Volumetric Analysis of the Brain Structures of Children with Down’s Syndrome: A 3D MRI Study

Year 2021, Volume: 38 Issue: 2, 197 - 203, 03.04.2021

Fatma Öz , Niyazi Acer , Yasin Ceviz Recep Eröz , Halit Canatan , Bircan Yucekaya

Abstract

Introduction: Down’s syndrome (DS) is one of the most common genetic causes of mental and cognitive retardation. In fact, it results in a number of characteristic neuropsychological and physical symptoms, including mental retardation. The aim of this study was to compare the brain structure volumes of children with DS to those of healthy children using MRI Studio in order to investigate whether there exists correlation between the developmental stages of DS and the results of both the Denver II Developmental Screening Test and magnetic resonance imaging (MRI) quantitative analysis.
Method: Five children diagnosed with Down’s syndrome (age range = 2–6 years) were matched for gender and age with five healthy comparison subjects. To analyse the overall and regional brain volumes, high-resolution MRI scans were performed and a morphometric analysis was conducted via MRI Studio software. The MRI T1 volumetric images were normalised using a linear transformation, which was followed by large deformation diffeomorphic metric mapping.
Results: Significant decreases (p < 0.05) in the volumes of the right pons, cerebellum and left superior frontal gyrus (prefrontal cortex) were observed in the children with DS when compared with the control group (p < 0.05). Although decreases were detected in the regional volumes of other brain locations, they were not significant (p > 0.05). It was further found that the developmental retardation observed in the children with DS, as detected using the Denver II test, increased due to decreases in the volumes of certain regions of the brain, although this was also not statistically significant (p > 0.05).
Conclusion: The results of this study generally confirm the findings of prior studies concerning the overall patterns of the brain volumes in childeren with DS and also provide new evidence of the abnormal volumes of specific regional tissue components among such a population. These results suggest that the brain volume reduction associated with DS may primarily be due to early developmental differences rather than neurodegenerative changes.

Keywords

Down’s syndrome;, brain volume;, MRI;, Denver II Test

Supporting Institution

Erciyes University Scientific Research Projects Coordination Unit

Project Number

2013/4728.

