Biology subjects

Troisi Lopez, E.

Publications and source records attributed to Troisi Lopez, E..

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

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

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↗

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

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

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↗

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↗

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

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↗

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.

neuroscience↗

bioRxiv2020

Clinical connectome fingerprints of cognitive decline

Brain connectome fingerprinting is rapidly rising as a novel influential field in brain network analysis. Yet, it is still unclear whether connectivity fingerprints could be effectively used for mapping and predicting disease progression from human brain data. We hypothesize that dysregulation of brain activity in disease would reflect in worse subject identification. Hence, we propose a novel framework, Clinical Connectome Fingerprinting, to detect individual connectome features from clinical populations. We show that "clinical fingerprints" can map individual variations between elderly healthy subjects and patients undergoing cognitive decline in functional connectomes extracted from magnetoencephalography data. We find that identifiability is reduced in patients as compared to controls, and show that these connectivity features are predictive of the individual Mini-Mental State Examination (MMSE) score in patients. We hope that the proposed methodology can help in bridging the gap between connectivity features and biomarkers of brain dysfunction in large-scale brain networks.

neuroscience↗

bioRxiv2020

Loss of integration of brain netwroks after one night of sleep deprivation underlies worsening of attentive functions

The topology of brain networks changes according to environmental demands and can be described within the framework of graph theory. We hypothesized that 24-hours long sleep deprivation (SD) causes functional rearrangements of the brain topology so as to impair optimal communication, and that such rearrangements relate to the performance in specific cognitive tasks, namely the ones specifically requiring attention. Thirty-two young men underwent resting-state MEG recording and assessments of attention and switching abilities before and after SD. We found loss of integration of brain network and a worsening of attention but not of switching abilities. These results show that brain network changes due to SD affect switching abilities, worsened attention and induce large-scale rearrangements in the functional networks.

neuroscience↗