Evidence map›Paper›PMID 41446119›Full record

ArticlebioRxiv : the preprint server for biology2025

Distributed fMRI patterns coupled to low-frequency cardiorespiratory dynamics provide markers of aging.

Shiyu Wang, Richard Song, Laurent M Lochard, Jiawen Fan, Yamin Li, Kimberly Rogge-Obando, Caroline Martin, Sarah E Goodale, Haatef Pourmotabbed, J Mason Harding and 10 more

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

5 · Who and what money

Authors and funding

20 authors.

Shiyu WangDepartment of Biomedical Engineering, Vanderbilt University, Nashville, TN, 37240, USA.
Richard SongDepartment of Computer Science, Vanderbilt University, Nashville, TN, 37240, USA.
Laurent M LochardDepartment of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, 37240, USA.
Jiawen FanAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, 02129, USA.
Yamin LiDepartment of Computer Science, Vanderbilt University, Nashville, TN, 37240, USA.
Kimberly Rogge-ObandoGraduate Program in Neuroscience, Vanderbilt University, Nashville, TN, 37240, USA.
Caroline MartinDepartment of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, 37240, USA.
Sarah E GoodaleDepartment of Biomedical Engineering, Vanderbilt University, Nashville, TN, 37240, USA.
Haatef PourmotabbedDepartment of Biomedical Engineering, Vanderbilt University, Nashville, TN, 37240, USA.
J Mason HardingDepartment of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, 37240, USA.
Terra LeeDepartment of Neuroscience, Vanderbilt University, Nashville, TN, 37240, USA.
Chang LiDepartment of Computer Science, Vanderbilt University, Nashville, TN, 37240, USA.
Shengchao ZhangDepartment of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, 37240, USA.
Roza G BayrakDepartment of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, 37240, USA.
Taylor BoltDepartment of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, 90095, United States.
Jason S NomiDepartment of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, 90095, United States.
Lucina Q UddinDepartment of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, 90095, United States.
Jingyuan E ChenAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, 02129, USA.
Mara MatherLeonard Davis School of Gerontology, Department of Psychology, Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA.
Catie ChangDepartment of Biomedical Engineering, Vanderbilt University, Nashville, TN, 37240, USA.

Funding

ABCD-USA Consortium: Coordinating CenterU24DA041147 · NIDA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SANDRA A BROWN, TERRY L. JERNIGAN · 2015 to 2026
$54.7M
17/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT UCLAU01DA050987 · NIDA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SUSAN Y BOOKHEIMER, Mirella Dapretto · 2020 to 2026
$9.0M
Training in Fundamental NeuroscienceT32MH064913 · NIMH · VANDERBILT UNIVERSITY · PI IHRIE, REBECCA A, WINDER, DANNY G. · 2001 to 2022
$7.6M
Training Program for Innovative Engineering Research in Surgery and InterventionT32EB021937 · NIBIB · VANDERBILT UNIVERSITY · PI Dario J Englot, Michael Ian Miga · 2016 to 2026
$2.3M
fMRI physiological signatures of aging and Alzheimer's DiseaseRF1MH125931 · NIMH · VANDERBILT UNIVERSITY · PI CHANG, CATHERINE ELIZABETH · 2021 to 2021
$1.1M
Exploratory investigation of bilingualism, executive function, and brain organization in children with autismR21HD111805 · NICHD · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI UDDIN, LUCINA QAZI · 2023 to 2023
$432k
State-dependent connectivity of impaired autonomic brain networks underlying cardiorespiratory dysfunction in epilepsyF31NS143413 · NINDS · VANDERBILT UNIVERSITY · PI Haatef Pourmotabbed · 2025 to 2026
$100k
Characterizing vigilance in fMRI data and its relation to age-related cognitive impairment.F99AG079810 · NIA · VANDERBILT UNIVERSITY · PI GOODALE, SARAH E · 2022 to 2024
$65k
NIA NIH HHS F99 AG079810NIBIB NIH HHS T32 EB021937NICHD NIH HHS R21 HD111805NIDA NIH HHS U01 DA050987NIDA NIH HHS U24 DA041147NIMH NIH HHS RF1 MH125931NIMH NIH HHS T32 MH064913NINDS NIH HHS F31 NS143413
6 · The paper itself

Abstract

How aging affects brain-body connections can be investigated through changes in the coupling between functional magnetic resonance imaging (fMRI) signals and bodily autonomic processes across the adult lifespan. Recent studies using univariate approaches have identified age-related changes in the association between fMRI signals from multiple individual brain regions and low-frequency respiratory and cardiac activity. Here, we investigate if whole-brain spatial fMRI patterns associated with low-frequency physiological processes (heart rate and respiratory volume fluctuations) present generalizable changes with age. Data from human participants of both sexes are included in the analysis. We find that chronological age can be predicted statistically beyond chance from patterns of low-frequency fMRI-physiology coupling, even after accounting for individual differences in physiological signal characteristics and brain anatomy. Notably, brain areas implicated in central autonomic regulation, including nodes within salience and ventral attention networks (e.g., insula and middle cingulate cortex), are amongst the strongest contributors to age prediction. Further, we observe that after removing physiological effects from fMRI data, the residual blood oxygen level-dependent (BOLD) signal variability is still a reliable indicator of age. Together, these findings underscore the close integration between brain and body physiology, and highlight this interaction as a potential biomarker of the aging process.

Identifiers

PMID41446119
PMCPMC12724417

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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.