Whole-brain metabolic-effective coupling in healthy controls and its disruption in gliomas

2402 

Abstract Submission 

Giulia Vallini¹, Giorgia Baron¹, Giulia Pagnin¹, John Lee², Andrei Vlassenko², Manu Goyal², Diego Cecchin³, Maurizio Corbetta⁴, Alessandra Bertoldo³

¹University of Padua, Padua, Italy, ²Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, St Louis, MO, ³Padova Neuroscience Center, Padua, Italy, ⁴Clinica Neurologica, Department of Neuroscience, University of Padova, Padova, Italy, Padova, Padova

Giulia Vallini  
University of Padua
Padua, Italy

Giorgia Baron  
University of Padua
Padua, Italy

Giulia Pagnin  
University of Padua
Padua, Italy

John Lee  
Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology
St Louis, MO

Andrei Vlassenko  
Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology
St Louis, MO

Manu Goyal  
Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology
St Louis, MO

Diego Cecchin  
Padova Neuroscience Center
Padua, Italy

Maurizio Corbetta  
Clinica Neurologica, Department of Neuroscience, University of Padova, Padova, Italy
Padova, Padova

Alessandra Bertoldo  
Padova Neuroscience Center
Padua, Italy

Glucose stands as the primary fuel source driving the energy-demanding process of neuronal activity. Glucose metabolism can be explored in vivo in human by [¹⁸F]FDG PET. DCM-based effective connectivity (EC) provides a linear mechanistic view of brain dynamics by conceiving it as a mixture of dissipative and solenoidal flow speaking to the concept of kinetic energy (Benozzo et al. 2023). This study aims to explore the association between kinetic energy and glucose metabolism by comparing EC-derived functional flow patterns with FDG PET measurements in healthy controls (HCs), finally investigating the disruption of this coupling in glioma patients.

The rsfMRI data of 42 HCs are fully described in (Volpi et al. 2022), while rsfMRI data of 43 patients are outlined in (Silvestri et al. 2022). EC matrices, estimated through sparse DCM (Prando et al. 2020), are decomposed into Σ^-1 (partial correlation of the neuronal states) and S (differential cross-covariance, expressing the temporal directionality of neural activation) as in (Benozzo et al. 2023). From dynamic PET data (60-min acquisitions for both datasets), individual Metabolic Connectivity (MC) matrices, expressing the relationships between the metabolic states of different brain regions, are estimated according to (Volpi et al. 2022). Standardized Uptake Value Ratio (SUVR) is derived from static PET (sum of late frames 40-60 min) using whole-brain uptake for HCs (Byrnes et al. 2014) and ipsilateral cerebellum white matter for patients (Nozawa et al. 2015) as reference. We employ a clustered version of 100-area Yan Homotopic atlas (74 nodes, 7 RSNs) (Yan et al. 2023) plus 12 subcortical AAL3 ROIs (Rolls et al. 2020).
Partial Least Square Correlation (PLSC) is applied to HC pairs: Σ^-1-MC, S-MC (upper triangular matrices), SUVR-Σ^-1 and SUVR-S (nodal strengths for Σ^-1 and S, ROI values for SUVR). Generalizability of multivariate correlations were cross-validated using a 7-fold procedure. For each generalizable effective-metabolic pair, the regression line between the scores was identified, defining a normality band at the 90-percentile of the HC distances from the line. Finally, patients' metabolic and effective variables are projected onto the maximizing-covariance latent space identified in HCs. A tumor frequency map was obtained by combining tumor masks as weighted by the corresponding distance from the HC regression line (out-of-range patients only). Analysis workflow in Fig. 1.

Cross-validation reveals good generalizability for Σ^-1-MC and SUVR-S associations (Pearson's r>0.76). The scatterplots between metabolic and effective scores for HCs and patient projections are in Fig. 2B. In both pairs, some patients notably diverge from the expected metabolic-effective coupling independently of tumor volume. As shown in Fig. 2C, SUVR-S pair is mostly altered for temporo-parietal tumors, while Σ^-1-MC is mainly disrupted in patients with frontal lesions.

A dual metabolic-effective association emerges: one at local level (glucose uptake-node directionality) and one at network level (metabolic connectivity-partial correlation). Furthermore, our study unveils how these two distinct decoupling can differentiate patients based on the lesion location (and not its volume)-local disruption observed only for temporo-parietal lesions and network alterations for frontal lesions-providing novel insights into the physiopathology and the metabolic-kinetic link of glioma. While (Maleki Balajoo et al. 2022) already explored distinguishing pathologies (Alzheimer and Mild Cognitive Impairment) based on metabolism-function (FC metrics) coupling, our study is a pioneer in gliomas and the introduction of EC measures allows both to link metabolism to the concept of kinetic energy, but also to decouple neuronal activity from the adverse effects of hemodynamic convolution, proposing metabolic-effective coupling as a new biomarker.

Connectivity (eg. functional, effective, structural) ²

fMRI Connectivity and Network Modeling

PET Modeling and Analysis

Multi-Modal Imaging ¹

Cerebral Metabolism and Hemodynamics

FUNCTIONAL MRI

Positron Emission Tomography (PET)