Biology subjects

Rabuffo, G.

Publications and source records attributed to Rabuffo, G..

15 recordsLinked to original sources

bioRxiv2026

A personalized map of where, when, and how to stimulate the brain to elicit controlled responses

Brain stimulation has transformed the treatment of several neurological and psychiatric disorders and is now widely used to read out causal interactions in the human brain. However, its effects vary from one trial to the next, even when the stimulation parameters are kept identical. Identifying the sources of this variability and developing new strategies to reduce it are key to achieving more controllable interventions. Yet, exhaustive testing in real experiments is unfeasible, because each participant can only be probed at a handful of sites under conditions the experimenter cannot fully control. Here we use personalized brain models to map how stimulation responses vary across target sites and with the brains ongoing state. We show that stimulation responses are jointly determined by the targets position along the cortical unimodal-to-transmodal hierarchy and the brains global ongoing activity, with lower-activity states yielding larger responses. Accordingly, timing stimulation to low-activity periods reduces trial-to-trial variability in response magnitude. Even without state information, joint stimulation of specific region pairs reduces variability compared with stimulating either region alone. Together, these findings identify where, when, and how to stimulate the brain to achieve more reproducible responses, with concrete predictions for closed-loop and circuit-level stimulation protocols.

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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.

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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.

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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.

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bioRxiv2025

The Topological Architecture of Brain Identity

Accurately identifying individuals from brain activity--functional fingerprinting--is a powerful tool for understanding individual variability and detecting brain disorders. Most current approaches rely on functional connectivity (FC), which measures pairwise correlations between brain regions. However, FC is limited in capturing the higher-order, multiscale structure of brain organization. Here, we propose a novel fingerprinting method based on homological scaffolds, a topological repre-sentation derived from persistent homology of resting-state fMRI data. Using data from the Human Connectome Project (n = 100), we show that scaffold-based fingerprints achieve near-perfect identification accuracy ([~] 100%), outperforming FC-based methods (90%), and remain robust across preprocessing pipelines, atlas choices, and even with drastically shortened scan durations. Unlike FC, in which fingerprinting features localize within networks, scaffolds derive their discriminative power from inter-network connections, revealing the existence of individual mesoscale organizational signatures. Finally, we show that scaffolds bridge redundancy and synergy by balancing redundant information along high-FC border edges with synergistic interactions across the topological voids they enclose. These findings establish topological scaffolds as a powerful tool for capturing individual variability, revealing that unique signatures of brain organization are encoded in the interplay between mesoscale network integration and information dynamics.

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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).

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bioRxiv2024

Magnetoencephalography dimensionality reduction informed by dynamic brain states

Complex spontaneous brain dynamics mirror the large number of interactions taking place among regions, supporting higher functions. Such complexity is manifested in the inter-regional dependencies among signals derived from different brain areas, as observed utilising neuroimaging techniques, like magnetoencephalography. The dynamics of this data produce numerous subsets of active regions at any moment as they evolve. Notably, converging evidence shows that these states can be understood in terms of transient coordinated events that spread across the brain over multiple spatial and temporal scales. Those can be used as a proxy of the "effectiveness" of the dynamics, as they become stereotyped or disorganised in neurological diseases. However, given the high dimensional nature of the data, representing them has been challenging thus far. Dimensionality reduction techniques are typically deployed to describe complex interdependencies and improve their interpretability. However, many dimensionality reduction techniques lose information about the sequence of configurations that took place. Here, we leverage a newly described algorithm, PHATE (Potential of Heat-diffusion for Affinity-based Transition Embedding), specifically designed to preserve the dynamics of the system in the low-dimensional embedding space. We analysed source-reconstructed resting-state magnetoencephalography from 18 healthy subjects to represent the dynamics of the configuration in low-dimensional space. After reduction with PHATE, unsupervised clustering via K-means is applied to identify distinct clusters. The topography of the states is described, and the dynamics are represented as a transition matrix. All the results have been checked against null models, providing a parsimonious account of the large-scale, fast, aperiodic dynamics during resting-state.

