Abstract
Analysis of developmental brain networks is fundamentally important for basic developmental neuroscience. In this paper, we focus on the temporally-covarying connection patterns, called meta-networks, and develop a new mathematical model for meta-network decomposition. With the proposed model, we decompose the developmental structural correlation networks of cortical thickness into five meta-networks. Each meta-network exhibits a distinctive spatial connection pattern, and its covarying trajectory highlights the temporal contribution of the meta-network along development. Systematic analysis of the meta-networks and covarying trajectories provides insights into three important aspects of brain network development.
| Original language | English |
|---|---|
| Article number | 93 |
| Journal | Frontiers in Human Neuroscience |
| Volume | 13 |
| DOIs | |
| Publication status | Published - 2019 Feb 1 |
Fingerprint
Keywords
- Brain network development
- Cortical thickness
- Low rank
- Meta-network analysis
- Temporal smoothness
ASJC Scopus subject areas
- Neuropsychology and Physiological Psychology
- Neurology
- Psychiatry and Mental health
- Biological Psychiatry
- Behavioral Neuroscience
Cite this
Meta-network analysis of structural correlation networks provides insights into brain network development. / Xu, Xiaohua; He, Ping; Yap, Pew Thian; Zhang, Han; Nie, Jingxin; Shen, Dinggang.
In: Frontiers in Human Neuroscience, Vol. 13, 93, 01.02.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Meta-network analysis of structural correlation networks provides insights into brain network development
AU - Xu, Xiaohua
AU - He, Ping
AU - Yap, Pew Thian
AU - Zhang, Han
AU - Nie, Jingxin
AU - Shen, Dinggang
PY - 2019/2/1
Y1 - 2019/2/1
N2 - Analysis of developmental brain networks is fundamentally important for basic developmental neuroscience. In this paper, we focus on the temporally-covarying connection patterns, called meta-networks, and develop a new mathematical model for meta-network decomposition. With the proposed model, we decompose the developmental structural correlation networks of cortical thickness into five meta-networks. Each meta-network exhibits a distinctive spatial connection pattern, and its covarying trajectory highlights the temporal contribution of the meta-network along development. Systematic analysis of the meta-networks and covarying trajectories provides insights into three important aspects of brain network development.
AB - Analysis of developmental brain networks is fundamentally important for basic developmental neuroscience. In this paper, we focus on the temporally-covarying connection patterns, called meta-networks, and develop a new mathematical model for meta-network decomposition. With the proposed model, we decompose the developmental structural correlation networks of cortical thickness into five meta-networks. Each meta-network exhibits a distinctive spatial connection pattern, and its covarying trajectory highlights the temporal contribution of the meta-network along development. Systematic analysis of the meta-networks and covarying trajectories provides insights into three important aspects of brain network development.
KW - Brain network development
KW - Cortical thickness
KW - Low rank
KW - Meta-network analysis
KW - Temporal smoothness
UR - http://www.scopus.com/inward/record.url?scp=85069476808&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069476808&partnerID=8YFLogxK
U2 - 10.3389/fnhum.2019.00093
DO - 10.3389/fnhum.2019.00093
M3 - Article
AN - SCOPUS:85069476808
VL - 13
JO - Frontiers in Human Neuroscience
JF - Frontiers in Human Neuroscience
SN - 1662-5161
M1 - 93
ER -