Abstract
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.
| Original language | English |
|---|---|
| Article number | 8618298 |
| Pages (from-to) | 2494-2504 |
| Number of pages | 11 |
| Journal | IEEE Journal of Biomedical and Health Informatics |
| Volume | 23 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 2019 Nov |
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Keywords
- Functional brain network
- mild cognitive impairment (MCI)
- resting-state functional magnetic resonance imaging (rs-fMRI)
ASJC Scopus subject areas
- Biotechnology
- Computer Science Applications
- Electrical and Electronic Engineering
- Health Information Management
Cite this
Functional Brain Network Estimation with Time Series Self-Scrubbing. / Li, Weikai; Qiao, Lishan; Zhang, Limei; Wang, Zhengxia; Shen, DInggang.
In: IEEE Journal of Biomedical and Health Informatics, Vol. 23, No. 6, 8618298, 11.2019, p. 2494-2504.Research output: Contribution to journal › Article
}
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
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
UR - http://www.scopus.com/inward/citedby.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 -