Biology subjects

Sorrentino, P.

Publications and source records attributed to Sorrentino, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Structural topology shapes regional roles in neuronal avalanche initiation and termination

Whole-brain dynamics unfold as fast, discrete, transient events known as, neuronal avalanches, whose propagation is constrained by the connectome, that is, the large-scale scaffolding of structural connections wiring the brain. However, the role of the connectome in determining the topographies of the start and end of these transient cascades remains unclear. Leveraging resting-state Magnetoencephalography (MEG) recordings and diffusion magnetic resonance imaging (dMRI) tractography across 42 healthy individuals, we examined whether the structural architecture of white matter tracts dictates the spatial origins and the end of cortical avalanches. We found that the probability of triggering and finishing an avalanche is strongly related to the structural nodal strength, showing a positive correlation that is highly robust to non-parametric spatial permutation testing and bootstrapping. This structure-function coupling is highly stable and robust across different activation thresholds, demonstrating that the choice of avalanche extraction threshold does not drive or alter our main findings. Collectively, our results suggest that structural properties determine a region's propensity to initiate and terminate neuronal avalanches.

neuroscience↗

bioRxiv2026

FUSE: FUsing EEG-MEG in a Shared Embedding via self-supervised learning for BCI

Combining complementary neurophysiological modalities offers a promising strategy for improving motor imagery (MI) brain-computer interfaces (BCIs), but learning shared representations across modalities remains largely unexplored. Here, we propose a two-phase deep learning framework for multimodal EEG-MEG decoding that explicitly decouples representation learning from downstream classification. In the first phase, a convolutional encoder-decoder learns a shared latent representation by predicting the power spectral density (PSD) of EEG and MEG signals directly from time-domain activity, rather than using the conventional objective of reconstructing the input signal. In the second phase, the encoder is frozen and its learned representations are reused, without further adaptation, to perform the classification of the downstream MI-BCI task. The framework was evaluated on simultaneous EEG and MEG recordings from 20 participants. The learned representations consistently outperformed conventional handcrafted spectral features, increasing median classification accuracy from 0.734 to 0.794. The multimodal framework also improved performance over MEG alone (median accuracy from 0.680 to 0.794) and yielded a modest increase over EEG alone (median accuracy from 0.765 to 0.794), providing a more robust decoding strategy than single-modality approaches. Furthermore, the learned latent representations were transferable across participants, with more than half of cross-subject models performing within 0.01 accuracy of their subject-specific counterparts. These findings demonstrate that task-agnostic representation learning can capture physiologically meaningful multimodal neural representations that remain transferable across individuals, offering a promising foundation for more robust and reusable BCI pipelines.

neuroscience↗

bioRxiv2026

Structure-Constrained Intrinsic Timescales Across Tasks

Intrinsic neural timescale (INT) quantifies the persistence of spontaneous neural dynamics and offers a principled metric for characterizing brain-wide temporal organization. Although a hierarchy of INTs has been established during rest, how task engagement reconfigures this organization and how it is constrained by the structural connectome (SC) remain poorly understood. Here, we systematically mapped whole-brain INT using high-resolution fMRI data from the Human Connectome Project during rest and seven tasks spanning working memory, gambling, motor, language, social, relational, and emotion domains. Task engagement induced robust, regionally heterogeneous changes in INT while largely preserving the brain-wide temporal hierarchy across cognitive states. SC-INT coupling remained strong but consistently decreased during tasks, indicating that anatomical architecture continues to constrain INT, although its influence is attenuated under task demands. To investigate these findings mechanistically, we employed a multiscale, whole-brain neuronal-network model, which revealed that INT increase and peak within a broad critical-like regime. Strong SC-INT coupling, as observed empirically, emerged in the subcritical regime, weakened progressively with increasing network excitability, and reversed in the supercritical regime. These results demonstrate that task engagement reconfigures INTs while maintaining their hierarchical organization, suggesting that both resting and task states operate largely within a common subcritical dynamical regime.

