Biology subjects

Bertoldo, A.

Publications and source records attributed to Bertoldo, A..

13 recordsLinked to original sources

bioRxiv2026

Towards a scalable effective connectivity estimation in fMRI

Characterizing directed connections between brain regions is fundamental for understanding the brains network organization and how it supports cognitive processes. However, a reliable methodology allowing assessment of effective connectivity (EC) at the whole-brain level and across high-resolution parcellations has remained elusive. This limitation reflects a trade-off between model detail and scalability. Mechanistic approaches such as Dynamic Causal Modeling (DCM) provide a principled description of directed interactions but become impractical for large parcellations, whereas scalable methods based on linear-system inversion often fail to reliably recover key features of the underlying effective connectivity. Here, using data from the Human Connectome Project S1200 release, we show that the multivariate Ornstein-Uhlenbeck (mOU) approach for EC estimation can be adapted to yield EC estimates that are in broad agreement with those of an advanced DCM, at least when the spatial variability of the hemodynamic response falls within the physiological range characteristic of the healthy adult brain. Furthermore, thanks to its low computational cost, mOU can be easily scaled up to large neuroimaging cohorts and high-resolution parcellations. High-resolution, mOU-based EC outperforms functional connectivity for single-subject connectome fingerprinting. In addition, it enables a high-resolution characterization of the brains hierarchical organization and modular architecture by identifying directed interactions within and between communities and revealing driver and receiver regions. Overall, our methodology provides a reliable and scalable EC estimation pipeline of wide applicability to cognitive and network neuroscience.

neuroscience↗

bioRxiv2026

Individual-level metabolic connectivity captures cortical morphology and their coupling strengthens with age

PurposeCerebral glucose metabolism and cortical morphology are known to undergo significant changes across the lifespan, yet their network-level coordination remains poorly understood. This study aimed to investigate whether individual-level metabolic connectivity (MC) reflects underlying inter-areal morphometric similarity, and to determine how this metabolic-morphometric coupling evolves across the adult lifespan. MethodsDynamic [18F]FDG-PET and structural MRI data were acquired from 67 healthy adults (age range: 38-86 years). Individual MC networks were estimated based on the similarity between regional time-activity curves. Corresponding structural similarity networks were generated using the morphometric inverse divergence (MIND) framework, which integrates multiple vertex-wise features of cortical morphology. The correspondence between metabolic and structural networks was quantified at both global and local scales using Spearman correlations. General linear models were employed to assess age-related effects on MC-MIND similarity. ResultsMC demonstrated a robust positive association with cortical morphometric similarity ({rho} = 0.32, p < 0.0001), an association that persisted after distance correction and was replicated at the individual level. Regional coupling followed a topographic gradient, peaking in heteromodal association cortices and reaching its minimum in paralimbic areas. Crucially, morphology-metabolism alignment systematically strengthened with age at the global level ({beta} = 0.59, p < 0.001). Local age-related increases were spatially heterogeneous, predominantly affecting visual, dorsal parietal, and premotor cortices alongside adjacent multimodal regions. ConclusionIndividual-level MC captures the morphometric organisation of the brain. The age-related increase in morphology-metabolism coupling indicates that metabolic coordination becomes progressively more aligned with cortical architecture, consistent with reduced neuroenergetic flexibility in the ageing brain.

neuroscience↗

bioRxiv2025

Eigenmode decomposition of asymmetries in whole-brain effective connectivity reveals multiscale hierarchical dynamics

The human brain is a complex, hierarchical system operating far from equilibrium, yet the mechanisms linking its directed network architecture to temporal irreversibility remain largely unknown. While prior studies have revealed functional and structural hierarchies, no framework has captured the hierarchical organization of the brains directed network in relation to its non-equilibrium dynamics. Here, we present the first large-scale eigendecomposition of whole-brain effective connectivity (EC), estimated from resting-state fMRI using sparse Dynamic Causal Modeling. We isolate the irreversible component of EC, which encodes the directionality of information flow and forms a comprehensive hierarchical network of forward and backward interactions. This hierarchy strengthens in brain states approaching criticality, where slow, oscillatory modes dominate and reflects the influence of long-range anatomical connections that scaffold whole-brain information flow. Our decomposition provides the first multiscale quantification of temporal irreversibility, revealing dual counterpropagating streams along the unimodal-transmodal axis operating at distinct frequencies. Crucially, these hierarchical dynamics carry robust, individual-specific signatures, with unimodal networks contributing disproportionately to subject identifiability. Altogether, this work delivers the first dynamic, whole-brain characterization of effective connectivity-derived hierarchies across spatiotemporal scales and individuals, offering a unified framework for studying brain hierarchy, non-equilibrium dynamics, and subject identifiability.

