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Improving respiratory and heart rate variation estimation from resting-state BOLD fMRI across the lifespan using a functionally informed, tissue-aware deep learning framework.

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Paper

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The authors' code

Python · 1 line · 12 B · no license

  1. __all__ = []

__init__.py at commit 1e2a711, no license · at the source

Overview

Authors: Abdoljalil Addeh1,2,3,4, Karen Ardila1,2,3,4, Pattarawut Charatpangoon1,2,3,4, Ethan Church5, Rebecca J. Williams6, G. Bruce Pike3,4,7, M. Ethan MacDonald1,2,3,4
ORCID iDs: Abdoljalil Addeh, Karen Ardila, Pattarawut Charatpangoon, Rebecca J. Williams, G. Bruce Pike, M. Ethan MacDonald
  1. Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, Canada
  2. Department of Electrical & Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, Canada
  3. Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Canada
  4. Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada
  5. Department of Computer Science, McGill University, Montréal, Canada
  6. Brain-Behaviour Research Group, University of New England, Armidale, Australia
  7. Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Canada
Institutions: University of Calgary (Canada); Hotchkiss Brain Institute (Canada); McGill University (Canada); University of New England (Australia)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1346
Dates: received 26 July 2025; accepted 22 July 2026; published online 27 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1346 · PMID 42666759 · PMCID PMC13522955 · OpenAlex W7201877782
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism)
Methods: Spectral & time-frequency, Machine learning, Preprocessing, Connectivity, fMRI & imaging, Physiology & signal measures, Statistics
Keywords: fMRI, head motion parameters, heart rate variation, respiratory variation, dynamic functional connectivity
MeSH: Brain*, Deep Learning*, Heart Rate*, Magnetic Resonance Imaging*, Respiration*, Adolescent, Adult, Aged, Child, Child, Preschool, Connectome, Convolutional Neural Networks, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Young Adult (* major topic)
Topic: Optical Imaging and Spectroscopy Techniques (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Citations: not cited yet (Europe PMC); 39 references in the paper

Abstract

Accurate measurement of physiological signals such as respiration and cardiac activity is essential for modeling physiological confounds in BOLD-fMRI data. However, external monitoring devices such as respiratory belts and photoplethysmographs often suffer from signal loss, noise, or incomplete recordings, and many fMRI datasets lack physiological measurements altogether due to practical constraints. In this work, we propose a data-driven framework to reconstruct respiratory variation (RV) and heart rate variation (HRV) directly from resting-state fMRI data using a hybrid machine learning architecture that combines one-dimensional convolutional neural networks (1D-CNN) with gated recurrent units (GRUs). The model processes BOLD signals from 630 regions of interest (ROIs), spanning cortical and subcortical gray matter, white matter, and cerebrospinal fluid. For RV estimation, six head motion parameters were included as additional inputs to capture motion-related physiological information. The proposed method was trained and evaluated on three cohorts across the lifespan from the Human Connectome Project (HCP), including HCP in Development (HCP-D, ages 5–21 years), HCP in Young Adults (HCP-YA, ages 22–35 years), and HCP in Aging (HCP-A, ages 36–100 years). Age-specific architectural tuning was applied to accommodate developmental, neurovascular, and physiological variability across these populations. When compared against established CNN- and RNN-based baselines under matched preprocessing and native temporal resolution, the proposed architecture achieved consistent performance gains across all evaluation metrics. The largest relative improvements were observed for error-based and temporal alignment metrics, with MAE, MSE, and DTW reduced by approximately 7–10%, indicating lower absolute reconstruction error and improved temporal fidelity, while correlation-based improvements were more modest (approximately 5–7%). These results demonstrate the feasibility and effectiveness of reconstructing RV and HRV directly from fMRI time series using spatially and temporally enriched neural architectures. Building on earlier efforts in this domain, the proposed framework extends prior work through evaluation across multiple cohorts, broader anatomical coverage, and enhanced modeling capacity, offering a robust alternative for physiological modeling in the absence of external recordings.

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

Repository

Its files are read in the Code ↔ Paper reader above.

jaliladde/fMRI-Physio-Reconstruction

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 1e2a7113c31e06c858feba4a287663d49098b1a6, 23 March 2026
Languages: Python (9), Shell (6)
Size: 17 files, 15 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (5 files), TensorFlow (5 files), scikit-learn (2 files), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
16 files
At the source: github.com/jaliladde/fMRI-Physio-Reconstruction

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 15 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data and Code Availability

The datasets analyzed in this study are publicly available through the Human Connectome Project (HCP), including the Human Connectome Project-Development (HCP-D), Human Connectome Project-Young Adult (HCP-YA), and Human Connectome Project-Aging (HCP-A) datasets. The code implementing the proposed method is available on GitHub at https://github.com/jaliladde/fMRI-Physio-Reconstruction

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 7 authors, 5 keywords, 18 MeSH terms, 39 references.

Cite

This paper

Addeh, A., Ardila, K., Charatpangoon, P., Church, E., Williams, R. J., Pike, G. B., & MacDonald, M. E. (2026). Improving respiratory and heart rate variation estimation from resting-state BOLD fMRI across the lifespan using a functionally informed, tissue-aware deep learning framework. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1346. https://doi.org/10.1162/imag.a.1346

BibTeX

@article{addeh2026improving,
author = {Addeh, Abdoljalil and Ardila, Karen and Charatpangoon, Pattarawut and Church, Ethan and Williams, Rebecca J. and Pike, G. Bruce and MacDonald, M. Ethan},
title = {{Improving respiratory and heart rate variation estimation from resting-state BOLD fMRI across the lifespan using a functionally informed, tissue-aware deep learning framework}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = aug,
volume = {4},
pages = {IMAG.a.1346},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1346},
url = {https://doi.org/10.1162/imag.a.1346},
pmid = {42666759},
pmcid = {PMC13522955}
}

RIS

TY - JOUR
AU - Addeh, Abdoljalil
AU - Ardila, Karen
AU - Charatpangoon, Pattarawut
AU - Church, Ethan
AU - Williams, Rebecca J.
AU - Pike, G. Bruce
AU - MacDonald, M. Ethan
TI - Improving respiratory and heart rate variation estimation from resting-state BOLD fMRI across the lifespan using a functionally informed, tissue-aware deep learning framework
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/08/27
VL - 4
SP - IMAG.a.1346
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1346
UR - https://doi.org/10.1162/imag.a.1346
LA - en
ER -

CSL-JSON

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