Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
권호 표지
DOI QR코드

DOI QR Code

Magnetic Resonance Image Texture Analysis of the Periaqueductal Gray Matter in Episodic Migraine Patients without T2-Visible Lesions

  • Chen, Zhiye (Department of Radiology, Chinese PLA General Hospital) ;
  • Chen, Xiaoyan (Department of Neurology, Chinese PLA General Hospital) ;
  • Liu, Mengqi (Department of Radiology, Chinese PLA General Hospital) ;
  • Liu, Shuangfeng (Department of Radiology, Chinese PLA General Hospital) ;
  • Yu, Shengyuan (Department of Neurology, Chinese PLA General Hospital) ;
  • Ma, Lin (Department of Radiology, Chinese PLA General Hospital)
  • Received : 2017.05.05
  • Accepted : 2017.07.16
  • Published : 2018.02.01
⟨ Previous Next ⟩

Abstract

Objective: The periaqueductal gray matter (PAG), a small midbrain structure, presents dysfunction in migraine. However, the precise neurological mechanism is still not well understood. Herein, the aim of this study was to investigate the texture characteristics of altered PAG in episodic migraine (EM) patients based on high resolution brain structural magnetic resonance (MR) images. Materials and Methods: The brain structural MR images were obtained from 18 normal controls (NC), 18 EM patients and 16 chronic migraine (CM) patients using a 3T MR system. A PAG template was created using the International Consortium Brain Mapping 152 gray matter model, and the individual PAG segment was developed by applying the deformation field from the structural image segment to the PAG template. A grey level co-occurrence matrix was used to calculate the texture parameters including the angular second moment (ASM), contrast, correlation, inverse difference moment (IDM) and entropy. Results: There was a significant difference for ASM, IDM and entropy in the EM group ($998.629{\pm}0.162{\times}10^{-3}$, $999.311{\pm}0.073{\times}10^{-3}$, $916.354{\pm}0.947{\times}10^{-5}$) compared to that found in the NC group ($998.760{\pm}0.110{\times}10^{-3}$, $999.358{\pm}0.037{\times}10^{-3}$ and $841.198{\pm}0.575{\times}10^{-5}$) (p < 0.05). The entropy was significantly lower among the patients with CM ($864.116{\pm}0.571{\times}10^{-5}$) than that found among patients with EM (p < 0.05). The area under the receiver operating characteristic curve was 0.776 and 0.750 for ASM and entropy in the distinction of the EM from NC groups, respectively. ASM was negatively related to disease duration (DD) and the Migraine Disability Assessment Scale (MIDAS) scores in the EM group, and entropy was positively related to DD and MIDAS in the EM group (p < 0.05). Conclusion: The present study identified altered MR image texture characteristics of the PAG in EM. The identified texture characteristics could be considered as imaging biomarkers for EM.

Keywords

Acknowledgement

Supported by : China Postdoctoral Science Foundation, Foundation for Medical and Health Sci & Tech Innovation

