Evidence map›Paper›PMID 27592153›Full record

ReviewNeuroscience and biobehavioral reviews2016

Large-scale functional network overlap is a general property of brain functional organization: Reconciling inconsistent fMRI findings from general-linear-model-based analyses.

Jiansong Xu, Marc N Potenza, Vince D Calhoun, Rubin Zhang, Sarah W Yip, John T Wall, Godfrey D Pearlson, Patrick D Worhunsky, Kathleen A Garrison, Joseph M Moran

Abstract readReview
In one paragraph

Review in Neuroscience and biobehavioral reviews, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing 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

35 citing papers in PubMed.

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  11. Task-specific network interactions across key cognitive domains.Cerebral cortex (New York, N.Y. : 1991) · 2022
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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

10 authors.

Jiansong XuDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT 06510, United States. Electronic address: Jiansong.xu@yale.edu.
Marc N PotenzaDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT 06510, United States; Child Study Center, Yale University School of Medicine, New Haven, CT 06510, United States; Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06510, United States; CASAColumbia, Yale University School of Medicine, New Haven, CT 06510, United States; The Connecticut Mental Health Center, New Haven, CT 06519, United States.
Vince D CalhounDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT 06510, United States; The Mind Research Network & LBERI, Albuquerque, NM 87131, United States; Dept of ECE, The University of New Mexico, Albuquerque, NM, 87131, United States.
Rubin ZhangDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT 06510, United States.
Sarah W YipDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT 06510, United States; CASAColumbia, Yale University School of Medicine, New Haven, CT 06510, United States.
John T WallDepartment of Neurosciences, University of Toledo, Toledo, OH, 43614, United States.
Godfrey D PearlsonDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT 06510, United States; Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06510, United States; Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT 06106, United States.
Patrick D WorhunskyDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT 06510, United States.
Kathleen A GarrisonDepartment of Psychiatry, Yale University School of Medicine, New Haven, CT 06510, United States.
Joseph M MoranThe Center for Brain Sciences, Harvard University, Cambridge, MA, United States; U.S. Army Natick Soldier Research, Development and Engineering Center, United States.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
TSF to enhance treatment of cocaine dependenceP50DA009241 · NIDA · YALE UNIVERSITY · PI KOBER, HEDY · 1994 to 2018
$40.5M
SUBSTANCE ABUSEK12DA000167 · NIDA · YALE UNIVERSITY · PI STEPHANIE S O'MALLEY, Marc N Potenza · 1991 to 2026
$17.7M
Transcranial Direct Current Stimulation for Treatment of Auditory Verbal HalluciP20GM103472 · NIGMS · THE MIND RESEARCH NETWORK · PI THOMA, ROBERT J · 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
Translational Research of Cocaine, Striatum, and ImpulsivitiesP20DA027844 · NIDA · YALE UNIVERSITY · PI POTENZA, MARC N · 2010 to 2013
$3.6M
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
Neural mechanisms of CBT in cocaine dependence (Gender Differences Supplement)R01DA035058 · NIDA · YALE UNIVERSITY · PI CARROLL, KATHLEEN M., POTENZA, MARC N · 2013 to 2016
$2.3M
fMRI of CBT and CM for Cocaine DependenceR01DA020908 · NIDA · YALE UNIVERSITY · PI POTENZA, MARC N · 2005 to 2009
$1.7M
Neural mechanisms of galantamine treatment for cocaine dependenceK01DA039299 · NIDA · YALE UNIVERSITY · PI YIP, SARAH · 2016 to 2020
$905k
FMRI & DTI Study on Neural Predictor of Treatment Outcome of Cocaine DependenceK01DA027750 · NIDA · YALE UNIVERSITY · PI XU, JIANSONG · 2010 to 2014
$881k
Functional networks related to cocaine dependence and its treatment and relapseR01DA039136 · NIDA · YALE UNIVERSITY · PI POTENZA, MARC N · 2016 to 2018
$754k
NCATS NIH HHS UL1 TR001863NIBIB NIH HHS R01 EB006841NIBIB NIH HHS R01 EB020407NIDA NIH HHS K01 DA027750NIDA NIH HHS K01 DA039299NIDA NIH HHS K12 DA000167NIDA NIH HHS P20 DA027844NIDA NIH HHS P50 DA009241NIDA NIH HHS R01 DA020908NIDA NIH HHS R01 DA035058NIDA NIH HHS R01 DA039136NIGMS NIH HHS P20 GM103472
6 · The paper itself

Abstract

Functional magnetic resonance imaging (fMRI) studies regularly use univariate general-linear-model-based analyses (GLM). Their findings are often inconsistent across different studies, perhaps because of several fundamental brain properties including functional heterogeneity, balanced excitation and inhibition (E/I), and sparseness of neuronal activities. These properties stipulate heterogeneous neuronal activities in the same voxels and likely limit the sensitivity and specificity of GLM. This paper selectively reviews findings of histological and electrophysiological studies and fMRI spatial independent component analysis (sICA) and reports new findings by applying sICA to two existing datasets. The extant and new findings consistently demonstrate several novel features of brain functional organization not revealed by GLM. They include overlap of large-scale functional networks (FNs) and their concurrent opposite modulations, and no significant modulations in activity of most FNs across the whole brain during any task conditions. These novel features of brain functional organization are highly consistent with the brain's properties of functional heterogeneity, balanced E/I, and sparseness of neuronal activity, and may help reconcile inconsistent GLM findings.

Indexed as

BrainBrain MappingHumansMagnetic Resonance ImagingBalanced excitation and inhibitionDefault mode networkfMRIFunctional network overlapGLMICASparseness of neuronal activity

Identifiers

PMID27592153
PMCPMC5140707

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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.