Evidence map›Paper›PMID 38433682›Full record

ArticleHuman brain mapping2024

Comparison of approaches to control for intracranial volume in research on the association of brain volumes with cognitive outcomes.

Jingxuan Wang, Tanisha Hill-Jarrett, Peter Buto, Annie Pederson, Kendra D Sims, Scott C Zimmerman, Michelle A DeVost, Erin Ferguson, Benjamin Lacar, Yulin Yang and 6 more

Abstract read
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 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing 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

41 citing papers in PubMed.

  1. Article
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  4. Examination of poverty trajectories and brain volume as predictors of adolescent depression.Journal of research on adolescence : the official journal of the Society for Research on Adolescence · 2026
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  12. Metabolic, demographic, and behavioral risk factors predict brain structural variability in the general population: an analysis of the human connectome project young adults.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026
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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

16 authors.

Jingxuan WangDepartment of Epidemiology and Biostatistics, University of California, San Francisco, California, USA.ORCID 0000-0002-9387-0480
Tanisha Hill-JarrettDepartment of Epidemiology, Boston University, Boston, Massachusetts, USA.
Peter ButoDepartment of Epidemiology and Biostatistics, University of California, San Francisco, California, USA.
Annie PedersonDepartment of Epidemiology and Biostatistics, University of California, San Francisco, California, USA.
Kendra D SimsDepartment of Epidemiology and Biostatistics, University of California, San Francisco, California, USA.
Scott C ZimmermanDepartment of Epidemiology and Biostatistics, University of California, San Francisco, California, USA.
Michelle A DeVostDepartment of Epidemiology and Biostatistics, University of California, San Francisco, California, USA.
Erin FergusonDepartment of Epidemiology and Biostatistics, University of California, San Francisco, California, USA.
Benjamin LacarBakar Computational Health Sciences Institute, University of California, San Francisco, California, USA.
Yulin YangDepartment of Epidemiology and Biostatistics, University of California, San Francisco, California, USA.
Minhyuk ChoiDepartment of Epidemiology and Biostatistics, University of California, San Francisco, California, USA.
Michelle R CauncaMemory and Aging Center, Department of Neurology, University of California, San Francisco, California, USA.
Renaud La JoieMemory and Aging Center, Department of Neurology, University of California, San Francisco, California, USA.
Ruijia ChenDepartment of Epidemiology and Biostatistics, University of California, San Francisco, California, USA.
M Maria GlymourDepartment of Epidemiology, Boston University, Boston, Massachusetts, USA.
Sarah F AckleyDepartment of Epidemiology, Boston University, Boston, Massachusetts, USA.

Funding

Research Education ComponentP30AG062422 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HOWARD J ROSEN · 2019 to 2026
$36.9M
Translational Epidemiology - Training for Research on Aging and Chronic diseaseT32AG049663 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Elizabeth Rose Mayeda, Mark J Pletcher · 2016 to 2026
$5.8M
Closing the gap between observational research and randomized trials for prevention of Alzheimer's Disease and dementiaR01AG057869 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GRAFF, REBECCA ELIZABETH, POWER, MELINDA C · 2018 to 2022
$3.9M
Research Fellowship for Equity in Alzheimer's Disease and Brain HealthT32AG078115 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BRUCE L MILLER, VICTOR VALCOUR · 2022 to 2026
$1.7M
Lifecourse Stressors and Social Disparities in Cognitive Aging: The Roles of Social Networks and Sleep DisturbanceK00AG068431 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CHEN, RUIJIA · 2021 to 2024
$330k
Connectome-based spread of neurodegenerative processes in Alzheimer's diseaseK99AG065501 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LA JOIE, RENAUD CHARLES · 2020 to 2021
$253k
Mathematical Models of Tau-PET Measures and Cognitive Decline in Alzheimer’s Disease Across the LifespanK99AG073454 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ACKLEY, SARAH · 2022 to 2024
$250k
Community Empowerment, Vascular Risk, and ADRD Disparities: Translating Research to Public PolicyK99AG083121 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI SIMS, KENDRA D · 2023 to 2024
$243k
The impact of facility characteristics on racial disparities in ADRD mortality in nursing homesF31AG079651 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI DEVOST, MICHELLE ANNE · 2022 to 2023
$78k
The Impact of SARS-CoV-2 Infection and Vaccination on the Risk of Alzheimer’s Disease and Related DementiasF99AG083306 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI WANG, JINGXUAN · 2023 to 2025
$72k
NIA NIH HHS F31 AG079651NIA NIH HHS F31AG079651NIA NIH HHS F99 AG083306NIA NIH HHS F99AG083306NIA NIH HHS K00 AG068431NIA NIH HHS K00AG068431NIA NIH HHS K99 AG065501NIA NIH HHS K99AG065501NIA NIH HHS K99 AG073454NIA NIH HHS K99AG073454NIA NIH HHS K99 AG083121NIA NIH HHS K99AG083121NIA NIH HHS P30 AG062422NIA NIH HHS P30-AG062422NIA NIH HHS R01 AG057869NIA NIH HHS R01AG057869NIA NIH HHS T32 AG049663NIA NIH HHS T32AG049663NIA NIH HHS T32 AG078115NIA NIH HHS T32AG078115
6 · The paper itself

Abstract

Most neuroimaging studies linking regional brain volumes with cognition correct for total intracranial volume (ICV), but methods used for this correction differ across studies. It is unknown whether different ICV correction methods yield consistent results. Using a brain-wide association approach in the MRI substudy of UK Biobank (N = 41,964; mean age = 64.5 years), we used regression models to estimate the associations of 58 regional brain volumetric measures with eight cognitive outcomes, comparing no correction and four ICV correction approaches. Approaches evaluated included: no correction; dividing regional volumes by ICV (proportional approach); including ICV as a covariate in the regression (adjustment approach); and regressing the regional volumes against ICV in different normative samples and using calculated residuals to determine associations (residual approach). We used Spearman-rank correlations and two consistency measures to quantify the extent to which associations were inconsistent across ICV correction approaches for each possible brain region and cognitive outcome pair across 2320 regression models. When the association between brain volume and cognitive performance was close to null, all approaches produced similar estimates close to the null. When associations between a regional volume and cognitive test were not null, the adjustment and residual approaches typically produced similar estimates, but these estimates were inconsistent with results from the crude and proportional approaches. For example, when using the crude approach, an increase of 0.114 (95% confidence interval [CI]: 0.103-0.125) in fluid intelligence was associated with each unit increase in hippocampal volume. However, when using the adjustment approach, the increase was 0.055 (95% CI: 0.043-0.068), while the proportional approach showed a decrease of -0.025 (95% CI: -0.035 to -0.014). Different commonly used methods to correct for ICV yielded inconsistent results. The proportional method diverges notably from other methods and results were sometimes biologically implausible. A simple regression adjustment for ICV produced biologically plausible associations.

Indexed as

BrainCognitionHippocampusHumansIntelligenceMiddle AgedNeuroimagingbrain-wide association studiescognitive agingintracranial volume correctionreproducibilitystructural magnetic resonance imaging

Identifiers

PMID38433682
PMCPMC10910271

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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 OpenQuestion graph.