Biology subjects

Volpi, T.

Publications and source records attributed to Volpi, T..

5 recordsLinked to original sources

bioRxiv2025

Glucose Metabolism echoes Long-Range Temporal Correlations in the Human Brain

Intrinsic brain activity is characterized by pervasive long-range temporal correlations. While these scale-invariant dynamics are a fundamental hallmark of brain function, their implications for individual-level metabolic regulation remain poorly understood. Here, we address this gap by integrating resting-state functional Magnetic Resonance Imaging (fMRI) and dynamic [18F]FDG Positron Emission Tomography (PET) data acquired from the same cohort of participants. We uncover a systematic relationship between long-range temporal correlations, quantified via the Hurst exponent, and glucose metabolism. Our findings reveal that persistent temporal dependencies impose a measurable metabolic cost, with brains exhibiting higher long-range temporal correlations incurring greater energetic demands. Beyond glucose metabolism, we also show that these dynamics are likely supported by continuous biosynthetic processes, such as protein synthesis, which are critical for neural circuit maintenance and remodeling. Overall, our results suggest that a significant fraction of the brains so-called "Dark Energy" is actively spent to power spontaneous long-range temporal correlations.

neuroscience↗

bioRxiv2025

Unveiling functional-metabolic synergy in the healthy brain: multivariate integration of dynamic FDG-PET and resting-state fMRI

IntroductionDespite 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. MethodsTo this end, we employed multivariate Partial Least Squares Correlation (PLSC) to investigate the functional-metabolic relationship at both nodal and network level. From dynamic [18F]FDG-PET data we estimated parameters describing glucose metabolism --delivery rate (K1), phosphorylation rate (k3), and fractional uptake (Ki)-- 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. ResultsOur 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. ConclusionsBy leveraging PLSC, this work deepens our understanding of the functional-metabolic synergy in the healthy brain, providing new insights into its organization. Key PointsO_LIWe identified robust functional-metabolic synergy in the healthy brain using multivariate Partial Least Squares Correlation (PLSC), revealing strong associations between fMRI-derived functional connectivity and glucose metabolism from dynamic [{superscript 1}F]FDG-PET. C_LIO_LIAt the nodal level, glucose delivery (K1) is more strongly linked to FC strength than phosphorylation (k3) or uptake (Ki), especially in fMRI bands enriched with hemodynamic components. C_LIO_LIAt the network level, coupling between metabolic and functional connectivity is strongest in the canonical low-frequency fMRI band, suggesting that integrated metabolic support underpins large-scale functional integration across brain systems. C_LI

neuroscience↗

bioRxiv2024

The brain's "dark energy" puzzle upgraded: FDG uptake, delivery and phosphorylation, and their coupling with resting-state brain activity

The brains resting-state energy consumption is expected to be mainly driven by spontaneous activity. In our previous work, we extracted a wide range of features from resting-state fMRI (rs-fMRI), and used them to predict [18F]FDG PET SUVR as a proxy of glucose metabolism. Here, we expanded upon our previous effort by estimating [18F]FDG kinetic parameters according to Sokoloffs model, i.e., Ki (irreversible uptake rate), K1 (delivery), k3 (phosphorylation), in a large healthy control group. The parameters spatial distribution was described at a high spatial resolution. We showed that while K1 is the least redundant, there are relevant differences between Ki and k3 (occipital cortices, cerebellum and thalamus). Using multilevel modeling, we investigated how much of the regional variability of [18F]FDG parameters could be explained by a combination of rs-fMRI variables only, or with the addition of cerebral blood flow (CBF) and metabolic rate of oxygen (CMRO2), estimated from 15O PET data. We found that combining rs-fMRI and CMRO2 led to satisfactory prediction of individual Ki variance (45%). Although more difficult to describe, Ki and k3 were both most sensitive to local rs-fMRI variables, while K1 was sensitive to CMRO2. This work represents the most comprehensive assessment to date of the complex functional and metabolic underpinnings of brain glucose consumption.

