Evidence map›Paper›PMID 30214399›Full record

ArticleFrontiers in systems neuroscience2018

Positive Connectivity Predicts the Dynamic Intrinsic Topology of the Human Brain Network.

Jingyu Qian, Ibai Diez, Laura Ortiz-Terán, Christian Bonadio, Thomas Liddell, Joaquin Goñi, Jorge Sepulcre

Abstract read
In one paragraph

Article in Frontiers in systems neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

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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

7 authors.

Jingyu QianDepartment of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Harvard University, Boston, MA, United States.
Ibai DiezDepartment of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Harvard University, Boston, MA, United States.
Laura Ortiz-TeránDepartment of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Harvard University, Boston, MA, United States.
Christian BonadioDepartment of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Harvard University, Boston, MA, United States.
Thomas LiddellDepartment of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Harvard University, Boston, MA, United States.
Joaquin GoñiSchool of Industrial Engineering, Purdue University, West-Lafayette, IN, United States.
Jorge SepulcreDepartment of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Harvard University, Boston, MA, United States.

Funding

Indiana Clinical and Translational Sciences InstituteUL1TR001108 · NCATS · INDIANA UNIVERSITY INDIANAPOLIS · PI DENNE, SCOTT C., SHEKHAR, ANANTHA · 2013 to 2017
$23.3M
Postgraduate Training Program in Medical Imaging (PTPMI)T32EB013180 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI Ciprian Catana, Pedram Heidari · 2011 to 2026
$3.7M
Indiana Clinical and Translational Sciences InstituteTL1TR001107 · NCATS · INDIANA UNIVERSITY INDIANAPOLIS · PI DENNE, SCOTT C., SHEKHAR, ANANTHA · 2013 to 2017
$2.0M
Statistical models for multi-modal brain imaging studies of HIV-associated cognitive declineR01MH108467 · NIMH · INDIANA UNIVERSITY INDIANAPOLIS · PI HAREZLAK, JAROSLAW · 2015 to 2019
$2.0M
Integrative Bioinformatics Approaches to Human Brain Genomics and ConnectomicsR01EB022574 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI SHEN, LI · 2016 to 2019
$1.9M
In Vivo Visualization of TAU and Amyloid-Beta Networks for Early AD DetectionK23EB019023 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI SEPULCRE, JORGE · 2014 to 2018
$810k
NCATS NIH HHS TL1 TR001107NCATS NIH HHS UL1 TR001108NIBIB NIH HHS K23 EB019023NIBIB NIH HHS R01 EB022574NIBIB NIH HHS T32 EB013180NIMH NIH HHS R01 MH108467
6 · The paper itself

Abstract

Functional connectivity MRI (fcMRI) has become instrumental in facilitating research of human brain network organization in terms of coincident interactions between positive and negative synchronizations of large-scale neuronal systems. Although there is a common agreement concerning the interpretation of positive couplings between brain areas, a major debate has been made in disentangling the nature of negative connectivity patterns in terms of its emergence in several methodological approaches and its significance/meaning in specific neuropsychiatric diseases. It is still not clear what information the functional negative correlations or connectivity provides or how they relate to the positive connectivity. Through implementing stepwise functional connectivity (SFC) analysis and studying the causality of functional topological patterns, this study aims to shed light on the relationship between positive and negative connectivity in the human brain functional connectome. We found that the strength of negative correlations between voxel-pairs relates to their positive connectivity path-length. More importantly, our study describes how the spatio-temporal patterns of positive connectivity explain the evolving changes of negative connectivity over time, but not the other way around. This finding suggests that positive and negative connectivity do not display equivalent forces but shows that the positive connectivity has a dominant role in the overall human brain functional connectome. This phenomenon provides novel insights about the nature of positive and negative correlations in fcMRI and will potentially help new developments for neuroimaging biomarkers.

Indexed as

functional connectivity MRInegative connectivitypositive connectivitystepwise functional connectivitytopological causality

Identifiers

PMID30214399
PMCPMC6125351

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.