Evidence map›Paper›PMID 38520363›Full record

ArticleHuman brain mapping2024

Diffusion MRI harmonization via personalized template mapping.

Yihao Xia, Yonggang Shi

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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

2 authors.

Yihao XiaUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.ORCID 0000-0001-9327-1202
Yonggang ShiUSC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.ORCID 0000-0002-3466-4302

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
TR&D3: Intrinsic Surface MappingP41EB015922 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI VAN HORN, JOHN DARRELL · 2012 to 2022
$14.1M
Surface-Based Fiber Tracking and Modeling Techniques for Mapping the Superficial White Matter Connectome with Diffusion MRIR01EB022744 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Yonggang Shi · 2016 to 2026
$3.7M
Brainstem connectomes related to Alzheimer's diseaseRF1AG056573 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI SHI, YONGGANG · 2018 to 2018
$2.5M
Shape-based personalized AT(N) imaging markers of Alzheimer's diseaseRF1AG077578 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI SHI, YONGGANG · 2023 to 2023
$2.2M
Tau-induced connectome imaging markers of Alzheimer's diseaseRF1AG064584 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI SHI, YONGGANG · 2020 to 2020
$2.1M
Tau-associated pathological changes in reward networks for the prediction of neuropsychiatric symptoms in Alzheimer’s diseaseR21AG064776 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI SHI, YONGGANG · 2019 to 2020
$444k
NIA NIH HHS R21 AG064776NIA NIH HHS R21AG064776NIA NIH HHS RF1 AG056573NIA NIH HHS RF1AG056573NIA NIH HHS RF1 AG064584NIA NIH HHS RF1AG064584NIA NIH HHS RF1 AG077578NIA NIH HHS RF1AG077578NIA NIH HHS U19 AG078109NIA NIH HHS U19AG078109NIBIB NIH HHS P41 EB015922NIBIB NIH HHS P41EB015922NIBIB NIH HHS R01 EB022744NIBIB NIH HHS R01EB022744
6 · The paper itself

Abstract

One fundamental challenge in diffusion magnetic resonance imaging (dMRI) harmonization is to disentangle the contributions of scanner-related effects from the variable brain anatomy for the observed imaging signals. Conventional harmonization methods rely on establishing an atlas space to resolve anatomical variability and generate a unified inter-site mapping function. However, this approach is limited in accounting for the misalignment of neuroanatomy that still widely persists even after registration, especially in regions close to cortical boundaries. To overcome this challenge, we propose a personalized framework in this paper to more effectively address the confounding from the misalignment of neuroanatomy in dMRI harmonization. Instead of using a common template representing site-effects for all subjects, the main novelty of our method is the adaptive computation of personalized templates for both source and target scanning sites to estimate the inter-site mapping function. We integrate our method with the rotation invariant spherical harmonics (RISH) features to achieve the harmonization of dMRI signals between sites. In our experiments, the proposed approach is applied to harmonize the dMRI data acquired from two scanning platforms: Siemens Prisma and GE MR750 from the Adolescent Brain Cognitive Development dataset and compared with a state-of-the-art method based on RISH features. Our results indicate that the proposed harmonization framework achieves superior performance not only in reducing inter-site variations due to scanner differences but also in preserving sex-related biological variability in original cohorts. Moreover, we assess the impact of harmonization on the estimation of fiber orientation distributions and show the robustness of the personalized harmonization procedure in preserving the fiber orientation of original dMRI signals.

Indexed as

BrainDiffusion Magnetic Resonance ImagingAdolescentAdolescent DevelopmentHumansImage Processing, Computer-Assisteddiffusion MRIharmonizationpersonalized template

Identifiers

PMID38520363
PMCPMC10960558

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.