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bioRxiv2024

Linking neuronal avalanches with oscillatory and broadband 1/f activities in the resting human brain

ObjectiveNarrowband oscillations (NOs) and Broadband Arrhythmic Activity (BAA) are valuable conceptualizations extensively used to interpret brain data, with NOs linked to communication and synchronization and BAA encompassing scale-free dynamics and neuronal avalanches. Although both frameworks offer critical in-sights into brain function, they have largely evolved in parallel, with limited integration and no unifying mechanistic account of how these dynamics interact to generate transient, Salient Events (SEs). This gap is particularly pressing given recent interest in how SEs--brief ({approx} 100 ms) bursts of activity coordinated across brain regions--relate to large-scale brain function and cognition. To address this, we introduce a signal-level framework that links the Fourier spectral properties (oscillation-domain) of neural signals to the emergence of realistic SEs in the time-domain from NOs and BAA. MethodsOur approach is grounded in a novel concept--Spectral Group Delay Consistency (SGDC)--along with associated measures that quantify the temporal alignment of spectral components and capture the conditions under which Nos and BAA coalesce into transient, burst-like events. Unlike traditional power- or phase-based measures, or higher-order statistical metrics such as kurtosis and cokurtosis, SGDC provides a signal-level mechanistic account of how local and large-scale SEs emerge from the spectral structure of the underlying signals. Empirical validation is provided using source-reconstructed MEG data from a large cohort and a comprehensive array of features characterizing the statistical, spatiotemporal and spectral properties of observed SEs. ResultsWe found that the SEs identified in our empirical MEG dataset can be segregated based on their spectral signature in two main groups having different propagation patterns. Using generative models based on the SGDC mechanism we provide a theoretical framework to interpret these experimental results showing that cluster 2 events are specifically related to the long-range spread of narrowband alpha bursts across the brain network (i.e., SNEs: Salient Network Events), whereas cluster 1 events correspond to more short-lived and spatially localized fluctuations mainly promoted by the BAA (i.e., SLEs: Salient Local Events). We also provide analytical arguments and numerical simulations showing that a) high SGDC in specific narrow frequency bands, b) transient cross-regional coherent NOs and c) BAA, are all key ingredients for the emergence of realistic SNEs. SignificanceWe combine experimental evidence supported by a signal-level analytical framework and numerical simulations based on generative models to demonstrate that transient phase-structured alpha bursts, shaped by the SGDC mechanism, contribute to long-range coordination during rest. This extends the communication-through-coherence hypothesis to the transient domain. Additionally, SGDC links to findings that NOs interact with fast microstates ({approx}100 - 200 ms) and may modulate long-range dependencies across timescales. While previous studies have described SEs within the framework of neuronal avalanches, they often lacked a generative, signal-level account. Here, we bridge that divide by offering a mathematically grounded and empirically validated framework that accounts for oscillatory and aperiodic bursts perspectives on brain activity. HighlightsO_LISalient network events propagating across the brain during spontaneous resting state activity, are highly structured in terms of their spatial, temporal and spectral properties. C_LIO_LIThe spectral group delay consistency framework provides a signal-level mechanism that accounts for transient salient network events emerging from oscillatory components of the brain activity. C_LIO_LINarrowband oscillations and broadband arrhythmic activity interact to shape the timing and spatial extent of salient events. C_LIO_LISpectral group delay consistency, transient cross-regional coherent narrowband oscillations and broadband arrhythmic activity, are all key ingredients for the emergence of realistic salient network events. C_LIO_LISalient network events during rest reflect large-scale spreading of synchronized alpha band activity, which may play a functional role as a long-range interaction mechanism in the human brain. C_LI