References

• 1. Carducci F, Onorati P, Condoluci C, Di Gennaro G, Quarato PP, Pierallini A, et al. Whole-brain voxel-based morphometry study of children and adolescents with Down syndrome. Funct Neurol. 2013;28(1):19-28. PMID: 23731912.
• 2. Gunbey HP, Bilgici MC, Aslan K, Has AC, Ogur MG, Alhan A, et al. Structural brain alterations of Down's syndrome in early childhood evaluation by DTI and volumetric analyses. Eur Radiol. 2017;27(7):3013-3021. doi: 10.1007/s00330-016-4626-6.
• 3. Anderson JS, Nielsen JA, Ferguson MA, Burback MC, Cox ET, Dai L, Gerig G, Edgin JO, Korenberg JR. Abnormal brain synchrony in Down Syndrome. Neuroimage Clin. 2013;24(2):703-15. doi: 10.1016/j.nicl.2013.05.006.
• 4. Adeyemi EI, Giedd JN, Lee NR. A case study of brain morphometry in triplets discordant for Down syndrome. Am J Med Genet A. 2015;167A(5):1107-10. doi: 10.1002/ajmg.a.36820.
• 5. Rigoldi C, Galli M, Condoluci C, Carducci F, Onorati P, Albertini G. Gait analysis and cerebral volumes in Down's syndrome. Funct Neurol. 2009;24(3):147-52. PMID: 20018142
• 6. Menghini D, Costanzo F, Vicari S. Relationship between brain and cognitive processes in Down syndrome. Behav Genet. 2011;41(3):381-93. doi: 10.1007/s10519-011-9448-3.
• 7. Igual L, Soliva JC, Hernandez-Vela A, Escalera S, Jimenez X, Vilarroya O, et al. A fully-automatic caudate nucleus segmentation of brain MRI: Application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder. Biomed Eng Online 2011; 10: 105. doi: 10.1186/1475-925X-10-105.
• 8. Corson PW, Nopoulos P, Miller DD, Arndt S, Andreasen NC. Change in Basal Ganglia Volume Over 2 Years in Patients with Schizophrenia: Typical Versus Atypical Neuroleptics. Am J Psychiatry 1999; 156: 1200–4. doi: 10.1176/ajp.156.8.1200.
• 9. Ertekin T, Acer N, İçer S, Ilıca AT. Comparison of two methods for the estimation of subcortical volume and asymmetry using magnetic resonance imaging: a methodological study. Surg Radiol Anat 2013; 35: 301–9. doi: 10.1007/s00276-012-1036-6.
• 10. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. FSL. Neuroimage 2012; 62: 782–90. doi: 10.1016/j.neuroimage.2011.09.015.
• 11. Cox RW. AFNI: what a long strange trip it's been. Neuroimage 2012; 62: 743–7. doi: 10.1016/j.neuroimage.2011.08.056.
• 12. Goebel R. BrainVoyager-past, present, future. Neuroimage 2012; 62: 748-56. doi: 10.1016/j.neuroimage.2012.01.083.
• 13. Fischl B. FreeSurfer. Neuroimage 2012; 62: 774–81. doi: 10.1016/j.neuroimage.2012.01.021.
• 14. Ashburner J. SPM: a history. Neuroimage 2012; 62: 791–800. doi: 10.1016/j.neuroimage.2011.10.025.
• 15. Guenette JP, Stern RA, Tripodis Y, Chua AS, Schultz V, Sydnor VJ, et al. Automated versus manual segmentation of brain region volumes in former football players. Neuroimage Clin. 2018 Mar 21;18:888-896. doi: 10.1016/j.nicl.2018.03.026.
• 16. Jovicich J, Czanner S, Han X, Salat D, van der Kouwe A, Quinn B, Pacheco J, Albert M, Killiany R, Blacker D, Maguire P, Rosas D, Makris N, Gollub R, et al. MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths. Neuroimage 2009;46(1):177-92. doi: 10.1016/j.neuroimage.2009.02.010.
• 17. Wang Y, Xu Q, Luo J, Hu M, Zuo C. Effects of Age and Sex on Subcortical Volumes. Front Aging Neurosci 2019;11:259. https://doi.org/10.3389/fnagi.2019.00259.
• 18. Kocaman H, Acer N, Köseoğlu E, Gültekin M, Dönmez H. Evaluation of intracerebral ventricles volume of patients with Parkinson's disease using the atlas-based method: A methodological study. J Chem Neuroanat. 2019 Jul;98:124-130. doi: 10.1016/j.jchemneu.2019.04.005.
