Evidence map›Paper›PMID 23755002›Full record

ArticleFrontiers in human neuroscience2013

Combination of Resting State fMRI, DTI, and sMRI Data to Discriminate Schizophrenia by N-way MCCA + jICA.

Jing Sui, Hao He, Qingbao Yu, Jiayu Chen, Jack Rogers, Godfrey D Pearlson, Andrew Mayer, Juan Bustillo, Jose Canive, Vince D Calhoun

Abstract read
In one paragraph

Article in Frontiers in human neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed, 4 pooled it
–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

64 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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4 more citing papers are in PubMed but not listed here.

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

10 authors.

Jing SuiThe Mind Research Network, Lovelace Biomedical and Environmental Research Institute , Albuquerque, NM , USA ; LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences , Beijing , China.
Hao He
Qingbao Yu
Jiayu Chen
Jack Rogers
Godfrey D Pearlson
Andrew Mayer
Juan Bustillo
Jose Canive
Vince D Calhoun

Funding

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
NEURAL MECHANISMS OF SCHIZOPHRENIAP20RR021938 · NCRR · THE MIND RESEARCH NETWORK · PI JUNG, REX · 2008 to 2011
$9.4M
Bipolar & Schizophrenia Consortium for Parsing Intermediate PhenotypesR01MH077945 · NIMH · YALE UNIVERSITY · PI PEARLSON, GODFREY D · 2007 to 2019
$7.6M
Informed Data-Driven Fusion of Behavior, Brain Function, and GenesR01EB005846 · NIBIB · THE MIND RESEARCH NETWORK · PI CALHOUN, VINCE D · 2005 to 2016
$5.5M
QUANTITATIVE STRUCTURAL NEUROIMAGING IN PSYCHOSISR01MH043775 · NIMH · JOHNS HOPKINS UNIVERSITY · PI PEARLSON, GODFREY D · 1988 to 2003
$2.4M
Schizophrenia Biomarkers: Memory, Genes and fMRIR01MH074797 · NIMH · HARTFORD HOSPITAL · PI PEARLSON, GODFREY D · 2007 to 2010
$1.5M
NCRR NIH HHS P20 RR021938NIBIB NIH HHS R01 EB005846NIBIB NIH HHS R01 EB006841NIMH NIH HHS R01 MH043775NIMH NIH HHS R01 MH074797NIMH NIH HHS R01 MH077945
6 · The paper itself

Abstract

Multimodal brain imaging data have shown increasing utility in answering both scientifically interesting and clinically relevant questions. Each brain imaging technique provides a different view of brain function or structure, while multimodal fusion capitalizes on the strength of each and may uncover hidden relationships that can merge findings from separate neuroimaging studies. However, most current approaches have focused on pair-wise fusion and there is still relatively little work on N-way data fusion and examination of the relationships among multiple data types. We recently developed an approach called "mCCA + jICA" as a novel multi-way fusion method which is able to investigate the disease risk factors that are either shared or distinct across multiple modalities as well as the full correspondence across modalities. In this paper, we applied this model to combine resting state fMRI (amplitude of low-frequency fluctuation, ALFF), gray matter (GM) density, and DTI (fractional anisotropy, FA) data, in order to elucidate the abnormalities underlying schizophrenia patients (SZs, n = 35) relative to healthy controls (HCs, n = 28). Both modality-common and modality-unique abnormal regions were identified in SZs, which were then used for successful classification for seven modality-combinations, showing the potential for a broad applicability of the mCCA + jICA model and its results. In addition, a pair of GM-DTI components showed significant correlation with the positive symptom subscale of Positive and Negative Syndrome Scale (PANSS), suggesting that GM density changes in default model network along with white-matter disruption in anterior thalamic radiation are associated with increased positive PANSS. Findings suggest the DTI anisotropy changes in frontal lobe may relate to the corresponding functional/structural changes in prefrontal cortex and superior temporal gyrus that are thought to play a role in the clinical expression of SZ.

Indexed as

ALFFDTIGMmCCA + jICAmultimodal fusionresting state fMRIschizophreniasMRI

Identifiers

PMID23755002
PMCPMC3666029

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.