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Decreased medial prefrontal cortex volume in migraine without aura:
a multispectral MRI study

Sourena Soheili-Nezhad1,2, Alireza Sedghi3, Ferdinand Schweser4,5, Neda Jahanshad2, Paul M. Thompson2, Amir
Eslami Shahr Babaki6, Aida Tabrizi1, Mansoure Togha*1

Iranian Center of Neurological Research, Neuroscience Research Institute, Tehran University of Medical Sciences, Tehran, Iran


Imaging Genetics Center, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del
Rey, CA 90292, United States of America

Medical Informatics Laboratory, School of Computing, Queen’s University, Kingston, ON K7L2N8, Canada


Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY
14203, United States of America

Translational Imaging Center, Clinical and Translational Science Institute, University at Buffalo, Buffalo, NY 14203, United States of America


Chronic Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran


Corresponding author:

Migraine is a common and debilitating disease with a prevalence of 17.5% in women,
which is more than two-fold of its prevalence in the male population [1]. Although
classically considered an episodic disorder, new evidence suggests that migraine may
represent a progressive neuropathology with inter-ictal changes in brain function and
structure. Similar to various neurodevelopmental and neurodegenerative disorders,
migraine is predisposed by a strong genetic component (>50% [2]). Quantitative
neuroimaging has recently revealed disrupted brain networks in migraine, and
discovering such neuroimaging biomarkers may enlighten disease mechanisms and
guide future disease-modifying clinical trials.

underwent multimodal MRI using a 3T Siemens Magnetom Trio scanner and a
receive-only 12-channel head coil. Pulse sequences of the study were as follows: 1)
a 1mm isotropic T1-weighted MPRAGE anatomical scan; 2) an 11-min resting-state
fMRI scan with parameters TR=2.57s and voxel size of 3.4×3.4×3.0mm; 3) diffusion
MRI with 64 gradient directions and b-max=1000s/mm2; 4) an eight-echo 3D
gradient-echo pulse sequence with voxel size of 0.8×0.8×1.5mm and further
optimization for quantitative susceptibility mapping (QSM). Changes in resting-state
functional brain networks [3], volumetric differences in brain anatomy [4], and whitematter microstructure [5] were explored in order to characterize MRI correlates of
migraine by using various statistical neuroimaging toolboxes including ANTS, FSL,
MRtrix3 and in-house developed software. The HEIDI algorithm was used to
quantify variations in regional brain tissue susceptibility [6].

Materials and methods
We enrolled 36 female migraine without-aura patients (36.6 ± 8.8 years) and 33 agematched healthy subjects of the same sex (36.4 ± 8.8 years). Study subjects

Figure 3. The default-mode network of study population decomposed by ICA of restingstate fMRI data (Red-yellow: correlation/activation; Blue: anti-correlation/inhibition).

Figure 1. Tensor-based morphometry shows lower volume of right mPFC in migraine patients
(Blue: uncorrected p<0.05; Red: corrected p<0.05). The crossed lines show coordinates of the
most significant voxels (uncorrected p=2.4×10-5, 50,000 permutations).

Figure 4. The quantitative susceptibility map (QSM) of a healthy study subject: a) Raw
phase image of the 3D gradient-echo pulse sequence. b) The same image after phase
unwrapping. c) Removal of the background phase contribution by SHARP [7] reveals
local phase alterations. d) Final QSM map reconstructed by the HEIDI algorithm. The
QSM map demonstrates high contrast in revealing deep grey-matter, brainstem and
cerebellar nuclei due to high paramagnetic material content (iron and ferritin).

Figure 2. Left: fiber orientation density (FOD) map of a healthy study subject reconstructed
by constrained spherical deconvolution of diffusion MRI data. Right: the first diffusion
eigenvector showing the principal direction of white-matter tracts superimposed on the
fractional anisotropy (FA) map of the same subject.

At the time of preparation of this poster, tensor-based morphometry analysis has been
completed and revealed significant loss of brain volume in right medial prefrontal
cortex (mPFC) of migraine patients (corrected p<0.05, Figure 1) and suggestive loss
of volume in bilateral inferior frontal sulci (uncorrected p=2.4×10-5). Preprocessed
diffusion MR images revealed white-matter tracts and their crossing in study subjects
(Figure 2). Various resting-state functional networks including the default-mode
network were successfully decomposed from 17,250 preprocessed fMRI volumes of
the study population in the common template space (Figure 3). Changes in brain
tissue magnetization due to regional deposition susceptibility-altering material
including iron (ferritin) were assessed by QSM (Figure 4). These data will be used to

investigate multimodal neuroimaging biomarkers of migraine using whole-brain
exploratory methods. Our neuroimaging study may aid the diagnosis and treatment
of migraine by elucidating the dysfunctional pain network in patients.


Victor, T.W., et al., Migraine prevalence by age and sex in the United States: a life-span study. Cephalalgia, 2010. 30(9): p. 1065-72.
Cox, H.C., et al., Heritability and genome-wide linkage analysis of migraine in the genetic isolate of norfolk island. Gene, 2012. 494(1): p.
Beckmann, C.F. and S.M. Smith, Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE
transactions on medical imaging, 2004. 23(2): p. 137-152.
Hua, X., et al., Tensor-based morphometry as a neuroimaging biomarker for Alzheimer's disease: an MRI study of 676 AD, MCI, and
normal subjects. Neuroimage, 2008. 43(3): p. 458-469.
Tournier, J., F. Calamante, and A. Connelly, Determination of the appropriate b value and number of gradient directions for high‐angular‐
resolution diffusion‐weighted imaging. NMR in Biomedicine, 2013. 26(12): p. 1775-1786.
Schweser, F., et al., Quantitative susceptibility mapping for investigating subtle susceptibility variations in the human brain. Neuroimage,
2012. 62(3): p. 2083-2100.
Schweser, F., et al., Sophisticated Harmonic Artifact Reduction for Phase Data (SHARP). 2010.

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