Dark Energy and Long-Term Memory: How Neural Timescales Drive Brain Metabolism
Poster No:
1667
Submission Type:
Abstract Submission
Authors:
Anna Ridolfo1,2, Massimiliano Facca1, Claudia Tarricone1,2, Tommaso Volpi1,3, Andrei G. Vlassenko4, Manu S. Goyal4, Maurizio Corbetta1,5, Alessandra Bertoldo1,2
Institutions:
1Padova Neuroscience Center (PNC), University of Padua, Padua, Italy, 2Department of Information Engineering, University of Padua, Padua, Italy, 3Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA, 4Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University, St Louis, MO, USA, 5Department of Neuroscience, University of Padua, Padua, Italy
First Author:
Anna Ridolfo
Padova Neuroscience Center (PNC), University of Padua|Department of Information Engineering, University of Padua
Padua, Italy|Padua, Italy
Padova Neuroscience Center (PNC), University of Padua|Department of Information Engineering, University of Padua
Padua, Italy|Padua, Italy
Co-Author(s):
Claudia Tarricone
Padova Neuroscience Center (PNC), University of Padua|Department of Information Engineering, University of Padua
Padua, Italy|Padua, Italy
Padova Neuroscience Center (PNC), University of Padua|Department of Information Engineering, University of Padua
Padua, Italy|Padua, Italy
Tommaso Volpi
Padova Neuroscience Center (PNC), University of Padua|Department of Radiology and Biomedical Imaging, Yale University
Padua, Italy|New Haven, CT, USA
Padova Neuroscience Center (PNC), University of Padua|Department of Radiology and Biomedical Imaging, Yale University
Padua, Italy|New Haven, CT, USA
Andrei G. Vlassenko
Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University
St Louis, MO, USA
Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University
St Louis, MO, USA
Manu S. Goyal
Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University
St Louis, MO, USA
Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University
St Louis, MO, USA
Maurizio Corbetta
Padova Neuroscience Center (PNC), University of Padua|Department of Neuroscience, University of Padua
Padua, Italy|Padua, Italy
Padova Neuroscience Center (PNC), University of Padua|Department of Neuroscience, University of Padua
Padua, Italy|Padua, Italy
Alessandra Bertoldo
Padova Neuroscience Center (PNC), University of Padua|Department of Information Engineering, University of Padua
Padua, Italy|Padua, Italy
Padova Neuroscience Center (PNC), University of Padua|Department of Information Engineering, University of Padua
Padua, Italy|Padua, Italy
Introduction:
Measured brain activity emerges from the intricate interplay of patterns unfolding at distinct spatiotemporal scales. This interplay gives rise to a global covariance structure, commonly referred to as functional connectivity (FC), which is inherently shaped by the temporal variability of individual nodes. Recently, it has been suggested that the temporal autocorrelation of brain regions, a measure of long-term memory and intrinsic timescales of neural activity, may account for local and global functional network properties (Shinn et al. 2023). Furthermore, a temporally stable core and a more dynamically switching periphery have been proposed to describe the graded spatial variation of intrinsic timescales across the brain (Gao et al. 2020). Here, we investigate the metabolic consequences of such intrinsic organization by interweaving resting-state fMRI and [18F]FDG-PET data. Specifically, we examine the relationship between the long-term memory of neural activity, as indexed by the Hurst exponent (H; Campbell et al. 2022), and the different kinetic components of brain glucose use, under the hypothesis that regions showing high H also exhibit higher metabolic cost due to their slow local integration dynamics.
Methods:
Resting-state fMRI and [18F]FDG-PET were collected from 42 healthy subjects (Goyal et al. 2023; Volpi et al. 2024). All analyses were conducted using the Schaefer 300 regions, 7 networks parcellation, augmented by the Tian subcortical atlas (S1 version, 16 regions). The Hurst exponent was calculated using a wavelet-based approach implemented through the NonFractal MATLAB toolbox (You et al. 2012). This algorithm was chosen for its robustness in analyzing non-stationary series. A two-compartment three-rate constant kinetic model was applied to dynamic PET data to obtain the three microparameters (namely K1 [ml/cm3/min], k2 [min-1] and k3 [min-1]) and their linear combination Ki (metabolic fractional uptake [ml/cm3/min]), using a Variational Bayesian inference approach (Castellaro et al., 2017). The coupling between the Hurst exponent and kinetic parameters was assessed using Pearson's correlation. Statistical significance was assessed using the Moran Spectral Randomization (MSR) method when subcortical ROIs were included, and the spin-test when only cortical parcels were considered (N = 10,000 null maps). P-values were adjusted for multiple comparisons using the Bonferroni correction.
Results:
The Hurst exponent was heterogeneously distributed across the cortex. Considering Yeo's Resting-State Network (RSNs) partition, the Ventral Attention (VAN) and Default Mode (DMN) Networks exhibited Hurst exponent values significantly higher than chance (all pspin-corrected < 0.05, Figure 1a). The regional variation of H was positively associated with the regional distribution of the metabolic fractional uptake and one [18F]FDG microparameter: Ki (r = 0.52, pMSR-corrected = 0.0004, Figure 1b) and k3 (r = 0.35, pMSR-corrected = 0.01, Figure 1b).
Conclusions:
Our findings indicate a positive linear relationship between the timescale of neural activity, as measured by the Hurst exponent, and glucose metabolism, as measured by FDG-PET. The correlation observed with Ki, along with the diverging correlational patterns involving K1, k2, and k3, suggests that glucose phosphorylation as indexed by k3 may be a key contributor to this interplay. These results underscore the role of local and non-network neural dynamics in driving a portion of the brain's metabolic expenditure at rest.
Modeling and Analysis Methods:
PET Modeling and Analysis
Task-Independent and Resting-State Analysis 1
Physiology, Metabolism and Neurotransmission:
Cerebral Metabolism and Hemodynamics 2
Keywords:
FUNCTIONAL MRI
Positron Emission Tomography (PET)
Other - resting-state; metabolism; hurst exponent;
1|2Indicates the priority used for review
Abstract Information
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2. Castellaro, M. et al. (2017). A Variational Bayesian inference method for parametric imaging of PET data. NeuroImage, 150, 136–149. https://doi.org/10.1016/j.neuroimage.2017.02.009
3. Gao, R. et al. (2020). Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture. eLife, 9. https://doi.org/10.7554/elife.61277
4. Goyal, M. S. et al. (2023). Brain aerobic glycolysis and resilience in Alzheimer disease. Proceedings of the National Academy of Sciences, 120(7). https://doi.org/10.1073/pnas.2212256120
5. Shinn, M. et al. (2023). Functional brain networks reflect spatial and temporal autocorrelation. Nature Neuroscience, 26(5), 867–878. https://doi.org/10.1038/s41593-023-01299-3
6. Volpi, T. et al. (2024). The brain’s “dark energy” puzzle: How strongly is glucose metabolism linked to resting-state brain activity? Journal of Cerebral Blood Flow & Metabolism, 44(8), 1433–1449. https://doi.org/10.1177/0271678x241237974
7. You, N. W. et al. (2012). Fractal analysis of resting state functional connectivity of the brain. 2022 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn.2012.6252657