TY - JOUR
T1 - Spatiotemporal Analysis of Developing Brain Networks
AU - He, Ping
AU - Xu, Xiaohua
AU - Zhang, Han
AU - Li, Gang
AU - Nie, Jingxin
AU - Yap, Pew Thian
AU - Shen, Dinggang
N1 - Funding Information:
This work was supported in part by the Chinese National Natural Science Foundation under Grant Nos. 61402395, 61502412, 61379066, and 61300151, Natural Science Foundation of Jiangsu Province under contracts BK20151314, BK20140492, and BK20150459, Jiangsu Overseas Research and Training
Funding Information:
Data used in the preparation of this article were obtained from the Pediatric MRI Data Repository created by the NIH MRI Study of Normal Brain Development. This is a multi-site, longitudinal study of typically developing children, from ages newborn through young adulthood, conducted by the Brain Development Cooperative Group and supported by the National Institute of Child Health and Human Development, the National Institute on Drug Abuse, the National Institute of Mental Health, and the National Institute of Neurological Disorders and Stroke (Contract #s N01-HD02-3343, N01-MH9-0002, and N01-NS-9-2314, -2315, -2316, -2317, -2319, and -2320). A listing of the participating sites and a complete listing of the study investigators can be found at http://www.bic.mni.mcgill.ca/nihpd/info/ participating_centers.html. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH.
Funding Information:
Data used in the preparation of this article were obtained from the Pediatric MRI Data Repository created by the NIH MRI Study of Normal Brain Development. This is a multi-site, longitudinal study of typically developing children, from ages newborn through young adulthood, conducted by the Brain Development Cooperative Group and supported by the National Institute of Child Health and Human Development, the National Institute on Drug Abuse, the National Institute of Mental Health, and the National Institute of Neurological Disorders and Stroke (Contract #s N01-HD02-3343, N01-MH9-0002, and N01-NS-9-2314, -2315, -2316, -2317, -2319, and -2320). A listing of the participating sites and a complete listing of the study investigators can be found at http://www.bic.mni.mcgill.ca/nihpd/info/participating_centers.html. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH. Funding. This work was supported in part by the Chinese National Natural Science Foundation under Grant Nos. 61402395, 61502412, 61379066, and 61300151, Natural Science Foundation of Jiangsu Province under contracts BK20151314, BK20140492, and BK20150459, Jiangsu Overseas Research and Training Program for University Prominent Young and Middle-aged Teachers and Presidents, Jiangsu Government Scholarship for Overseas Studies. This work was also supported in part by National Institutes of Health grant MH100217, MH107815, MH108914.
Publisher Copyright:
© 2018 He, Xu, Zhang, Li, Nie, Yap and Shen.
PY - 2018/7/31
Y1 - 2018/7/31
N2 - Recent advances in MRI have made it easier to collect data for studying human structural and functional connectivity networks. Computational methods can reveal complex spatiotemporal dynamics of the human developing brain. In this paper, we propose a Developmental Meta-network Decomposition (DMD) method to decompose a series of developmental networks into a set of Developmental Meta-networks (DMs), which reveal the underlying changes in connectivity over development. DMD circumvents the limitations of traditional static network decomposition methods by providing a novel exploratory approach to capture the spatiotemporal dynamics of developmental networks. We apply this method to structural correlation networks of cortical thickness across subjects at 3–20 years of age, and identify four DMs that smoothly evolve over three stages, i.e., 3–6, 7–12, and 13–20 years of age. We analyze and highlight the characteristic connections of each DM in relation to brain development.
AB - Recent advances in MRI have made it easier to collect data for studying human structural and functional connectivity networks. Computational methods can reveal complex spatiotemporal dynamics of the human developing brain. In this paper, we propose a Developmental Meta-network Decomposition (DMD) method to decompose a series of developmental networks into a set of Developmental Meta-networks (DMs), which reveal the underlying changes in connectivity over development. DMD circumvents the limitations of traditional static network decomposition methods by providing a novel exploratory approach to capture the spatiotemporal dynamics of developmental networks. We apply this method to structural correlation networks of cortical thickness across subjects at 3–20 years of age, and identify four DMs that smoothly evolve over three stages, i.e., 3–6, 7–12, and 13–20 years of age. We analyze and highlight the characteristic connections of each DM in relation to brain development.
KW - Cortical thickness
KW - Developmental meta-network decomposition
KW - Developmental networks
KW - Non-negative matrix factorization
KW - Structural correlation networks
UR - http://www.scopus.com/inward/record.url?scp=85054802503&partnerID=8YFLogxK
U2 - 10.3389/fninf.2018.00048
DO - 10.3389/fninf.2018.00048
M3 - Article
AN - SCOPUS:85054802503
SN - 1662-5196
VL - 12
JO - Frontiers in Neuroinformatics
JF - Frontiers in Neuroinformatics
M1 - 48
ER -