi.org/10.1038/npp.2011.9

    Article  CAS  Google Scholar 

  55. Portugal LCL, Ramos TC, Fernandes O, et al (2023) Machine learning applied to fMRI patterns of brain activation in response to mutilation pictures predicts PTSD symptoms. https://doi.org/10.21203/rs.3.rs-2928305/v1

  56. Mourão-Miranda J, Oliveira L, Ladouceur CD et al (2012) Pattern recognition and functional neuroimaging help to discriminate healthy adolescents at risk for mood disorders from low risk adolescents. PLoS ONE 7:e29482. https://doi.org/10.1371/journal.pone.0029482

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  57. Marquand A, Howard M, Brammer M et al (2010) Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes. Neuroimage 49:2178–2189. https://doi.org/10.1016/j.neuroimage.2009.10.072

    Article  PubMed  Google Scholar 

  58. Varoquaux G, Raamana PR, Engemann DA et al (2017) Assessing and tuning brain decoders: cross-validation, caveats, and guidelines. Neuroimage 145:166–179. https://doi.org/10.1016/j.neuroimage.2016.10.038

    Article  PubMed  Google Scholar 

  59. Gill S, Mouches P, Hu S et al (2020) Using machine learning to predict dementia from neuropsychiatric symptom and neuroimaging data. J Alzheim Dis 75:277–288. https://doi.org/10.3233/jad-191169

    Article  Google Scholar 

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Acknowledgements

The authors would like to express their gratitude to all the subjects who participated in the study; to Professor Maurice Vincent for important discussions; to Luke Barbara for English revision of the manuscript; and Danielle Pimentel and Tania Maria Netto for technical support. We express our acknowledgements to “Coordenação de Aperfeiçoamento de Pessoal de Nível Superior” (CAPES) for their support.

Funding

OFJ, MA, and LRR were previously funded by CAPES foundation—“Coordenação de Aperfeiçoamento de Pessoal de Nível Superior”—Brazil. OFJ is funded by FAPERJ—“Fundação de Aparo à Pesquisa do Estado do Rio de Janeiro”.

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Authors and Affiliations

  1. Laboratory of Neuroimaging and Psychophysiology, Instituto de Psiquiatria, Faculdade de Medicina – Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil

    Orlando Fernandes Jr, Lucas Rego Ramos, Mariana Calixto Acchar & Tiago Arruda Sanchez

  2. Universidade Estacio de Sá (UNESA), Rio de Janeiro, RJ, Brazil

    Mariana Calixto Acchar

  3. Laboratório de Neurofisiolgia e Comportamento, Departamento de Fisiologia e Farmacologia, Instituto Biomédico – Universidade Federal Fluminense, Nitéroi, RJ, Brazil

    Orlando Fernandes Jr

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  1. Orlando Fernandes Jr
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Correspondence to Tiago Arruda Sanchez.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the Ethics Committee of the Federal University of Rio de Janeiro, Brazil, and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The ethical approval can be found in Brazilian’s National Health Council webpage at: https://plataformabrasil.saude.gov.br (identifier #429.485/2013). Informed consent was obtained from all individual participants included in the study.

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Fernandes, O., Ramos, L.R., Acchar, M.C. et al. Migraine aura discrimination using machine learning: an fMRI study during ictal and interictal periods. Med Biol Eng Comput (2024). https://doi.org/10.1007/s11517-024-03080-5

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