Back to Search
Start Over
Segregation of the regional radiomics similarity network exhibited an increase from late childhood to early adolescence: A developmental investigation
- Source :
- NeuroImage, Vol 302, Iss , Pp 120893- (2024)
- Publication Year :
- 2024
- Publisher :
- Elsevier, 2024.
-
Abstract
- Brain development is characterized by an increase in structural and functional segregation, which supports the specialization of cognitive processes within the context of network neuroscience. In this study, we investigated age-related changes in morphological segregation using individual Regional Radiomics Similarity Networks (R2SNs) constructed with a longitudinal dataset of 494 T1-weighted MR scans from 309 typically developing children aged 6.2 to 13 years at baseline. Segertation indices were defined as the relative difference in connectivity strengths within and between modules and cacluated at the global, system and local levels. Linear mixed-effect models revealed longitudinal increases in both global and system segregation indices, particularly within the limbic and dorsal attention network, and decreases within the ventral attention network. Superior performance in working memory and inhibitory control was associated with higher system-level segregation indices in default, frontoparietal, ventral attention, somatomotor and subcortical systems, and lower local segregation indices in visual network regions, regardless of age. Furthermore, gene enrichment analysis revealed correlations between age-related changes in local segregation indices and regional expression levels of genes related to developmental processes. These findings provide novel insights into typical brain developmental changes using R2SN-derived segregation indices, offering a valuable tool for understanding human brain structural and cognitive maturation.
Details
- Language :
- English
- ISSN :
- 10959572
- Volume :
- 302
- Issue :
- 120893-
- Database :
- Directory of Open Access Journals
- Journal :
- NeuroImage
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.61e6383bcd5647518e9acbcf03949142
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.neuroimage.2024.120893