Biology subjects

Quarantelli, M.

Publications and source records attributed to Quarantelli, M..

7 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

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↗

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↗

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↗

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

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

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↗