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Derivation of machine learning brain aging biomarkers for a set of forty thousand functional connectomes.

Overview

Authors: Nicolas Honnorat1, Di Wang1, Ngoc-Huynh Ho1, David Martinez1, Sachintha Ransara Brandigampala1, Susan R Heckbert2, Mohsen Bahrami3, Jayandra Jung Himali1,4, Charlie DeCarli5, Alexa Beiser4, Timothy M Hughes3, Sudha Seshadri1, Mohamad Habes1
ORCID iDs: Nicolas Honnorat
  1. Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Disease, University of Texas Health Science Center at San Antonio, 4940 Charles Katz Drive, San Antonio, 78229, TX, USA
  2. Department of Epidemiology, University of Washington School of Public Health, 3980 15th Ave NE, Box 351621, Seattle, 98195, WA, USA
  3. Section of Gerontology and Geriatric Medicine Department, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, 27157, NC, USA
  4. Department of Biostatistics, Boston University School of Public Health, 715 Albany Street, Boston, 02118, MA, USA
  5. Department of Neurology, University of California Davis, 1651 Alhambra Blvd Suite 200A, Sacramento, 95816, CA, USA
Institutions: The University of Texas at San Antonio Health Science Center (United States); University of Washington (United States); Wake Forest University Health Sciences (United States); Wake Forest University (United States); Boston University (United States); University of California, Davis (United States)
Journal: Brain research bulletin, volume 237, article 111815
Dates: published online 5 March 2026; in print April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.brainresbull.2026.111815 · PMID 41794271 · PMCID PMC13202619 · OpenAlex W7133829981
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: structural MRI / diffusion (modality), fMRI (modality), human (organism), clinical / translational (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity, fMRI & imaging
Keywords: Functional MRI, Aging
MeSH: Aging*, Brain*, Connectome*, Machine Learning*, Biomarkers, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIA NIH HHS (R01 AG083865, R01 AG085571, P30 AG066546, R01 AG049607, R01 AG054076, R01 AG080821, U24 AG074855, K25 AG090707); NINDS NIH HHS (R01 NS017950); NHLBI NIH HHS (R01 HL127659); National Institutes of Health (P30AG066546); National Heart Lung and Blood Institute (UL1-TR-001079, UL1-TR-001420); William and Ella Owens Medical Research Foundation; National Center for Advancing Translational Sciences (UL1-TR-000040)
Citations: not cited yet (Europe PMC); 69 references in the paper

Abstract

Various Magnetic Resonance Imaging modalities were developed to explore the brain. Among them, functional MRI is of key importance for studying brain activity and its neural substrates. Recent works have pointed out that machine learning can use neuroimaging data to predict brain age. This approach is crucial not only for understanding the effects of aging but also for refining diagnostics because many chronic and neurodegenerative diseases appear as accelerated aging. Unfortunately, the prediction of brain age is particularly challenging for functional data due to the large dimension of the high-resolution connectomes usually derived to summarize the functional organization of the brain and their particular mathematical properties. In this work, we investigate the prediction of brain age from functional data on a large scale by creating a set of forty thousand functional connectomes via the processing of the resting-state fMRI scans of four cohort studies. This dataset is used to explore the ability of various connectome transformations and machine learning strategies to achieve accurate age predictions. We hope that our results will open the way for more reliable functional brain age measures.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper links to its data, not to its authors' code: see the Data section.

Tracing map

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Data

Datasets cited

Data availability

FHS data access procedures can be obtained here: https://www.framinghamheartstudy.org/fhs-for-researchers/. The Human Connectome Project minimally preprocessed young adult dataset imaging data and associated NIH Toolbox measures are publicly available at https://db.humanconnectome.org/. MESA consortium can be contacted via the following website: https://www.mesa-nhlbi.org/. The UKBB data mentioned in this work is available to all researchers and can be accessed upon approval of the UK Biobank (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 13 authors, 2 keywords, 8 MeSH terms, 7 funders, 52 references.

Cite

This paper

Honnorat, N., Wang, D., Ho, N.-H., Martinez, D., Brandigampala, S. R., Heckbert, S. R., Bahrami, M., Himali, J. J., DeCarli, C., Beiser, A., Hughes, T. M., Seshadri, S., & Habes, M. (2026). Derivation of machine learning brain aging biomarkers for a set of forty thousand functional connectomes. Brain research bulletin, 237, 111815. https://doi.org/10.1016/j.brainresbull.2026.111815

BibTeX

@article{honnorat2026derivation,
author = {Honnorat, Nicolas and Wang, Di and Ho, Ngoc-Huynh and Martinez, David and Brandigampala, Sachintha Ransara and Heckbert, Susan R and Bahrami, Mohsen and Himali, Jayandra Jung and DeCarli, Charlie and Beiser, Alexa and Hughes, Timothy M and Seshadri, Sudha and Habes, Mohamad},
title = {{Derivation of machine learning brain aging biomarkers for a set of forty thousand functional connectomes}},
journal = {Brain research bulletin},
year = {2026},
month = mar,
volume = {237},
pages = {111815},
publisher = {Elsevier BV},
issn = {0361-9230},
doi = {10.1016/j.brainresbull.2026.111815},
url = {https://doi.org/10.1016/j.brainresbull.2026.111815},
pmid = {41794271},
pmcid = {PMC13202619}
}

RIS

TY - JOUR
AU - Honnorat, Nicolas
AU - Wang, Di
AU - Ho, Ngoc-Huynh
AU - Martinez, David
AU - Brandigampala, Sachintha Ransara
AU - Heckbert, Susan R
AU - Bahrami, Mohsen
AU - Himali, Jayandra Jung
AU - DeCarli, Charlie
AU - Beiser, Alexa
AU - Hughes, Timothy M
AU - Seshadri, Sudha
AU - Habes, Mohamad
TI - Derivation of machine learning brain aging biomarkers for a set of forty thousand functional connectomes
T2 - Brain research bulletin
J2 - Brain Res Bull
PY - 2026
DA - 2026/03/05
VL - 237
SP - 111815
SN - 0361-9230
PB - Elsevier BV
DO - 10.1016/j.brainresbull.2026.111815
UR - https://doi.org/10.1016/j.brainresbull.2026.111815
LA - en
ER -

CSL-JSON

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