References

  1. Welch KM, Nagesh V, Aurora SK, Gelman N. Periaqueductal gray matter dysfunction in migraine: cause or the burden of illness? Headache 2001;41:629-637 https://doi.org/10.1046/j.1526-4610.2001.041007629.x
  2. Smith GS, Savery D, Marden C, López Costa JJ, Averill S, Priestley JV, et al. Distribution of messenger RNAs encoding enkephalin, substance P, somatostatin, galanin, vasoactive intestinal polypeptide, neuropeptide Y, and calcitonin generelated peptide in the midbrain periaqueductal grey in the rat. J Comp Neurol 1994;350:23-40 https://doi.org/10.1002/cne.903500103
  3. Kruit MC, Launer LJ, Overbosch J, van Buchem MA, Ferrari MD. Iron accumulation in deep brain nuclei in migraine: a population-based magnetic resonance imaging study. Cephalalgia 2009;29:351-359 https://doi.org/10.1111/j.1468-2982.2008.01723.x
  4. Tepper SJ, Lowe MJ, Beall E, Phillips MD, Liu K, Stillman MJ, et al. Iron deposition in pain-regulatory nuclei in episodic migraine and chronic daily headache by MRI. Headache 2012;52:236-243 https://doi.org/10.1111/j.1526-4610.2011.02056.x
  5. Raskin NH, Hosobuchi Y, Lamb S. Headache may arise from perturbation of brain. Headache 1987;27:416-420 https://doi.org/10.1111/j.1526-4610.1987.hed2708416.x
  6. Gee JR, Chang J, Dublin AB, Vijayan N. The association of brainstem lesions with migraine-like headache: an imaging study of multiple sclerosis. Headache 2005;45:670-677 https://doi.org/10.1111/j.1526-4610.2005.05136.x
  7. Haas DC, Kent PF, Friedman DI. Headache caused by a single lesion of multiple sclerosis in the periaqueductal gray area. Headache 1993;33:452-455 https://doi.org/10.1111/j.1526-4610.1993.hed3308452.x
  8. Lin GY, Wang CW, Chiang TT, Peng GS, Yang FC. Multiple sclerosis presenting initially with a worsening of migraine symptoms. J Headache Pain 2013;14:70 https://doi.org/10.1186/1129-2377-14-70
  9. Tortorella P, Rocca MA, Colombo B, Annovazzi P, Comi G, Filippi M. Assessment of MRI abnormalities of the brainstem from patients with migraine and multiple sclerosis. J Neurol Sci 2006;244:137-141 https://doi.org/10.1016/j.jns.2006.01.015
  10. Fragoso YD, Brooks JB. Two cases of lesions in brainstem in multiple sclerosis and refractory migraine. Headache 2007;47:852-854 https://doi.org/10.1111/j.1526-4610.2007.00823.x
  11. Wang Y, Wang XS. Migraine-like headache from an infarction in the periaqueductal gray area of the midbrain. Pain Med 2013;14:948-949 https://doi.org/10.1111/pme.12096
  12. Chen Z, Chen X, Liu M, Liu S, Ma L, Yu S. Nonspecific periaqueductal gray lesions on T2WI in episodic migraine. J Headache Pain 2016;17:101 https://doi.org/10.1186/s10194-016-0695-9
  13. Chen Z, Chen X, Liu M, Liu S, Ma L, Yu S, et al. Disrupted functional connectivity of periaqueductal gray subregions in episodic migraine. J Headache Pain 2017;18:36 https://doi.org/10.1186/s10194-017-0747-9
  14. Herlidou-Meˆme S, Constans JM, Carsin B, Olivie D, Eliat PA, Nadal-Desbarats L, et al. MRI texture analysis on texture test objects, normal brain and intracranial tumors. Magn Reson Imaging 2003;21:989-993 https://doi.org/10.1016/S0730-725X(03)00212-1
  15. Nachimuthu DS, Baladhandapani A. Multidimensional texture characterization: on analysis for brain tumor tissues using MRS and MRI. J Digit Imaging 2014;27:496-506 https://doi.org/10.1007/s10278-013-9669-5
  16. Mahmoud-Ghoneim D, Toussaint G, Constans JM, de Certaines JD. Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas. Magn Reson Imaging 2003;21:983-987 https://doi.org/10.1016/S0730-725X(03)00201-7
