Evidence map›Paper›PMID 42267358›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2026

Analytic Bounds on GAMLSS Model Variability of Normative White Matter Brain Charts.

Michael E Kim, Gaurav Rudravaram, Adam Saunders, Chenyu Gao, Karthik Ramadass, Nancy R Newlin, Praitayini Kanakaraj, Sam Bogdanov, Derek Archer, Timothy J Hohman and 29 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

39 authors.

Michael E KimVanderbilt University, Department of Computer Science, Nashville, TN, USA.
Gaurav RudravaramVanderbilt University, Department of Electrical and Computer Engineering, Nashville, TN, USA.
Adam SaundersVanderbilt University, Department of Electrical and Computer Engineering, Nashville, TN, USA.
Chenyu GaoVanderbilt University, Department of Electrical and Computer Engineering, Nashville, TN, USA.
Karthik RamadassVanderbilt University, Department of Electrical and Computer Engineering, Nashville, TN, USA.
Nancy R NewlinVanderbilt University, Department of Computer Science, Nashville, TN, USA.
Praitayini KanakarajVanderbilt University, Department of Computer Science, Nashville, TN, USA.
Sam BogdanovVanderbilt University, Medical Scientist Training Program, Nashville, TN, USA.
Derek ArcherVanderbilt University Medical Center, Vanderbilt Memory and Alzheimer's Center, Nashville, TN, USA.
Timothy J HohmanVanderbilt University Medical Center, Vanderbilt Memory and Alzheimer's Center, Nashville, TN, USA.
Angela L JeffersonVanderbilt University Medical Center, Department of Neurology, Nashville, TN, USA.
Victoria L MorganVanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, TN, USA.
Alexandra RocheVanderbilt University Institute of Imaging Science, Nashville, TN, USA.
Dario J EnglotVanderbilt University Medical Center, Department of Neurological Surgery, Nashville, TN, USA.
Susan M ResnickLaboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
Lori L Beason HeldLaboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
Murat BilgelLaboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
Laurie E CuttingPeabody College of Education and Human Development, Department of Special Education, Nashville, Tennessee, USA.
Laura A BarqueroPeabody College of Education and Human Development, Department of Special Education, Nashville, Tennessee, USA.
Micah A D'ArchangelPeabody College of Education and Human Development, Department of Special Education, Nashville, Tennessee, USA.
Tin Q NguyenPeabody College of Education and Human Development, Department of Special Education, Nashville, Tennessee, USA.
Kathryn L HumphreysVanderbilt University, Department of Psychology and Human Development, Nashville, TN, USA.
Yanbin NiuVanderbilt University, Department of Psychology and Human Development, Nashville, TN, USA.
Sophia Vinci-BooherVanderbilt University, Department of Psychology and Human Development, Nashville, TN, USA.
Carissa J CascioUniversity of Kansas, Life Span Institute and Department of Psychology, Lawrence, KS, USA.
Kimberly R PechmanVanderbilt University Medical Center, Vanderbilt Memory and Alzheimer's Center, Nashville, TN, USA.
Niranjana ShashikumarVanderbilt University Medical Center, Vanderbilt Memory and Alzheimer's Center, Nashville, TN, USA.
HABS-HD Study Team
Alzheimer’s Disease Neuroimaging Initiative
BIOCARD Study Team
Zhiyuan LiPark University, Department of Computer Science, Parkville, MO, USA.
Simon N VandekarVanderbilt University Medical Center, Department of Biostatistics, Nashville, TN, USA.
Panpan ZhangVanderbilt University Medical Center, Department of Biostatistics, Nashville, TN, USA.
John C GoreVanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, TN, USA.
Yihao LiuVanderbilt University, Department of Electrical and Computer Engineering, Nashville, TN, USA.
Lianrui ZuoVanderbilt University, Department of Electrical and Computer Engineering, Nashville, TN, USA.
Kurt G SchillingVanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, TN, USA.
Daniel C MoyerVanderbilt University, Department of Computer Science, Nashville, TN, USA.
Bennett A LandmanVanderbilt University, Department of Computer Science, Nashville, TN, USA.

