There is a plethora of studies showing sex-biased neurological and neuropsychiatric conditions. In this regard, there is an evident prevalence of sex-biased conditions that also show symptomatological differences between males and females . Examples of female-biased conditions include anxiety, anorexia nervosa, and depression, while male-biased conditions include dyslexia, autism, attention deficit/hyperactivity disorder, language impairment, and Tourette syndrome [1–4].
Therefore, investigating structural brain similarities and differences between males and females will identify where and how female and male brains differ which will aid in determining risk and resilience factors of such conditions, as well as distinguish associated mechanisms.
Moreover, it is clear that males and females have a capacity to have different behavioral and psychophysiological attributes. Given that, structural differences can be associated with differences in physiology and behavior . Moreover, it is clear that there is a functional significance of brain structural findings and correlations between brain volume and cognitive performance . Therefore, findings of sex-biased differences in brain structure will ultimately help in identifying mechanisms underlying behavioral, physiological, psychophysiological endpoints in males and females.
Although there is convincing evidence of differences in male and female brains from brain morphometry [6–10], there does remain some studies that found no such sex differences [11–13]. Also, contradicting results were found in previous region-of-interest and whole-brain studies of sex differences in developing human brains .
Furthermore, in regards to brain white matter, sex effects on global white matter (WM) still remain unclear . One study found the there is significant difference in white matter between males and females , while another, more recent, study found no significant gender effects on white matter .
Given the above, it appears, that there is still no consensus on whether or not there are sex differences in whole-brain and regional white matter between the two human sexes. Also, if such a difference does exist, it still remains unclear which white matter regions show such a difference between males and females, thus, setting the basis for the rationale of this study. In this study we conducted a voxel-based morphometry (VBM) analysis to investigate white matter differences between males and females, and we have also investigated whether sex affects global white matter volume across different age groups.
All subjects’ brain images were acquired from an online cohort provided by Imperial College London (www.brain-development.org). The male group included 192 T1-weighted MRI brain images of normal and healthy male participants (mean age: 46.92, median age: 46, std. dev.: 15.5). The female group included 192 T1-weighted MRI brain images of normal and healthy female participants (mean age: 46.92, median age: 46, std. dev.: 15.5).
The brain images acquired from brain-development.org were all T1-weighted. The MRI acquisitions were from three different MRI scanners from three different hospitals in London. At Guy’s Hospital a 1.5 T Philips Medical Systems Gyroscan Intera MRI system was used (TR = 9.8, TE = 4.6, reconstruction diameter = 240, flip angle = 8). The Institute of Psychiatry a 1.5 T GE MRI system was used, and at Hammersmith Hospital a 3T Philips Medical Systems Intera MRI system was used (TR = 9.6, TE = 4.6, reconstruction diameter = 240, flip angle = 8).
Voxel-based morphometry and image preprocessing
Voxel-based morphometry (VBM) was performed using SPM12 (The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK). All 384 (192 male, 192 female) brain images were analyzed using SPM12. To optimize normalization, reorientation was done so that each brain image was centered at the anterior commissure (AC-PC orientation). Then each brain image was segmented into cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM). To minimize between-subject structural variations, WM segmented images were respectively normalized to WM population templates generated from the complete set of images using the registration method: Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) . The spatially normalized WM images were then smoothed using an 8 mm FWHM isotropic kernel.
The spatially normalized and smoothed WM images were subjected to a two-sample t-test in SPM12. The first sample included the 192 male WM images, and the second sample included the 192 female WM images. The threshold for statistical significance was set at p<0.05 with family wise error (FWE) correction for the following comparisons: male>female and female>male.
Demographic differences between the two groups were assessed using independent sample t-tests. All statistical analyses were performed using SPSS software (IBM SPSS Statistics for Windows Version 22.0). The 2 groups of participants differed significantly only for education (p=.01). There was no difference in age. Thus, education level was entered as a covariate in the VBM statistical analyses to control for the effect of these variables in the main results.
We tested the WM volume changes between males and females using a t-test model for independent samples in SPM12. In the t test model, all scans of the two groups were included within one design matrix. The model also included the education for each subject as a covariate of no interest. Contrasts between the two groups (males vs. females) were performed to investigate the main WM regional volume differences. For all contrasts, the statistical threshold was set to Family Wise Error (FWE) p < 0.05, corrected for multiple comparisons.
