Functional Brain Network Estimation with Time Series Self-Scrubbing

Weikai Li, Lishan Qiao, Limei Zhang, Zhengxia Wang, DInggang Shen

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


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 languageEnglish
Article number8618298
Pages (from-to)2494-2504
Number of pages11
JournalIEEE Journal of Biomedical and Health Informatics
Issue number6
Publication statusPublished - 2019 Nov


  • 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


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