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Estimating sparse functional connectivity networks via hyperparameter-free learning model.

Functional connectivity networks (FCNs) provide a potential way for understanding the brain organizational patterns and diagnosing neurological diseases. Currently, researchers have proposed many methods for FCN construction, among which the most classic example is Pearson's correlation (PC). Despit...

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Published in:Artificial Intelligence in Medicine Vol. 111
Main Authors: Sun, Lei, Xue, Yanfang, Zhang, Yining, Qiao, Lishan, Zhang, Limei, Liu, Mingxia
Format: equations & formulas research tables/charts Journal Article
Published: Elsevier B.V. Jan2021
Online Access:View this record in EBSCOhost
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        09333657
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      jtl: Artificial Intelligence in Medicine
      issn: 09333657
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    pubinfo:
      dt: Jan2021
      vid: 111
      pid: 1004
      pub: Elsevier B.V.
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        148120845
        148120845
        NLM33461688
        148120845
        10.1016/j.artmed.2020.102004
        NLM33461688
        148120845
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        atl: Estimating sparse functional connectivity networks via hyperparameter-free learning model.
      aug:
        au:
          Sun, Lei
          Xue, Yanfang
          Zhang, Yining
          Qiao, Lishan
          Zhang, Limei
          Liu, Mingxia
        affil: School of Mathematics Science, Liaocheng University, Liaocheng 252000, China
      sug:
        subj:
          Brain
          Magnetic Resonance Imaging
          Brain Mapping
          Human
          Comparative Studies
          Multicenter Studies
          Evaluation Research
          Validation Studies
      ab: Functional connectivity networks (FCNs) provide a potential way for understanding the brain organizational patterns and diagnosing neurological diseases. Currently, researchers have proposed many methods for FCN construction, among which the most classic example is Pearson's correlation (PC). Despite its simplicity and popularity, PC always results in dense FCNs, and thus a thresholding strategy is usually needed in practice to sparsify the estimated FCNs prior to the network analysis, which undoubtedly causes the problem of threshold parameter selection. As an alternative to PC, sparse representation (SR) can directly generate sparse FCNs due to the l1 regularizer in the estimation model. However, similar to the thresholding scheme used in PC, it is also challenging to determine suitable values for the regularization parameter in SR. To circumvent the difficulty of parameter selection involved in these traditional methods, we propose a hyperparameter-free method for FCN construction based on the global representation among fMRI time courses. Interestingly, the proposed method can automatically generate sparse FCNs, without any thresholding or regularization parameters. To verify the effectiveness of the proposed method, we conduct experiments to identify subjects with mild cognitive impairment (MCI) and Autism spectrum disorder (ASD) from normal controls (NCs) based on the estimated FCNs. Experimental results on two benchmark databases demonstrate that the achieved classification performance of our proposed scheme is comparable to four conventional methods.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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