TY - GEN
T1 - Multi-LSTM Networks for Accurate Classification of Attention Deficit Hyperactivity Disorder from Resting-State fMRI Data
AU - Liu, Rui
AU - Huang, Zhi An
AU - Jiang, Min
AU - Tan, Kay Chen
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61876162 and Grant 61673328, in part by the Shenzhen Scientific Research and Development Funding Program under Grant JCYJ20180307123637294, and in part by the Research Grants Council of the Hong Kong SAR under Grant CityU11202418 and Grant CityU11209219. (Corresponding authors: Zhi-An Huang; Min Jiang.)
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/23
Y1 - 2020/10/23
N2 - Attention deficit hyperactivity disorder (ADHD) is a widespread mental disorder among young children. Due to the complex pathological mechanisms and clinical symptoms, the diagnosis of ADHD is still challenging. In this paper, we propose a novel multi-network of long short term memory (multi-LSTM) for the identification of ADHD. The Gaussian mixture model (GMM) is introduced to cluster different regions of interests (ROIs) for feature selection. Then, the data augmentation and phenotypic information are used to further improve the classification performance. The simulation experiment demonstrates that the proposed model outperforms the state-of-the-art methods based on the multi-site ADHD-200 global competition dataset. It is anticipated that the proposed ROI-based clustering method and multi-LSTM model can provide valuable insights into the auxiliary diagnosis of ADHD from the rs-fMRI signal.
AB - Attention deficit hyperactivity disorder (ADHD) is a widespread mental disorder among young children. Due to the complex pathological mechanisms and clinical symptoms, the diagnosis of ADHD is still challenging. In this paper, we propose a novel multi-network of long short term memory (multi-LSTM) for the identification of ADHD. The Gaussian mixture model (GMM) is introduced to cluster different regions of interests (ROIs) for feature selection. Then, the data augmentation and phenotypic information are used to further improve the classification performance. The simulation experiment demonstrates that the proposed model outperforms the state-of-the-art methods based on the multi-site ADHD-200 global competition dataset. It is anticipated that the proposed ROI-based clustering method and multi-LSTM model can provide valuable insights into the auxiliary diagnosis of ADHD from the rs-fMRI signal.
KW - Attention deficit hyperactivity disorder (ADHD)
KW - functional magnetic resonance imaging (fMRI)
KW - Long short-term memory (LSTM)
KW - regions of interests (ROIs)
UR - http://www.scopus.com/inward/record.url?scp=85098492315&partnerID=8YFLogxK
U2 - 10.1109/IAI50351.2020.9262176
DO - 10.1109/IAI50351.2020.9262176
M3 - Conference article published in proceeding or book
AN - SCOPUS:85098492315
T3 - 2nd International Conference on Industrial Artificial Intelligence, IAI 2020
SP - 1
EP - 6
BT - 2nd International Conference on Industrial Artificial Intelligence, IAI 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Industrial Artificial Intelligence, IAI 2020
Y2 - 23 October 2020 through 25 October 2020
ER -
