Segregation of the regional radiomics similarity network exhibited an increase from late childhood to early adolescence: A developmental investigation

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.

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Publication Details

Journal
NeuroImage
Published
2024-10-18
DOI
https://doi.org/10.1016/j.neuroimage.2024.120893
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
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article

Segregation of the regional radiomics similarity network exhibited an increase from late childhood to early adolescence: A developmental investigation

Yanpei Wang, Jia‐Hong Gao, Debin Zeng, Shaozheng Qin et al.
NeuroImage
Radiomics and Machine Learning in Medical Imaging
article

Segregation of the regional radiomics similarity network exhibited an increase from late childhood to early adolescence: A developmental investigation

Yanpei Wang, Jia‐Hong Gao, Debin Zeng, Shaozheng Qin, Tianyuan Lei, Weiwei Men, Shuping Tan, Sha Tao, Yong He, Tengda Zhao, Xiaoxi Dong, Xiaodan Chen, Mingming Hu, Qi Dong, Shuyu Li, Zhiying Pan, Lei Chu, Xuhong Liao, Daoyang Wang, Qiongling Li, Yirong He
article en

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.

NeuroImageVol. 302
Hangzhou Normal University (CN), Peking University (CN), Beijing Normal University (CN), Chinese Institute for Brain Research (CN), Beijing HuiLongGuan Hospital (CN), Center for Life Sciences (CN), Beihang University (CN)
Reduced inequalities
Openalex Percentile: Top 69%
Radiomics and Machine Learning in Medical Imaging
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