Evidence map›Paper›PMID 26974435›Full record

ArticlePloS one2016

Seeking Optimal Region-Of-Interest (ROI) Single-Value Summary Measures for fMRI Studies in Imaging Genetics.

Yunxia Tong, Qiang Chen, Thomas E Nichols, Roberta Rasetti, Joseph H Callicott, Karen F Berman, Daniel R Weinberger, Venkata S Mattay

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

33 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

8 authors.

Yunxia TongClinical and Translational Neuroscience Branch, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, United States of America.
Qiang ChenLieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, United States of America.
Thomas E NicholsDepartment of Statistics, University of Warwick, Warwick, United Kingdom.
Roberta RasettiDepartment of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Joseph H CallicottClinical and Translational Neuroscience Branch, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, United States of America.
Karen F BermanClinical and Translational Neuroscience Branch, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, United States of America.
Daniel R WeinbergerLieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, United States of America.
Venkata S MattayLieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, United States of America.

Funding

Multimodal Neuroimaging of Gene-Brain Relationships with a Focus on Williams Syndrome and 7q11.23 Duplication SyndromeZIAMH002863 · NIMH · NATIONAL INSTITUTE OF MENTAL HEALTH · PI BERMAN, KAREN FAITH · 2009 to 2025
$27.6M
Neuroimaging of Brain Circuits and Neurogenetic Mechanisms in Normal CognitionZIAMH002717 · NIMH · NATIONAL INSTITUTE OF MENTAL HEALTH · PI BERMAN, KAREN FAITH · 2009 to 2024
$21.8M
Imaging of Neuropsychiatric Disorders with Developmental and Genetic MechanismsZIAMH002652 · NIMH · NATIONAL INSTITUTE OF MENTAL HEALTH · PI BERMAN, KAREN FAITH · 2009 to 2025
$17.3M
Multimodal Imaging: Genetic and Environmental Effects in NeuropsychiatryZIAMH002942 · NIMH · NATIONAL INSTITUTE OF MENTAL HEALTH · PI BERMAN, KAREN FAITH · 2013 to 2022
$12.2M
Intramural NIH HHSMedical Research Council G0900908
6 · The paper itself

Abstract

A data-driven hypothesis-free genome-wide association (GWA) approach in imaging genetics studies allows screening the entire genome to discover novel genes that modulate brain structure, chemistry, and function. However, a whole brain voxel-wise analysis approach in such genome-wide based imaging genetic studies can be computationally intense and also likely has low statistical power since a stringent multiple comparisons correction is needed for searching over the entire genome and brain. In imaging genetics with functional magnetic resonance imaging (fMRI) phenotypes, since many experimental paradigms activate focal regions that can be pre-specified based on a priori knowledge, reducing the voxel-wise search to single-value summary measures within a priori ROIs could prove efficient and promising. The goal of this investigation is to evaluate the sensitivity and reliability of different single-value ROI summary measures and provide guidance in future work. Four different fMRI databases were tested and comparisons across different groups (patients with schizophrenia, their siblings, vs. normal control subjects; across genotype groups) were conducted. Our results show that four of these measures, particularly those that represent values from the top most-activated voxels within an ROI are more powerful at reliably detecting group differences and generating greater effect sizes than the others.

Indexed as

Databases as TopicDemographyGenotypeHumansImaging, Three-DimensionalMagnetic Resonance ImagingReproducibility of Results

Identifiers

PMID26974435
PMCPMC4790904

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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.