Evidence map›Paper›PMID 39526419›Full record

ArticleHuman brain mapping2024

A Site-Wise Reliability Analysis of the ABCD Diffusion Fractional Anisotropy and Cortical Thickness: Impact of Scanner Platforms.

Yezhi Pan, L Elliot Hong, Ashley Acheson, Paul M Thompson, Neda Jahanshad, Alyssa H Zhu, Jiaao Yu, Chixiang Chen, Tianzhou Ma, Ho-Ling Liu and 5 more

Abstract readMulticenter Study
In one paragraph

Article in Human brain mapping, 2024. 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.

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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Yezhi PanMaryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, USA.ORCID 0009-0001-4014-0302
L Elliot HongDepartment of Psychiatry and Behavioral Science, University of Texas Health Science Center, Houston, Texas, USA.
Ashley AchesonDepartment of Psychiatry, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.
Paul M ThompsonImaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Neda JahanshadImaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Alyssa H ZhuImaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Jiaao YuDepartment of Mathematics, University of Maryland, College Park, Maryland, USA.
Chixiang ChenInstitute for Health Computing, University of Maryland, North Bethesda, Maryland, USA.
Tianzhou MaDepartment of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, USA.
Ho-Ling LiuDepartment of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Jelle VeraartCenter for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.
Els FieremansCenter for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.
Nicole R KarcherDepartment of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA.
Peter KochunovDepartment of Psychiatry and Behavioral Science, University of Texas Health Science Center, Houston, Texas, USA.
Shuo ChenMaryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, USA.ORCID 0000-0002-7990-4947

Funding

The Effects of Kynurenine Aminotransferase Inhibition in People with SchizophreniaP50MH103222 · NIMH · UNIVERSITY OF MARYLAND BALTIMORE · PI BUCHANAN, ROBERT W · 2014 to 2023
$25.2M
TR&D 4: Revealing Microstructure: Biophysical modeling and validation for discovery and clinical careP41EB017183 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Christopher M Collins · 2014 to 2026
$19.3M
Psychophysiological Precursors of AlcoholismR01AA012207 · NIAAA · UNIV OF ARKANSAS FOR MED SCIS · PI ACHESON, ASHLEY, KOCHUNOV, PETER V. · 2001 to 2025
$9.0M
Mesoscopic Biomarkers of Neurodegeneration with Diffusion MRIR01NS088040 · NINDS · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI FIEREMANS, ELS, NOVIKOV, DMITRY S · 2014 to 2023
$5.2M
SOLAR-Eclipse Computational Tools for Imaging GeneticsR01EB015611 · NIBIB · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI KOCHUNOV, PETER V. · 2012 to 2024
$5.0M
Towards Multisystem-Brain Successful Aging in Schizophrenia SpectrumR01MH116948 · NIMH · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI HONG, L ELLIOT ELLIOT · 2018 to 2022
$3.7M
Lifespan Vascular Biology on White MatterRF1NS114628 · NINDS · UNIVERSITY OF MARYLAND BALTIMORE · PI HONG, L ELLIOT ELLIOT, KOCHUNOV, PETER V. · 2020 to 2020
$3.1M
Global studies into the Genetic Architecture of the Brain's White Matter Network through Harmonized and Coordinated Analyses in the ENIGMA-ConsortiumR01MH134004 · NIMH · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Neda Jahanshad · 2023 to 2026
$2.6M
Neurotransmitters in Schizophrenia using high-field MR SpectroscopyR01MH096263 · NIMH · JOHNS HOPKINS UNIVERSITY · PI BARKER, PETER B, ROWLAND, LAURA M · 2012 to 2016
$2.5M
A Multivariate Mediation and Deep Learning Framework for Genome-Connectome -Substance Use ResearchDP1DA048968 · NIDA · UNIVERSITY OF MARYLAND BALTIMORE · PI CHEN, SHUO · 2019 to 2023
$2.3M
The Vascular Axis in Schizophrenia Brain-Body AgingR01MH133812 · NIMH · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI L Elliot Elliot Hong · 2024 to 2026
$2.2M
Multimodal neuroimaging study of relational learning in schizophreniaR01MH094520 · NIMH · UNIVERSITY OF MARYLAND BALTIMORE · PI ROWLAND, LAURA M · 2012 to 2016
$1.9M
NIAAA NIH HHS R01 AA012207NIA NIH HHS R21 AG087904NIBIB NIH HHS P41 EB017183NIBIB NIH HHS R01 EB015611NIDA NIH HHS 1DP1DA048968-01NIDA NIH HHS DP1 DA048968NIDA NIH HHS K01 DA059603NIH HHS P50 MH103222NIH HHS R01 AA012207NIH HHS R01 EB015611NIH HHS R01 MH094520NIH HHS R01 MH096263NIH HHS S10 OD023696NIMH NIH HHS P50 MH103222NIMH NIH HHS R01 MH094520NIMH NIH HHS R01 MH096263NIMH NIH HHS R01 MH116948NIMH NIH HHS R01 MH133812NIMH NIH HHS R01 MH134004NIMH NIH HHS RF1 MH123163NINDS NIH HHS R01 NS088040NINDS NIH HHS R01 NS114628NINDS NIH HHS RF1 NS114628
6 · The paper itself

Abstract

The Adolescent Brain and Cognitive Development (ABCD) project is the largest study of adolescent brain development. ABCD longitudinally tracks 11,868 participants aged 9-10 years from 21 sites using standardized protocols for multi-site MRI data collection and analysis. While the multi-site and multi-scanner study design enhances the robustness and generalizability of analysis results, it may also introduce nonbiological variances including scanner-related variations, subject motion, and deviations from protocols. ABCD imaging data were collected biennially within a period of ongoing maturation in cortical thickness and integrity of cerebral white matter. These changes can bias the classical test-retest methodologies, such as intraclass correlation coefficients (ICC). We developed a site-wise adaptive ICC (AICC) to evaluate the reliability of imaging-derived phenotypes while accounting for ongoing brain development. AICC iteratively estimates the population-level age-related brain development trajectory using a weighted mixed model and updates age-corrected site-wise reliability until convergence. We evaluated the test-retest reliability of regional fractional anisotropy (FA) measures from diffusion tensor imaging and cortical thickness (CT) from structural MRI data for each site. The mean AICC for 20 FA tracts across sites was 0.61 ± 0.19, lower than the mean AICC for CT in 34 regions across sites, 0.76 ± 0.12. Remarkably, sites using Siemens scanners consistently showed significantly higher AICC values compared with those using GE/Philips scanners for both FA (AICC = 0.71 ± 0.12 vs. 0.46 ± 0.17, p < 0.001) and CT (AICC = 0.80 ± 0.10 vs. 0.69 ± 0.11, p < 0.001). These findings demonstrate site-and-scanner related variations in data quality and underscore the necessity for meticulous data curation in subsequent association analyses.

Indexed as

Cerebral CortexDiffusion Tensor ImagingAdolescentAnisotropyBrain Cortical ThicknessChildFemaleHumansLongitudinal StudiesMaleReproducibility of Resultsbrain developmentdiffusion tensor imaginglongitudinalquality controlstructural MRItest–retest reliability

Identifiers

PMID39526419
PMCPMC11551787

What Socratic 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 Socratic graph.