Cortical Thickness in Migraine: A Coordinate-Based Meta-Analysis.

LiQin Sheng, HaiRong Ma, YuanYuan Shi, ZhenYu Dai, JianGuo Zhong, Fei Chen, PingLei Pan
Author Information
  1. LiQin Sheng: Department of Neurology, Kunshan Hospital of Traditional Chinese Medicine, Suzhou, China.
  2. HaiRong Ma: Department of Neurology, Kunshan Hospital of Traditional Chinese Medicine, Suzhou, China.
  3. YuanYuan Shi: Department of Central Laboratory, School of Medicine, Affiliated Yancheng Hospital, Southeast University, Yancheng, China.
  4. ZhenYu Dai: Department of Radiology, School of Medicine, Affiliated Yancheng Hospital, Southeast University, Yancheng, China.
  5. JianGuo Zhong: Department of Neurology, School of Medicine, Affiliated Yancheng Hospital, Southeast University, Yancheng, China.
  6. Fei Chen: Department of Radiology, School of Medicine, Affiliated Yancheng Hospital, Southeast University, Yancheng, China.
  7. PingLei Pan: Department of Central Laboratory, School of Medicine, Affiliated Yancheng Hospital, Southeast University, Yancheng, China.

Abstract

Cortical thickness (CTh) via surface-based morphometry analysis is a popular method to characterize brain morphometry. Many studies have been performed to investigate CTh abnormalities in migraine. However, the results from these studies were not consistent and even conflicting. These divergent results hinder us to obtain a clear picture of brain morphometry regarding CTh alterations in migraine. Coordinate-based meta-analysis (CBMA) is a promising technique to quantitatively pool individual neuroimaging studies to identify consistent brain areas involved. Electronic databases (PubMed, EMBASE, Web of Science, China National Knowledge Infrastructure, WanFang, and SinoMed) and other sources (bioRxiv and reference lists of relevant articles and reviews) were systematically searched for studies that compared regional CTh differences between patients with migraine and healthy controls (HCs) up to May 15, 2020. A CBMA was performed using the Seed-based d Mapping with Permutation of Subject Images approach. In total, we identified 16 studies with 17 datasets reported that were eligible for the CBMA. The 17 datasets included 872 patients with migraine (average sample size 51.3, mean age 39.6 years, 721 females) and 949 HCs (average sample size 59.3, mean age 44.2 years, 680 females). The CBMA detected no statistically significant consistency of CTh alterations in patients with migraine relative to HCs. Sensitivity analysis and subgroup analysis verified this result to be robust. Metaregression analyses revealed that this CBMA result was not confounded by age, gender, aura, attack frequency per month, and illness duration. Our CBMA adds to the evidence of the replication crisis in neuroimaging research that is increasingly recognized. Many potential confounders, such as underpowered sample size, heterogeneous patient selection criteria, and differences in imaging collection and methodology, may contribute to the inconsistencies of CTh alterations in migraine, which merit attention before planning future research on this topic.