Additionally, the global WM volume for each respective WM image was calculated in ml using Get_totals (www0.cs.ucl.ac.uk/staff/g.ridgway/vbm/get_totals.m). Then a two sample t-test (threshold: p<0.05) was performed, using SPSS software (IBM SPSS Statistics for Windows Version 22.0), for male vs. female global WM volumes, and between the following age groups for the same comparison: 20-39, 40-59, 60-79 years old.
For the male to female comparison, there were 15 brain regions that showed significant difference in WM. These regions were larger in males compared to females. The brain regions that showed a significant difference between sexes included: frontal, temporal, parietal, occipital, and insular regions (Table 1, Figure 1). On the other hand, the female>male comparison only showed one WM brain region that was significantly larger for female compared to male. This region (Figure 2) was the WM of the postcentral gyrus in the right parietal lobe (MNI coordinates: 34,-32,51; t value: 4.94; z score: 4.86; p(FWE) = 0.028).
As for global WM volume comparisons between the male and female groups, there was no statistical significance (p > 0.05). Also, all three age group (20-39, 40-59, 60-79) comparisons between males and females, respectively, showed no significant WM volume differences between males and females (p > 0.05).
The current study used VBM to quantify any sex differences in human brain white matter morphology from a set of 384 MRI brain scans. The aim of this study was to examine where differences in WM volume exist between males and females, and whether or not there exists a significant sex effect on global WM volume. We did find regional differences in WM across sex. In our VBM analysis, we found that males have significantly larger WM in certain regions in all cerebral lobes respectively (Table 1, Figure 1), and that females only showed larger WM in the region of the postcentral gyrus in the right parietal lobe (Figure 2). These results cannot be explained by a general assumption that a male’s brain is bigger than a female’s brain. Indeed we did not find any additional significant differences in global WM between males and females.
Although, previous work found that males exhibit larger increase in WM volume during adolescence , there is contradicting evidence of effects of sex on global WM volume [16; 19] . Also localized sexual dimorphism of the adult human brain remains unresolved [13; 14; 20; 21].
In this study we found that males did not show significantly larger global WM volume than females. We also found that even across age groups (20-39, 40-59, 60-79 years old) males did not show significantly larger global WM volume compared to females. We take these results in light of previous work [16; 19] in which Lemaitre et al. (2005) found significant difference between male and female global WM volumes while Smith et al. (2007) found no such significant difference. In this case our results are consistent with that of Smith et al. (2007). Therefore, providing further research evidence that in the comparison of global WM brain volume between males and females there was no significant difference.
Results of gender differences with respect to regional and global brain volumes remain contradictory. Our results of sexual dimorphism of the human brain are consistent with previous studies [21–23]. We found regional brain white matter sexual dimorphism, but we did not find any evidence of such dimorphism for global brain white matter. Although sex differences in brain structure may explain differences in behavior, conditions, and psychophysiology, studies that showed regional brain volume differences have been less consistent . This is one of the main reasons this study was conducted, and in order to provide another insight on any global and regional brain WM differences between males and females. Also, reaching a consensus on regional brain differences between the two sexes will greatly help in explaining sex-biased behavior, cognitive function, psychophysiology, neurological conditions and underpinnings of various pathologies.
