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DisConICA: a Software Package for Assessing Reproducibility of Brain Networks and their Discriminability across Disorders
Syed, Mohammed A.1,2,3; Yang, Zhi4; Rangaprakash, D.1,5; Hu, Xiaoping6; Dretsch, Michael N.7,8,9; Katz, Jeffrey S.1,9,10,11; Denney, Thomas S., Jr.1,9,10,11; Deshpande, Gopikrishna1,9,10,11,12,13
First AuthorSyed, Mohammed A.
Correspondent Emailgopikrishna deshpande gopi@auburn.edu
2020
Source PublicationNEUROINFORMATICS
ISSN1539-2791
Subtypearticle
Volume18Issue:1Pages:87-107
Contribution Rank4
Abstract

There is a lack of objective biomarkers to accurately identify the underlying etiology and related pathophysiology of disparate brain-based disorders that are less distinguishable clinically. Brain networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) has been a popular tool for discovering candidate biomarkers. Specifically, independent component analysis (ICA) of rs-fMRI data is a powerful multivariate technique for investigating brain networks. However, ICA-derived brain networks that are not highly reproducible within heterogeneous clinical populations may exhibit mean statistical separation between groups, yet not be sufficiently discriminative at the individual-subject level. We hypothesize that functional brain networks that are most reproducible in subjects within clinical and control groups separately, but not when the two groups are merged, may possess the ability to discriminate effectively between the groups even at the individual-subject level. In this study, we present DisConICA or "Discover Confirm Independent Component Analysis", a software package that implements the methodology in support of our hypothesis. It relies on a "discover-confirm" approach based upon the assessment of reproducibility of independent components (representing brain networks) obtained from rs-fMRI (discover phase) using the gRAICAR (generalized Ranking and Averaging Independent Component Analysis by Reproducibility) algorithm, followed by unsupervised clustering analysis of these components to evaluate their ability to discriminate between groups (confirm phase). The unique feature of our software package is its ability to seamlessly interface with other software packages such as SPM and FSL, so that all related analyses utilizing features of other software can be performed within our package, thus providing a one-stop software solution starting with raw DICOM images to the final results. We showcase our software using rs-fMRI data acquired from US Army soldiers returning from the wars in Iraq and Afghanistan who were clinically grouped into the following groups: PTSD (posttraumatic stress disorder), comorbid PCS (post-concussion syndrome) + PTSD, and matched healthy combat controls. This software package along with test data sets is available for download at .

KeywordFunctional MRI Independent component analysis Reproducibility Clustering Posttraumatic stress disorder Post-concussion syndrome
DOI10.1007/s12021-019-09422-1
Indexed BySCI
Language英语
Funding OrganizationNational Science Foundation - NSF ; U.S. Army Medical Research and Materials Command (MRMC) ; National Science Foundation of China ; Foundation of Beijing Key Laboratory of Mental Disorders ; Beijing Nova Program for Science and Technology ; Outstanding Young Investigator Award of Institute of Psychology, Chinese Academy of Sciences ; NIH
Funding ProjectNational Science Foundation - NSF[0966278] ; U.S. Army Medical Research and Materials Command (MRMC)[00007218] ; National Science Foundation of China[81270023] ; Foundation of Beijing Key Laboratory of Mental Disorders[2014JSJB03] ; Beijing Nova Program for Science and Technology[XXJH2015B079] ; Outstanding Young Investigator Award of Institute of Psychology, Chinese Academy of Sciences[Y4CX062008] ; NIH[DA033393] ; NIH[R01EY025978]
WOS Research AreaComputer Science ; Neurosciences & Neurology
WOS SubjectComputer Science, Interdisciplinary Applications ; Neurosciences
WOS IDWOS:000514280700006
PublisherHUMANA PRESS INC
WOS KeywordABNORMAL FUNCTIONAL CONNECTIVITY ; INDEPENDENT COMPONENT ANALYSIS ; ANTERIOR CINGULATE CORTEX ; VETERANS ; PTSD ; REGISTRATION ; MAXIMIZATION ; PRECUNEUS
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/30976
Collection中国科学院行为科学重点实验室
Corresponding AuthorDeshpande, Gopikrishna
Affiliation1.Auburn Univ, AU MRI Res Ctr, Dept Elect & Comp Engn, 560 Devall Dr,Suite 266D, Auburn, AL 36849 USA
2.Auburn Univ, Dept Comp Sci & Software Engn, Auburn, AL 36849 USA
3.Boeing Co, Seattle, WA USA
4.Chinese Acad Sci, Inst Psychol, Key Lab Behav Sci, Beijing, Peoples R China
5.Northwestern Univ, Dept Radiol, Chicago, IL USA
6.Univ Calif Riverside, Dept Bioengn, Riverside, CA 92521 USA
7.US Army, Aeromed Res Lab, Ft Rucker, AL USA
8.US Army, Joint Base Lewis McCord, Med Res Directorate West, Tacoma, WA USA
9.Auburn Univ, Dept Psychol, Auburn, AL 36849 USA
10.Auburn Univ, Ctr Neurosci, Birmingham, AL 36849 USA
11.Alabama Adv Imaging Consortium, Birmingham, AL 36207 USA
12.Natl Inst Mental Hlth & Neurosci, Dept Psychiat, Bangalore, Karnataka, India
13.Auburn Univ, Ctr Hlth Ecol & Equ Res, Auburn, AL 36849 USA
Recommended Citation
GB/T 7714
Syed, Mohammed A.,Yang, Zhi,Rangaprakash, D.,et al. DisConICA: a Software Package for Assessing Reproducibility of Brain Networks and their Discriminability across Disorders[J]. NEUROINFORMATICS,2020,18(1):87-107.
APA Syed, Mohammed A..,Yang, Zhi.,Rangaprakash, D..,Hu, Xiaoping.,Dretsch, Michael N..,...&Deshpande, Gopikrishna.(2020).DisConICA: a Software Package for Assessing Reproducibility of Brain Networks and their Discriminability across Disorders.NEUROINFORMATICS,18(1),87-107.
MLA Syed, Mohammed A.,et al."DisConICA: a Software Package for Assessing Reproducibility of Brain Networks and their Discriminability across Disorders".NEUROINFORMATICS 18.1(2020):87-107.
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