Funding

The Health & Aging Brain Study - Health Disparities (HABS-HD)U19AG078109 · NIA · UNIVERSITY OF NORTH TEXAS HLTH SCI CTR · PI Sid E O'Bryant · 2022 to 2026
$181.1M
Vanderbilt Institute for Clinical and Translational Research (VICTR) -Identifying correlates of functional immunity in SARS-CoV-2 convalescent plasmaUL1TR002243 · NCATS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Paul A. Harris, Wesley H Self · 2017 to 2026
$130.7M
Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
Technology and Remote Assessment CoreU19AG063911 · NIA · MAYO CLINIC ROCHESTER · PI Bradley F Boeve · 2019 to 2026
$120.9M
Early Onset AD Consortium - the LEAD Study (LEADS)U01AG057195 · NIA · INDIANA UNIVERSITY INDIANAPOLIS · PI APOSTOLOVA, LIANA G, CARRILLO, MARIA C · 2018 to 2023
$70.3M
Smartphone-Based "Burst" Cognitive AssessmentsP01AG003991 · NIA · WASHINGTON UNIVERSITY · PI Suzanne Elizabeth Schindler · 1985 to 2026
$69.5M
The Clinical Significance of Incidental White Matter Lesions on MRI Amongst a Diverse Population with Cognitive Complaints (INDEED)U19NS120384 · NINDS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI DAVID K JOHNSON · 2020 to 2026
$61.9M
THERAPEUTIC EFFECTS OF INTRA-NASAL INSULIN DETEMIRP50AG005136 · NIA · UNIVERSITY OF WASHINGTON · PI GRABOWSKI, THOMAS J. · 1985 to 2019
$57.2M
Cellular and molecular mechanisms governing cortical surface area overgrowth in iPSC-derived neural cells from longitudinally characterized individuals with autismR01HD055741 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Joseph Piven · 2007 to 2026
$52.6M
The natural history of AB accumulation in preclinical ADP01AG026276 · NIA · WASHINGTON UNIVERSITY · PI MORRIS, JOHN · 2005 to 2025
$49.5M
SUPPLEMENT TO RUSH ALZHEIMERS DISEASE CENTER COREP30AG010161 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI LEURGANS, SUE E. · 1991 to 2020
$49.1M
Satellite Diagnostic and Treatment Clinic CoreP50AG008702 · NIA · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI DE JAGER, PHILIP L · 1989 to 2019
$46.3M
NCATS NIH HHS UL1 TR000445NCATS NIH HHS UL1 TR000448NCATS NIH HHS UL1 TR002243NCRR NIH HHS U24 RR021382NIA NIH HHS K01 AG073584NIA NIH HHS K24 AG046373NIA NIH HHS P01 AG003991NIA NIH HHS P01 AG026276NIA NIH HHS P20 AG068024NIA NIH HHS P20 AG068053NIA NIH HHS P20 AG068077NIA NIH HHS P20 AG068082NIA NIH HHS P30 AG008017NIA NIH HHS P30 AG010129NIA NIH HHS P30 AG010133NIA NIH HHS P30 AG010161NIA NIH HHS P30 AG013846NIA NIH HHS P30 AG013854NIA NIH HHS P30 AG019610NIA NIH HHS P30 AG035982NIA NIH HHS P30 AG053760NIA NIH HHS P30 AG062421NIA NIH HHS P30 AG062422NIA NIH HHS P30 AG062428NIA NIH HHS P30 AG062429NIA NIH HHS P30 AG062677NIA NIH HHS P30 AG062715NIA NIH HHS P30 AG066444NIA NIH HHS P30 AG066462NIA NIH HHS P30 AG066468NIA NIH HHS P30 AG066506NIA NIH HHS P30 AG066507NIA NIH HHS P30 AG066508NIA NIH HHS P30 AG066509NIA NIH HHS P30 AG066511NIA NIH HHS P30 AG066512NIA NIH HHS P30 AG066514NIA NIH HHS P30 AG066515NIA NIH HHS P30 AG066518NIA NIH HHS P30 AG066519NIA NIH HHS P30 AG066530NIA NIH HHS P30 AG066546NIA NIH HHS P30 AG072931NIA NIH HHS P30 AG072946NIA NIH HHS P30 AG072947NIA NIH HHS P30 AG072958NIA NIH HHS P30 AG072959NIA NIH HHS P30 AG072972NIA NIH HHS P30 AG072973NIA NIH HHS P30 AG072975NIA NIH HHS P30 AG072976NIA NIH HHS P30 AG072977NIA NIH HHS P30 AG072978NIA NIH HHS P30 AG072979NIA NIH HHS P30 AG072980NIA NIH HHS P50 AG005133NIA NIH HHS P50 AG005136NIA NIH HHS P50 AG005142NIA NIH HHS P50 AG008702NIA NIH HHS P50 AG016573NIA NIH HHS P50 AG016574NIA NIH HHS P50 AG033514NIA NIH HHS P50 AG047266NIA NIH HHS P50 AG047270NIA NIH HHS P50 AG047366NIA NIH HHS