neuroscience↗

bioRxiv2026

Temporal persistence and structural organization of neuronal avalanche dynamics

Brain activity can be understood as a sequence of neuronal avalanches, i.e., transient episodes of coordinated activation that emerge across scales, from individual neurons and local networks to whole-brain dynamics. Avalanches are typically characterized by features such as size, duration, number of active components, and the silent time separating consecutive events. Although these features have been extensively characterized through their marginal distributions, their temporal organization and dependence on the underlying brain architecture remain poorly understood, leaving us without a framework for embedding neuronal avalanches within slower brain dynamics. Here, we analyzed eyes-closed resting-state magnetoencephalography recordings and the corresponding structural connectomes from 30 healthy participants to investigate the dynamics of avalanche sizes and silent times. We found that large avalanches preferentially followed short silent times, whereas small avalanches were more likely to occur after long silent periods. Based on the empirical joint distributions of avalanche size and silent time, we could define four types of events occurring above chance levels (avalanche large or small, preceding pause long or short). Mixed categories, combining a small value of one feature with a large value of the other, occurred more frequently than expected, while same-category events happened less often than chance. Furthermore, consecutive events tended to remain in the same category, a phenomenon referred to as persistence. We next investigated whether a brain region's connectivity profile shapes its propensity to participate in avalanches of different sizes. More strongly connected regions participated most often in small avalanches, whereas weakly connected regions were preferentially recruited during large avalanches. This pattern may reflect the greater sensitivity of highly connected hubs to fluctuations propagating through the network, resulting in frequent but spatially contained events. By contrast, the recruitment of more peripheral regions may require broader and stronger collective activity, occurring only during rarer, large-scale avalanches. In contrast, regional participation showed no clear association with the silent time preceding an avalanche. Together, these findings show that neuronal avalanches are neither temporally independent nor anatomically unconstrained: their sequence retains a memory of preceding events, while structural topology shapes which regions are recruited as avalanches grow. By connecting avalanche dynamics with slower temporal organization and the structural connectome, our results provide a multiscale framework for understanding how transient events are embedded within ongoing brain activity.

neuroscience↗

bioRxiv2026

Departure from OFF-State Microstate Dynamics Tracks Levodopa Response in Parkinson's Disease

Although dopaminergic therapies in Parkinsons disease primarily restore dopamine within nigrostriatal circuits, symptoms are better indexed by whole-brain dynamics than by local activity. In this manuscript, we hypothesize that L-Dopa therapy affects whole-brain dynamics alike, which in turn relate to clinical improvement. To test this hypothesis, we conducted a repeated-measures, source-reconstructed MEG study in 13 bradykinetic-dominant PD patients, recording resting-state cortical activity OFF medication and [~]1 hour after levodopa administration (ON). We characterize brain dynamics using a microstate framework, in which transition probabilities between microstates are used to contrast pathological OFF-state dynamics with those in the ON-state. Microstate dynamics were stable within medication states but reconfigured by L-Dopa, with greater departures from the OFF-state pattern associated, at the individual level, with larger clinical improvements. Our results suggest that individualized changes in microstate dynamics may serve as a neurophysiological marker of dopaminergic responsiveness.

neuroscience↗

bioRxiv2026

Data-driven gait cycle decomposition based on whole-body coordination dynamics

The study of human locomotion has long relied on descriptive frameworks of the gait cycle, which have provided essential insights into the functional phases of walking and their underlying biomechanical demands. While these models remain highly informative, they are largely based on observational analyses and may not fully capture the continuous, global coordination that characterizes human movement. The present study proposes an integrated framework to study whole-body coordination. This framework combines network theory with non-negative matrix factorization (NNMF) to treat gait as a dynamic system of coordinated joint interactions. Using three-dimensional kinematic data from 60 healthy subjects, we constructed a representation of whole-body coordination across time, named "dynamic kinectome". It was then decomposed using NNMF to extract spatial patterns of joint coordination and their corresponding temporal activations, allowing for an interpretable characterization of locomotor organization while preserving physiological meaning. Our analysis extracted six robust, highly consistent, and symmetrical coordination patterns across participants, effectively capturing the primary functional subtasks of locomotion. Rather than challenging classical phase descriptions, these findings enrich them by showing how coordination emerges as a continuous, often proactive process that can extend across conventional phase boundaries and systematically integrates the upper limbs for dynamic stability. Overall, this study provides a holistic, data-driven perspective on human locomotion, offering a promising basis for future investigations in motor control and may contribute to the development of sensitive biomarkers for clinical and rehabilitative applications.

