Evidence map›Paper›PMID 39149257›Full record

ArticlebioRxiv : the preprint server for biology2024

MRI-based quantification of cardiac-driven brain biomechanics for early detection of neurological disorders.

Tyler C Diorio, Javid Abderezzai, Eric Nauman, Mehmet Kurt, Yunjie Tong, Vitaliy L Rayz

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

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

6 authors.

Tyler C DiorioPurdue University, (Weldon School of Biomedical Engineering), West Lafayette, (IN), USA.ORCID 0000-0001-6371-9424
Javid AbderezzaiUniversity of Washington, (Department of Mechanical Engineering), Seattle, (WA), USA.ORCID 0000-0001-5236-7582
Eric NaumanUniversity of Cincinnati, (Department of Biomedical Engineering), Cincinnati, (OH), USA.ORCID 0000-0002-8907-5716
Mehmet KurtUniversity of Washington, (Department of Mechanical Engineering), Seattle, (WA), USA.ORCID 0000-0002-5618-0296
Yunjie TongPurdue University, (Weldon School of Biomedical Engineering), West Lafayette, (IN), USA.ORCID 0000-0001-6052-8913
Vitaliy L RayzPurdue University, (Weldon School of Biomedical Engineering), West Lafayette, (IN), USA.

Funding

3T MRI Scanner dedicated to Life Sciences ResearchS10OD012336 · OD · PURDUE UNIVERSITY · PI DYDAK, ULRIKE · 2015 to 2015
$2.0M
Image based modeling of biomechanical factors for risk assessment of developing periventricular white matter hyperintensitiesR21AG068962 · NIA · PURDUE UNIVERSITY · PI TONG, YUNJIE · 2021 to 2021
$423k
NIA NIH HHS R21 AG068962NIH HHS S10 OD012336
6 · The paper itself

Abstract

We present a pipeline to quantify biomechanical environment of the brain using solely MRI-derived data in order to elucidate the role of biomechanical factors in neurodegenerative disorders. Neurological disorders, like Alzheimer's and Parkinson's diseases, are associated with physical changes, including the accumulation of amyloid-β and tau proteins, damage to the cerebral vasculature, hypertension, atrophy of the cortical gray matter, and lesions of the periventricular white matter. Alterations in the external mechanical environment of cells can trigger pathological processes, and it is known that AD causes reduced stiffness in the brain tissue during degeneration. However, there appears to be a significant lag time between microscale changes and macroscale obstruction of neurological function in the brain. Here, we present a pipeline to quantify the whole brain biomechanical environment to bridge the gap in understanding how underlying brain changes affect macroscale brain biomechanics. This pipeline enables image-based quantification of subject-specific displacement field of the whole brain to subject-specific strain, strain rate, and stress across 133 labeled functional brain regions. We have focused our development efforts on utilizing solely MRI-derived data to facilitate clinical applicability of our approach and have emphasized automation in all aspects of our methods to reduce operator dependance. Our pipeline has the potential to improve early detection of neurological disorders and facilitate the identification of disease before widespread, irreversible damage has occurred.

Indexed as

Alzheimer’s diseaseMRINeurodegenerationwhite matter lesions

Identifiers

PMID39149257
PMCPMC11326150

What OpenQuestion holds

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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 OpenQuestion graph.