Evidence map›Paper›PMID 38712293›Full record

ArticlebioRxiv : the preprint server for biology2024

Microstructural Mapping of Neural Pathways in Alzheimer's Disease using Macrostructure-Informed Normative Tractometry.

Yixue Feng, Bramsh Q Chandio, Julio E Villalon-Reina, Sophia I Thomopoulos, Talia M Nir, Sebastian Benavidez, Emily Laltoo, Tamoghna Chattopadhyay, Himanshu Joshi, Ganesan Venkatasubramanian and 7 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. 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

5 · Who and what money

Authors and funding

17 authors.

Yixue FengImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States.ORCID 0000-0003-1015-4209
Bramsh Q ChandioImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States.
Julio E Villalon-ReinaImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States.
Sophia I ThomopoulosImaging Genetics Center, Mark and Mary 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, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States.ORCID 0000-0002-7106-7443
Sebastian BenavidezImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States.
Emily LaltooImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States.
Tamoghna ChattopadhyayImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States.
Himanshu JoshiMultimodal Brain Image Analysis Laboratory National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India.
Ganesan VenkatasubramanianTranslational Psychiatry Laboratory, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India.
John P JohnMultimodal Brain Image Analysis Laboratory National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India.
Neda JahanshadImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States.ORCID 0000-0003-4401-8950
Robert I ReidDepartment of Information Technology, Mayo Clinic and Foundation, Rochester, MN, United States.ORCID 0000-0003-2391-8650
Clifford R JackDepartment of Radiology, Mayo Clinic and Foundation, Rochester, MN, United States.ORCID 0000-0001-7916-622X
Michael W WeinerDepartment of Radiology and Biomedical Imaging, UCSF School of Medicine, San Francisco, CA, United States.
Paul M ThompsonImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States.ORCID 0000-0002-4720-8867
Alzheimers Disease Neuroimaging Initiative

Funding

Project 1U19AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI MICHAEL W WEINER · 2016 to 2026
$226.7M
High resolution mapping of the genetic risk for disease in the aging brainR01AG059874 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI JAHANSHAD, NEDA · 2018 to 2022
$3.1M
India ENIGMA Initiative for Global Aging & Mental HealthR01AG060610 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI GANESAN, VENKATASUBRAMANIAN, JOHN, JOHN P · 2019 to 2024
$2.8M
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
FiberNET: Deep learning to evaluate brain tract integrity worldwide and in ADR01AG057892 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI THOMPSON, PAUL M · 2024 to 2024
$638k
NIA NIH HHS R01 AG057892NIA NIH HHS R01 AG059874NIA NIH HHS R01 AG060610NIA NIH HHS RF1 AG057892NIA NIH HHS U19 AG024904NINDS NIH HHS RF1 NS136995
6 · The paper itself

Abstract

Introduction: Diffusion MRI is sensitive to the microstructural properties of brain tissues, and shows great promise in detecting the effects of degenerative diseases. However, many approaches analyze single measures averaged over regions of interest, without considering the underlying fiber geometry. Methods: Here, we propose a novel Macrostructure-Informed Normative Tractometry (MINT) framework, to investigate how white matter microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia. We compare MINT-derived metrics with univariate metrics from diffusion tensor imaging (DTI), to examine how fiber geometry may impact interpretation of microstructure. Results: In two multi-site cohorts from North America and India, we find consistent patterns of microstructural and macrostructural anomalies implicated in MCI and dementia; we also rank diffusion metrics' sensitivity to dementia. Discussion: We show that MINT, by jointly modeling tract shape and microstructure, has potential to disentangle and better interpret the effects of degenerative disease on the brain's neural pathways.

Indexed as

Alzheimer’s diseaseanomaly detectiondeep generative modelsdiffusion MRInormative modelingtractometrytransfer learning

Identifiers

PMID38712293
PMCPMC11071453

What Socratic holds

Textmetadata
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.