bioengineering↗

bioRxiv2026

A microcircuit model of astrocytic potassium buffering and neural synchronization

Neural synchronization is fundamental to brain function and, when it becomes excessive, underlies pathological conditions such as epilepsy. Among brain regions, the temporal lobes, and the hippocampus in particular, exhibit the highest epileptogenic potential, with mesial temporal lobe epilepsy representing the most prevalent form of the condition in humans. Within the hippocampus, extracellular potassium dynamics are central to non-synaptic epileptiform activity, and astrocytic potassium buffering mechanisms have emerged as key regulators of network excitability. Yet the specific contributions of astrocytic gap-junction coupling and potassium spatial buffering to neuronal synchronization across different spatial scales remain poorly understood. To address this gap, we developed a microcircuit biophysical model consisting of two astrocyte-neuron modules, each comprising one astrocyte coupled to five neurons. Astrocyte-neuron interactions are mediated exclusively through shared extracellular potassium dynamics. Using a reduced astrocyte model that captures both local membrane and syncytial potassium buffering, we systematically investigated how astrocytic potassium handling shapes neuronal activity patterns and inter-module synchronization. Our results demonstrate that astrocytes prevent the emergence of pathological states -- such as sustained ictal activity and depolarization block, by stabilizing extracellular potassium levels. Furthermore, we show that astrocytic gap-junction coupling strength critically regulates phase synchronization between neuronal modules: stronger coupling promotes inter-module synchrony under physiological conditions, whereas impaired astrocytic function drives networks toward pathological hypersynchronization when extracellular potassium is elevated. These findings support the hypothesis that astrocytic networks impose modularity on hippocampal neuronal assemblies, and suggest that astrocytic connexins may represent a relevant therapeutic target in epilepsy and other disorders characterized by aberrant neural synchronization. Author summary

neuroscience↗

bioRxiv2026

Hormone-Dependent Microstates and the Reconfiguration of Resting-State Dynamics across the Menstrual Cycle

While behavioral fluctuations across the menstrual cycle (MC) are well-documented, the neural underpinnings of these changes remain elusive. This study investigated the hypothesis that cyclic variations in sex hormones modulate large-scale brain activation patterns. To test this, longitudinal magnetoencephalographic (MEG) recordings were acquired and source reconstructed from 24 naturally cycling women across three distinct MC phases: early follicular, peri-ovulatory, and mid-luteal. Microstate analysis was employed to characterize large-scale cortical dynamics as "visits" to specific global configurations (i.e., maps) of brain activity. Our results revealed significant variations in the occurrence of specific microstate maps, particularly between the early follicular and mid-luteal phases. Furthermore, the occurrence of these specific configurations was significantly associated with fluctuations in hormone levels. Critically, both the hormonal levels and microstate dynamics were predictive of individual longitudinal changes in psychological well-being. These findings propose a neurophysiological substrate for the behavioral effects of hormonal cycling, identifying specific topographic maps whose dynamics are sensitive to the hormonal profile and carry predictive power for psychological health. Collectively, these results underscore the necessity of accounting for the MC in neuroimaging research and introduce a novel framework for defining microstates (Hormone-Dependent Microstates - HDMs) with respect to slowly changing dynamical properties across a month-long timescale.

neuroscience↗

bioRxiv2026

Modularity-dependent storage of dynamic spiking patterns: bridging micro- and mesoscopic representations

