Investigation of cortical thickness and volume during spontaneous attacks of migraine without aura: a 3-Tesla MRI study

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Abstract

Background

Structural imaging has revealed changes in cortical thickness in migraine patients compared to healthy controls is reported, but presence of dynamic cortical and subcortical changes during migraine attack versus inter-ictal phase is unknown. The aim of the present study was to investigate possible changes in cortical thickness during spontaneous migraine attacks. We hypothesized that pain-related cortical area would be affected during the attack compared to an inter-ictal phase.

Methods

Twenty-five patients with migraine without aura underwent three-dimensional T1-weighted imaging on a 3-Tesla MRI scanner during spontaneous and untreated migraine attacks. Subsequently, 20 patients were scanned in the inter-ictal phase, while 5 patients did not show up for the inter-ictal scan. Four patients were excluded from the analysis because of bilateral migraine pain and another one patient was excluded due to technical error in the imaging. Longitudinal image processing was done using FreeSurfer. Repeated measures ANOVA was used for statistical analysis and to control for multiple comparison the level of significance was set at p = 0.025.

Results

In a total of 15 patients, we found reduced cortical thickness of the precentral ( p = 0.023), pericalcarine ( p = 0.024), and temporal pole ( p = 0.017) cortices during the attack compared to the inter-ictal phase. Cortical volume was reduced in prefrontal ( p = 0.018) and pericalcarine ( p = 0.017) cortices. Hippocampus volume was increased during attack ( p = 0.007). We found no correlations between the pain side or any other clinical parameters and the reduced cortical size.

Conclusion

Spontaneous migraine attacks are accompanied by transient reduced cortical thickness and volume in pain-related areas. The findings constitute a fingerprint of acute pain in migraine patients, which can be used as a possible biomarker to predict antimigraine treatment effect in future studies.

Trial registration

The study was registered at ClinicalTrials.gov ( NCT02202486).

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Most cited references 44

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An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

Rahul Desikan, Florent Ségonne, Bruce Fischl … (2006)

In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.

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Cortical surface-based analysis. I. Segmentation and surface reconstruction.

Anders Dale, Bruce Fischl, Martin I. Sereno (1999)

Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging. Copyright 1999 Academic Press.

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Measuring the thickness of the human cerebral cortex from magnetic resonance images.

B Fischl, A M Dale (2000)

Accurate and automated methods for measuring the thickness of human cerebral cortex could provide powerful tools for diagnosing and studying a variety of neurodegenerative and psychiatric disorders. Manual methods for estimating cortical thickness from neuroimaging data are labor intensive, requiring several days of effort by a trained anatomist. Furthermore, the highly folded nature of the cortex is problematic for manual techniques, frequently resulting in measurement errors in regions in which the cortical surface is not perpendicular to any of the cardinal axes. As a consequence, it has been impractical to obtain accurate thickness estimates for the entire cortex in individual subjects, or group statistics for patient or control populations. Here, we present an automated method for accurately measuring the thickness of the cerebral cortex across the entire brain and for generating cross-subject statistics in a coordinate system based on cortical anatomy. The intersubject standard deviation of the thickness measures is shown to be less than 0.5 mm, implying the ability to detect focal atrophy in small populations or even individual subjects. The reliability and accuracy of this new method are assessed by within-subject test-retest studies, as well as by comparison of cross-subject regional thickness measures with published values.

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Author and article information

Contributors

Faisal Mohammad Amin:

ORCID: http://orcid.org/0000-0003-3803-3599

faisal@dadlnet.dk

Journal

Journal ID (nlm-ta): J Headache Pain

Journal ID (iso-abbrev): J Headache Pain

Title: The Journal of Headache and Pain

Publisher: Springer Milan (Milan )

ISSN (Print): 1129-2369

ISSN (Electronic): 1129-2377

Publication date (Electronic): 21 August 2021

Publication date PMC-release: 21 August 2021

Publication date Collection: 2021

Volume: 22

Issue: 1

Electronic Location Identifier: 98

Affiliations

[1 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Danish Headache Center, Department of Neurology, Faculty of Health and Medical Sciences, , Rigshospitalet Glostrup, University of Copenhagen, ; Valdemar Hansens Vej 5, 2600 Glostrup, Denmark

[2 ]GRID grid.419416.f, ISNI 0000 0004 1760 3107, Headache Science & Neurorehabilitation Center, , IRCCS Mondino Foundation, ; Pavia, Italy

[3 ]GRID grid.8982.b, ISNI 0000 0004 1762 5736, Department of Brain and Behavioral Sciences, , University of Pavia, ; Pavia, Italy

[4 ]GRID grid.475435.4, Functional Imaging Unit, Department of Clinical Physiology, Nuclear Medicine and PET,Faculty of Health and Medical Sciences, , Rigshospitalet, University of Copenhagen, ; Glostrup, Denmark

[5 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Centre for Neuropsychiatric Schizophrenia Research, CNSR and Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, Mental Health Centre Glostrup, , University of Copenhagen, ; 2600 Glostrup, Denmark

[6 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Department of Radiology, , Herlev-Gentofte Hospital, University of Copenhagen, ; Herlev, Denmark

Author information

Faisal Mohammad Amin http://orcid.org/0000-0003-3803-3599

Article

Publisher ID: 1312

DOI: 10.1186/s10194-021-01312-9

PMC ID: 8380396

PubMed ID: 34418951

SO-VID: 5ac71d01-d356-4a2f-980a-c394cd3c9855

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 29 April 2021

Date accepted : 3 August 2021

Funding

Funded by: Lundbeckfonden (DK)

Award ID: R155-2014-171

Award Recipient : Messoud Ashina

Custom metadata

ScienceOpen disciplines: Anesthesiology & Pain management

Keywords: migraine cortex,pain cortex,migraine brain,cortical volume,migraine attack

Data availability:

ScienceOpen disciplines: Anesthesiology & Pain management

Keywords: migraine cortex, pain cortex, migraine brain, cortical volume, migraine attack

Investigation of cortical thickness and volume during spontaneous attacks of migraine without aura: a 3-Tesla MRI study

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Trial registration

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Irish Journal of Paramedicine

Most cited references 44

An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

Cortical surface-based analysis. I. Segmentation and surface reconstruction.

Measuring the thickness of the human cerebral cortex from magnetic resonance images.

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