Evidence map›Paper›PMID 40894788›Full record

ArticlebioRxiv : the preprint server for biology2025

Streamline Density Normalization: A Robust Approach to Mitigate Bundle Variability in Multi-Site Diffusion MRI.

Yixue Feng, Yuhan Shuai, Julio E Villalón-Reina, Bramsh Q Chandio, Sophia I Thomopoulos, Talia M Nir, Neda Jahanshad, Paul M Thompson

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. 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

8 authors.

Yixue FengImaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States.ORCID 0000-0003-1015-4209
Yuhan ShuaiImaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States.
Julio E Villalón-ReinaImaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States.
Bramsh Q ChandioImaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States.
Sophia I ThomopoulosImaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States.ORCID 0000-0002-0046-4070
Talia M NirImaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States.
Neda JahanshadImaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States.
Paul M ThompsonImaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States.

Funding

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
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
Training in the Multiscale and Multimodal Analysis of Biomarkers in Alzheimer’s DiseaseT32AG058507 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI ARTHUR W TOGA · 2018 to 2026
$2.1M
NIA NIH HHS T32 AG058507NIMH NIH HHS R01 MH134004NINDS NIH HHS RF1 NS136995
6 · The paper itself

Abstract

Tractometry enables quantitative analysis of tissue microstructure is sensitive to variability introduced during tractography and bundle segmentation. Differences in processing parameters and bundle geometry can lead to inconsistent streamline reconstructions and sampling, ultimately affecting the reproducibility of tractometry analysis. In this study, we introduce Streamline Density Normalization (SDNorm), a supervised two-step method designed to reduce variability in bundle reconstructions. SDNorm first computes streamline weights using linear regression to match a subject's bundle to a template streamline density map, then iteratively prunes streamlines to achieve a target density using a novel metric called

Indexed as

bundle segmentationtractographytractometry

Identifiers

PMID40894788
PMCPMC12393452

What Socratic 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 Socratic graph.