R01 AG015819NIA NIH HHS R01 AG017917NIA NIH HHS R01 AG019771NIA NIH HHS R01 AG021155NIA NIH HHS R01 AG021910NIA NIH HHS R01 AG022018NIA NIH HHS R01 AG027161NIA NIH HHS R01 AG034962NIA NIH HHS R01 AG037639NIA NIH HHS R01 AG043434NIA NIH HHS R01 AG045571NIA NIH HHS R01 AG052560NIA NIH HHS R01 AG053509NIA NIH HHS R01 AG053993NIA NIH HHS R01 AG054047NIA NIH HHS R01 AG054073NIA NIH HHS R01 AG054110NIA NIH HHS R01 AG055005NIA NIH HHS R01 AG056031NIA NIH HHS R01 AG056258NIA NIH HHS R01 AG056405NIA NIH HHS R01 AG056531NIA NIH HHS R01 AG056534NIA NIH HHS R01 AG058533NIA NIH HHS R01 AG058724NIA NIH HHS R01 AG059716NIA NIH HHS R01 AG061788NIA NIH HHS R01 AG062276NIA NIH HHS R01 AG062826NIA NIH HHS R01 AG064233NIA NIH HHS R01 AG067482NIA NIH HHS R01 AG067781NIA NIH HHS R01 AG068338NIA NIH HHS R01 AG069453NIA NIH HHS R01 AG070862NIA NIH HHS R01 AG073235NIA NIH HHS R01 AG077444NIA NIH HHS R01 AG079280NIA NIH HHS R35 AG072262NIA NIH HHS R56 AG037639NIA NIH HHS R56 AG045571NIA NIH HHS R56 AG074321NIA NIH HHS RF1 AG054047NIA NIH HHS U01 AG024904NIA NIH HHS U01 AG057195NIA NIH HHS U01 AG068057NIA NIH HHS U19 AG063911NIA NIH HHS U19 AG073153NIA NIH HHS U19 AG078109NIA NIH HHS U24 AG067418NIA NIH HHS U24 AG072122NIA NIH HHS U24 AG074855NIBIB NIH HHS K01 EB032898NIBIB NIH HHS P41 EB015922NIBIB NIH HHS R01 EB009352NIBIB NIH HHS R01 EB017230NICHD NIH HHS F31 HD104385NICHD NIH HHS L40 HD119876NICHD NIH HHS P50 HD103537NICHD NIH HHS R01 HD044073NICHD NIH HHS R01 HD055741NICHD NIH HHS R01 HD067254NICHD NIH HHS R01 HD089474NICHD NIH HHS R01 HD109151NICHD NIH HHS R01 HD114489NICHD NIH HHS R37 HD095519NIDA NIH HHS RC2 DA029475NIDCD NIH HHS R01 DC008552NIGMS NIH HHS T32 GM007347NIGMS NIH HHS T32 GM152284NIH HHS S10 OD020154NIH HHS S10 OD023680NIH HHS S10 OD026738NIMH NIH HHS P50 MH071616NIMH NIH HHS R01 MH094639NIMH NIH HHS R01 MH129634NINDS NIH HHS P30 NS098577NINDS NIH HHS R01 NS049096NINDS NIH HHS R01 NS075075NINDS NIH HHS R01 NS108445NINDS NIH HHS R01 NS110130NINDS NIH HHS R01 NS112252NINDS NIH HHS R01 NS134625NINDS NIH HHS U19 NS120384NINDS NIH HHS UH2 NS100599NINDS NIH HHS UH3 NS100599RRD VA I01 RX001534RRD VA I21 RX001381
6 · The paper itself

Abstract

Brain charts, or normative models of quantitative neuroimaging measures, can identify trajectories of brain development and abnormalities in groups and individuals by leveraging large populations. Recent work has extended these brain charts to model microstructural and macrostructural features of white matter. Assessments of variance for these brain charts are necessary to determine whether the models being used for these data are stable. We implement an analytic approach to characterize variability of the parameters in previously released brain charts created using the generalized additive models for location, scale, and shape (GAMLSS) framework. Additionally, we empirically validate the accuracy of each analytic model through a comparison to a bootstrapping approach from 0.2 to 90 years of age. We find that across all models, the analytic coefficient of variation (COV) remains below 5% for ages greater than 0.25 years, with the maximum empirical observed COV reaching 7% at 0.2 years of age. Further, the empirical assessment shows high agreement with the analytic assessment, with COV estimates averaged across the lifespan for all models having a Pearson correlation coefficient of 0.776 and a mean difference of 4 x 10

Indexed as

model variabilitynormative modelingwhite matter

Identifiers

PMID42267358
PMCPMC13245483

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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.