Biological systems rely on asynchronous and temporally overlapping dynamics, allowing for the concurrent activation of multiple processes. This principle is particularly evident in brain function, where cognitive tasks engage distributed, interacting regions rather than sequentially isolated ones. To investigate the mechanisms enabling such coordination, we study a modular spiking neural network composed of leaky integrate-and-fire neurons and governed by spike-timing-dependent plasticity (STDP). Our model stores modular spatiotemporal patterns both at the mesoscopic level (sequences of modules) and at the microscopic level (precise spike timings) and includes a parameter, , which regulates the degree of temporal overlap between modules activations. By tuning , the network transitions from sequential to overlapping regimes, ranging from synfire chain-like dynamics to fully co-activated modules. We investigate how the temporal structure influences the networks capacity to encode and selectively retrieve multiple dynamical patterns, while considering biological constraints such as the cost of long-range connectivity. Our results offer insight into how spatiotemporal coding and network organisation support robust, large-scale memory storage and replay.

neuroscience↗

bioRxiv2026

EEG dynamics in Lewy body diseases is related to clinical fluctuations and degeneration of locus coeruleus

This work investigates aperiodic bursts of neuronal activity, referred to as neuronal avalanches, which are thought to support flexible information processing in Lewy body (LB) disease. We compared avalanche-derived features across LB disease phenotypes (prodromal dementia with LB and Parkinsons disease, PD), with healthy controls (HC). Based on the premise that focal neurodegeneration alters whole-brain dynamics, we examined whether the "richness" of braindynamics was associated with clinical symptoms and with the microstructural integrity of catecholaminergic nuclei - the right caudal locus coeruleus (rcLC) and the substantia nigra pars compacta (SNpc). The sample comprised 30 HC, 56 cognitively normal individuals with core clinical features of dementia with LB (CN-CCF), 34 individuals with mild cognitive impairment with LB (MCI-LB), and 30 advanced PD patients. Neuronal avalanches were extracted from source-reconstructed resting-state EEG and described by their count, the number of unique spatial patterns (flexibility), hemispheric symmetry of flexibility, and avalanche transition matrices. LC and SNpc integrity were assessed using neuromelanin-sensitive MRI and free-water diffusion imaging, respectively. Lewy body disease groups exhibited increased flexibility relative to HC, which was, in prodromal groups, associated with cognitive fluctuations and REM sleep behavior disorder symptoms, but not parkinsonism. Flexibility was negatively related to rcLC integrity, which was lowest in MCI-LB. These findings suggest that elevated flexibility reflects dysregulated brain dynamics linked to malignant non-motor features of LB diseases and to the caudal LC involvement.

neuroscience↗

bioRxiv2025

The role of structural heterogeneity in shaping whole-brain dynamics across scales

The brain must rapidly and dynamically recruit the appropriate brain regions to respond to environmental changes and sustain flexible cognition. An extensive body of literature views the ability to spontaneously explore a rich repertoire of activity patterns, even during rest, as a correlate of the capacity to respond rapidly to internal fluctuations and external stimuli. Moreover, brain flexibility at rest successfully indexes pathological conditions such as Parkinsons disease, multiple sclerosis, and amyotrophic lateral sclerosis. However, previous work provides no information about the contribution of each region to the flexibility of brain dynamics. Given the functional and structural heterogeneity of brain regions, we hypothesized their roles in sustaining flexibility to be heterogeneous and related to regional structural properties. To systematically investigate these hypotheses, we define the flexibility gradient (FG), a new measure, which quantifies regional contributions to flexibility. We employed three datasets spanning different recording techniques and a range of spatial and temporal scales: 47 MEG recordings, 11 EEG recordings, and 77 fMRI recordings along with the corresponding tractographies. We found that FG is not homogeneously distributed, with associative and occipital regions contributing the most. This gradient is symmetric across left and right hemispheres, and the structural properties of brain regions correlate with their contribution to flexibility. Our findings are stable across modalities and temporal scales, highlighting that brain flexibility is not only a global dynamical property but also a spatially structured feature shaped by the anatomical organization of the brain.

neuroscience↗

bioRxiv2025

Intra- and Interhemispheric Signatures of Criticality at the Onset of Synchronization

