Biology subjects

Sorrentino, P.

Publications and source records attributed to Sorrentino, P..

At least 37 records · Page 2Linked to original sources

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.

neuroscience↗

bioRxiv2024

Criticality explains structure-function relationships in the human brain

Healthy brain exhibits a rich dynamical repertoire, with flexible spatiotemporal patterns replays on both microscopic and macroscopic scales. How do fixed structural connections yield a diverse range of dynamic patterns in spontaneous brain activity? We hypothesize that the observed relationship between empirical structure and functional patterns is best explained when the microscopic neuronal dynamics is close to a critical regime. Using a modular Spiking Neuronal Network model based on empirical connectomes, we posit that multiple stored functional patterns can transiently reoccur when the system operates near a critical regime, generating realistic brain dynamics and structural-functional relationships. The connections in the model are chosen as to force the network to learn and propagate suited modular spatiotemporal patterns. To test our hypothesis, we employ magnetoencephalography and tractography data from five healthy individuals. We show that the critical regime of the model is able to generate realistic features, and demonstrate the relevance of near-critical regimes for physiological brain activity.

neuroscience↗

bioRxiv2024

A neural mass model with neuromodulation

The study of brain activity and its function requires the development of computational models alongside experimental investigations to explore different effects of multiple mechanisms at play in the central nervous system. Chemical neuromodulators, such as dopamine, play central roles in regulating the dynamics of neuronal populations. In this work, we propose a modular framework to capture the effects of neuromodulators at the neural mass level. Using this framework, we formulate a specific model for dopamine dynamics affecting D1-type receptors. We detail the dynamical repertoire associated with dopamine concentration evolution and characterize the transitions across qualitatively different oscillatory states as a function of relevant parameters, along with the variations in frequency of the emergent multi-scale oscillations.

neuroscience↗

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

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

bioRxiv2023

Intermodal consistency of whole-brain connectivity and signal propagation delays

Measuring the propagation of perturbations across the human brain and their transmission delays is critical for network neuroscience, but it is a challenging problem that still requires cross-validation approaches. Here, we compare results from a recently introduced, non-invasive technique of functional delays estimation from source-reconstructed electro/magnetoencephalography, to the corresponding findings from a large dataset of cortico-cortical evoked potentials estimated from intracerebral stimulations of epileptic pharmaco-resistant patients. The two methods yield significantly similar probabilistic connectivity maps and signal propagation delays. This similarity suggests a correspondence between the mechanisms underpinning the propagation of spontaneously generated scale-free perturbations (i.e. neuronal avalanches observed in resting state activity studied using magnetoencephalography) and the spreading of cortico-cortical evoked potentials. This manuscript provides evidence for the accuracy of a subject-specific estimate of functional delays obtained non-invasively from reconstructed sources. Conversely, our findings show that estimates obtained from externally-induced perturbations capture physiological activities. In conclusion, this manuscript constitutes a cross-validation between two different modalities. Importantly, the capability to measure delays non-invasively (as per MEG) paves the way for the inclusion of functional delays in personalized large-scale brain models as well as in diagnostic and prognostic algorithms.

neuroscience↗

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.

neuroscience↗

bioRxiv2022

Brain fingerprint is based on the aperiodic, scale-free, neuronal activity

The possibility to identify subjects from their brain activity was met enthusiastically, as it bears the possibility to individualize brain analyses. However, the nature of the processes generating subject-specific features remains unknown, as the literature does not point to specific mechanisms. In particular, most of the current literature uses techniques that are based on the assumption of stationarity (e.g. Pearsons correlation), which do not hypothesize any mechanisms, and crashes against a large body of literature showing the complex, highly non-linear nature of brain activity. In this paper, we hypothesize that intermittent moments when large, non-linear perturbations spread across the brain (defined as neuronal avalanches in the context of critical dynamics) are the ones that carry subject-specific information, and that contribute the most to identifiability. To test this hypothesis, we apply the recently-developed avalanche transition matrix (ATM) to source reconstructed magnetoencephalographic data, as to characterize subject-speficic fast dynamics. Then, we perform identifiability analysis based on the ATMs, and compared the performance to more classical ways of estimating large-scale connections (which assume stationareity). We demonstrate that selecting the moments and places where neuronal avalanches spread improves identifiability (p<0.0001, permutation testing), despite the fact that most ot the data (i.e. the linear part) are discarded. Our results show that the non-linear part of the brain signals carries most of the subject-specific information, shading light on the nature of the processes that underlie subject-identifiability. Borrowing from statistical mechanics, a solid branch of physics, we provide a principled way to link emergent large-scale personalized activations to non-observable, microscopic processes.

