Evidence map›Paper›PMID 33747675›Full record

ArticleIEEE access : practical innovations, open solutions2020

Sparse representation and dictionary learning model incorporating group sparsity and incoherence to extract abnormal brain regions associated with schizophrenia.

Peng Peng, Yongfeng Ju, Yipu Zhang, Kaiming Wang, Suying Jiang, Yuping Wang

Open access · goldAbstract read
In one paragraph

Article in IEEE access : practical innovations, open solutions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.3field-weighted citation impact, top 45% 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

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

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

6 authors at 2 institutions in 2 countries.

Peng PengThe school of Electronics and Control Engineering, Chang'an University, Xi'an, Shaanxi, 710049, China.
Yongfeng JuThe school of Electronics and Control Engineering, Chang'an University, Xi'an, Shaanxi, 710049, China.
Yipu ZhangThe school of Electronics and Control Engineering, Chang'an University, Xi'an, Shaanxi, 710049, China.
Kaiming WangThe school of Science, Chang'an University, Xi'an, Shaanxi, 710049, China.
Suying JiangThe school of Information Engineering, Chang'an University, Xi'an, Shaanxi, 710049, China.
Yuping WangDepartment of Biomedical Engineering, Tulane University, New Orleans, LA, 70118, USA.
Chang'an University · CNTulane University · US

Funding

Trans-omics Integration of Multi-omics Studies for OsteoporosisU19AG055373 · NIA · TULANE UNIVERSITY OF LOUISIANA · PI Qi Zhao · 2017 to 2026
$24.3M
Transcranial Direct Current Stimulation for Treatment of Auditory Verbal HalluciP20GM103472 · NIGMS · THE MIND RESEARCH NETWORK · PI ABBOTT, CHRISTOPHER C · 2012 to 2017
$14.3M
Multivariate methods for identifying multitask/multimodal brain imaging biomarkersR01EB006841 · NIBIB · THE MIND RESEARCH NETWORK · PI VINCE D CALHOUN, Sergey Plis · 2007 to 2026
$9.8M
Informed Data-Driven Fusion of Behavior, Brain Function, and GenesR01EB005846 · NIBIB · THE MIND RESEARCH NETWORK · PI CALHOUN, VINCE D · 2005 to 2016
$5.5M
Male/Female differences in psychosis and mood disorders:Dynamic imaging-genomic models for characterizing and predicting psychosis and mood dR01MH118695 · NIMH · GEORGIA STATE UNIVERSITY · PI ADALI, TULAY, CALHOUN, VINCE D · 2019 to 2023
$3.8M
Data-driven solutions for temporal, spatial, and spatiotemporal dynamic functional connectivityR01MH123610 · NIMH · GEORGIA STATE UNIVERSITY · PI ADALI, TULAY, CALHOUN, VINCE D · 2021 to 2025
$3.1M
Epigenomewide DNA Methylation Study for Osteoporosis RiskR01AR059781 · NIAMS · TULANE UNIVERSITY OF LOUISIANA · PI DENG, HONG-WEN · 2012 to 2016
$3.1M
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
Integration of brain imaging with genomic and epigenomic dataR01MH104680 · NIMH · TULANE UNIVERSITY OF LOUISIANA · PI CALHOUN, VINCE D, DENG, HONG-WEN · 2014 to 2017
$2.1M
Integration of fMRI imaging, genomics, network and biological knowledgeR01MH107354 · NIMH · TULANE UNIVERSITY OF LOUISIANA · PI WANG, YU-PING · 2015 to 2018
$1.9M
Integration of multiscale genomic data for comprehensive analysis of complex diseR01GM109068 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI WANG, YU-PING · 2014 to 2018
$1.6M
NIAMS NIH HHS R01 AR059781NIA NIH HHS U19 AG055373NIBIB NIH HHS R01 EB005846NIBIB NIH HHS R01 EB006841NIBIB NIH HHS R01 EB020407NIGMS NIH HHS P20 GM103472NIGMS NIH HHS R01 GM109068NIMH NIH HHS R01 MH104680NIMH NIH HHS R01 MH107354NIMH NIH HHS R01 MH118695NIMH NIH HHS R01 MH123610
6 · The paper itself

Abstract

Schizophrenia is a complex mental illness, the mechanism of which is currently unclear. Using sparse representation and dictionary learning (SDL) model to analyze functional magnetic resonance imaging (fMRI) dataset of schizophrenia is currently a popular method for exploring the mechanism of the disease. The SDL method decomposed the fMRI data into a sparse coding matrix

Indexed as

abnormal brain regionsGroup sparsityincoherenceschizophreniasparse representation and dictionary learning

Identifiers

PMID33747675
PMCPMC7971409
OpenAlexW3033694671

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.