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Sexual dimorphism in the human brain: Evidence from neuroimaging

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Sacher,  Julia
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Clinic for Cognitive Neurology, University of Leipzig, Germany;

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Neumann,  Jane
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Integrated Research and Treatment Center Adiposity Diseases, University of Leipzig, Germany;

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Okon-Singer,  Hadas
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

Gotowiek,  Sarah
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Villringer,  Arno
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Clinic for Cognitive Neurology, University of Leipzig, Germany;

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Citation

Sacher, J., Neumann, J., Okon-Singer, H., Gotowiek, S., & Villringer, A. (2013). Sexual dimorphism in the human brain: Evidence from neuroimaging. Magnetic Resonance Imaging, 31(3), 366-375. doi:10.1016/j.mri.2012.06.007.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-A750-C
Abstract
In recent years, more and more emphasis has been placed on the investigation of sex differences in the human brain. Noninvasive neuroimaging techniques represent an essential tool in the effort to better understand the effects of sex on both brain structure and function. In this review, we provide a comprehensive summary of the findings that were collected in human neuroimaging studies in vivo thus far: we explore sexual dimorphism in the human brain at the level of (1) brain structure, in both gray and white matter, observed by voxel-based morphometry (VBM) and diffusion tensor imaging (DTI), respectively; (2) baseline neural activity, studied using resting-state functional magnetic resonance imaging (rs-fMRI) and positron emission tomography (PET); (3) neurochemistry, visualized by means of neuroreceptor ligand PET; and (4) task-related neural activation, investigated using fMRI. Functional MRI findings from the literature are complemented by our own meta-analysis of fMRI studies on sex-specific differences in human emotional processing. Specifically, we used activation likelihood estimation (ALE) to provide a quantitative approach to mapping the consistency of neural networks involved in emotional processing across studies. The presented evidence for sex-specific differences in neural structure and function highlights the importance of modeling sex as a contributing factor in the analysis of brain-related data.