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A Bayesian Spatiotemporal Model for Joint Estimation of Brain Activation and Connectivity in fMRI Studies.

Overview

Authors: Parisa Naseri1,2, Yuliya Shapovalova1, Ioan Gabriel Bucur1, Charlotte Cambier van Nooten1, Giancarlo Valente2, Tom Heskes1
ORCID iDs: Parisa Naseri, Ioan Gabriel Bucur, Charlotte Cambier van Nooten, Giancarlo Valente, Tom Heskes
  1. Institute for Computing and Information Sciences, Radboud University, Nijmegen, the Netherlands
  2. Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
Institutions: Radboud University Nijmegen (Netherlands); Maastricht University (Netherlands)
Journal: Statistics in medicine, volume 45, issue 20-22, article e70712
Dates: received 20 October 2025; accepted 12 August 2026; published online 25 August 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/sim.70712 · PMID 42639870 · PMCID PMC13504729 · OpenAlex W7204180234
Open access: hybrid, a free copy (OpenAlex)
Status: data only
Categories: fMRI (modality), human (organism), computational (subfield)
Methods: Statistics, fMRI & imaging
Keywords: Bayesian inference, brain activation, brain connectivity, functional magnetic resonance imaging, spatiotemporal modeling
MeSH: Brain*, Magnetic Resonance Imaging*, Bayes Theorem, Brain Mapping, Computer Simulation, Connectome, Humans, Models, Statistical, Signal-To-Noise Ratio (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Radboud Universiteit; NIMH NIH HHS (U54 MH091657); WU-Minn Consortium (1U54MH091657)
Citations: not cited yet (Europe PMC); 71 references in the paper

Abstract

Task‐based functional magnetic resonance imaging (fMRI) experiments play a crucial role in modern data‐driven neuroscience research. These studies often aim to understand how external stimuli trigger activation in specific brain regions and to explore functional connectivity patterns among predefined regions, commonly referred to as regions of interest (ROIs). Accurately estimating both brain activation and inter‐regional connectivity is challenging due to complex spatiotemporal correlations and low signal‐to‐noise ratios inherent in fMRI data. This paper introduces a joint spatiotemporal Bayesian framework that simultaneously models activation and connectivity across multiple subjects while estimating the hemodynamic response function (HRF) for each region. Spatial dependencies are captured via an unweighted graph‐Laplacian prior on regression and autoregressive coefficients, and region‐specific random effects are modeled using a Bayesian Gaussian graphical model to reflect connectivity among ROIs. We evaluate the performance of the model through simulation studies, demonstrating robust estimation under realistic low signal‐to‐noise conditions. The approach is then applied to a multisubject motor task dataset from the Human Connectome Project (HCP), mapping brain motor areas associated with specific movements (e.g., finger, toe, tongue) and assessing their activation and lateralization in response to visual cues. The model is further evaluated on the Individual Brain Charting (IBC) dataset, a high‐resolution 3T fMRI dataset designed for fine‐grained cognitive mapping across multiple tasks. Finally, the model is validated through comparisons with the classical general linear model (GLM), which is commonly used in the neuroscience community, highlighting the advantages of our Bayesian approach in jointly capturing activation and connectivity patterns.

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 Statement

The data that support the findings of this study are available in the Human Connectome Project at https://www.humanconnectome.org/. These data were derived from the following resources available in the public domain: – HCP Young Adult, https://www.humanconnectome.org/study/hcp‐young‐adult (https://www.humanconnectome.org/study/hcp-young-adult) 3mm‐preprocessed data from the IBC project, https://search.kg.ebrains.eu/instances/Dataset/ed615ee5‐fdaa‐4f1d‐8fcd‐8c55d05a4e2d (https://search.kg.ebrains.eu/instances/Dataset/ed615ee5-fdaa-4f1d-8fcd-8c55d05a4e2d).

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 3, 28 September 2026

  • Publisher: — → Wiley
  • Authors: added Charlotte Cambier van Nooten (0000-0001-8057-7333); removed Charlotte Cambier van Nooten

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 9 MeSH terms, 3 funders, 49 references.

Cite

This paper

Naseri, P., Shapovalova, Y., Bucur, I. G., van Nooten, C. C., Valente, G., & Heskes, T. (2026). A Bayesian Spatiotemporal Model for Joint Estimation of Brain Activation and Connectivity in fMRI Studies. Statistics in medicine, 45(20-22), e70712. https://doi.org/10.1002/sim.70712

BibTeX

@article{naseri2026bayesian,
author = {Naseri, Parisa and Shapovalova, Yuliya and Bucur, Ioan Gabriel and van Nooten, Charlotte Cambier and Valente, Giancarlo and Heskes, Tom},
title = {{A Bayesian Spatiotemporal Model for Joint Estimation of Brain Activation and Connectivity in fMRI Studies}},
journal = {Statistics in medicine},
year = {2026},
month = sep,
volume = {45},
number = {20-22},
pages = {e70712},
publisher = {Wiley},
issn = {0277-6715},
doi = {10.1002/sim.70712},
url = {https://doi.org/10.1002/sim.70712},
pmid = {42639870},
pmcid = {PMC13504729}
}

RIS

TY - JOUR
AU - Naseri, Parisa
AU - Shapovalova, Yuliya
AU - Bucur, Ioan Gabriel
AU - van Nooten, Charlotte Cambier
AU - Valente, Giancarlo
AU - Heskes, Tom
TI - A Bayesian Spatiotemporal Model for Joint Estimation of Brain Activation and Connectivity in fMRI Studies
T2 - Statistics in medicine
J2 - Stat Med
PY - 2026
DA - 2026/09/01
VL - 45
IS - 20-22
SP - e70712
SN - 0277-6715
PB - Wiley
DO - 10.1002/sim.70712
UR - https://doi.org/10.1002/sim.70712
LA - en
ER -

CSL-JSON

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