Functional Brain Network Estimation With Time Series Self-Scrubbing

• Published in: IEEE journal of biomedical and health informatics Vol. 23; no. 6; pp. 2494 - 2504
• Main Authors: Li, Weikai, Qiao, Lishan, Zhang, Limei, Wang, Zhengxia, Shen, Dinggang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biomarkers; Brain; Brain - diagnostic imaging; Brain - physiology; Brain mapping; Brain Mapping - methods; Cognitive ability; Cognitive Dysfunction - diagnostic imaging; Correlation; Data points; Databases, Factual; Estimation; Functional brain network; Functional magnetic resonance imaging; Head movement; Humans; Image Interpretation, Computer-Assisted - methods; Informatics; Magnetic Resonance Imaging - methods; mild cognitive impairment (MCI); Neurodegenerative diseases; Optimization; Pipelines; Rest - physiology; resting-state functional magnetic resonance imaging (rs-fMRI); Time series; Time series analysis; Washing
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2019.2893880

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.