The cerebral cortex must flexibly alternate between locally segregated activity that supports specialization and long-range interactions that enable integration. How cortical networks balance these competing demands remains unclear. We propose that fluctuations around a critical point between ordered and disordered phases provide a natural framework to understand coordinated neuronal activity across scales. Using simultaneous spiking recordings from the left and right prefrontal cortex (PFC) of freely behaving rats, we found that signatures of criticality emerged at the onset of neuronal synchronization, both locally within individual hemispheres and globally across the combined population. Over time, cortical activity explored a continuum of intra-and inter-hemispheric synchrony levels, including states in which neurons were locally desynchronized yet maintained interhemispheric coupling. A computational model operating near its critical regime reproduced these empirical patterns, capturing the characteristic relationship between local and long-range coordination. These results suggest that cortical networks achieve flexible transitions between local and global computation by fluctuating around a critical regime.

neuroscience↗

bioRxiv2025

The Connectome Modulates Critical Brain Dynamics Across Local and Global Scales

Neuronal activity in the brain has been hypothesized to operate near criticality-a dynamical regime poised between order and disorder that maximizes information processing, adaptability, and dynamic range. While criticality has been extensively studied at local scales (within neuronal populations) and at global scales (across interacting brain regions), the interplay between these levels remains poorly understood. Here, we propose a multiscale computational framework that bridges local and global criticality within a single, mechanistic model. At the mesoscopic level, individual brain regions are represented by neural mass models tuned near the transition between asynchronous and synchronous regimes. These regions are then coupled via an empirically derived mouse connectome to investigate how structural connectivity shapes the emergence of large-scale coordination. We show that (i) local near critical dynamics for an isolated brain region can be faithfully reproduced within a mean-field model framework, (ii) local distance to criticality is modulated by long-range coupling, (iii) whole-brain simulations reveal non-linear gradients of timescales and heterogeneous shifts towards/away from local criticality, and (iv) global criticality, manifested in scale-free avalanche distributions and optimal functional connectivity, emerges when local populations are locally tuned near criticality and coupled within an optimal range. These results demonstrate that local and global criticality are dynamically intertwined but not directly aligned, and that their relationship depends on the underlying structural connectivity. Our multiscale modeling framework provides a tractable tool for generating testable hypotheses on how brain criticality co-arises across scales and how it may be modulated in health and disease.

neuroscience↗

bioRxiv2025

Pre-stimulus Brain States Predict and Control Variability in Stimulation Responses

Does the ongoing brain state determine how it responds to localized electrical stimulation? This fundamental question has major implications for neuroscience and medicine, as stimulation outcomes remain highly variable even when identical parameters (how) and target sites (where) are used. Such unpredictability undermines reproducibility, limits clinical reliability, and forces current protocols to rely on empirical trial-and-error rather than principled, evidence-based strategies. Mounting evidence suggests that the brains state prior to stimulation-a crucial when factor-shapes responses, yet the most reliable predictive markers remain unknown. Here, we systematically characterize the links between pre-stimulus (spontaneous) activity and post-stimulus (evoked) responses using simultaneous high-density EEG and stereotactic EEG from 36 epilepsy patients across[~] 320 sessions (>10,000 individual stimulations). We show that large-scale neural dynamics robustly predict stimulation outcomes, with a subset of measures-particularly network synchronization, functional connectivity, and spatiotemporal signal diversity-consistently forecasting responses across sessions. Whole-brain activity enhanced prediction compared to local assessments, and predictability varied across networks, being strongest in sensorimotor and visual regions. These findings establish a quantitative framework for state-dependent brain stimulation: by timing interventions to optimal pre-stimulus states, variability can be reduced and reproducibility enhanced. Our results directly address the fundamental question of where and when to stimulate, providing a pathway toward evidence-based protocols with improved therapeutic precision.

neuroscience↗

bioRxiv2025

Next generation neural mass model with dopamine modulation mediated by D1-type receptors

Neuromodulation is a complex process in which chemical substances modulate brain activity, allowing its rich repertoire of behaviors. Among these substances, dopamine has a preponderant role, being involved in several mechanisms. Moreover, dysfunctions in the dopamine connections has been observed in pathology, such as Parkinsons disease and schizophrenia. To investigate the mechanism of neuromodulation, we expand a previously proposed mean-field formalism, that describes the average activity of a neural population, by adding the effect of dopamine modulation. This mean-field reduction allows for a direct comparison with the underlying neural network to test its ability to qualitatively reproduce population behavior. The resulting mathematical framework is able to capture network activity in distinct dynamical regimes and transitions between them. Thus, this approach provides a reliable foundation for the development of personalized medicine tools to study how the effect of dopamine modulation on single brain region affects whole brain behavior.

