Brain functional-metabolic relationships:rs-fMRI/dynamic [18F]FDG-PET multivariate integration
Poster No:
1602
Submission Type:
Abstract Submission
Authors:
Claudia Tarricone1,2, Giulia Vallini1, Giorgia Baron1,3, Erica Silvestri1, Tommaso Volpi4, Andrei G. Vlassenko5, Manu S. Goyal5, Alessandra Bertoldo1,2
Institutions:
1Department of Information Engineering, University of Padua, Padua, Italy/Padua, 2Padova Neuroscience Center, University of Padua, Padua, Italy, Italy, 3IRCCS San Camillo Hospital, Venice, Venice, Italy, Italy, 4Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 5Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University, St Louis, MO
First Author:
Claudia Tarricone
Department of Information Engineering, University of Padua|Padova Neuroscience Center, University of Padua
Padua, Italy/Padua|Padua, Italy, Italy
Department of Information Engineering, University of Padua|Padova Neuroscience Center, University of Padua
Padua, Italy/Padua|Padua, Italy, Italy
Co-Author(s):
Giorgia Baron
Department of Information Engineering, University of Padua|IRCCS San Camillo Hospital, Venice
Padua, Italy/Padua|Venice, Italy, Italy
Department of Information Engineering, University of Padua|IRCCS San Camillo Hospital, Venice
Padua, Italy/Padua|Venice, Italy, Italy
Andrei G. Vlassenko
Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University
St Louis, MO
Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University
St Louis, MO
Manu S. Goyal
Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University
St Louis, MO
Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University
St Louis, MO
Alessandra Bertoldo
Department of Information Engineering, University of Padua|Padova Neuroscience Center, University of Padua
Padua, Italy/Padua|Padua, Italy, Italy
Department of Information Engineering, University of Padua|Padova Neuroscience Center, University of Padua
Padua, Italy/Padua|Padua, Italy, Italy
Introduction:
The human brain, while accounting for just 2% of body mass, demands substantial glucose and oxygen even at rest, emphasizing the critical relationship between brain function and metabolism (Raichle, 2006). At the individual level, resting-state fMRI functional connectivity (FC) reveals network organization, while metabolic connectivity matrices (MC) from [18F]FDG PET complementarily describe the brain network based on its glucose metabolism (Volpi et al., 2023). While moderate correlations between FC and local metabolic indices have been observed (Palombit et al., 2022; Volpi et al., 2024), the interplay between FC and glycolytic parameters like glucose inflow and phosphorylation rate, and MC, is underexplored. We aim to leverage Partial Least Squares Correlation (PLSC) to investigate local and network-level functional-metabolic coupling, providing a novel multimodal perspective.
Methods:
Rs-fMRI and dynamic [18F]FDG-PET data of 42 healthy subjects were analysed (Goyal et al., 2023). fMRI data were band-pass filtered into: 0.008-0.1 Hz (F1), maximizing the presence of spontaneous low-frequency oscillations; 0.008-0.21 Hz (F2), including more hemodynamic contributions (Tong et al., 2019). Both datasets were parcellated using a clustered Yan functional atlas (74 ROIs/7 RSNs) and 12 subcortical areas (AAL3). Individual F1 and F2 FC matrices were computed via Pearson correlation. From PET data, the blood to tissue influx rate K1 [ml/cm3/min], the phosphorylation rate k3 [min-1] and the fractional metabolic uptake Ki [ml/cm3/min] were estimated via kinetic modelling and Variational Bayesian inference (Castellaro et al., 2017); MC matrices were calculated as in (Volpi et al., 2023). PLSC was applied to investigate: ROI-level coupling of single-subject metabolic parameters (K1, k3, Ki) with FC node strength (FCSTR); network-level coupling of the upper triangular portion of FC and MC. K-fold-cross-validation was used to assess generalizability of significant PLSC results. Bonferroni-corrected Wilcoxon rank tests were used to detect significant differences in fMRI and PET measures between latent score subgroups (positive vs. negative scores) for each generalizable pair.
