Evidence map›Paper›PMID 41648304›Full record

ArticlebioRxiv : the preprint server for biology2026

Tractometry-Based Quantification of Along-Tract White-Matter Hemispheric Asymmetry in Alzheimer's Disease.

Bramsh Qamar Chandio, Yixue Feng, Iyad Ba Gari, Jonathan Davis Alibrando, Sophia I Thomopoulos, Julio E Villalón-Reina, Kenny Liou, Sunanda Somu, Hannah Yoo, Talia M Nir and 5 more

Abstract readPreprint
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

15 authors.

Bramsh Qamar ChandioDepartment of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, USA.
Yixue FengImaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
Iyad Ba GariImaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
Jonathan Davis AlibrandoImaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
Sophia I ThomopoulosImaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
Julio E Villalón-ReinaImaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
Kenny LiouImaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
Sunanda SomuImaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
Hannah YooImaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
Talia M NirImaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
Eleftherios GaryfallidisDepartment of Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN, USA.
Eileen LüdersSchool of Psychology, University of Auckland, Auckland, New Zealand.
Fang-Cheng YehDepartment of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA.
Neda JahanshadImaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
Paul M ThompsonImaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.

Funding

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
TR&D3: Intrinsic Surface MappingP41EB015922 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI TOGA, ARTHUR W · 2012 to 2022
$14.1M
CRCNS: Community-supported open-source software for computational neuroanatomyR01EB027585 · NIBIB · TRUSTEES OF INDIANA UNIVERSITY · PI Eleftherios Garyfallidis · 2018 to 2026
$2.6M
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
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
NIA NIH HHS RF1 AG057892NIA NIH HHS U01 AG068057NIBIB NIH HHS P41 EB015922NIBIB NIH HHS R01 EB027585NIMH NIH HHS R01 MH134004NINDS NIH HHS RF1 NS136995
6 · The paper itself

Abstract

White-matter hemispheric asymmetry is a fundamental property of human brain organization and is known to change in aging, neurodevelopment, and neurodegenerative disorders. Tractometry analyzes diffusion-derived microstructural measures along the full length of tracts, localizing changes to specific tract-segments rather than collapsing tracts into a single value. Yet, existing frameworks lack a principled way to quantify left-right hemispheric asymmetries along homologous tracts. Here, we introduce an asymmetry-aware tractometry framework that integrates a symmetric white-matter atlas with BUAN (Bundle Analytics) to enable anatomically consistent, along-tract comparison of homologous pathways. By defining homologous bundles with a shared template and consistent orientation, each left-hemisphere segment is directly matched to its right-hemisphere counterpart, enabling principled, segment-wise comparison and revealing spatially localized asymmetries along-tract. Applying this framework to diffusion MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) comprising 1,215 subjects, we demonstrate how this approach reveals systematic left-right asymmetries across major white-matter pathways and show how these patterns differentiate cognitively normal (CN) individuals from those with mild cognitive impairment (MCI) and dementia. This method provides a sensitive and anatomically grounded tool for studying hemispheric specialization and its disruption in aging and disease, and establishes a general approach for asymmetry-aware tractometry in population neuroimaging studies.

Indexed as

AsymmetryBUANDiffusion MRITractometryWhite Matter

Identifiers

PMID41648304
PMCPMC12871689

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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.