J Neurol Surg B Skull Base 2022; 83(04): 443-450
DOI: 10.1055/s-0041-1731033
Original Article

A Volumetric Study of the Corpus Callosum in the Turkish Population

Handan Soysal
1   Department of Anatomy, Faculty of Dentistry, Ankara Yıldırım Beyazıt University, Ankara, Turkey
,
Niyazi Acer
2   Department of Anatomy, Faculty of Medicine, Arel University, İstanbul, Turkey
,
Meltem Özdemir
3   Department of Radiology, Dışkapı Yıldırım Beyazıt Health Application and Research Center, Medical Sciences University, Ankara, Turkey
,
Önder Eraslan
3   Department of Radiology, Dışkapı Yıldırım Beyazıt Health Application and Research Center, Medical Sciences University, Ankara, Turkey
› Author Affiliations
Funding None.

Abstract

Objective The aim of this study is to measure the average corpus callosum (CC) volume of healthy Turkish humans and to analyze the effects of gender and age on volumes, including the genu, truncus, and splenium parts of the CC.

Patients and Methods Magnetic resonance imaging brain scans were obtained from 301 healthy male and female subjects, aged 11 to 84 years. The median age was 42 years (min–max: 11–82) in females and 49 years (min–max: 12–84) in males. Corpus callosum and its parts were calculated by using MRICloud. CC volumes of each subject were compared with those of the age and gender groups.

Results All volumes of the CC were significantly higher in males than females. All left volumes except BCC were significantly higher than the right volumes in both males and females. The oldest two age groups (50–69 and 70–84 years) were found to have higher bilateral CC volumes, and bilateral BCC volumes were also higher than in the other two age groups (11–29 and 30–49 years).

Conclusion The results suggest that compared with females/males, females have a faster decline in the volume of all volumes of the CC. We think that quantitative structural magnetic resonance data of the brain is vital in understanding human brain function and development.

Note

Research was conducted on human participants. All procedures performed in this study comply with the ethical standards of the institution. Prior to this review, the approval of the institutional review board was obtained. This study was approved by the Clinical Research Ethics Committee of Dışkapı Yıldırım Beyazıt Training and Research Hospital. A signed form was obtained from each participant indicating that the patient was informed and approved.




Publication History

Received: 04 December 2020

Accepted: 07 May 2021

Article published online:
30 June 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 
  • References

