Gender difference on myelin content in healthy young adult brain: a quantitative magnetic resonance imaging study at 1.5 Tesla

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Kingkarn Aphiwatthanasumet
Arunee Hematulin

Abstract

Background: Diffusion tensor imaging (DTI) of cerebral white matter integrity in healthy young adult is not well studied due to anisotropy variation in brain regions with complex fiber architecture. Investigation how such variation of gender differences may offer a discernible clinical benefit.


Objectives: To evaluate gender difference on white matter tissue properties in healthy young adult brain using T1 weighted and high-resolution DTI at 1.5 Tesla MRI.


Materials and methods: Twenty healthy volunteers (10 men, 10 women, age 20-24 years) underwent DTI for quantification of apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values. A T1 weighted sequence was chosen to provide anatomical reference while diffusion-weighted sequence was chosen to provide DTI. Visualization of white matter fiber tracts were obtained with FiberTrak software of the manufacturer. Selected tracts were constructed along corpus callosum, cingulum-cingulate gyrus, and corticospinal tract. To determine water diffusion in certain regions, ADC and FA values were measured. Finally, the two-sample t-test was performed to evaluate the difference between genders and p-values below 0.05 were considered statistically significant.


Results: The ADC values of men ranged from 0.675 to 0.926 mm2s-1 while these values ranged from 0.671 to 0.918 mm2s-1 for women. In thalamus, a significant ADC difference was found (t=2.781, p<0.05) and otherwise no significant ADC differences were seen. For men, the FA values ranged from 0.175 to 0.832 while these values ranged from 0.163 to 0.845 for women. The highest FA value was found in corpus callosum. Two-sample t-test showed no significant FA differences between genders (p>0.05).


Conclusion: There was no significant FA difference between men and women's brains while the mean ADC values in thalamus was statistically significant different. However, there is still no clear correlation of ADC and FA values regarding the gender differences on white matter integrity.

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How to Cite
Aphiwatthanasumet, K., & Hematulin, A. (2020). Gender difference on myelin content in healthy young adult brain: a quantitative magnetic resonance imaging study at 1.5 Tesla. Journal of Associated Medical Sciences, 53(3), 15-23. Retrieved from https://he01.tci-thaijo.org/index.php/bulletinAMS/article/view/240637
Section
Radiologic Technology

References

[1] Lazar M. Mapping brain anatomical connectivity using white matter tractography. NMR Biomed 2010; 23(7):821-35.

[2] Gerrish AC, Thomas AG, Dineen RA, editors. Brain white matter tracts: functional anatomy and clinical relevance. Semin Ultrasound CT MR 2014; Elsevier.

[3] Mandonnet E, Sarubbo S, Petit L. The nomenclature of human white matter association pathways: proposal for a systematic taxonomic anatomical classification. Front Neuroanat 2018; 12:94.

[4] Callaghan MF, Freund P, Draganski B, Anderson E, Cappelletti M, Chowdhury R, et al. Widespread age-related differences in the human brain microstructure revealed by quantitative magnetic resonance imaging. Neurobiol Aging 2014; 35(8): 1862-72.

[5] Yeatman JD, Wandell BA, Mezer AA. Lifespan maturation and degeneration of human brain white matter. Nat Commun 2014; 5(1): 1-12.

[6] Liu H, Yang Y, Xia Y, Zhu W, Leak RK, Wei Z, et al. Aging of cerebral white matter. Ageing Res Rev 2017; 34: 64-76.

[7] Marner L, Nyengaard JR, Tang Y, Pakkenberg B. Marked loss of myelinated nerve fibers in the human brain with age. J Comp Neurol 2003; 462(2): 144-52.

[8] Peters A. The effects of normal aging on myelin and nerve fibers: a review. J Neurocytol 2002; 31(8-9): 581-93.

[9] Erten-Lyons D, Woltjer R, Kaye J, Mattek N, Dodge HH, Green S, et al. Neuropathologic basis of white matter hyperintensity accumulation with advanced age. Neurology 2013; 81(11): 977-83.

[10] Fields RD. A new mechanism of nervous system plasticity: activity-dependent myelination. Nat Rev Neurosci 2015; 16(12): 756-67.

