Evidence map›Paper›PMID 29867410›Full record

ArticleFrontiers in human neuroscience2018

Diagnosis of Autism Spectrum Disorders Using Multi-Level High-Order Functional Networks Derived From Resting-State Functional MRI.

Feng Zhao, Han Zhang, Islem Rekik, Zhiyong An, Dinggang Shen

Open access · goldAbstract read
In one paragraph

Article in Frontiers in human neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 3 pooled it
7.2field-weighted citation impact, top 2% 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

38 citing papers in PubMed, 3 syntheses or guidelines pooled it, 115 citations in OpenAlex.

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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

5 authors at 3 institutions in 4 countries.

Feng ZhaoSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, China.
Han ZhangDepartment of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, United States.
Islem RekikBASIRA Lab, CVIP Group, Computing, School of Science and Engineering, University of Dundee, Dundee, United Kingdom.
Zhiyong AnSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, China.
Dinggang ShenDepartment of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, United States.
Shandong Institute of Business and Technology · CNUniversity of North Carolina at Chapel Hill · USUniversity of Dundee · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Functional brain networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used for Autism Spectrum Disorder (ASD) diagnosis. Typically, these networks are constructed by calculating functional connectivity (FC) between any pair of brain regions of interest (ROIs), i.e., using Pearson's correlation between rs-fMRI time series. However, this can only be called as a

Indexed as

autism spectrum disorderbrain networkhigh-order functional connectivitylearning-based classificationresting-state fMRI

Identifiers

PMID29867410
PMCPMC5960713
OpenAlexW2803679388

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.