  • 1 Pandya DN, Karol EA, Heilbronn D. The topographical distribution of interhemispheric projections in the corpus callosum of the rhesus monkey. Brain Res 1971; 32 (01) 31-43
  • 2 Sakai T, Mikami A, Suzuki J. et al. Developmental trajectory of the corpus callosum from infancy to the juvenile stage: comparative MRI between chimpanzees and humans. PLoS One 2017; 12 (06) e0179624
  • 3 Firat A, Tezer Filik FI. Morphometry of the Corpus Callosum. Arch Anat Physiol 2016; 1 (01) 004-006
  • 4 Hardan AY, Pabalan M, Gupta N. et al. Corpus callosum volume in children with autism. Psychiatry Res 2009; 174 (01) 57-61
  • 5 de Moura MTM, Zanetti MV, Duran FLS. et al. Corpus callosum volumes in the 5 years following the first-episode of schizophrenia: effects of antipsychotics, chronicity and maturation. Neuroimage Clin 2018; 18: 932-942
  • 6 Gilbert AR. Keshavan M.S. MRI structural findings in Schizophrenia. Rev Bras Psiquiatr 2001; 23 (Supl I ): 15-18
  • 7 Di Paola M, Spalletta G, Caltagirone C. In vivo structural neuroanatomy of corpus callosum in Alzheimer's disease and mild cognitive impairment using different MRI techniques: a review. J Alzheimers Dis 2010; 20 (01) 67-95
  • 8 Hermann B, Hansen R, Seidenberg M, Magnotta V, O'Leary D. Neurodevelopmental vulnerability of the corpus callosum to childhood onset localization-related epilepsy. Neuroimage 2003; 18 (02) 284-292
  • 9 Lacerda ALT, Brambilla P, Sassi RB. et al. Anatomical MRI study of corpus callosum in unipolar depression. J Psychiatr Res 2005; 39 (04) 347-354
  • 10 Brambilla P, Nicoletti MA, Sassi RB. et al. Magnetic resonance imaging study of corpus callosum abnormalities in patients with bipolar disorder. Biol Psychiatry 2003; 54 (11) 1294-1297
  • 11 Sullivan EV, Rosenbloom MJ, Desmond JE, Pfefferbaum A. Sex differences in corpus callosum size: relationship to age and intracranial size. Neurobiol Aging 2001; 22 (04) 603-611
  • 12 Tanaka-Arakawa MM, Matsui M, Tanaka C. et al. Developmental changes in the corpus callosum from infancy to early adulthood: a structural magnetic resonance imaging study. PLoS One 2015; 10 (03) e0118760
  • 13 Arda KN, Akay S. The relationship between corpus callosum morphometric measurements and age/gender characteristics: a comprehensive MR imaging study. J Clin Imaging Sci 2019; 9 (33) 33
  • 14 Zilles K, Kawashima R, Dabringhaus A, Fukuda H, Schormann T. Hemispheric shape of European and Japanese brains: 3-D MRI analysis of intersubject variability, ethnical, and gender differences. Neuroimage 2001; 13 (02) 262-271
  • 15 Ünalmış D, Acer N, Yılmaz S, Tokpınar A, Doğan S, Demir H. The calculation of the femoral condyle cartilage volume and surface area in patients with osteoarthritis. Enciyes Med J 2020; 42 (02) 178-184
  • 16 Igual L, Soliva JC, Hernández-Vela A. 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
  • 17 Herron TJ, Kang X, Woods DL. Automated measurement of the human corpus callosum using MRI. Front Neuroinform 2012; 6 (25) 25
  • 18 Kim SG, Lee H, Chung MK. et al. Agreement between the white matter connectivity based on the tensor-based morphometry and the volumetric white matter parcellations based on diffusion tensor imaging. NIH public. Proc IEEE Int Symp Biomed Imaging 2012; 42-45
  • 19 Acer N, Bastepe-Gray S, Sagiroglu A. et al. Diffusion tensor and volumetric magnetic resonance imaging findings in the brains of professional musicians. J Chem Neuroanat 2018; 88: 33-40
  • 20 Keller SS, Gerdes JS, Mohammadi S. et al. Volume estimation of the thalamus using freesurfer and stereology: consistency between methods. Neuroinformatics 2012; 10 (04) 341-350
  • 21 Poretti A, Mall V, Smitka M. et al. Macrocerebellum: significance and pathogenic considerations. Cerebellum 2012; 11 (04) 1026-1036
  • 22 Soldea O, Ekin A, Soldea DF. et al. Segmentation of anatomical structures in brain MR images using atlases in FSL - a quantitative approach. International Conference on Pattern Recognition.. Accessed 2010 at: https://ieeexplore.ieee.org/abstract/document/5595776?section=abstract
