@inproceedings{4e54cfce48fa45d397960c7beee1f982,
title = "Early Development of Infant Brain Complex Network",
abstract = "The infant brain experiences explosive growth in the first few years of life. The developing topology of the functional network mirrors the emergence of complex cognitive functions. However, early development of brain topological properties in infants is still largely unclear due to the dearth of high-quality longitudinal infant functional MRI (fMRI) data. In this study, we employed advanced methods to investigate the developmental trajectories of various network features on high-resolution, longitudinal fMRI data of infants from birth to 2 years of age. The developmental trajectories of various global and nodal metrics were evaluated with linear mixed-effect modeling. We then investigated the association between these developmental trajectories and the visual reception ability, an important skill that could shape the future development of other cognitive functions. Four global metrics (shortest path length, global efficiency, local efficiency, and sigma (i.e., small-worldness)) showed significant developmental changes to facilitate more efficient information processing. Significant developmental changes were also found in the nodal characters with a prominent spatial specificity, and some brain regions showed increasing importance along the development. Most importantly, different associations between developmental trajectories in both global and nodal network characters and varied visual reception ability were revealed. This is the first longitudinal study on the early development of the brain functional connectome and its potential relationship to the individual variability of the visual abilities. These findings provide valuable knowledge for better understanding of normative and abnormal neurodevelopment in the first few years of life.",
keywords = "Complex network, Development, Graph theory, Infant, Longitudinal, Resting-state fMRI, Visual reception",
author = "Weixiong Jiang and Han Zhang and Hsu, {Li Ming} and Dan Hu and Guoshi Li and Ye Wu and Dinggang Shen",
note = "Funding Information: This work utilizes approaches developed by an NIH grant (1U01MH110274) and the efforts of the UNC/UMN Baby Connectome Project (BCP) Consortium. This work was also supported in part by an NIH grant MH117943. Funding Information: Acknowledgments. This work utilizes approaches developed by an NIH grant (1U01MH110274) and the efforts of the UNC/UMN Baby Connectome Project (BCP) Consortium. This work was also supported in part by an NIH grant MH117943. Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 ; Conference date: 13-10-2019 Through 17-10-2019",
year = "2019",
doi = "10.1007/978-3-030-32245-8_92",
language = "English",
isbn = "9783030322441",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "832--840",
editor = "Dinggang Shen and Pew-Thian Yap and Tianming Liu and Peters, {Terry M.} and Ali Khan and Staib, {Lawrence H.} and Caroline Essert and Sean Zhou",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings",
address = "Germany",
}