[11] Allen M, Wang X, Burgess JD, Watzlawik J, Serie DJ, Younkin CS, et al. Conserved brain myelination networks are altered in Alzheimer's and other neurodegenerative diseases. Alzheimer's Dementia 2018; 14(3): 352-66.

[12] Harada CN, Love MCN, Triebel KL. Normal cognitive aging. Clin Geriatr Med 2013; 29(4): 737-52.

[13] Schmidt R, Schmidt H, Haybaeck J, Loitfelder M, Weis S, Cavalieri M, et al. Heterogeneity in age-related white matter changes. Acta Neuropathol 2011; 122(2): 171-85.

[14] Basser PJ, Mattiello J, LeBihan D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson Ser B 1994; 103(3): 247-54.

[15] Schmithorst VJ, Wilke M, Dardzinski BJ, Holland SK. Cognitive functions correlate with white matter architecture in a normal pediatric population: a diffusion tensor MRI study. Hum Brain Mapp 2005; 26(2): 139-47.

[16] Deutsch GK, Dougherty RF, Bammer R, Siok WT, Gabrieli JD, Wandell B. Children's reading performance is correlated with white matter structure measured by diffusion tensor imaging. Cortex 2005; 41(3): 354-63.

[17] Beaulieu C, Plewes C, Paulson LA, Roy D, Snook L, Concha L, et al. Imaging brain connectivity in children with diverse reading ability. NeuroImage 2005; 25(4): 1266-71.

[18] Shimony J, Burton H, Epstein A, McLaren D, Sun S, Snyder A. Diffusion tensor imaging reveals white matter reorganization in early blind humans. Cereb Cortex 2006; 16(11): 1653-61.

[19] Yoshikawa K, Nakata Y, Yamada K, Nakagawa M. Early pathological changes in the parkinsonian brain demonstrated by diffusion tensor MRI. J Neurol Neurosurg Psychiatry 2004; 75(3): 481-4.

[20] Bozzali M, Falini A, Cercignani M, Baglio F, Farina E, Alberoni M, et al. Brain tissue damage in dementia with Lewy bodies: an in vivo diffusion tensor MRI study. Brain 2005; 128(7): 1595-604.

[21] Bozzali M, Falini A, Franceschi M, Cercignani M, Zuffi M, Scotti G, et al. White matter damage in Alzheimer's disease assessed in vivo using diffusion tensor magnetic resonance imaging. J Neurol Neurosurg Psychiatry 2002; 72(6): 742-6.

[22] Adler CM, Holland SK, Schmithorst V, Wilke M, Weiss KL, Pan H, et al. Abnormal frontal white matter tracts in bipolar disorder: a diffusion tensor imaging study. Bipolar Disord 2004; 6(3): 197-203.

[23] LI TQ, Mathews VP, Wang Y, Dunn D, Kronenberger W. Adolescents with disruptive behavior disorder investigated using an optimized MR diffusion tensor imaging protocol. Ann N Y Acad Sci 2005; 1064(1): 184-92.

[24] Distortion-free diffusion imaging with TSE. Fieldstrength. 2014; 1: 37–40.

[25] Alsop DC. Phase insensitive preparation of single-shot RARE: application to diffusion imaging in humans. Magn Reson Med 1997; 38(4): 527-533.

[26] Jellison BJ, Field AS, Medow J, Lazar M, Salamat MS, Alexander AL. Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns. Am J Neuroradiol 2004; 25(3): 356-69.

[27] Ding X-Q, Finsterbusch J, Wittkugel O, Saager C, Goebell E, Fitting T, et al. Apparent diffusion coefficient, fractional anisotropy and T2 relaxation time measurement. Clin Neuroradiol 2007; 17(4): 230-8.

[28] Hunsche S, Moseley ME, Stoeter P, Hedehus M. Diffusion-tensor MR imaging at 1.5 and 3.0 T: initial observations. Radiology 2001; 221(2): 550-6.

[29] Helenius J, Soinne L, Perkiö J, Salonen O, Kangasmäki A, Kaste M, et al. Diffusion-weighted MR imaging in normal human brains in various age groups. Am J Neuroradiol 2002; 23(2): 194-9.

