Investigating sexual dimorphism in human brain structure by combining multiple indexes of brain morphology and source-based morphometry

Abstract

Computational morphometry of magnetic resonance images represents a powerful tool for studying macroscopic differences in human brains. In the present study (N participants = 829), we combined different techniques and measures of brain morphology to investigate one of the most compelling topics in neuroscience: sexual dimorphism in human brain structure. When accounting for overall larger male brains, results showed limited sex differences in gray matter volume (GMV) and surface area. On the other hand, we found larger differences in cortical thickness, favoring both males and females, arguably as a result of region-specific differences. We also observed higher values of fractal dimension, a measure of cortical complexity, for males versus females across the four lobes. In addition, we applied source-based morphometry, an alternative method for measuring GMV based on the independent component analysis. Analyses on independent components revealed higher GMV in fronto-parietal regions, thalamus and caudate nucleus for females, and in cerebellartemporal cortices and putamen for males, a pattern that is largely consistent with previous findings.

Cingulate cortex morphology impacts on neurofunctional activity and behavioral performance in interference tasks

“…For the group analysis gender and years of formal education were entered as nuisance covariates. Gender was considered as a covariate because of evidence of gender-related differences in aMCC sulcal pattern distribution (see 20,64,65 ) and cortical complexity [66][67][68] . Years of formal education were entered in the model as a covariate, since education has been reported to impact on Stroop RTs 69 .…”

Human hippocampal astrocytes: Computational dissection of their transcriptome, sexual differences and exosomes across ageing and mild‐cognitive impairment

“…Several publications have reported the existence of sexual differences as a global feature of brain functioning (Alshammari, 2021; Berchtold et al, 2008; Cieri et al, 2022; del Mauro et al, 2022; Guebel et al, 2016; Mazur et al, 2021; Pallayova et al, 2019; Taylor et al, 2021; Yang et al, 2021). Therefore, herein it was tested whether this phenomenon also occurs in the astrocytic population.…”

Resting‐state fMRI functional connectome of C9orf72 mutation status

“…Connectome matrices were entered into the second‐level general linear model to compare the functional connectivity network profiles of patients' subgroups with directional contrasts (i.e., C9orf72 + vs. ALSm , C9orf72 − vs. ALSm , and C9orf72 + vs. C9orf72 −). Age, sex assigned at birth, and TIV were included as nuisance covariates 37–39 . The functional network connectivity (FNC) method implemented in CONN was adopted to perform cluster‐level inferences on the rs‐fMRI networks of ROIs 26,35 .…”

“…Age, sex assigned at birth, and TIV were included as nuisance covariates. 37 , 38 , 39 The functional network connectivity (FNC) method implemented in CONN was adopted to perform cluster‐level inferences on the rs‐fMRI networks of ROIs. 26 , 35 Briefly, FNC first identifies sets of related ROIs with a data‐driven hierarchical clustering procedure.…”