Sex differences of signal complexity at resting-state functional magnetic resonance imaging and their associations with the estrogen-signaling pathway in the brain

• Published in: Cognitive neurodynamics Vol. 18; no. 3; pp. 973 - 986
• Main Authors: Zhao, Cheng-li, Hou, Wenjie, Jia, Yanbing, Sahakian, Barbara J., Luo, Qiang
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.06.2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Biochemistry; Biomedical and Life Sciences; Biomedicine; Brain; Brain research; Cerebellum; Cognitive Psychology; Communication; Complexity; Computer Science; Electroencephalography; Entropy; Estrogen receptors; Estrogens; Females; Functional magnetic resonance imaging; Gender differences; Gene expression; Genes; Magnetic resonance imaging; Medical imaging; Neural plasticity; Neuroimaging; Neuromodulation; Neurosciences; Research Article; Sex differences; Sex hormones; Signal transduction; Somatosensory cortex; Structure-function relationships; Time series; Women; Sample entropy; Default mode network; Sex differences; Resting-state functional magnetic resonance imaging; Estrogen-signaling pathway
• ISSN: 1871-4080; 1871-4099
• DOI: 10.1007/s11571-023-09954-y

Sex differences in the brain have been widely reported and may hold the key to elucidating sex differences in many medical conditions and drug response. However, the molecular correlates of these sex differences in structural and functional brain measures in the human brain remain unclear. Herein, we used sample entropy (SampEn) to quantify the signal complexity of resting-state functional magnetic resonance imaging (rsfMRI) in a large neuroimaging cohort (N = 1,642). The frontoparietal control network and the cingulo-opercular network had high signal complexity while the cerebellar and sensory motor networks had low signal complexity in both men and women. Compared with those in male brains, we found greater signal complexity in all functional brain networks in female brains with the default mode network exhibiting the largest sex difference. Using the gene expression data in brain tissues, we identified genes that were significantly associated with sex differences in brain signal complexity. The significant genes were enriched in the gene sets that were differentially expressed between the brain cortex and other tissues, the estrogen-signaling pathway, and the biological function of neural plasticity. In particular, the G-protein-coupled estrogen receptor 1 gene in the estrogen-signaling pathway was expressed more in brain regions with greater sex differences in SampEn. In conclusion, greater complexity in female brains may reflect the interactions between sex hormone fluctuations and neuromodulation of estrogen in women.