Results:
The results suggest that delivery processes, rather than actual glucose metabolism, modulate the coupling of FC and glucose kinetics. Specifically, only K1-FCSTR and FC-MC pairs were generalizable in both F1 and F2, with the scores showing a positive linear trend (Fig.2a,c). Stronger ROI-level coupling in F2 linked regions with both higher K1 and FCSTR. Subgroups significant differences were found in occipital and subcortical K1 values, while not in FCSTR values (Fig.2b). Stronger FC-MC coupling was found in F1, where higher unimodal FC corresponded to more integrated uni- and trans-modal MC. Both FC and MC were significantly different between the two subgroups (Fig.2d): positive scores showed an overall higher FC, especially SOMMOT, DORSATTN, and SALVENTATTN, being metabolically supported by CONT-SOMMOT and CONT-SALVENTATTN interactions, and partially by CONT-LIMBIC and CONT-DMN.
Conclusions:
A stronger K1-FCSTR coupling in F2 reflects the hemodynamic and vasomotor contributions to F2 (Yuen et al., 2019), and highlights a robust link between glucose influx rate and FC (Amend et al., 2019). The lack of significant differences in FCSTR scores and the k3-FCSTR and Ki-FCSTR non-generalizability likely reflect the sensitivity of FCSTR largely to blood flow and glucose transport, and confirms previous findings of a mostly local function-metabolism coupling (Volpi et al., 2024). The strong FC-MC link demonstrates the importance of metabolic uni-transmodal integration to sustain unimodal functional networks, characterized by less variable connectivity patterns (Volpi et al., 2024). By leveraging PLSC, this study underscores the importance of network-level analysis and within-individual multivariate approaches to comprehensively capture variability in functional-metabolic relationships.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Multivariate Approaches 1
PET Modeling and Analysis
Task-Independent and Resting-State Analysis
Physiology, Metabolism and Neurotransmission:
Cerebral Metabolism and Hemodynamics
Keywords:
Cerebral Blood Flow
FUNCTIONAL MRI
Multivariate
Positron Emission Tomography (PET)
Statistical Methods
Other - PLSC; [18F]FDG metabolism; functional connectivity; metabolic connectivity; kinetic modelling
1|2Indicates the priority used for review
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2. Castellaro, M., Rizzo, G., Tonietto, M., Veronese, M., Turkheimer, F. E., Chappell, M. A., & Bertoldo, A. (2017). A Variational Bayesian inference method for parametric imaging of PET data. Neuroimage, 150, 136–149.
3. Goyal, M. S., Blazey, T., Metcalf, N. V., McAvoy, M. P., Strain, J. F., Rahmani, M., Durbin, T. J., Xiong, C., Benzinger, T. L.-S., & Morris, J. C. (2023). Brain aerobic glycolysis and resilience in Alzheimer disease. Proceedings of the National Academy of Sciences, 120(7), e2212256120.
4. Palombit, A., Silvestri, E., Volpi, T., Aiello, M., Cecchin, D., Bertoldo, A., & Corbetta, M. (2022). Variability of regional glucose metabolism and the topology of functional networks in the human brain. NeuroImage, 257, 119280. https://doi.org/10.1016/j.neuroimage.2022.119280
5. Raichle, M. E. (2006). The brain’s dark energy. Science, 314(5803), 1249–1250.
6. Tong, Y., Hocke, L. M., & Frederick, B. B. (2019). Low Frequency Systemic Hemodynamic “Noise” in Resting State BOLD fMRI: Characteristics, Causes, Implications, Mitigation Strategies, and Applications. Frontiers in Neuroscience, 13, 787. https://doi.org/10.3389/fnins.2019.00787
7. Volpi, T., Silvestri, E., Aiello, M., Lee, J. J., Vlassenko, A. G., Goyal, M. S., Corbetta, M., & Bertoldo, A. (2024). The brain’s “dark energy” puzzle: How strongly is glucose metabolism linked to resting-state brain activity? Journal of Cerebral Blood Flow & Metabolism, 0271678X241237974. https://doi.org/10.1177/0271678X241237974
8. Volpi, T., Vallini, G., Silvestri, E., Francisci, M. D., Durbin, T., Corbetta, M., Lee, J. J., Vlassenko, A. G., Goyal, M. S., & Bertoldo, A. (2023). A new framework for metabolic connectivity mapping using bolus [18F]FDG PET and kinetic modeling. Journal of Cerebral Blood Flow & Metabolism, 43(11), 1905–1918. https://doi.org/10.1177/0271678X231184365
9. Yuen, N. H., Osachoff, N., & Chen, J. J. (2019). Intrinsic Frequencies of the Resting-State fMRI Signal: The Frequency Dependence of Functional Connectivity and the Effect of Mode Mixing. Frontiers in Neuroscience, 13. https://doi.org/10.3389/fnins.2019.00900