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bioRxiv2023

Neuronal Avalanches in Naturalistic Speech and Music Listening

Neuronal avalanches are cascade-like events ubiquitously observed across imaging modalities and scales. Aperiodic timing and topographic distribution of these events have been related to the systemic physiology of brain states. However, it is still unknown whether neuronal avalanches are correlates of cognition, or purely reflect physiological properties. In this work, we investigate this question by analyzing intracranial recordings of epileptic participants during rest and passive listening of naturalistic speech and music stimuli. During speech or music listening, but not rest, participants brains "tick" together, as the timing of neuronal avalanches is stimulus-driven and hence correlated across participants. Auditory regions are strongly participating in coordinated neuronal avalanches, but also associative regions, indicating both the specificity and distributivity of cognitive processing. The subnetworks where such processing takes place during speech and music largely overlap, especially in auditory regions, but also diverge in associative cortical sites. Finally, differential pathways of avalanche propagation across auditory and non-auditory regions differentiate brain network dynamics during speech, music and rest. Overall, these results highlight the potential of neuronal avalanches as a neural index of cognition. Authors summaryNeuronal avalanches consist of collective network events propagating across the brain in short-lived and aperiodic instances. These salient events have garnered a great interest for studying the physics of cortical dynamics, and bear potential for studying brain data also in purely neuroscientific contexts. In this work we investigated neuronal avalanches to index cognition, analyzing an intracranial stereo electroencephalography (iEEG) dataset during speech, music listening and resting state in epileptic patients. We show that neuronal avalanches are consistently driven by music and speech stimuli: avalanches co-occur in participants listening to the same auditory stimulus; avalanche topography differs from resting state, presenting partial similarities during speech and music; avalanche propagation changes during speech, music, and rest conditions, especially along the pathways between auditory and non auditory regions. Our work underlines the distributed nature of auditory stimulus processing, supporting neuronal avalanches as a valuable and computationally advantageous framework for the study of cognition in humans.

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bioRxiv2023

Probing the Mechanisms of Global Brain Reconfiguration after Local Manipulations

Understanding how localized brain interventions translate into whole-brain dynamics is crucial for deciphering neural function and tailoring therapeutic strategies. Combining mouse experimental datasets of focal interventions (thalamic lesion and chemogenetic silencing of cortical hubs), we demonstrate both local and global effects. Using whole-brain simulations of experimental data, we not only confirm the distributed nature of local manipulations but also offer mechanistic insights into these processes. Our simulations predict specific alterations in firing rates and spectral characteristics across specific brain networks, leading to structured changes in functional connectivity patterns. Some of these predictions have been empirically validated. Notably, the affected brain subnetworks--and their resultant signatures of change--are contingent on the original intervention site, suggesting a method to accurately localize the source of alteration. Our results provide a general framework for interpreting localized intervention effects, offering insights that could refine clinical interventions for focal brain disorders by enabling targeted circuit-level neuromodulation strategies.

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bioRxiv2023

Altered spread of large scale brain dynamics is influenced by cortical thickness organization in temporal lobe epilepsy: a MRI and hdEEG study

Temporal lobe epilepsy (TLE) is a brain network disorder characterized by alterations at both the structural and the functional level. It remains unclear how structure and function are related and whether this has any clinical relevance. In the present work, we adopted a novel methodological approach investigating how network structural features influence the large-scale dynamics. The functional network was defined by the spatio-temporal spreading of aperiodic bursts of activations (neuronal avalanches), as observed utilizing high-density electroencephalography (hdEEG) in TLE patients. The structural network was modeled as the region-based thickness covariance. Loosely speaking, we quantified the similarity of the cortical thickness of any two brain regions, both across groups, and at the individual level, the latter utilizing a novel approach to define the personalized covariance network (pCN). In order to compare the structural and functional networks (at the nodal level), we studied the correlation between the probability that a wave of activity would propagate from a source to a target region, and the similarity of the source region thickness as compared to other target brain regions. Building on the recent evidence that large-waves of activities pathologically spread through the epileptogenic network in TLE, also during resting state, we hypothesize that the structural cortical organization might influence such altered spatio-temporal dynamics. We observed a stable cluster of structure-function correlation in the bilateral limbic areas across subjects, highlighting group specific features for left, right and bilateral TLE. The involvement of contralateral areas was observed in unilateral TLE. We showed that in temporal lobe epilepsy alterations of structural and functional networks pair in the regions where seizures propagate and are linked to disease severity. In this study we leveraged on a well-defined model of neurological disease and pushed forward personalization approaches potentially useful in clinical practice. Finally, the methods developed here could be exploited to investigate the relationship between structure-function networks at subject level in other neurological conditions.