neuroscience↗

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↗

bioRxiv2023

Analyzing asymmetry in brain hierarchies with a linear state-space model of resting-state fMRI data

The study of functional brain connectivity in resting-state functional magnetic resonance imaging (rsfMRI) data has traditionally focused on zero-lag statistics. However, recent research has emphasized the need to account for dynamic aspects due to the complex patterns of time-varying co-activations among brain regions. In this regard, the importance of non-zero-lag statistics in studying complex brain interactions has been emphasized, both in terms of modeling and data analysis. Here, we show how a time-lag description is incorporated within the framework of dynamic causal modeling (DCM) resulting in an asymmetric state interaction matrix known as effective connectivity (EC). Asymmetry in EC is conventionally associated with the directionality of interactions between brain regions and is frequently employed to distinguish between incoming and outgoing node connections. We will revisit this interpretation by employing a decomposition of the EC matrix. This decomposition enables us to isolate the steady-state differential crosscovariance matrix, which is responsible for modeling the information flow and introducing time irreversibility. In other words, by modeling the off-diagonal part of the differential covariance, the system landscape may exhibit a curl steady-state flow component that breaks detailed balance and diverges the dynamics from equilibrium. Our empirical results reveal that only the outgoing strengths of the EC matrix relate with the flow described by the differential cross-covariance, while the so-called incoming strengths are primarily driven by the zero-lag covariance, specifically the precision matrix, thus reflecting conditional independence rather than directionality.

neuroscience↗

bioRxiv2023

Revealing the spatial pattern of brain hemodynamic sensitivity to healthy aging through sparse DCM

Age-related changes in the BOLD response could reflect neuro-vascular coupling modifications rather than simply impairments in neural functioning. In this study, we propose the use of a generative dynamic causal model (DCM) to decouple neuronal and vascular factors in the BOLD signal, with the aim of characterizing the whole-brain spatial pattern of hemodynamic sensitivity to healthy aging, as well as to test the role of hemodynamic features as independent predictors in an age-classification model.In this view, DCM was applied to the resting-state fMRI data of a cohort of 126 healthy individuals in a wide age range, providing reliable estimates of the hemodynamic response function (HRF) for each subject and each region of interest. Then, some features characterizing each HRF curve were extracted and used to fit a multivariate logistic regression model to predict the age class of each individual. Ultimately, we tested the final predictive model on an independent dataset of 338 healthy subjects selected from the Human Connectome Project Aging (HCP-A) and Development (HCP-D) cohorts. Our results entail the spatial heterogeneity of the age effects on the hemodynamic component, since its impact resulted to be strongly region- and population-specific, discouraging any space-invariant corrective procedures that attempt to correct for vascular factors when carrying out functional studies involving groups with different ages. Moreover, we demonstrated that a strong interaction exists between some specific hemodynamic features and age, further supporting the essential role of the hemodynamic factor as independent predictor of biological aging, rather than a simple confounding variable. Significance statementBy inferring region-wise hemodynamic profiles at the individual level, this is the first study providing an exhaustive whole-brain characterization of the hemodynamic sensitivity to healthy aging, reporting further evidence of the vascular changes across the adult lifespan. Using a predictive framework, we analysed the statistical influence of advancing age on individual regional hemodynamic attributes, offering a quantitative evaluation of the diverse hemodynamic bias across different brain regions. We unveiled a specific set of hemodynamic predictors to discriminate young from elderly people, mainly describing vascular properties of right-hemispheric areas. This suggests the asymmetric nature of vascular degeneration processes affecting the human brain at the latest stage of life, other than a potential biomarker that could be relevant for brain-age prediction.

neuroscience↗

bioRxiv2023

Controlling target brain regions by optimal selection of input nodes

The network control theory framework holds great potential to inform neurostimulation experiments aimed at inducing desired activity states in the brain. However, the current applicability of the framework is limited by inaccurate modeling of brain dynamics, and an overly ambitious focus on whole-brain activity control. In this work, we leverage recent progress in linear modeling of brain dynamics (effective connectivity) and we exploit the concept of target controllability to focus on the control of a single region or a small subnetwork of nodes. We discuss when control may be possible with a reasonably low energy cost and few stimulation loci, and give general predictions on where to stimulate depending on the subset of regions one wishes to control. Importantly, using the robustly asymmetric effective connectome instead of the symmetric structural connectome (as in previous research), we highlight the fundamentally different roles in- and out-hubs have in the control problem, and the relevance of inhibitory connections. The large degree of inter-individual variation in the effective connectome implies that the control problem is best formulated at the individual level, but we discuss to what extent group results may still prove useful.