Keywords

References

  1. Neuroimage. 2018 Feb 15;167:104-120 [PMID: 29155184]
  2. eNeuro. 2014 Dec 30;1(1):e20.14 [PMID: 25893216]
  3. Cephalalgia. 2020 May;40(6):575-585 [PMID: 32299230]
  4. Neuroimage Clin. 2016 Feb 17;11:322-327 [PMID: 27298761]
  5. Neuropsychopharmacology. 2020 Mar;45(4):703-712 [PMID: 31694045]
  6. Proc Natl Acad Sci U S A. 2000 Sep 26;97(20):11050-5 [PMID: 10984517]
  7. Lancet Neurol. 2019 May;18(5):459-480 [PMID: 30879893]
  8. J Headache Pain. 2017 Dec;18(1):74 [PMID: 28733941]
  9. Neuroimage. 2008 Mar 1;40(1):187-96 [PMID: 18096408]
  10. Hum Brain Mapp. 2019 Dec 1;40(17):5142-5154 [PMID: 31379049]
  11. EBioMedicine. 2017 Jul;21:228-235 [PMID: 28633986]
  12. JAMA Psychiatry. 2017 Jan 01;74(1):47-55 [PMID: 27829086]
  13. Korean J Radiol. 2018 Jul-Aug;19(4):767-776 [PMID: 29962883]
  14. Cephalalgia. 2015 Aug;35(9):783-91 [PMID: 25414472]
  15. Curr Opin Neurol. 2016 Jun;29(3):309-13 [PMID: 26886355]
  16. Headache. 2015 Jun;55(6):762-77 [PMID: 26084235]
  17. Neuroimaging Clin N Am. 2019 May;29(2):301-324 [PMID: 30926119]
  18. Neurosci Biobehav Rev. 2018 Jan;84:151-161 [PMID: 29180258]
  19. Pain. 2015 Jul;156(7):1232-1239 [PMID: 25775358]
  20. Nat Rev Neurosci. 2013 May;14(5):365-76 [PMID: 23571845]
  21. Headache. 2018 Feb;58(2):346-353 [PMID: 28796284]
  22. Pediatr Neurol. 2020 Jun;107:1-6 [PMID: 32192818]
  23. Neuroimage. 2019 Feb 1;186:174-184 [PMID: 30389629]
  24. Neuroimage. 2020 May 1;211:116608 [PMID: 32032737]
  25. Neuroimage. 2016 Jan 15;125:267-279 [PMID: 26463175]
  26. Sleep Med Rev. 2018 Dec;42:111-118 [PMID: 30093361]
  27. Psychol Med. 2018 Sep;48(12):2001-2010 [PMID: 29239286]
  28. Mol Psychiatry. 2017 Jun;22(6):900-909 [PMID: 27137745]
  29. Auton Neurosci. 2019 Mar;217:41-48 [PMID: 30704974]
  30. PLoS One. 2012;7(6):e38234 [PMID: 22675527]
  31. Nat Neurosci. 2017 Feb 23;20(3):299-303 [PMID: 28230846]
  32. J Affect Disord. 2019 Apr 1;248:34-41 [PMID: 30711867]
  33. PLoS One. 2017 Jul 6;12(7):e0179590 [PMID: 28683072]
  34. J Neuroimaging. 2018 Sep;28(5):515-523 [PMID: 29766613]
  35. Front Psychiatry. 2014 Feb 10;5:13 [PMID: 24575054]
  36. Radiology. 2013 Jul;268(1):170-80 [PMID: 23533286]
  37. Neuroimage. 2015 Feb 15;107:107-115 [PMID: 25498430]
  38. Cephalalgia. 2011 Oct;31(14):1452-8 [PMID: 21911412]
  39. BMJ. 1997 Sep 13;315(7109):629-34 [PMID: 9310563]
  40. Lancet Neurol. 2019 Aug;18(8):795-804 [PMID: 31160203]
  41. Eur J Neurol. 2014 Feb;21(2):287-e13 [PMID: 24200371]
  42. Cephalalgia. 2014 Dec;34(14):1125-33 [PMID: 24728304]
  43. Hum Brain Mapp. 2015 Sep;36(9):3472-85 [PMID: 26033168]
  44. Trends Cardiovasc Med. 2020 Oct;30(7):424-430 [PMID: 31679956]
  45. Brain Topogr. 2021 May;34(3):384-401 [PMID: 33606142]
  46. Prog Neuropsychopharmacol Biol Psychiatry. 2019 Jan 10;88:287-302 [PMID: 30118825]
  47. Headache. 2019 Feb;59(2):180-191 [PMID: 30468246]
  48. Cephalalgia. 2019 Apr;39(5):665-673 [PMID: 30525946]
  49. Neuroimage. 2013 Apr 15;70:122-31 [PMID: 23261638]
  50. J Headache Pain. 2020 Jul 11;21(1):89 [PMID: 32652927]
  51. Pharmacol Ther. 2017 Apr;172:151-170 [PMID: 27919795]
  52. Pain. 2019 Jul;160(7):1634-1643 [PMID: 30839431]
  53. Ann Transl Med. 2020 Jan;8(1):12 [PMID: 32055603]
  54. Expert Rev Neurother. 2018 Jul;18(7):533-544 [PMID: 29883214]
  55. J Neurol Neurosurg Psychiatry. 2016 Jul;87(7):741-9 [PMID: 26733600]
  56. BMJ. 2009 Jul 21;339:b2535 [PMID: 19622551]
  57. Front Neuroinform. 2015 Apr 24;9:12 [PMID: 25964757]
  58. Brain. 2020 Jun 1;143(6):e45 [PMID: 32363400]
  59. Hum Brain Mapp. 2013 Nov;34(11):3000-9 [PMID: 22807270]
  60. J Clin Neurosci. 2019 Apr;62:180-183 [PMID: 30472336]
  61. Neuroimage. 2006 Aug 1;32(1):180-94 [PMID: 16651008]
  62. Neuroimage. 2010 Aug 1;52(1):158-71 [PMID: 20362677]
  63. Neuroimage. 2009 Feb 15;44(4):1324-33 [PMID: 19038349]
  64. PLoS One. 2016 Jan 26;11(1):e0146913 [PMID: 26812647]
  65. Brain. 2018 Mar 1;141(3):776-785 [PMID: 29360944]
  66. J Neurol. 2017 Sep;264(9):2031-2039 [PMID: 28321564]
  67. Neuroimage. 2009 Nov 1;48(2):371-80 [PMID: 19559801]
  68. Semin Neurol. 2018 Apr;38(2):182-190 [PMID: 29791944]
  69. Cephalalgia. 2014 Dec;34(14):1115-24 [PMID: 24781111]
  70. J Vis Exp. 2019 Nov 27;(153): [PMID: 31840658]
  71. Front Neurosci. 2021 Jan 06;14:600423 [PMID: 33488349]
  72. JAMA Psychiatry. 2015 Dec;72(12):1243-51 [PMID: 26558708]
  73. Cephalalgia. 2016 May;36(6):526-33 [PMID: 26378082]
  74. Stat Methods Med Res. 2019 Dec;28(12):3741-3754 [PMID: 30514161]
  75. Brain. 2012 Aug;135(Pt 8):2546-59 [PMID: 22843414]
  76. Neuroimage. 2018 Aug 1;176:56-70 [PMID: 29673966]