[30] Peper JS, Mandl RC, Braams BR, De Water E, Heijboer AC, Koolschijn PCM, et al. Delay discounting and frontostriatal fiber tracts: a combined DTI and MTR study on impulsive choices in healthy young adults. Cereb Cortex 2013; 23(7): 1695-702.

[31] Jeurissen B, Leemans A, Tournier JD, Jones DK, Sijbers J. Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Hum Brain Mapp 2013; 34(11): 2747-66.

[32] Smith ME. A regional survey of myelin development: some compositional and metabolic aspects. J Lipid Res 1973; 14(5): 541-51.

[33] Xin J, Zhang XY, Tang Y, Yang Y. Brain differences between men and women: Evidence from deep learning. Front Neurosci 2019; 13: 185.

[34] Kanaan RA, Allin M, Picchioni M, Barker GJ, Daly E, Shergill SS, et al. Gender differences in white matter microstructure. PLoS One 2012; 7(6).

[35] Kanaan RA, Chaddock C, Allin M, Picchioni MM, Daly E, Shergill SS, et al. Gender influence on white matter microstructure: a tract-based spatial statistics analysis. PLoS One 2014; 9(3).

[36] Hu Y, Xu Q, Li K, Zhu H, Qi R, Zhang Z, et al. Gender differences of brain glucose metabolic networks revealed by FDG-PET: evidence from a large cohort of 400 young adults. PLoS One. 2013; 8(12).

[37] Vassal F, Schneider F, Nuti C. Intraoperative use of diffusion tensor imaging-based tractography for resection of gliomas located near the pyramidal tract: comparison with subcortical stimulation mapping and contribution to surgical outcomes. Br J Neurosurg 2013; 27(5): 668-75.

[38] Sollmann N, Kelm A, Ille S, Schröder A, Zimmer C, Ringel F, et al. Setup presentation and clinical outcome analysis of treating highly language-eloquent gliomas via preoperative navigated transcranial magnetic stimulation and tractography. J Neurosurg 2018; 44(6): E2.

[39] Henry RG, Berman JI, Nagarajan SS, Mukherjee P, Berger MS. Subcortical pathways serving cortical language sites: initial experience with diffusion tensor imaging fiber tracking combined with intraoperative language mapping. NeuroImage 2004; 21(2): 616-22.

[40] Kamada K, Todo T, Morita A, Masutani Y, Aoki S, Ino K, et al. Functional monitoring for visual pathway using real-time visual evoked potentials and optic-radiation tractography. Oper Neurosurg 2005; 57(suppl_1): 121-7.

[41] Powell H, Parker G, Alexander D, Symms M, Boulby P, Wheeler-Kingshott C, et al. MR tractography predicts visual field defects following temporal lobe resection. Neurology 2005; 65(4): 596-9.

[42] Königs M, Pouwels PJ, van Heurn LE, Bakx R, Vermeulen RJ, Goslings JC, et al. Relevance of neuroimaging for neurocognitive and behavioral outcome after pediatric traumatic brain injury. Brain Imaging Behav 2018; 12(1): 29-43.

[43] Mohamed AZ, Corrigan F, Collins-Praino LE, Plummer SL, Soni N, Nasrallah FA. Evaluating spatiotemporal microstructural alterations following diffuse traumatic brain injury. NeuroImage 2020; 25: 102136.

[44] Huisman TA, Loenneker T, Barta G, Bellemann ME, Hennig J, Fischer JE, et al. Quantitative diffusion tensor MR imaging of the brain: field strength related variance of apparent diffusion coefficient (ADC) and fractional anisotropy (FA) scalars. Eur J Raiol 2006; 16(8): 1651.

[45] Fox R, Sakaie K, Lee J-C, Debbins J, Liu Y, Arnold D, et al. A validation study of multicenter diffusion tensor imaging: reliability of fractional anisotropy and diffusivity values. Am J Neuroradiol 2012; 33(4): 695-700.

[46] Okada T, Miki Y, Fushimi Y, Hanakawa T, Kanagaki M, Yamamoto A, et al. Diffusion-tensor fiber tractography: intraindividual comparison of 3.0-T and 1.5-T MR imaging. Radiology 2006; 238(2): 668-78.