TY - GEN
T1 - Deep learning based decomposition of brain networks
AU - Lee, Pilsub
AU - Choi, Myungwon
AU - Kim, Daegyeom
AU - Lee, Suji
AU - Jeong, Hyun Ghang
AU - Han, Cheol E.
N1 - Funding Information:
ACKNOWLEDGMENT This research was supported by the Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education (2016R1D1A1B03934990), and by ICT R&D program of MSIP/IITP (R7124-16-0004, Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding). These funding sources were not involved in the creation of the study protocol, data analysis, or in writing the manuscript.
Funding Information:
National Research Foundation (NRF) of Korea funded by the Ministry of Education (2016R1D1A1B03934990)
Publisher Copyright:
© 2019 IEEE.
PY - 2019/3/18
Y1 - 2019/3/18
N2 - A brain network is the essence of the intelligence where it consists of nodes that are anatomically defined brain regions, and edges that connect a pair of brain regions. The diffusion-weighted magnetic resonance (MR) images and the advances in computer-Aided tractography algorithms let us know strong association between human brain networks and cognitive functions. Brain regions dedicated to a certain specific cognitive function were spatially clustered and efficiently connected each other; it is called local functional segregation. However, it is not well known that such a local segregation is associated with a certain sub-network which may act as a building block of the brain network. In this work, using a graph auto-encoder, we extracted building blocks of brain networks and investigate whether they are affected by a neurological disease, Alzheimer's disease. We found that the brain network of each person is linear summation of the learned building blocks. Also, the activation levels of these building blocks vary in the normal controls and patients with Alzheimer's disease, showing that network deterioration in the disease group.
AB - A brain network is the essence of the intelligence where it consists of nodes that are anatomically defined brain regions, and edges that connect a pair of brain regions. The diffusion-weighted magnetic resonance (MR) images and the advances in computer-Aided tractography algorithms let us know strong association between human brain networks and cognitive functions. Brain regions dedicated to a certain specific cognitive function were spatially clustered and efficiently connected each other; it is called local functional segregation. However, it is not well known that such a local segregation is associated with a certain sub-network which may act as a building block of the brain network. In this work, using a graph auto-encoder, we extracted building blocks of brain networks and investigate whether they are affected by a neurological disease, Alzheimer's disease. We found that the brain network of each person is linear summation of the learned building blocks. Also, the activation levels of these building blocks vary in the normal controls and patients with Alzheimer's disease, showing that network deterioration in the disease group.
KW - Alzheimer's disease
KW - brain networks
KW - graph auto-encoder
KW - graph convolutional neural network
UR - http://www.scopus.com/inward/record.url?scp=85063884338&partnerID=8YFLogxK
U2 - 10.1109/ICAIIC.2019.8669055
DO - 10.1109/ICAIIC.2019.8669055
M3 - Conference contribution
AN - SCOPUS:85063884338
T3 - 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
SP - 349
EP - 354
BT - 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
Y2 - 11 February 2019 through 13 February 2019
ER -
