Evidence map›Paper›PMID 31187352›Full record

ArticleNeuroinformatics2020

DisConICA: a Software Package for Assessing Reproducibility of Brain Networks and their Discriminability across Disorders.

Mohammed A Syed, Zhi Yang, D Rangaprakash, Xiaoping Hu, Michael N Dretsch, Jeffrey S Katz, Thomas S Denney, Gopikrishna Deshpande

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neuroinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Mohammed A SyedAU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA.ORCID 0000-0002-5898-7632
Zhi YangKey Laboratory of Behavioral Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
D RangaprakashAU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA.
Xiaoping HuDepartment of Bioengineering, University of California Riverside, Riverside, CA, USA.
Michael N DretschU.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, USA.
Jeffrey S KatzAU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA.
Thomas S DenneyAU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA.
Gopikrishna DeshpandeAU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA. gopi@auburn.edu.

Funding

IMPROVEMENT AND APPLICATIONS OF FUNCTIONAL MRIR01EB002009 · NIBIB · EMORY UNIVERSITY · PI HU, XIAOPING P · 2003 to 2008
$2.3M
Crossmodal Correspondences Between Visual and Auditory FeaturesR01EY025978 · NEI · EMORY UNIVERSITY · PI DESHPANDE, GOPIKRISHNA, NYGAARD, LYNNE C · 2015 to 2017
$1.8M
Assessing Large-scale Connectivities in Prenatal Cocaine Exposure Affected BrainsR01DA033393 · NIDA · UNIVERSITY OF GEORGIA · PI HU, XIAOPING P, LIU, TIANMING · 2012 to 2016
$1.6M
NEI NIH HHS R01 EY025978NIBIB NIH HHS R01 EB002009NIDA NIH HHS R01 DA033393
6 · The paper itself

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 https://bitbucket.org/masauburn/disconica.

Indexed as

Data AnalysisSoftwareAdultAlgorithmsBrainBrain MappingFemaleHumansMagnetic Resonance ImagingMaleNerve NetNervous System DiseasesReproducibility of ResultsYoung AdultClusteringFunctional MRIIndependent component analysisPost-concussion syndromePosttraumatic stress disorderReproducibility

Identifiers

PMID31187352
PMCPMC6904532

What OpenQuestion holds

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Registered trials

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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.