Evidence map›Paper›PMID 30250625›Full record

ArticleCognitive neurodynamics2018

Examining stability of independent component analysis based on coefficient and component matrices for voxel-based morphometry of structural magnetic resonance imaging.

Qing Zhang, Guoqiang Hu, Lili Tian, Tapani Ristaniemi, Huili Wang, Hongjun Chen, Jianlin Wu, Fengyu Cong

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Article in Cognitive neurodynamics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

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5citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Qing Zhang *1Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.
Guoqiang Hu *2Department of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.
Lili Tian3Department of Psychology, University of Jyvaskyla, Jyvaskyla, Finland.
Tapani Ristaniemi4Faculty of Information Technology, University of Jyvaskyla, Jyvaskyla, Finland.
Huili Wang5School of Foreign Languages, Dalian University of Technology, Dalian, China.
Hongjun Chen5School of Foreign Languages, Dalian University of Technology, Dalian, China.
Jianlin Wu1Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.
Fengyu Cong2Department of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.ORCID 0000-0003-0058-2429

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Independent component analysis (ICA) on group-level voxel-based morphometry (VBM) produces the coefficient matrix and the component matrix. The former contains variability among multiple subjects for further statistical analysis, and the latter reveals spatial maps common for all subjects. ICA algorithms converge to local optimization points in practice and the mostly applied stability investigation approach examines the stability of the extracted components. We found that the practically stable components do not guarantee to produce the practically stable coefficients of ICA decomposition for the further statistical analysis. Consequently, we proposed a novel approach including two steps: (1), the stability index for the coefficient matrix and the stability index for the component matrix were examined, respectively; (2) the two indices were multiplied to analyze the stability of ICA decomposition. The proposed approach was used to study the sMRI data of Type II diabetes mellitus group and the healthy control group (HC). Group differences in VBM were found in the superior temporal gyrus. Besides, it was revealed that the VBMs of the region of the HC group were significantly correlated with Montreal Cognitive Assessment (MoCA) describing the level of cognitive disorder. In contrast to the widely applied approach to investigating the stability of the extracted components for ICA decomposition, we proposed to examine the stability of ICA decomposition by fusion the stability of both coefficient matrix and the component matrix. Therefore, the proposed approach can examine the stability of ICA decomposition sufficiently.

Indexed as

Back-projectionCoefficient matrixComponent matrixDiabetesIndependent component analysisMontreal cognitive assessmentStabilityVoxel-based morphometry

Identifiers

PMID30250625
PMCPMC6139102

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.