  • 23 Sakamoto R, Marano C, Miller MI. et al. Cloud-based brain magnetic resonance image segmentation and parcellation system for individualized prediction of cognitive worsening. J Healthc Eng 2019; 2019: 9507193
  • 24 Mori S, Ceritoglu C, Li Y. et al. MRICloud: delivering high-throughput MRI neuroinformatics as cloud-based software as a service. Comput Sci Eng 2016; 18 (05) 21-35
  • 25 Wu D, Faria AV, Younes L, Ross CA, Mori S, Miller MI. Whole-brain segmentation and change-point analysis of anatomical brain MRI-application in premanifest huntington's disease. J Vis Exp 2018; 136 (136) 1-9
  • 26 Luders E, Toga AW, Thompson PM. Why size matters: differences in brain volume account for apparent sex differences in callosal anatomy: the sexual dimorphism of the corpus callosum. Neuroimage 2014; 84: 820-824
  • 27 Lee BY, Sohn JH, Choi MH. et al. A volumetric study of the corpus callosum in 20s and 40s Korean people. Brain Struct Funct 2009; 213 (4-5): 463-467
  • 28 Ardekani BA, Figarsky K, Sidtis JJ. Sexual dimorphism in the human corpus callosum: an MRI study using the OASIS brain database. Cereb Cortex 2013; 23 (10) 2514-2520
  • 29 Guz W, Pazdan D, Stachyra S. et al. Analysis of corpus callosum size depending on age and sex. Folia Morphol (Warsz) 2019; 78 (01) 24-32
  • 30 Hwang SJ, Ji EK, Lee EK. et al. Gender differences in the corpus callosum of neonates. Neuroreport 2004; 15 (06) 1029-1032
  • 31 DeLacoste-Utamsing C, Holloway RL. Sexual dimorphism in the human corpus callosum. Science 1982; 216 (4553): 1431-1432
  • 32 Shiino A, Chen YW, Tanigaki K. et al. Sex-related difference in human white matter volumes studied: inspection of the corpus callosum and other white matter by VBM. Sci Rep 2017; 7 (39818): 39818
  • 33 Holloway RL, de Lacoste MC. Sexual dimorphism in the human corpus callosum: an extension and replication study. Hum Neurobiol 1986; 5 (02) 87-91
  • 34 Pujol J, Vendrell P, Junqué C, Martí-Vilalta JL, Capdevila A. When does human brain development end? Evidence of corpus callosum growth up to adulthood. Ann Neurol 1993; 34 (01) 71-75
  • 35 Allen JS, Damasio H, Grabowski TJ, Bruss J, Zhang W. Sexual dimorphism and asymmetries in the gray-white composition of the human cerebrum. Neuroimage 2003; 18 (04) 880-894
  • 36 Leonard CM, Towler S, Welcome S. et al. Size matters: cerebral volume influences sex differences in neuroanatomy. Cereb Cortex 2008; 18 (12) 2920-2931
  • 37 Giedd JN, Blumenthal J, Jeffries NO. et al. Development of the human corpus callosum during childhood and adolescence: a longitudinal MRI study. Prog Neuropsychopharmacol Biol Psychiatry 1999; 23 (04) 571-588
  • 38 Clarke JM, Zaidel E. Anatomical-behavioral relationships: corpus callosum morphometry and hemispheric specialization. Behav Brain Res 1994; 64 (1-2): 185-202
  • 39 Luders E, Rex DE, Narr KL. et al. Relationships between sulcal asymmetries and corpus callosum size: gender and handedness effects. Cereb Cortex 2003; 13 (10) 1084-1093
  • 40 Makowski C, Béland S, Kostopoulos P. et al. Evaluating accuracy of striatal, pallidal, and thalamic segmentation methods: comparing automated approaches to manual delineation. Neuroimage 2018; 170: 182-198
  • 41 Turgut M. et al. (eds.) Island of Reil (Insula) in the Human Brain chapter Niyazi Acer and Mehmet Turgut Measurements of the Insula Volume Using MRI. Springer International Publishing AG, part of Springer Nature; 2018: 101-111 Accessed 2018 at: https://www.springer.com/gp/book/9783319754673
  • 42 Rezende TJR, Campos BM, Hsu J. et al. Test-retest reproducibility of a multi-atlas automated segmentation tool on multimodality brain MRI. Brain Behav 2019; 9 (10) e01363
  • 43 Li Y, Liu P, Li Y. et al. ASL-MRICloud: an online tool for the processing of ASL MRI data. NMR Biomed 2019; 32 (02) e4051