neuroscience↗

bioRxiv2023

Macroscale coupling between structural and effective connectivity in the mouse brain

How the emergent functional connectivity (FC) relates to the underlying anatomy (structural connectivity, SC) is one of the biggest questions of modern neuroscience. At the macro-scale level, no one-to-one correspondence between structural and functional links seems to exist. And we posit that to better understand their coupling, two key aspects should be taken into account: the directionality of the structural connectome and the limitations of describing network functions in terms of FC. Here, we employed an accurate directed SC of the mouse brain obtained by means of viral tracers, and related it with single-subject effective connectivity (EC) matrices computed by applying a recently developed DCM to whole-brain resting-state fMRI data. We analyzed how SC deviates from EC and quantified their couplings by conditioning both on the strongest SC links and EC links. We found that when conditioning on the strongest EC links, the obtained coupling follows the unimodal-transmodal functional hierarchy. Whereas the reverse is not true, as there are strong SC links within high-order cortical areas with no corresponding strong EC links. This mismatch is even more clear across networks. Only the connections within sensory motor networks align both in terms of effective and structural strength.

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↗

bioRxiv2018

Dynamic 11C-PiB PET shows cerebrospinal fluid flow alterations in Alzheimer’s disease and multiple sclerosis

Cerebrospinal fluid (CSF) plays an important role in the clearance of solutes and maintenance of brain homeostasis. 11C-PiB PET was recently proposed as a tool for detection of CSF clearance alterations in Alzheimers disease. The current study seeks to investigate the magnitude of 11C-PiB PET signal in the lateral ventricles of an independent group of Alzheimers and mild cognitive impairment subjects. We have also evaluated multiple sclerosis as a model of disease with CSF clearance alterations without amyloid-beta tissue accumulation.\n\nMethodsA set of Alzheimers and mild cognitive impairment subjects and a set of multiple sclerosis subjects with matched healthy controls underwent MRI and dynamic 11C-PiB PET. Manual lateral ventricle regions of interest were generated from MRI data. PET data was analysed using a simplified reference tissue model with cerebellum or a supervised reference region, for the Alzheimers and multiple sclerosis datasets, respectively. Magnitude of 11C-PiB signal in the lateral ventricles was calculated as area under curve from 35 to 80 minutes and standard uptake value ratio (SUVR) from 50 to 70 minutes. Compartmental modelling analysis was performed on a separate dataset containing Alzheimers and matched healthy control data with an arterial input function to further understand the kinetics of the lateral ventricular 11C-PiB signal.\n\nResultsAnalysis of variance revealed significant group differences in lateral ventricular SUVR across the Alzheimers, mild cognitive impairment, and healthy control groups (p=0.004). Additional pairwise comparisons revealed significantly lower lateral ventricular SUVR in Alzheimers compared to healthy controls (p<0.001) and mild cognitive impairment (p=0.029). Lateral ventricular SUVR was also significantly lower in multiple sclerosis compared to healthy controls (p=0.008). Compartmental modelling analysis revealed significantly lower uptake rates of 11C-PiB signal from blood (p=0.005) and brain tissue (p=0.004) to the lateral ventricles in Alzheimers compared to healthy controls. This analysis also revealed significantly lower clearance of 11C-PiB signal out of the lateral ventricles in Alzheimers compared to healthy controls (p=0.002).\n\nConclusionOverall, these results indicate that dynamic 11C-PiB PET can be used to observe pathological changes in cerebrospinal fluid dynamics and that cerebrospinal fluid-mediated clearance is reduced in Alzheimers disease and multiple sclerosis compared to healthy controls.

neuroscience↗

bioRxiv2018

Archetypes in human behavior and their brain correlates: An evolutionary trade-off approach

Organisms perform multiple tasks and in doing so face critical trade-offs. According to Pareto optimality theory, such trade-offs lead to the evolution of phenotypes that are distributed in a portion of the trait-space resembling a polytope, whose vertices represent the specialists at one of the traits (archetypes).\n\nWe applied this theory to the variability of cognitive and behavioral scores measured in 1206 individuals from the Human Connectome Project. Among all possible 300 combinations of pairs of traits, we found the best fit to Pareto optimality when individuals were plotted in the trait-space of time preferences for reward, evaluated with the Delay Discounting task. This task requires choosing either immediate smaller rewards or delayed larger rewards. Time preference for reward identified three archetypes, which accounted for variability on many cognitive, personality, and socio-economic status scores, differences in brain structure, as well as in functional connectivity between prefrontal cortex, basal ganglia, and amygdala, regions associated with reward and their regulation. There was only a weak association with genetics. In summary, time preference for reward reflects a core variable that biases human phenotypes via natural and cultural selection.

neuroscience↗