Texture-based brain networks for characterization of healthy subjects from MRI

Abstract

Brain networks have been widely used to study the relationships between brain regions based on their dynamics using, e.g. fMRI or EEG, and to characterize their real physical connections using DTI. However, few studies have investigated brain networks derived from structural properties; and those have been based on cortical thickness or gray matter volume. The main objective of this work was to investigate the feasibility of obtaining useful information from brain networks derived from structural MRI, using texture features. We also wanted to verify if texture brain networks had any relation with established functional networks. T1-MR images were segmented using AAL and texture parameters from the gray-level co-occurrence matrix were computed for each region, for 760 subjects. Individual texture networks were used to evaluate the structural connections between regions of well-established functional networks; assess possible gender differences; investigate the dependence of texture network measures with age; and single out brain regions with different texture-network characteristics. Although around 70% of texture connections between regions belonging to the default mode, attention, and visual network were greater than the mean connection value, this effect was small (only between 7 and 15% of these connections were larger than one standard deviation), implying that texture-based morphology does not seem to subside function. This differs from cortical thickness-based morphology, which has been shown to relate to functional networks. Seventy-five out of 86 evaluated regions showed significant (ANCOVA, p < 0.05) differences between genders. Forty-four out of 86 regions showed significant (ANCOVA, p < 0.05) dependence with age; however, the R2 indicates that this is not a linear relation. Thalamus and putamen showed a very unique texture-wise structure compared to other analyzed regions. Texture networks were able to provide useful information regarding gender and age-related differences, as well as for singling out specific brain regions. We did not find a morphological texture-based subsidy for the evaluated functional brain networks. In the future, this approach will be extended to neurological patients to investigate the possibility of extracting biomarkers to help monitor disease evolution or treatment effectiveness.

Texture‐based morphometry in relation to apolipoprotein ε4 genotype, ageing and sex in a midlife population

“…In a relatively young cohort (mean age 39 years), they found that textural connectivity differed between males and females (75/86 regions) and varied with ageing (44/86 regions). In this study (da Silveira et al, 2023 ) they reported that for most of the areas (amongst them the postcentral gyri, precuneus, areas in the frontal, parietal lobes and temporal pole), males tended to show higher textural similarity amongst neighbouring regions, the cerebellum was an exception; female participants showed higher similarity within it. Despite the methodological differences, this finding can be seen as in line with our findings where females show higher energy/homogeneity and lower contrast/entropy within the cerebellum.…”

“…Texture‐based brain networks have been investigated in relation to ageing and sex. The study by da Silveira et al ( 2023 ) investigated textural connectivity based on brain ROIs using GLCM features calculated within these ROIs (AAL) atlas. In a relatively young cohort (mean age 39 years), they found that textural connectivity differed between males and females (75/86 regions) and varied with ageing (44/86 regions).…”

“…Another plausible explanation is that this difference in the textural profile of the ventricles could relate to different CSF flow patterns and the associated introduced noise, differences that can be investigated with other techniques such as phase contrast MR angiography (Sakhare et al, 2019 ). A study examining textural networks found an association between textural features and age for 44 of 86 examined regions (da Silveira et al, 2023 ). The age distribution of this study was very broad (18–90), with a mean age of 39 years; 257 of the 760 participants were aged between 46 and 75.…”