Biology subjects

Facca, M.

Publications and source records attributed to Facca, M..

3 recordsLinked to original sources

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↗