Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 349-354 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538678220 |
| DOIs | |
| Publication status | Published - 2019 Mar 18 |
| Event | 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019 - Okinawa, Japan Duration: 2019 Feb 11 → 2019 Feb 13 |
Publication series
| Name | 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019 |
|---|
Conference
| Conference | 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019 |
|---|---|
| Country | Japan |
| City | Okinawa |
| Period | 19/2/11 → 19/2/13 |
Fingerprint
Keywords
- Alzheimer's disease
- brain networks
- graph auto-encoder
- graph convolutional neural network
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Science Applications
- Artificial Intelligence
Cite this
Deep learning based decomposition of brain networks. / Lee, Pilsub; Choi, Myungwon; Kim, Daegyeom; Lee, Suji; Jeong, Hyun-Ghang; Han, Cheol E.
1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 349-354 8669055 (1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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.
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
UR - http://www.scopus.com/inward/citedby.url?scp=85063884338&partnerID=8YFLogxK
U2 - 10.1109/ICAIIC.2019.8669055
DO - 10.1109/ICAIIC.2019.8669055
M3 - Conference contribution
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.
ER -