Word Cloud

Created with Highcharts 10.0.0migraineCThCBMAstudiesmorphometryanalysisbrainalterationspatientsHCssamplesizeageCorticalthicknesssurface-basedManyperformedresultsconsistentmeta-analysisneuroimagingdifferencesd17datasetsaverage3meanyearsfemalesresultresearchviapopularmethodcharacterizeinvestigateabnormalitiesHoweverevenconflictingdivergenthinderusobtainclearpictureregardingCoordinate-basedpromisingtechniquequantitativelypoolindividualidentifyareasinvolvedElectronicdatabasesPubMedEMBASEWebScienceChinaNationalKnowledgeInfrastructureWanFangSinoMedsourcesbioRxivreferencelistsrelevantarticlesreviewssystematicallysearchedcomparedregionalhealthycontrolsMay152020usingSeed-basedMappingPermutationSubjectImagesapproachtotalidentified16reportedeligibleincluded8725139672194959442680detectedstatisticallysignificantconsistencyrelativeSensitivitysubgroupverifiedrobustMetaregressionanalysesrevealedconfoundedgenderauraattackfrequencypermonthillnessdurationaddsevidencereplicationcrisisincreasinglyrecognizedpotentialconfoundersunderpoweredheterogeneouspatientselectioncriteriaimagingcollectionmethodologymaycontributeinconsistenciesmeritattentionplanningfuturetopicThicknessMigraine:Coordinate-BasedMeta-Analysiscoordinate-basedcorticalseed-basedmappingpermutationsubjectimages

Similar Articles

Cited By