Evidence map›Paper›PMID 32506636›Full record

ArticleBrain and behavior2020

Dynamic visual cortical connectivity analysis based on functional magnetic resonance imaging.

Le Zhao, Weiming Zeng, Yuhu Shi, Weifang Nie, Jiajun Yang

Abstract read
In one paragraph

Article in Brain and behavior, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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.

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Le ZhaoLaboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China.
Weiming ZengLaboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China.ORCID 0000-0002-3810-8258
Yuhu ShiLaboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China.
Weifang NieLaboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China.
Jiajun YangLaboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai, China.

Funding

National Natural Science Foundation of China 31470954National Natural Science Foundation of China 31870979Shanghai Sailing Program 19YF1419000Shanghai University of Medicine & Health Sciences Seed Foundation Project SFP-18-20-14-006
6 · The paper itself

Abstract

backgroundStudies of brain functional connectivity (FC) and effective connectivity (EC) using the functional magnetic resonance imaging (fMRI) have advanced our understanding of functional organization on visual cortex of human brain. The current studies mainly focus on static or dynamic connectivity, while the relationships between them have not been well characterized especially for static EC (sEC) and dynamic EC (dEC), as well as the consistency characteristics of changing trend of dFCs and dECs, which is of great importance to reveal the neural information processing mechanism in visual cortex region.

methodIn this study, we explore these relationships among several subareas of human visual cortex (V1-V5) by calculating the connection intensity and information flow among them over time by sliding window method, which are defined by Pearson correlation coefficient and Granger causality analysis, respectively, in each window.

resultsThe results demonstrate that there are extensive connections existing in human visual network, which are time-varying both in resting and task-related states. sFC intensity is negatively correlated with the variance of dFC, while sEC intensity is positively correlated with the variance of dEC. Furthermore, we also find that dFC within visual cortex at rest shows more consistency, while dEC shows less compared with task state in changing trend.

conclusionTherefore, this study provides novel findings about dynamics of connectivity in human visual cortex from the perspective of functional and effective connectivity.

Indexed as

Visual CortexBrainBrain MappingHumansMagnetic Resonance ImagingNeural PathwaysNeuroimagingdynamic effective connectivitydynamic functional connectivityfMRIGranger causalityvisual cortex

Identifiers

PMID32506636
PMCPMC7375061

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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.