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Unveiling Functional-Metabolic Synergy in the Healthy Brain: Multivariate Integration of Dynamic [<sup>18</sup>F]FDG-PET and Resting-State fMRI.

Overview

Authors: Claudia Tarricone1,2, Giulia Vallini1, Giorgia Baron1,3,4, Massimiliano Facca1,2, Erica Silvestri1, Tommaso Volpi2,5, Andrei G Vlassenko6, Manu S Goyal6, Alessandra Bertoldo1,2
ORCID iDs: Claudia Tarricone, Giulia Vallini, Giorgia Baron, Erica Silvestri, Tommaso Volpi, Andrei G Vlassenko, Manu S Goyal, Alessandra Bertoldo
  1. Department of Information Engineering, University of Padova, Padova, Italy
  2. Padova Neuroscience Center, University of Padova, Padova, Italy
  3. Defitech Chair of Clinical Neuroengineering, Neuro‐X Institute (INX), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
  4. Defitech Chair of Clinical Neuroengineering, INX, EPFL Valais, Sion, Switzerland
  5. Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA
  6. Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Missouri, USA
Institutions: University of Padua (Italy); École Polytechnique Fédérale de Lausanne (Switzerland); Yale University (United States); Mallinckrodt (United States) (United States)
Journal: Human brain mapping, volume 47, issue 14, article e70651
Dates: received 21 May 2025; accepted 25 August 2026; published online 30 September 2026; in print October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/hbm.70651 · PMID 42816435 · PMCID PMC13626976 · OpenAlex W7214943937
Open access: gold, a free copy (OpenAlex)
Status: empty repository (how the code is found and verified)
Categories: fMRI (modality), PET / SPECT (modality), human (organism)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
Keywords: BOLD frequency bands, dynamic [18F]FDG‐PET, functional‐metabolic integration, hemodynamics, partial least squares correlation
MeSH: Brain*, Connectome*, Glucose*, Magnetic Resonance Imaging*, Nerve Net*, Positron-Emission Tomography*, Adult, Female, Fluorodeoxyglucose F18, Humans, Male, Multimodal Imaging, Radiopharmaceuticals (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Recovery and Resilience Plan; Project MNESYS; Ministero dell’Università e della Ricerca (DN. 1553 11.10.2022)
Citations: not cited yet (Europe PMC); 116 references in the paper

Abstract

Despite accounting for only 2% of body weight, the human brain requires significant amounts of glucose, even at rest, underscoring the importance of functional‐metabolic relationships. Previous studies revealed moderate associations between resting‐state fMRI functional connectivity (FC) and local metabolism via [18F]FDG‐PET, yet much remains to be understood, particularly regarding their coupling between functional and metabolic networks. To this end, we employed multivariate Partial Least Squares Correlation (PLSC) to investigate the functional‐metabolic relationship at both nodal and network levels. From dynamic [18F]FDG‐PET data we estimated parameters describing glucose metabolism—delivery rate (K 1), phosphorylation rate (k 3), and fractional uptake (K i)—and generated within‐individual metabolic connectivity (MC) networks. FC was derived from fMRI data filtered into two frequency bands and summarized as region‐wise strength to capture nodal characteristics. Our findings revealed that glucose delivery is linked with FC strength, particularly when fMRI signal frequencies include greater hemodynamic contributions. Even stronger functional‐metabolic coupling occurs at the network level in the low‐frequency fMRI band, with higher MC between sensory/attention and transmodal networks supporting stronger FC within sensory/attention areas. By leveraging PLSC, this work deepens our understanding of the functional‐metabolic synergy in the healthy brain, providing new insights into its organization.

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

Code

No file of the authors' code could be read here: it is described below, and read at its source.

FairUnipd/ConsensusClustering-YanAtlas

License: none: the authors keep all their rights
State: the link answers, verified on 12 November 2026
Evidence: files inventoried
Commit: 1232402682a5f6a8ce1236535ef64c406b0ba47d, 18 October 2024
Size: 4 files, 0 scripts
Software Heritage: not archived
Found in: the text, “Structural MRI Preprocessing”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 2 checks, the latest on 12 November 2026: the link answers
  • 12 November 2026: the link answers
  • 3 October 2026: the link answers
At the source: github.com/FairUnipd/ConsensusClustering-YanAtlas

Tracing map

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Data

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

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy or ethical restrictions.

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

Versions

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Version 1, 6 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 5 keywords, 13 MeSH terms, 3 funders, 116 references.

Cite

This paper

Tarricone, C., Vallini, G., Baron, G., Facca, M., Silvestri, E., Volpi, T., Vlassenko, A. G., Goyal, M. S., & Bertoldo, A. (2026). Unveiling Functional-Metabolic Synergy in the Healthy Brain: Multivariate Integration of Dynamic [<sup>18</sup>F]FDG-PET and Resting-State fMRI. Human brain mapping, 47(14), e70651. https://doi.org/10.1002/hbm.70651

BibTeX

@article{tarricone2026unveiling,
author = {Tarricone, Claudia and Vallini, Giulia and Baron, Giorgia and Facca, Massimiliano and Silvestri, Erica and Volpi, Tommaso and Vlassenko, Andrei G and Goyal, Manu S and Bertoldo, Alessandra},
title = {{Unveiling Functional-Metabolic Synergy in the Healthy Brain: Multivariate Integration of Dynamic [\<sup\>18\</sup\>F]FDG-PET and Resting-State fMRI}},
journal = {Human brain mapping},
year = {2026},
month = oct,
volume = {47},
number = {14},
pages = {e70651},
publisher = {Wiley},
issn = {1065-9471},
doi = {10.1002/hbm.70651},
url = {https://doi.org/10.1002/hbm.70651},
pmid = {42816435},
pmcid = {PMC13626976}
}

RIS

TY - JOUR
AU - Tarricone, Claudia
AU - Vallini, Giulia
AU - Baron, Giorgia
AU - Facca, Massimiliano
AU - Silvestri, Erica
AU - Volpi, Tommaso
AU - Vlassenko, Andrei G
AU - Goyal, Manu S
AU - Bertoldo, Alessandra
TI - Unveiling Functional-Metabolic Synergy in the Healthy Brain: Multivariate Integration of Dynamic [<sup>18</sup>F]FDG-PET and Resting-State fMRI
T2 - Human brain mapping
J2 - Hum Brain Mapp
PY - 2026
DA - 2026/10/01
VL - 47
IS - 14
SP - e70651
SN - 1065-9471
PB - Wiley
DO - 10.1002/hbm.70651
UR - https://doi.org/10.1002/hbm.70651
LA - en
ER -

CSL-JSON

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