neuroscience↗

bioRxiv2022

Measuring brain critical dynamics to inform Brain-Computer Interfaces

Large-scale interactions among multiple brain regions manifest as bursts of activations called neuronal avalanches, which reconfigure according to the task at hand and, hence, might constitute natural candidates to design brain-computer interfaces (BCI). To test this hypothesis, we used source-reconstructed magneto/electroencephalography, during resting state and a motor imagery task performed within a BCI protocol. To track the probability that an avalanche would spread across any two regions we built an avalanche transition matrix (ATM) and demonstrated that the edges whose transition probabilities significantly differed between conditions hinged selectively on premotor regions in all subjects. Furthermore, we showed that the topology of the ATMs allows task-decoding above the current gold standard. Hence, our results suggest that Neuronal Avalanches might capture interpretable differences between tasks that can be used to inform brain-computer interfaces.

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bioRxiv2021

The kinectome: a comprehensive kinematic map of human motion in health and disease

Effective human movement requires the coordinated participation of the whole musculoskeletal system. Here we propose to represent the human body movements as a network (that we named "kinectome"), where nodes are body parts, and edges are defined as the correlations of the accelerations between each pair of body parts during gait. We apply this framework in healthy individuals and patients with Parkinsons disease (PD). The network dynamics in Parkinsons display high variability, as conveyed by the high variance and the modular structure in the patients kinectomes. Furthermore, our analysis identified a set of anatomical elements that are specifically related to the balance impairment in PD. Furthermore, each participant could be identified based on its kinectome patterns, akin to a "fingerprint" of movement, confirming that our approach captures relevant features of gait. We hope that applying network approaches to human kinematics yields new insights to characterize human movement.

bioengineering↗

bioRxiv2021

On the topochronic map of the human brain dynamics

Two structurally connected brain regions are more likely to interact, with the lengths of the structural bundles, their widths, myelination, and the topology of the structural connectome influencing the timing of the interactions. We introduce an in vivo approach for measuring functional delays across the whole brain using magneto/electroencephalography and integrating them with the structural bundles. The resulting topochronic map of the functional delays/velocities shows that larger bundles have faster velocities. We estimated the topochronic map in multiple sclerosis patients, who have damaged myelin sheaths, and controls, demonstrating greater delays in patients across the network and that structurally lesioned tracks were slowed down more than unaffected ones. We provide a novel framework for estimating functional transmission delays in vivo at the single-subject and single-fiber level. One-Sentence SummaryA non-invasive estimation of the individual deterministic spatio-temporal scaffold underlying the evolution of brain dynamics.

neuroscience↗

bioRxiv2020

Brain networks and cognitive impairment in Parkinson's disease

AimThe aim of the present study is to investigate the relations between both functional connectivity and brain networks with cognitive decline, in patients with Parkinsons disease (PD). IntroductionPD phenotype is not limited to motor impairment but, rather, a wide range of non-motor disturbances can occur, cognitive impairment being one of the commonest. However, how the large-scale organization of brain activity differs in cognitively impaired patients, as opposed to cognitively preserved ones, remains poorly understood. MethodsStarting from source-reconstructed resting-state magnetoencephalography data, we applied the PLM to estimate functional connectivity, globally and between brain areas, in PD patients with and without cognitive impairment (respectively PD-CI and PD-NC), as compared to healthy subjects (HS). Furthermore, using graph analysis, we characterized the alterations in brain network topology and related these, as well as the functional connectivity, to cognitive performance. ResultsWe found reduced global and nodal PLM in several temporal (fusiform gyrus, Heschls gyrus and inferior temporal gyrus), parietal (postcentral gyrus), and occipital (lingual gyrus) areas within the left hemisphere, in the gamma band, in PD-CI patients, as compared to PD-NC and HS. With regard to the global topological features, PD-CI patients, as compared to HS and PD-NC patients, showed differences in multi frequencies bands (delta, alpha, gamma) in the Leaf fraction, Tree hierarchy (both higher in PD-CI) and Diameter (lower in PD-CI). Finally, we found statistically significant correlations between the MoCA test and both the Diameter in delta band and the Tree Hierarchy in the alpha band. ConclusionOur work points to specific large-scale rearrangements that occur selectively in cognitively compromised PD patients and correlated to cognitive impairment.

neuroscience↗

bioRxiv2020

Detection of cross-frequency coupling between brain areas: an extension of phase-linearity measurement.