neuroscience↗

bioRxiv2024

Connecting the Dots: Approaching a Standardized Nomenclature for Molecular Connectivity Combining Data and Literature

PET-based connectivity computation is a molecular approach that complements fMRI-derived functional connectivity. However, the diversity of methodologies and terms employed in PET connectivity analysis has resulted in ambiguities and confounded interpretations, highlighting the need for a standardized nomenclature. Drawing parallels from other imaging modalities, we propose "molecular connectivity" as an umbrella term to characterize statistical dependencies between PET signals across brain regions at the individual level (within-subject). Like fMRI resting-state functional connectivity, "molecular connectivity" leverages temporal associations in the PET signal to derive brain network associations. Another within-subject approach evaluates regional similarities of tracer kinetics, which are unique in PET imaging, thus referred to as "kinetic connectivity". On the other hand, "molecular covariance" denotes group-level computations of covariance matrices across-subject. Further specification of the terminology can be achieved by including the employed radioligand, such as "metabolic connectivity/covariance" for [18F]FDG as well as "tau/amyloid covariance" for [18F]flutemetamol / [18F]flortaucipir. To augment these distinctions, high-temporal resolution functional [18F]FDG PET scans from 17 healthy participants were analysed with common techniques of molecular connectivity and covariance, allowing for a data-driven support of the above terminology. Our findings demonstrate that temporal band-pass filtering yields structured network organization, whereas other techniques like 3rd order polynomial fitting, spatio-temporal filtering and baseline normalization require further methodological refinement for high-temporal resolution data. Conversely, molecular covariance from across-subject data provided a simple means to estimate brain region interactions through regularized or sparse inverse covariance estimation. A standardized nomenclature in PET-based connectivity research can reduce ambiguity, enhance reproducibility, and facilitate interpretability across radiotracers and imaging modalities. Via a data-driven approach, this work provides a transparent framework for categorizing and comparing PET-derived connectivity and covariance metrics, laying the foundation for future investigations in the field.

neuroscience↗

bioRxiv2022

A new framework for metabolic connectivity mapping using bolus FDG PET and kinetic modelling

PurposeMetabolic connectivity (MC) has been previously proposed as the covariation of static [18F]FDG PET images across participants, which we call across-individual MC (ai-MC). In few cases, MC has also been inferred from dynamic [18F]FDG signals, similarly to fMRI functional connectivity (FC), which we term within-individual MC (wi-MC). The validity and interpretability of both MC approaches is an important open issue. Here we reassess this topic, aiming to 1) develop a novel methodology for wi-MC estimation; 2) compare ai-MC maps obtained using different [18F]FDG parameters (K1, i.e. tracer transport rate, k3, i.e. phosphorylation rate, Ki, i.e. tracer uptake rate, and the standardized uptake value ratio, SUVR); 3) assess the interpretability of ai-MC and wi-MC in comparison to structural and functional connectivity (FC) measures. MethodsWe analyzed dynamic [18F]FDG data from 54 healthy adults using kinetic modelling to quantify the macro- and microparameters describing the tracer behavior (i.e. Ki, K1, k3). We also calculated SUVR. From the across-individual correlation of SUVR, Ki, K1, k3, we obtained four different ai-MC matrices. A new approach based on Euclidean distance was developed to calculate wi-MC from PET time-activity curves. ResultsWe identified Euclidean similarity as the most appropriate metric to calculate wi-MC. ai-MC networks changed with different [18F]FDG parameters (k3 MC vs. SUVR MC, r = 0.44). We found that wi-MC and ai-MC matrices are dissimilar (maximum r = 0.37), and that the match with FC is higher for wi-MC (Dice similarity: 0.47-0.63) than for ai-MC (0.24-0.39). ConclusionOur data demonstrate that individual-level MC from dynamic [18F]FDG data using Euclidean similarity is feasible and yields interpretable matrices that bear similarity to resting-state fMRI FC measures.

bioengineering↗