  17. de Oliveira MS, Betting LE, Mory SB, Cendes F, Castellano G. Texture analysis of magnetic resonance images of patients with juvenile myoclonic epilepsy. Epilepsy Behav 2013;27:22-28 https://doi.org/10.1016/j.yebeh.2012.12.009
  18. Caselato GR, Kobayashi E, Bonilha L, Castellano G, Rigas AH, Li LM, et al. Hippocampal texture analysis in patients with familial mesial temporal lobe epilepsy. Arq Neuropsiquiatr 2003;61 Suppl 1:83-87 https://doi.org/10.1590/S0004-282X2003000100015
  19. Certaines JDD, Larcher T, Duda D, Azzabou N, Eliat PA, Escudero LM, et al. Application of texture analysis to muscle MRI: 1-What kind of information should be expected from texture analysis? EPJ Nonlinear Biomed Phys 2015;3:1-14 https://doi.org/10.1140/epjnbp/s40366-015-0016-2
  20. Chang CW, Ho CC, Chen JH. ADHD classification by a texture analysis of anatomical brain MRI data. Front Syst Neurosci 2012;6:66
  21. de Oliveira MS, Balthazar ML, D‘Abreu A, Yasuda CL, Damasceno BP, Cendes F, et al. MR imaging texture analysis of the corpus callosum and thalamus in amnestic mild cognitive impairment and mild Alzheimer disease. AJNR Am J Neuroradiol 2011;32:60-66 https://doi.org/10.3174/ajnr.A2232
  22. Haralick RM, Shanmugam K, Dinstein IH. Textural Features for Image Classification. IEEE Trans Syst Man Cybern 1973;smc- 3:610-621 https://doi.org/10.1109/TSMC.1973.4309314
  23. Rajkovic N, Kolarevic D, Kanjer K, Milosevic NT, Nikolic-Vukosavljevic D, Radulovic M. Comparison of Monofractal, Multifractal and gray level Co-occurrence matrix algorithms in analysis of Breast tumor microscopic images for prognosis of distant metastasis risk. Biomed Microdevices 2016;18:83 https://doi.org/10.1007/s10544-016-0103-x
  24. Headache Classification Committee of the International Headache Society (IHS). The international classification of headache disorders, 3rd edition (beta version). Cephalalgia 2013;33:629-808 https://doi.org/10.1177/0333102413485658
  25. Maier W, Buller R, Philipp M, Heuser I. The Hamilton Anxiety Scale: reliability, validity and sensitivity to change in anxiety and depressive disorders. J Affect Disord 1988;14:61-68 https://doi.org/10.1016/0165-0327(88)90072-9
  26. Hamilton M. Development of a rating scale for primary depressive illness. Br J Soc Clin Psychol 1967;6:278-296 https://doi.org/10.1111/j.2044-8260.1967.tb00530.x
  27. Ashburner J, Friston KJ. Voxel-based morphometry--the methods. Neuroimage 2000;11:805-821 https://doi.org/10.1006/nimg.2000.0582
  28. Walker RF, Jackway P, Longstaff ID. Improving co-occurrence matrix feature discrimination. DICTA '95, 3rd Conference on Digital Image Computing: Techniques and Application; 1995 December 6-8; 643-648
  29. Mohanaiah P, Sathyanarayana P, Gurukumar L. Image texture feature extraction using GLCM approach. Int J Sci Res Publ 2013;3:1-5
  30. Benarroch EE. Periaqueductal gray: an interface for behavioral control. Neurology 2012;78:210-217 https://doi.org/10.1212/WNL.0b013e31823fcdee
  31. An X, Bandler R, Ongür D, Price JL. Prefrontal cortical projections to longitudinal columns in the midbrain periaqueductal gray in macaque monkeys. J Comp Neurol 1998;401:455-479 https://doi.org/10.1002/(SICI)1096-9861(19981130)401:4<455::AID-CNE3>3.0.CO;2-6
  32. Herbert H, Saper CB. Organization of medullary adrenergic and noradrenergic projections to the periaqueductal gray matter in the rat. J Comp Neurol 1992;315:34-52 https://doi.org/10.1002/cne.903150104
  33. Yezierski RP. Spinomesencephalic tract: projections from the lumbosacral spinal cord of the rat, cat, and monkey. J Comp Neurol 1988;267:131-146 https://doi.org/10.1002/cne.902670109