4.3 Article

Multimodal Image Analysis of Sexual Dimorphism in Developing Childhood Brain

Journal

BRAIN TOPOGRAPHY
Volume 34, Issue 3, Pages 257-268

Publisher

SPRINGER
DOI: 10.1007/s10548-021-00823-7

Keywords

Sexual dimorphism; Developing childhood brain; Multimodal MR image analysis; Structural MR brain images; Diffusion tensor imaging (DTI)

Funding

  1. Natural Science Foundation of Shanghai [20ZR1426300]
  2. Shanghai Jiao Tong University Scientific and Technological Innovation Funds [2019QYB02]
  3. Shanghai Science and Technology Talents Program [18XD1403200]
  4. National Natural Science Foundation of China (NSFC) [6181101049]

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Using multimodal brain image analysis, specific patterns of sexual dimorphism in developing brain structure of early childhood were identified, with the volumes of right precuneus and right postcentral being more related to sexual dimorphism. Connections between certain brain regions were also found to be more relevant to sexual dimorphism, shedding light on the observed sex differences in developing childhood brain structure and connectivity.
It is well known that there exist great differences in human brain anatomy and functions between males and females. With the development of noninvasive neuroimaging techniques, the sex differences in adult human brain have been well studied in some researches. However, the sexual dimorphism of human brain anatomy and functions has not been sufficiently described during the developmental period of early childhood brain when the sex differences emerge in behavior. This study was to identify specific patterns of the sexual dimorphism in developing brain structure of early childhood using multimodal brain image analysis. We have performed a multivariate and data-driven analysis by combining multiple neuroimaging technologies including the 3D T1-weighted structural MR images (sMRI) and diffusion tensor imaging (DTI) in a prospective cohort of 188 children (128 males and 60 females) between the ages of 0 and 15. First, the brain images were segmented into 90 regions of interest (ROIs) based on the AAL template to extract the ROI volume and connectivity features. Then, the individual multimodal imaging biomarkers were identified associated with the sex differences. Finally, the selected features from multi-modality neuroimages were combined using multi-kernel support vector machine for classifications of male or female. The method achieved sex classification accuracy 72% for the children between the ages of 0 and 15. And the volumes of right precuneus and right postcentral were more related to sexual dimorphism of developing brain structure than those of other regions in sMRI (p < 0.002). In DTI, the connections between right middle occipital gyrus and right inferior occipital gyrus, between left superior marginal gyrus and left middle temporal gyrus, and between right triangle inferior frontal gyrus and right putamen are also more relevant to the sexual dimorphism than others (p < 0.005). The brain regions and connections related to the sexual differences were identified with sMRI and DTI in early developing brain structure by using the multimodal image analysis. And these sexually dimorphic patterns of brain may be related to the observed sex differences in developing childhood brain structure and connectivity.

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