Evidence map›Paper›PMID 42465495›Full record

ArticlebioRxiv : the preprint server for biology2026

Comparing Harmonization Approaches for Protocol-Related Variability in Multisite Diffusion MRI Data.

Kenny Liou, Sophia I Thomopoulos, Julio E Villalon-Reina, Hannah Yoo, Yuhan Shuai, Sasha Chehrzadeh, Arvin Arani, Bret Borowski, Robert I Reid, Prashanthi Vemuri and 6 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Kenny LiouMark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.ORCID 0009-0005-4377-4641
Sophia I ThomopoulosMark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.ORCID 0000-0002-0046-4070
Julio E Villalon-ReinaMark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
Hannah YooMark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
Yuhan ShuaiMark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
Sasha ChehrzadehMark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
Arvin AraniDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Bret BorowskiDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Robert I ReidDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.ORCID 0000-0003-2391-8650
Prashanthi VemuriDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.ORCID 0000-0003-4286-0589
Clifford R JackDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.ORCID 0000-0001-7916-622X
Michael W WeinerDepartment of Radiology, School of Medicine, University of California, San Francisco, CA, USA.
Neda JahanshadMark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
Paul M ThompsonMark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
Talia M NirMark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
Alzheimer’s Disease Neuroimaging Initiative

Funding

Project 1U19AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI ARTHUR W TOGA · 2016 to 2026
$226.7M
Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease BiobanksU01AG068057 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Christos Davatzikos, Heng Huang · 2020 to 2026
$20.7M
ENIGMA World Aging CenterR01AG058854 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI THOMPSON, PAUL M · 2021 to 2025
$3.3M
FiberNET: Deep learning to evaluate brain tract integrity worldwide and in ADRF1AG057892 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI THOMPSON, PAUL M · 2020 to 2023
$2.6M
Worldwide Tractometry Initiative to Investigate Brain Microstructure, Cognitive Impairment & Dementia in Parkinsons DiseaseRF1NS136995 · NINDS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI JAHANSHAD, NEDA, THOMPSON, PAUL M · 2024 to 2024
$2.2M
High Capacity, High Performance Storage System for NeuroscienceS10OD032285 · OD · UNIVERSITY OF SOUTHERN CALIFORNIA · PI TOGA, ARTHUR W · 2022 to 2022
$1.7M
Causal and Event Based Modeling of Brain Alterations in ADRDR01AG087513 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Neda Jahanshad · 2025 to 2026
$1.2M
NIA NIH HHS R01 AG058854NIA NIH HHS R01 AG087513NIA NIH HHS RF1 AG057892NIA NIH HHS U01 AG068057NIA NIH HHS U19 AG024904NIH HHS S10 OD032285NINDS NIH HHS RF1 NS136995
6 · The paper itself

Abstract

Diffusion MRI (dMRI) enables assessment of white matter microstructural abnormalities in Alzheimer's disease (AD), and multisite datasets enable more robust modeling of non-biological variation that can confound analyses. The Alzheimer's Disease Neuroimaging Initiative (ADNI) includes over 10 dMRI protocols, necessitating robust methods to model protocol-related variability when pooling data. Here, we compared three harmonization approaches: (1) mixed-effects models, (2) ComBat-GAM, and (3) eHarmonize, a reference-based lifespan method. We assessed their ability to reduce protocol-related variability in diffusion tensor imaging fractional anisotropy (FA) and mean diffusivity (MD) while preserving associations with cognitive impairment (CI), and amyloid-beta (Aβ) and tau PET burden in 1,086 ADNI3/4 participants. All approaches yielded more closely aligned FA/MD distributions across protocols. Associations with clinical indicators of CI were highly consistent across approaches, whereas PET associations were less widespread and more variable. Overall, multiple strategies effectively modeled protocol-related variability while preserving AD-related associations.

Indexed as

ADNIAlzheimer’s Diseasediffusion MRIDTIHarmonization

Identifiers

PMID42465495
PMCPMC13370964

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.