The current paper proposes a method to estimate phase to phase cross-frequency coupling between brain areas, applied to broadband signals, without any a priori hypothesis about the frequency of the synchronized components. N:m synchronization is the only form of cross-frequency synchronization that allows the exchange of information at the time resolution of the faster signal, hence likely to play a fundamental role in large-scale coordination of brain activity. The proposed method, named cross-frequency phase linearity measurement (CF-PLM), builds and expands upon the phase linearity measurement, an iso-frequency connectivity metrics previously published by our group. The main idea lies in using the shape of the interferometric spectrum of the two analyzed signals in order to estimate the strength of cross-frequency coupling. Here, we demonstrate that the CF-PLM successfully retrieves the (different) frequencies of the original broad-band signals involved in the connectivity process. Furthermore, if the broadband signal has some frequency components that are synchronized in iso-frequency and some others that are synchronized in cross-frequency, our methodology can successfully disentangle them and describe the behaviour of each frequency component separately. We first provide a theoretical explanation of the metrics. Then, we test the proposed metric on simulated data from coupled oscillators synchronized in iso- and cross-frequency (using both Rossler and Kuramoto oscillator models), and subsequently apply it on real data from brain activity, using source-reconstructed Magnetoencephalography (MEG) data. In the synthetic data, our results show reliable estimates even in the presence of noise and limited sample sizes. In the real signals, components synchronized in cross-frequency are retrieved, together with their oscillation frequencies. All in all, our method is useful to estimate n:m synchronization, based solely on the phase of the signals (independently of the amplitude), and no a-priori hypothesis is available about the expected frequencies. Our method can be exploited to more accurately describe patterns of cross-frequency synchronization and determine the central frequencies involved in the coupling.

neuroscience↗

bioRxiv2020

The structural connectome constrains fast brain dynamics

Brain activity during rest displays complex, rapidly evolving patterns in space and time. Structural connections comprising the human connectome are hypothesized to impose constraints on the dynamics of this activity. Here, we use magnetoencephalography (MEG) to quantify the extent to which fast neural dynamics in the human brain are constrained by structural connections inferred from diffusion MRI tractography. We characterize the spatio-temporal unfolding of whole-brain activity at the millisecond scale from source-reconstructed MEG data, estimating the probability that any two brain regions will significantly deviate from baseline activity in consecutive time epochs. We find that the structural connectome relates to, and likely affects, the rapid spreading of neuronal avalanches, evidenced by a significant association between these transition probabilities and structural connectivity strengths (r=0.37, p<0.0001). This finding opens new avenues to study the relationship between brain structure and neural dynamics.

neuroscience↗

bioRxiv2020

The effects of different frequencies of rhythmic acoustic stimulation on gait kinematics and trunk sway in healthy elderly population

The use of rhythmic acoustic stimulation (RAS) in improving gait and balance in healthy elderly subjects has been widely investigated. However, methodologies and results are often controversial. In this study, we hypothesize that both the kinematic features of gait and stability, depend on the frequency at which RAS is administered. Our aim was to observe, through 3D Gait Analysis, the effect of different types of RAS (at a fixed frequency or based on the average cadence of each subject) on both gait spatio-temporal parameters and stability. The latter was estimated through an innovative measure, the trunk displacement index (TDI) that we have recently implemented. We observed that the low frequencies RAS led to a general slowdown of gait, which did not provide any clear benefit and produced harmful effects on stability when the frequency became too low compared to the individual natural frequency. On the contrary, the high frequencies of RAS showed a slight acceleration of gait, accompanied by better stability (as documented by a lower TDI value), regardless of the type of RAS. Finally, the RAS equal to the individual natural cadence also produced an increase in stability.

neuroscience↗

bioRxiv2020

Functional brain network topology across the menstrual cycle is sex hormone dependent and correlates with the individual well-being

The menstrual cycle is known to influence the behaviour. The neuronal bases of this phenomenon are poorly understood. We hypothesized that hormones, might affect the large-scale organization of the brain functional networks and that, in turn, such changes might have behavioural correlates in terms of the affective state. To test our hypothesis, we took advantage of magnetoencephalography to investigate brain topology in early follicular, ovulatory and luteal phases, in twenty-four naturally-cycling women without signs of anxiety and/or depression. We show that in the alpha band the betweenness centrality (BC) of the right posterior cingulate gyrus (PCG) during the ovulatory phase is increased and the rise is predicted by the levels of estradiol. We also demonstrate that the increase in the BC is related to improved subjective well-being that, in turn, is correlated to the estradiol levels. The increased topological centrality of the PCG during the ovulatory phase could have implications in reproductive psychology.

neuroscience↗