Elsevier

NeuroImage

Volume 61, Issue 4, 16 July 2012, Pages 931-940
NeuroImage

Combined structural and resting-state functional MRI analysis of sexual dimorphism in the young adult human brain: An MVPA approach

https://doi.org/10.1016/j.neuroimage.2012.03.080Get rights and content

Abstract

There has been growing interest recently in the use of multivariate pattern analysis (MVPA) to decode information from high-dimensional neuroimaging data. The present study employed a support vector machine-based MVPA approach to identify the complex patterns of sex differences in brain structure and resting-state function. We also aimed to assess the role of anatomy on functional sex differences during rest. One hundred and forty healthy young Chinese adults (70 men and 70 women) underwent structural and resting-state functional MRI scans. Gray matter density and regional homogeneity (ReHo) were used to map brain structure and resting-state function, respectively. After combining these two feature vectors into one union-vector, a pattern classifier was designed using principal component analysis and linear support vector machine to identify brain areas that had distinct characteristics between the groups. We found that: (1) male and female brains were different with a mean classification accuracy of 89%; (2) sex differences in gray matter density were widely distributed in the brain, notably in the occipital lobe and the cerebellum; (3) men primarily showed higher ReHo in their right hemispheres and women tended to show greater ReHo in their left hemispheres; (4) about 50% of brain areas with functional sex differences exhibited significant positive correlations between gray matter density and ReHo. Our results suggest that sex is an important factor that account for interindividual variability in the healthy brain.

Introduction

At a population level, sex differences exist in many behavioral and cognitive domains. For example, men generally perform better in visuospatial processing (Rizk-Jackson et al., 2006, Voyer et al., 1995), whereas women tend to outperform men in verbal fluency (Hyde and Linn, 1988), facial emotion recognition (Rahman et al., 2004), and emotional memory (Canli et al., 2002). Because a variety of changes in behavioral conditions are associated with alterations of the brain, the evaluation of sex differences in brain structure and function using imaging methodologies is an important step toward understanding the neural basis that underlies sex differences in behavior. Moreover, insight into sex differences in the healthy brain is important for sex-specific diagnosis and treatment of many psychiatric disorders, which differ in prevalence and symptoms between men and women (Cosgrove et al., 2007, Lenroot and Giedd, 2010).

Many studies have described sex differences in the brain using structural and functional magnetic resonance imaging (MRI) techniques. It has been consistently shown that the brain size of women is smaller than that of men (Goldstein et al., 2001). In addition, there are specific brain regions which show morphological differences between men and women, including the hypothalamus, amygdala, parahippocampal gyrus, occipital lingual gyrus, inferior frontal gyrus, anterior cingulate gyrus, and cerebellum (Chen et al., 2007, Fan et al., 2010, Good et al., 2001, Takahashi et al., 2011). Using various experimental designs, functional imaging studies have shown different patterns of brain activation between men and women even when controlling for performance differences. For example, during the mental rotation task, studies have found that men showed stronger parietal activation, whereas women showed greater inferior frontal activation (Hugdahl et al., 2006, Weiss et al., 2003). Sex-related hemispheric lateralization was found in language processing (Shaywitz et al., 1995) and memory for emotional material (Canli et al., 2002). These functional findings indicate that male and female brains may use different neural mechanisms for performing certain tasks (Cahill, 2006). Although sex-specific brain activations have been well documented by previous studies, the possible sex-related differences in baseline or resting state of brain function are largely unexplored. It has been demonstrated that functionally related brain areas share low-frequency fluctuations in the resting-state fMRI (rs-fMRI) signal (Biswal et al., 2010, Fox and Raichle, 2007). Therefore we hypothesized that men and women may exhibit different patterns of spontaneous brain activity during the resting state.

In recent years, there has been an increasing interest in the use of multivariate pattern analysis (MVPA) for analyzing neuroimaging data (Dosenbach et al., 2010, Mourao-Miranda et al., 2005, Shen et al., 2010, Zhu et al., 2008). MVPA applies powerful pattern classification algorithms to extract spatial and/or temporal patterns in the neuroimaging data that differentiate between cognitive tasks, mental states, behaviors, or other variables of interest (reviewed in Norman et al., 2006, Pereira et al., 2009). Compared with traditional mass-univariate methods, MVPA can often extract additional information from high-dimensional neuroimaging data. A potential reason may be that mass-univariate methods consider each individual variable separately, whereas MVPA takes into account patterns of information that might be presented across multiple variables, and thus may have increased sensitivity in detecting subtle and spatially distributed sex differences in the brain.

In this study, we employed an MVPA approach to identify the complex patterns of sex differences in brain structure and resting-state function in a group of 140 healthy young individuals. Gray matter density, as measured from structural MRI (sMRI) data, was used to examine sex differences in brain structure. A local functional connectivity measurement, regional homogeneity (ReHo), was used to map resting-state brain function, which reflects regional synchrony of spontaneous fMRI signals (Zang et al., 2004). A pattern classifier was designed using principal component analysis (PCA) and linear support vector machine (SVM) to identify brain areas that had distinct characteristics between the male and female groups. Correlation analyses between gray matter density and ReHo were also performed in brain areas that exhibited significant functional sex differences, to determine the brain areas in which sex differences in resting-state brain function depend on the underlying anatomical properties. Additionally, to determine whether sex differences in the human brain were independent of the larger sizes of the male brains, the same SVM-based MVPA approach was also performed on a subset of 35 male and 35 female subjects who were matched for brain size.

Section snippets

Subjects

A cohort of healthy young volunteers was recruited by the Institutional Review Board of the State Key Laboratory for Cognitive Neuroscience and Learning, Beijing Normal University. The present study was restricted to 70 male (21.2 ± 1.9 years old, range 18–26) and 70 female (20.6 ± 1.6 years old, range 18–25) subjects. All subjects were right-handed native Chinese speakers. No subject had suffered major head trauma, had a history of alcohol or drug dependence, or had neurological disorders. Written

Global tissue volume and head movement parameters

Global volumes of GM, WM, CSF, and total brain are given in Table 1. We observed that men had significant greater tissue volumes than women, and the average brain size of men was about 11% larger than that of women. For functional data, the male and female groups had no significant difference in head movement (Table 1; P = 0.156 for translational movement and P = 0.340 for rotational movement).

Classification accuracy

To examine whether there were robust sex differences in brain structure and resting-state function, and to

Discussion

We employed an MVPA approach to identify sex differences in gray matter density and ReHo. By combining structural and functional information, MVPA successfully extracted reliable differences between male and female brains with a mean classification rate of 89%. MVPA revealed that sex differences in gray matter density were widely distributed in the brain, notably in the occipital lobe and the cerebellum. It was also found that men primarily showed higher ReHo in their right hemisphere and women

Conclusion

In conclusion, this study showed reliable sex differences in the brain using an SVM-based MVPA approach, suggesting that sex should be considered as a non-negligible factor in structural and resting-state fMRI analysis. It was found that sex differences in resting-state brain function exhibited lateralized patterns. Moreover, the observed functional sex differences can partly be accounted for by the underlying anatomical properties. Our findings of sex differences in the healthy brain may

Acknowledgments

The authors thank the anonymous reviewers for constructive suggestions. This work was supported by the National Basic Research Program of China (2011CB707802), the National High-Tech Program of China (2012AA011601), and the National Science Foundation of China (60835005, 61003202).

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