Evidence map›Paper›PMID 30921608›Full record

ArticleNeuroImage. Clinical2019

Group ICA for identifying biomarkers in schizophrenia: 'Adaptive' networks via spatially constrained ICA show more sensitivity to group differences than spatio-temporal regression.

Mustafa S Salman, Yuhui Du, Dongdong Lin, Zening Fu, Alex Fedorov, Eswar Damaraju, Jing Sui, Jiayu Chen, Andrew R Mayer, Stefan Posse and 4 more

Open access · goldAbstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in NeuroImage. Clinical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
60citing papers in PubMed, 2 pooled it
7.7field-weighted citation impact, top 2% of its field
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

60 citing papers in PubMed, 2 syntheses or guidelines pooled it, 111 citations in OpenAlex.

  1. Pooled it
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  3. ΔBiological psychiatry. Cognitive neuroscience and neuroimaging · 2026
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  10. Independent Filter Analysis for Group Discrimination in fMRI.bioRxiv : the preprint server for biology · 2025
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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

14 authors at 5 institutions in 2 countries.

Mustafa S SalmanDepartment of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA; The Mind Research Network, Albuquerque, NM, USA.
Yuhui DuThe Mind Research Network, Albuquerque, NM, USA; School of Computer & Information Technology, Shanxi University, Taiyuan, China. Electronic address: ydu@mrn.org.
Dongdong LinThe Mind Research Network, Albuquerque, NM, USA.
Zening FuThe Mind Research Network, Albuquerque, NM, USA.
Alex FedorovDepartment of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA; The Mind Research Network, Albuquerque, NM, USA.
Eswar DamarajuDepartment of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA; The Mind Research Network, Albuquerque, NM, USA.
Jing SuiThe Mind Research Network, Albuquerque, NM, USA; Brainnetome Center and National Laboratory of Pattern Recognition, University of Chinese Academy of Sciences, Beijing, China.
Jiayu ChenThe Mind Research Network, Albuquerque, NM, USA.
Andrew R MayerThe Mind Research Network, Albuquerque, NM, USA.
Stefan PosseDepartment of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA; Department of Neurology, University of New Mexico, Albuquerque, NM, USA; Department of Physics and Astronomy, University of New Mexico, Albuquerque, NM, USA.
Daniel H MathalonDepartment of Psychiatry, University of California, San Francisco, CA, USA; San Francisco VA Medical Center, San Francisco, CA, USA.
Judith M FordDepartment of Psychiatry, University of California, San Francisco, CA, USA.
Theodorus Van ErpDepartment of Psychiatry and Human Behavior, University of California Irvine, CA, USA.
Vince D CalhounDepartment of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA; The Mind Research Network, Albuquerque, NM, USA.
Mind Research Network · USSan Francisco VA Medical Center · USUniversity of California, Irvine · USUniversity of California, San Francisco · USUniversity of New Mexico · US

Funding

Transcranial Direct Current Stimulation for Treatment of Auditory Verbal HalluciP20GM103472 · NIGMS · THE MIND RESEARCH NETWORK · PI CALHOUN, VINCE D · 2012 to 2017
$14.3M
NEURAL MECHANISMS OF SCHIZOPHRENIAP20RR021938 · NCRR · THE MIND RESEARCH NETWORK · PI HANLON, FAITH MARIE · 2008 to 2011
$9.4M
Unified multivariate data-driven solutions for static and dynamic brain connectivityR01EB020407 · NIBIB · THE MIND RESEARCH NETWORK · PI ADALI, TULAY, CALHOUN, VINCE D · 2015 to 2018
$2.7M
Data-driven approaches to identify biomarkers from multimodal imaging big dataR56MH117107 · NIMH · THE MIND RESEARCH NETWORK · PI SUI, JING · 2018 to 2018
$453k
NCRR NIH HHS P20 RR021938NIBIB NIH HHS R01 EB020407NIGMS NIH HHS P20 GM103472NIMH NIH HHS R56 MH117107
6 · The paper itself

Abstract

Brain functional networks identified from fMRI data can provide potential biomarkers for brain disorders. Group independent component analysis (GICA) is popular for extracting brain functional networks from multiple subjects. In GICA, different strategies exist for reconstructing subject-specific networks from the group-level networks. However, it is unknown whether these strategies have different sensitivities to group differences and abilities in distinguishing patients. Among GICA, spatio-temporal regression (STR) and spatially constrained ICA approaches such as group information guided ICA (GIG-ICA) can be used to propagate components (indicating networks) to a new subject that is not included in the original subjects. In this study, based on the same a priori network maps, we reconstructed subject-specific networks using these two methods separately from resting-state fMRI data of 151 schizophrenia patients (SZs) and 163 healthy controls (HCs). We investigated group differences in the estimated functional networks and the functional network connectivity (FNC) obtained by each method. The networks were also used as features in a cross-validated support vector machine (SVM) for classifying SZs and HCs. We selected features using different strategies to provide a comprehensive comparison between the two methods. GIG-ICA generally showed greater sensitivity in statistical analysis and better classification performance (accuracy 76.45 ± 8.9%, sensitivity 0.74 ± 0.11, specificity 0.79 ± 0.11) than STR (accuracy 67.45 ± 8.13%, sensitivity 0.65 ± 0.11, specificity 0.71 ± 0.11). Importantly, results were also consistent when applied to an independent dataset including 82 HCs and 82 SZs. Our work suggests that the functional networks estimated by GIG-ICA are more sensitive to group differences, and GIG-ICA is promising for identifying image-derived biomarkers of brain disease.

Indexed as

AdultBrainDatabases, FactualFemaleHumansMagnetic Resonance ImagingMaleNerve NetPrincipal Component AnalysisSchizophreniaClassificationfMRIFunctional networkGIG-ICAICASchizophreniaSpatio-temporal regression

Identifiers

PMID30921608
PMCPMC6438914
OpenAlexW2953079170

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.