neuroscience↗

bioRxiv2025

Neuronal avalanches as a predictive biomarker of BCI performance- towards a tool to guide tailored training

Motor imagery-based Brain-Computer Interfaces (BCIs) restore control in persons with motor impairments, but up to 30% of users struggle--a phenomenon known as "BCI inefficiency". This study tackles a key limitation of current protocol: the use of fixed-length sessions training paradigms that ignore individual learning variability. We propose a novel approach based on neuronal avalanches-- spatiotemporal cascades of brain activities--as biomarkers to characterize and predict user-specific learning. From electroencephalography data across four sessions in 20 subjects, we characterized avalanches by their length and their spatiotemporal size. These features showed significant training and task effects and were found to correlate to BCI performance across sessions. We further assessed their ability to predict BCI success through longitudinal models, achieving up to 91% accuracy, improved by spatial filtering on selected brain regions. These findings demonstrate the utility of neuronal avalanche dynamics as robust biomarkers for BCI training, supporting the development of personalized protocols aimed at mitigating BCI illiteracy.

neuroscience↗

bioRxiv2025

Artificial Intelligence for automatic movement recognition: a network-based approach

Introductionautomatic movement recognition is often used to support various fields such as clinical, sports, and security. To date, there is a lack of a classification feature that is both interpretable and not movement-specific, characteristics that would enhance generalization and adaptability. Previous studies on motion analysis have shown that coordination properties extracted from full-body movement using network theory can describe specific movement characteristics, making coordination a potential feature for classification. Methodstherefore, we leveraged kinematic data from 168 individuals performing 30 different movements, published in an online dataset. Using network theory, we reduced data dimensionality, obtaining a coordination matrix called the kinectome. By applying support vector machine algorithms, we compared the classification performance of the kinectome with that of principal component analysis, used as an alternative data reduction method. Resultsthe classification accuracy of the kinectome (0.99 {+/-} 0.01) was significantly higher (pFDR < 0.001) than that of PCA (0.96 {+/-} 0.04). Moreover, unlike PCA, the kinectome demonstrated resilience to data loss, robustness to derived measures, independence from the classification algorithm, and clear interpretability of features. Discussionour results suggest that kinnectome-based features could capture interpretable changes between movements that could pave the way to new automatic movement recognition approaches dedicated to a wide range of applications, in particular sport training and physical readaptation, and designed for non-data scientists experts.

bioengineering↗

bioRxiv2024

Principles and Operation of Virtual Brain Twins

Current clinical methods often overlook individual variability by relying on population-wide trials, while mechanism-based trials remain underutilized in neuroscience due to the brains complexity. A Virtual Brain Twin (VBT) is a personalized digital replica of an individuals brain, integrating structural and functional brain data into advanced computational models and inference algorithms. By bridging the gap between molecular mechanisms, whole-brain dynamics, and imaging data, VBTs enhance the understanding of (patho)physiological mechanisms, advancing insights into both healthy and disordered brain function. Central to VBT is the network modeling that couples mesoscopic representation of neuronal activity through white matter connectivity, enabling the simulation of brain dynamics at a network level. This transformative approach provides interpretable predictive capabilities, supporting clinicians in personalizing treatments and optimizing interventions. This manuscript outlines the key components of VBT development, covering the conceptual, mathematical, technical, and clinical aspects. We describe the stages of VBT construction-from anatomical coupling and modeling to simulation and Bayesian inference-and demonstrate their applications in resting-state, healthy aging, epilepsy, and multiple sclerosis. Finally, we discuss potential extensions to other neurological disorders, such as Parkinsons disease, and explore future applications in consciousness research and brain-computer interfaces, paving the way for advancements in personalized medicine and brain-machine integration. "The brain is conceived as a self-organizing system operating close to instabilities where its activities are governed by collective variables, the order parameters, that enslave the individual parts, i.e., the neurons." Professor Hermann Haken (1927-2024).

neuroscience↗