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bioRxiv2023

Large-scale brain signatures of fluid dynamics and responsiveness linked to consciousness

Signatures of consciousness are found in spectral and temporal properties of neuronal activity. Among these, spatiotemporal complexity after a perturbation has recently emerged as a robust metric to infer levels of consciousness. Perturbation paradigms remain, however, difficult to perform routinely. To discover alternative paradigms and metrics, we systematically explore brain stimulation and resting-state activity in a whole-brain model. We find that perturbational complexity only occurs when the brain model operates within a specific dynamical regime, in which spontaneous activity produces a large degree of functional network reorganizations referred to as being fluid. The regime of high brain fluidity is characterized by a small battery of metrics drawn from dynamical systems theory and predicts the impact of consciousness-altering drugs (Xenon, Propofol, and Ketamine). We validate the predictions in a cohort of 15 subjects at various stages of consciousness and demonstrate their agreement with previously reported perturbational complexity, but in a more accessible paradigm. Beyond the facilitation in clinical use, the metrics highlight complexity properties of brain dynamics in support of the emergence of consciousness.

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bioRxiv2022

The structured flow on the brain's resting state manifold

Spontaneously fluctuating brain activity patterns that emerge at rest have been linked to brains health and cognition. Despite detailed descriptions of the spatio-temporal brain patterns, our understanding of their generative mechanism is still incomplete. Using a combination of computational modeling and dynamical systems analysis we provide a mechanistic description of the formation of a resting state manifold via the network connectivity. We demonstrate that the symmetry breaking by the connectivity creates a characteristic flow on the manifold, which produces the major data features across scales and imaging modalities. These include spontaneous high amplitude co-activations, neuronal cascades, spectral cortical gradients, multistability and characteristic functional connectivity dynamics. When aggregated across cortical hierarchies, these match the profiles from empirical data. The understanding of the brains resting state manifold is fundamental for the construction of task-specific flows and manifolds used in theories of brain function such as predictive coding. In addition, it shifts the focus from the single recordings towards brains capacity to generate certain dynamics characteristic of health and pathology.

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bioRxiv2020

Neuronal cascades shape whole-brain functional dynamics at rest

At rest, mammalian brains display remarkable spatiotemporal complexity, evolving through recurrent brain states on a slow timescale of the order of tens of seconds. While the phenomenology of the resting state dynamics is valuable in distinguishing healthy and pathological brains, little is known about its underlying mechanisms. Here, we identify neuronal cascades as a potential mechanism. Using full-brain network modeling, we show that neuronal populations, coupled via a detailed structural connectome, give rise to large-scale cascades of firing rate fluctuations evolving at the same time scale of resting-state networks. The ignition and subsequent propagation of cascades depend upon the brain state and connectivity of each region. The largest cascades produce bursts of Blood-Oxygen-Level-Dependent (BOLD) co-fluctuations at pairs of regions across the brain, which shape the simulated resting-state network dynamics.We experimentally confirm these theoretical predictions. We demonstrate the existence and stability of intermittent epochs of functional connectivity comprising BOLD co-activation bursts in mice and human fMRI. We then provide evidence for the existence and leading role of the neuronal cascades in humans with simultaneous EEG/fMRI recordings. These results show that neuronal cascades are a major determinant of spontaneous fluctuations in brain dynamics at rest. 1 Significance StatementFunctional connectivity and its dynamics are widely used as a proxy of brain function and dysfunction. Their neuronal underpinnings remain unclear. Using connectome-based modeling, we link the fast microscopic neuronal scale to the slow emergent whole-brain dynamics. We show that cascades of neuronal activations spontaneously propagate in resting state-like conditions. The largest neuronal cascades result in the co-fluctuation of Blood-Oxygen-Level-Dependent signals at pairs of brain regions, which in turn translate to stable brain states. Thus, we provide a theoretical framework for the emergence and the dynamics of resting-state networks. We verify these predictions in empirical mouse fMRI and human EEG/fMRI datasets measured in resting states conditions. Our work sheds light on the multiscale mechanisms of brain function.

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bioRxiv2020

Time scale separation of information processing between sensory and associative regions

A hierarchy of local timescales with a back (sensory)-to-front (prefrontal) gradient reflects brain region specialization. However, cognitive processes emerge from the coordinated activity across regions, and the corresponding timescales should refer to the interactions rather than to regional activity. Using edgewise connectivity on magnetoencephalography signals, we demonstrate a reverse front-to-back gradient when non-local interactions are prominent. Thus, the timescales are dynamic and reconfigure between back-to-front and front-to-back patterns.

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