Evidence map›Paper›PMID 31328535›Full record

ArticleBrain connectivity2019

Functional Connectivities Are More Informative Than Anatomical Variables in Diagnostic Classification of Autism.

Aina Eill, Afrooz Jahedi, Yangfeifei Gao, Jiwandeep S Kohli, Christopher H Fong, Seraphina Solders, Ruth A Carper, Faramarz Valafar, Barbara A Bailey, Ralph-Axel Müller

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Brain connectivity, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

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

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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.

Aina EillBrain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.
Afrooz JahediBrain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.
Yangfeifei GaoBrain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.
Jiwandeep S KohliBrain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.
Christopher H FongBrain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.
Seraphina SoldersBrain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.
Ruth A CarperBrain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.
Faramarz ValafarDepartment of Bioinformatics and Medical Informatics, San Diego State University, San Diego, California.
Barbara A BaileyDepartment of Mathematics and Statistics, San Diego State University, San Diego, California.
Ralph-Axel MüllerBrain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.

Funding

Integrity and Dynamic Processing Efficiency of Networks in ASDR01MH101173 · NIMH · SAN DIEGO STATE UNIVERSITY · PI MUELLER, RALPH-AXEL · 2014 to 2018
$3.2M
Linking Local Activity and Functional Connectivity in AutismR01MH081023 · NIMH · SAN DIEGO STATE UNIVERSITY · PI MUELLER, RALPH-AXEL · 2009 to 2013
$1.9M
Multimodal Imaging of Social Brain Networks in ASDK01MH097972 · NIMH · SAN DIEGO STATE UNIVERSITY · PI FISHMAN, INNA · 2012 to 2015
$599k
NIMH NIH HHS K01 MH097972NIMH NIH HHS R01 MH081023NIMH NIH HHS R01 MH101173
6 · The paper itself

Abstract

Machine learning techniques have been implemented to reveal brain features that distinguish people with autism spectrum disorders (ASDs) from typically developing (TD) peers. However, it remains unknown whether different neuroimaging modalities are equally informative for diagnostic classification. We combined anatomical magnetic resonance imaging (aMRI), diffusion weighted imaging (DWI), and functional connectivity MRI (fcMRI) using conditional random forest (CRF) for supervised learning to compare how informative each modality was in diagnostic classification. In-house data (

Indexed as

Magnetic Resonance ImagingAdolescentAutism Spectrum DisorderBrainChildCohort StudiesConnectomeDiagnosis, Computer-AssistedFemaleHumansMaleNeural PathwaysSupervised Machine Learningautismdiagnostic classificationmachine learningMRImultimodal imaging

Identifiers

PMID31328535
PMCPMC6798803

What OpenQuestion holds

Textmetadata
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.