• 19. Palancı Ö, Kalaycıoğlu A, Acer N, Eyüpoğlu İ, Çakmak V. Volume Calculation of Brain Structures in Migraine Disease by Using MriStudio. NeuroQuantology 2018;16(10):8-13. doi: 10.14704/nq.2018.16.10.1692.
• 20. Mori S, et al. MRICloud: Delivering High-Throughput MRI Neuroinformatics as Cloud-Based Software as a Service. Computing in Science & Engineering. 2016;18(5):21–35. doi: 10.1109/MCSE.2016.93.
• 21. Acer N, Turgut M. Measurements of the Insula Volume Using MRI. In: Turgut M, Yurttaş C, Shane Tubbs R, editors. Island of Reil (Insula) in the Human Brain. Springer International Publishing AG, part of Springer Nature 2018. p. 101-111.
• 22. Rezende TJR, Campos BM, Hsu J, Li Y, Ceritoglu C, Kutten K, et al. Test-retest reproducibility of a multi-atlas automated segmentation tool on multimodality brain MRI. Brain Behav 2019;9 (10):e01363. doi: 10.1002/brb3.1363.
• 23. Jiang H, van Zijl PC, Kim J, Pearlson GD, Mori S. DtiStudio: resource program for diffusion tensor computation and fiber bundle tracking. Comput Methods Programs Biomed. 2006;81(2):106-16. doi: 10.1016/j.cmpb.2005.08.004.
• 24. Ceritoglu C, Tang X, Chow M, Hadjiabadi D, Shah D, Brown T, et al. Computational analysis of LDDMM for brain mapping. Front Neurosci 2013; 27(7): 151. doi: 10.3389/fnins.2013.00151.
• 25. Morey RA, Petty CM, Xu Y, Hayes JP, Wagner HR, Lewis DV, et al. A comparison of automated segmentation and manual tracing for quantifying hippocampal and amygdala volumes. Neuroimage 2009; 45(3): 855-66. doi: 10.1016/j.neuroimage.2008.12.033.
• 26. Morey RA, Selgrade ES, Wagner HR, Huettel SA, Wang L, McCarthy G, et al. Scan-rescan reliability of subcortical brain volumes derived from automated segmentation. Hum Brain Mapp 2010; 31(11): 1751-62. doi: 10.1002/hbm.20973.
• 27. Acer N, Dolu N, Zararsiz G, Dogan MS, Gumus K, Ozmen S, et al. Anatomical characterization of ADHD using an atlas-based analysis: A diffusion tensor imaging study. The Eurobiotech Journal 2017;1:46-56. doi: 10.24190/ISSN2564-615X/2017/01.08.
• 28. Acer N, Bastepe-Gray S, Sagiroglu A, Gumus KZ, Degirmencioglu L, Zararsiz G, et al. Diffusion tensor and volumetric magnetic resonance imaging findings in the brains of professional musicians. J Chem Neuroanat. 2018;88:33-40. doi: 10.1016/j.jchemneu.2017.11.003.
• 29. Yalaz K, Anlar B, Bayoğlu B. "Denver II Gelişimsel Tarama Testi Türkiye Standardizasyonu." Denver II Developmental Screening Test Handbook. Ankara: Anıl Grup Matbaacılık; 2010.
• 30. Mori S, Oishi K, Jiang H, Jiang L, Li X, et al. Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage. 2008;40(2):570-582. doi: 10.1016/j.neuroimage.2007.12.035.
• 31. Miller MI, Beg MF, Ceritoglu C, Stark C. Increasing the power of functional maps of the medial temporal lobe by using large deformation diffeomorphic metric mapping. Proc Natl Acad Sci U S A. 2005 Jul 5;102(27):9685-90. doi: 10.1073/pnas.0503892102.
• 32. Oishi K, Faria A, Jiang H, Li X, Akhter K, Zhang J, et al. Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and Alzheimer's disease participants. Neuroimage 2009; 46(2): 486–99. doi: 10.1016/j.neuroimage.2009.01.002.
• 33. Faria AV, Hoon A, Stashinko E, Li X, Jiang H, Mashayekh A, et al. Quantitative analysis of brain pathology based on MRI and brain atlases-applications for cerebral palsy. Neuroimage 2011; 54(3):1854–61. doi: 10.1016/j.neuroimage.2010.09.061.
