Biology subjects

Marzetti, L.

Publications and source records attributed to Marzetti, L..

4 recordsLinked to original sources

bioRxiv2025

Meditation induces shifts in neural oscillations, braincomplexity and critical dynamics: Novel insights fromMEG

While the beneficial impacts of meditation are increasingly acknowledged, its underlying neural mechanisms remain poorly understood. We examined the electrophysiological brain signals of expert Buddhist monks during two established meditation methods known as Samatha and Vipassana, which employ focused attention and open monitoring technique. By combining source-space magnetoencephalography (MEG) with advanced signal processing and machine learning tools, we provide an unprecedented assessment of the role of brain oscillations, complexity and criticality in meditation. In addition to power spectral density (PSD), we computed long-range temporal correlations (LRTC), deviation from criticality coefficient (DCC), Lempel-Ziv complexity (LZC), 1/f slope, Higuchi fractal dimension (HFD), and spectral entropy. Our findings indicate increased levels of neural signal complexity during both meditation practices compared to the resting state, along-side widespread reductions in gamma-band LRTC and 1/f slope. Importantly, the DCC analysis revealed a separation between Samatha and Vipassana, suggesting that their distinct phenomenological properties are mediated by specific computational characteristics of their dynamic states. Furthermore, in contrast to most previous reports, we observed a decrease in oscillatory gamma power during meditation, a divergence we attribute to the correction of the power spectrum by the 1/f slope. We discuss how these results advance our comprehension of the neural processes associated with focused attention and open monitoring meditation practices.

neuroscience↗

bioRxiv2025

A Mean-Field Approach to Criticality in Spiking Neural Networks for Reservoir Computing

Reservoir computing is a neural network paradigm for processing temporal data by exploiting the dynamics of a fixed, high-dimensional system, enabling efficient computation with reduced complexity compared to fully trainable recurrent networks. This work presents an analytical framework for configuring in the critical regime a reservoir based on spiking neural networks with a highly general topology. Specifically, we derive and solve a mean-field equation that governs the evolution of the average membrane potential in leaky integrate-and-fire neurons, and provide an approximation for the critical point. This framework reduces the need for an extensive online fine-tuning, offering a streamlined path to near-optimal network performance from the outset. Through extensive simulations, we validate the theoretical predictions by analyzing the networks spiking dynamics and quantifying its computational capacity using the information-based Lempel-Ziv-Welch complexity near criticality. Finally, we explore self-organized quasi-criticality by implementing a local learning rule for synaptic weights, demonstrating that the networks dynamics remain close to the theoretical critical point. Beyond AI, our approach and findings also have significant implications for computational neuroscience, providing a principled framework for quantitatively understanding how biological networks leverage criticality for efficient information processing.

neuroscience↗

bioRxiv2023

Intrinsic motor network connectivity predicts corticospinal excitability

State-dependent non-invasive brain stimulation (NIBS) informed by electroencephalography (EEG) has contributed to the understanding of NIBS inter-subject and inter-session variability. While these approaches focused on local EEG characteristics, it is acknowledged that the brain exhibits an intrinsic long-range dynamic organization in networks. This proof-of-concept study explores whether EEG connectivity of the primary motor cortex (M1) in the pre-stimulation period aligns with the motor network (MN) and how MN state affects responses to transcranial magnetic stimulation (TMS) of M1. One thousand suprathreshold TMS pulses were delivered to left M1 in 8 subjects at rest, with simultaneous EEG. Motor evoked potentials (MEPs) were measured from the right hand. Source-space functional connectivity of left M1 to the whole-brain was assessed using the imaginary part of the Phase Locking Value at the frequency of the sensorimotor {micro}-rhythm in a 1-second window before the pulse. Group-level connectivity revealed functional links between left M1, left supplementary motor area, and right M1. Also, pulses delivered at high MN connectivity states result in a greater MEP amplitude compared to low connectivity states. At single-subject level, this relation is more expressed in subjects that feature an overall high cortico-spinal excitability. In conclusion, this study paves the way for MN connectivity based NIBS. HighlightsO_LIEEG pre-stimulus connectivity of left M1 largely corresponds to the motor network C_LIO_LIStronger motor network (MN) connectivity corresponds to greater MEP amplitudes C_LIO_LILinear regression models based on MN connectivity predicts MEP amplitude C_LI

neuroscience↗

bioRxiv2018

Analysing linear multivariate pattern transformations in neuroimaging data

Most connectivity metrics in neuroimaging research reduce multivariate activity patterns in regions-of-interests (ROIs) to one dimension, which leads to a loss of information. Importantly, it prevents us from investigating the transformations between patterns in different ROIs. Here, we applied linear estimation theory in order to robustly estimate the linear transformations between multivariate fMRI patterns with a cross-validated Tikhonov regularisation approach. We derived three novel metrics that describe different features of these voxel-by-voxel mappings: goodness-of-fit, sparsity and pattern deformation. The goodness-of-fit describes the degree to which the patterns in an input region can be described as a linear transformation of patterns in an output region. The sparsity metric, which relies on a Monte Carlo procedure, was introduced in order to test whether the transformation mostly consists of one-to-one mappings between voxels in different regions. Furthermore, we defined a metric for pattern deformation, i.e. the degree to which the transformation rotates or rescales the input patterns. As a proof of concept, we applied these metrics to an event-related fMRI data set consisting of four subjects that has been used in previous studies. We focused on the transformations from early visual cortex (EVC) to inferior temporal cortex (ITC), fusiform face area (FFA) and parahippocampal place area (PPA). Our results suggest that the estimated linear mappings are able to explain a significant amount of variance of the three output ROIs. The transformation from EVC to ITC shows the highest goodness-of-fit, and those from EVC to FFA and PPA show the expected preference for faces and places as well as animate and inanimate objects, respectively. The pattern transformations are sparse, but sparsity is lower than would have been expected for one-to-one mappings, thus suggesting the presence of one-to-few voxel mappings. ITC, FFA and PPA patterns are not simple rotations of an EVC pattern, indicating that the corresponding transformations amplify or dampen certain dimensions of the input patterns. While our results are only based on a small number of subjects, they show that our pattern transformation metrics can describe novel aspects of multivariate functional connectivity in neuroimaging data.

neuroscience↗