TY - JOUR
T1 - Functional Brain Network Estimation with Time Series Self-Scrubbing
AU - Li, Weikai
AU - Qiao, Lishan
AU - Zhang, Limei
AU - Wang, Zhengxia
AU - Shen, DInggang
N1 - Funding Information:
Manuscript received September 5, 2018; revised December 12, 2018; accepted January 13, 2019. Date of publication January 18, 2019; date of current version November 6, 2019. This work was supported in part by the National Natural Science Foundation of China (61300154 and 61402215), in part by the Natural Science Foundation of Shandong Province (ZR2018MF020), in part by the Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJQN201800716 and KJ175492), in part by the Natural Science Foundation Project of CQCSTC (2018jcyjAX0398), in part by the Shanghai Municipal Planning Commission of Science and Research Fund (201740010), and in part by the NIH under Grants EB022880, AG049371, and AG042599. (Corresponding author: Lishan Qiao.) W. Li is with the School of Mathematics Science, Liaocheng University, Liaocheng 252000, China, and with the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 211106, China (e-mail:,leeweikai@outlook.com).
Publisher Copyright:
© 2013 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Functional brain network (FBN) is becoming an increasingly important measurement for exploring cerebral mechanisms and mining informative biomarkers that assist diagnosis of some neurodegenerative disorders. Despite its effectiveness to discover valuable hidden patterns in the human brain, the estimated FBNs are often heavily influenced by the quality of the observed data (e.g., blood oxygen level dependent signal series). In practice, a preprocessing pipeline is usually employed for improving data quality. With this in mind, some data points (volumes or time course in the time series) are still not clean enough, due to artifacts including spurious resting-state processes (head movement, mind-wandering). Therefore, not all volumes in the fMRI time series can contribute to the subsequent FBN estimation. To address this issue, we propose a novel FBN estimation method by introducing a latent variable as an indicator of the data quality, and develop an alternating optimization algorithm for jointly scrubbing the data and estimating FBN simultaneously. To further illustrate the effectiveness of the proposed method, we conduct experiments on two public datasets to identify subjects with mild cognitive impairment from normal controls based on the estimated FBNs, and achieve improved accuracies than the baseline methods.
AB - Functional brain network (FBN) is becoming an increasingly important measurement for exploring cerebral mechanisms and mining informative biomarkers that assist diagnosis of some neurodegenerative disorders. Despite its effectiveness to discover valuable hidden patterns in the human brain, the estimated FBNs are often heavily influenced by the quality of the observed data (e.g., blood oxygen level dependent signal series). In practice, a preprocessing pipeline is usually employed for improving data quality. With this in mind, some data points (volumes or time course in the time series) are still not clean enough, due to artifacts including spurious resting-state processes (head movement, mind-wandering). Therefore, not all volumes in the fMRI time series can contribute to the subsequent FBN estimation. To address this issue, we propose a novel FBN estimation method by introducing a latent variable as an indicator of the data quality, and develop an alternating optimization algorithm for jointly scrubbing the data and estimating FBN simultaneously. To further illustrate the effectiveness of the proposed method, we conduct experiments on two public datasets to identify subjects with mild cognitive impairment from normal controls based on the estimated FBNs, and achieve improved accuracies than the baseline methods.
KW - Functional brain network
KW - mild cognitive impairment (MCI)
KW - resting-state functional magnetic resonance imaging (rs-fMRI)
UR - http://www.scopus.com/inward/record.url?scp=85068529910&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2019.2893880
DO - 10.1109/JBHI.2019.2893880
M3 - Article
C2 - 30668484
AN - SCOPUS:85068529910
VL - 23
SP - 2494
EP - 2504
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
SN - 2168-2194
IS - 6
M1 - 8618298
ER -