• 34. Faria AV, Zhang J, Oishi K, Li X, Jian H, Akhter K, et al. Atlas-based analysis of neurodevelopment from infancy to adult hood using diffusion tensor imaging and applications for automated abnormality detection. Neuroimage 2010; 52(2): 415–28. doi: 10.1016/j.neuroimage.2010.04.238.
• 35. Pinter JD, Eliez S, Schmitt JE, Capone GT, Reiss AL. Neuroanatomy of Down's syndrome: a high-resolution MRI study. Am J Psychiatry. 2001;158(10):1659-65. doi: 10.1176/appi.ajp.158.10.1659.
• 36. Kates WR, Folley BS, Lanham DC, Capone GT, Kaufmann WE. Cerebral growth in Fragile X syndrome: review and comparison with Down syndrome. Microsc Res Tech. 2002 May 1;57(3):159-67. doi: 10.1002/jemt.10068.
• 37. Rigoldi C, Galli M, Condoluci C, Carducci F, Onorati P, Albertini G. Gait analysis and cerebral volumes in Down's syndrome. Funct Neurol. 2009 Jul-Sep;24(3):147-52. PMID: 20018142.
• 38. Ilg W, Golla H, Thier P, Giese MA. Specific influences of cerebellar dysfunctions on gait. Brain. 2007 Mar;130(Pt 3):786-98. doi: 10.1093/brain/awl376.
• 39. Strick PL, Dum RP, Fiez JA. Cerebellum and nonmotor function. Annu Rev Neurosci. 2009;32:413-34. doi: 10.1146/annurev.neuro.31.060407.125606.
• 40. du Boisgueheneuc F, Levy R, Volle E, Seassau M, Duffau H, Kinkingnehun S, et al. Functions of the left superior frontal gyrus in humans: a lesion study. Brain. 2006;129(12):3315-28. doi: 10.1093/brain/awl244.
• 41. Bletsch A, Mann C, Andrews DS, Daly E, Tan GMY, Murphy DGM, et al. Down syndrome is accompanied by significantly reduced cortical grey-white matter tissue contrast. Hum Brain Mapp. 2018;39(10):4043-4054. doi: 10.1002/hbm.24230.
• 42. Goldberg II, Harel M, Malach R. When the brain loses its self: prefrontal inactivation during sensorimotor processing. Neuron. 2006;50(2):329-39. doi: 10.1016/j.neuron.2006.03.015.
• 43. Raz N, Torres IJ, Briggs SD, Spencer WD, Thornton AE, Loken WJ, et al. Selective neuroanatomic abnormalities in Down's syndrome and their cognitive correlates: evidence from MRI morphometry. Neurology. 1995;45(2):356-66. doi: 10.1212/wnl.45.2.356.
• 44. Komaki H, Hamaguchi H, Hashimoto T. Assessment of the brainstem and the cerebellar lesions and myelination using magnetic resonance images in children with Down syndrome. No To Hattatsu. 1999;31(5):422-7. Japanese. PMID: 10487067.
• 45. Roizen NJ, Patterson D. Down's syndrome. Lancet. 2003;361(9365):1281-9. doi: 10.1016/S0140-6736(03)12987-X.
• 46. Shott SR, Joseph A, Heithaus D. Hearing loss in children with Down syndrome. Int J Pediatr Otorhinolaryngol. 2001;61(3):199-205. doi: 10.1016/s0165-5876(01)00572-9.
• 47. Fujii Y, Aida N, Niwa T, Enokizono M, Nozawa K, Inoue T. A small pons as a characteristic finding in Down syndrome: A quantitative MRI study. Brain Dev. 2017;39(4):298-305. doi: 10.1016/j.braindev.2016.10.016.
• 48. Keller SS, Gerdes JS, Mohammadi S, Kellinghaus C, Kugel H, Deppe K, et al. Volume estimation of the thalamus using freesurfer and stereology: consistency between methods. Neuroinformatics. 2012;10(4):341-50. doi: 10.1007/s12021-012-9147-0.
• 49. Poretti A, Mall V, Smitka M, Grunt S, Risen S, Toelle SP, et al. Macrocerebellum: significance and pathogenic considerations. Cerebellum 2012; 11(4): 1026-36. doi: 10.1007/s12311-012-0379-1.
• 50. Zhou J, Rajapakse JC. Segmentation of subcortical brain structures using fuzzy templates. Neuroimage 2005; 28: 915–24. doi: 10.1016/j.neuroimage.2005.06.037.