Baron-Cohen S, Lombardo M V, Auyeung B, Ashwin E, Chakrabarti B, Knickmeyer R. Why are autism spectrum conditions more prevalent in males? PLoS Biol., 2011, 9 (6): e1001081.CrossrefPubMedWeb of ScienceGoogle Scholar
Rutter M, Caspi A, Moffitt TE. Using sex differences in psychopathology to study causal mechanisms: unifying issues and research strategies, J. Child Psychol. Psychiatry., 2003, 44 (8): 1092–115.CrossrefPubMedGoogle Scholar
Ruigrok AN V, Salimi-Khorshidi G, Lai M-C, Baron-Cohen S, Lombardo M V, Tait RJ, et al. A meta-analysis of sex differences in human brain structure, Neurosci. Biobehav. Rev., 2014, 39 (100): 34–50. Web of ScienceCrossrefPubMedGoogle Scholar
Gur RC, Turetsky BI, Matsui M, Yan M, Bilker W, Hughett P, et al. Sex differences in brain gray and white matter in healthy young adults: correlations with cognitive performance, J. Neurosci., 1999, 19 (10): 4065–72. PubMedGoogle Scholar
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 (4): 880–94.CrossrefPubMedGoogle Scholar
Paus T, Otaky N, Caramanos Z, MacDonald D, Zijdenbos A, D’Avirro D, etal. In vivo morphometry of the intrasulcal gray matter in the human cingulate, paracingulate, and superior-rostral sulci: hemispheric asymmetries, gender differences and probability maps, J. Comp. Neurol., 1996, 376 (4): 664–73.PubMedCrossrefGoogle Scholar
Courchesne E, Chisum HJ, Townsend J, Cowles A, Covington J, Egaas B, et al. Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers, Radiology, 2000, 216 (3): 672–82. CrossrefPubMedGoogle Scholar
Blatter DD, Bigler ED, Gale SD, Johnson SC, Anderson C V, Burnett BM, et al. Quantitative volumetric analysis of brain MR: normative database spanning 5 decades of life, AJNR. Am. J. Neuroradiol., 1995, 16 (2): 241–51.PubMedGoogle Scholar
Cosgrove KP, Mazure CM, Staley JK. Evolving Knowledge of Sex Differences in Brain Structure, Function, and Chemistry, Biol. Psychiatry, 2007, 62 (8): 847–855. CrossrefPubMedWeb of ScienceGoogle Scholar
Hsu J-L, Leemans A, Bai C-H, Lee C-H, Tsai Y-F, Chiu H-C, et al. Gender differences and age-related white matter changes of the human brain: A diffusion tensor imaging study, Neuroimage, 2008, 39 (2): 566–577. CrossrefWeb of ScienceGoogle Scholar
Lemaître H, Crivello F, Grassiot B, Alpérovitch A, Tzourio C, Mazoyer B. Age- and sex-related effects on the neuroanatomy of healthy elderly, Neuroimage 2005, 26 (3): 900–911. CrossrefPubMedGoogle Scholar
Smith CD, Chebrolu H, Wekstein DR, Schmitt FA, Markesbery WR. Age and gender effects on human brain anatomy: A voxel-based morphometric study in healthy elderly, Neurobiol. Aging, 2007, 28 (7): 1075–1087. CrossrefWeb of SciencePubMedGoogle Scholar
Perrin JS, Leonard G, Perron M, Pike GB, Pitiot A, Richer L, et al. Sex differences in the growth of white matter during adolescence, Neuroimage 2009, 45 (4): 1055–1066. PubMedCrossrefWeb of ScienceGoogle Scholar
Lemaître H, Crivello F, Grassiot B, Alpérovitch A, Tzourio C, Mazoyer B. Age- and sex-related effects on the neuroanatomy of healthy elderly, Neuroimage 2005, 26 (3): 900–11. CrossrefPubMedGoogle Scholar
Goldstein JM, Seidman LJ, Horton NJ, Makris N, Kennedy DN, Caviness VS, et al. Normal sexual dimorphism of the adult human brain assessed by in vivo magnetic resonance imaging, Cereb. Cortex, 2001, 11 (6): 490–7. CrossrefPubMedGoogle Scholar
Szeszko PR, Vogel J, Ashtari M, Malhotra AK, Bates J, Kane JM, et al. Sex differences in frontal lobe white matter microstructure: a DTI study, Neuroreport 2003, 14 (18): 2469–73.PubMedCrossrefGoogle Scholar
Schmithorst VJ, Holland SK, Dardzinski BJ. Developmental differences in white matter architecture between boys and girls, Hum. Brain Mapp., 2008, 29 (6): 696–710. CrossrefWeb of SciencePubMedGoogle Scholar
About the article
Published Online: 2017-05-31
Citation Information: Translational Neuroscience, Volume 8, Issue 1, Pages 49–53, ISSN (Online) 2081-6936, DOI: https://doi.org/10.1515/tnsci-2017-0009.
© 2017 Ali K. Bourisly et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0