Biology subjects

Barzon, G.

Publications and source records attributed to Barzon, G..

6 recordsLinked to original sources

bioRxiv2026

Towards a scalable effective connectivity estimation in fMRI

Characterizing directed connections between brain regions is fundamental for understanding the brains network organization and how it supports cognitive processes. However, a reliable methodology allowing assessment of effective connectivity (EC) at the whole-brain level and across high-resolution parcellations has remained elusive. This limitation reflects a trade-off between model detail and scalability. Mechanistic approaches such as Dynamic Causal Modeling (DCM) provide a principled description of directed interactions but become impractical for large parcellations, whereas scalable methods based on linear-system inversion often fail to reliably recover key features of the underlying effective connectivity. Here, using data from the Human Connectome Project S1200 release, we show that the multivariate Ornstein-Uhlenbeck (mOU) approach for EC estimation can be adapted to yield EC estimates that are in broad agreement with those of an advanced DCM, at least when the spatial variability of the hemodynamic response falls within the physiological range characteristic of the healthy adult brain. Furthermore, thanks to its low computational cost, mOU can be easily scaled up to large neuroimaging cohorts and high-resolution parcellations. High-resolution, mOU-based EC outperforms functional connectivity for single-subject connectome fingerprinting. In addition, it enables a high-resolution characterization of the brains hierarchical organization and modular architecture by identifying directed interactions within and between communities and revealing driver and receiver regions. Overall, our methodology provides a reliable and scalable EC estimation pipeline of wide applicability to cognitive and network neuroscience.

neuroscience↗

bioRxiv2026

Control of cortical population activity with patterned microstimulation

Closed-loop control of cortical activity is a central goal in systems neuroscience and clinical neuromodulation, but most approaches either rely on detailed circuit models that are unattainable in vivo or on open-loop stimulation tuned by trial and error. Here we introduce REACHable manifold Control (REACH-Ctrl), a data-driven brain-computer interface that achieves real-time control of population spiking activity using patterned microstimulation and multi-electrode recordings. REACH-Ctrl learns a finite-horizon controllability map directly from short training epochs in which random multi-electrode pulse sequences are delivered through a subset of electrodes while recording evoked responses. From these input-output data, it identifies the "reachable manifold" of population states and computes low-current microstimulation sequences that steer activity toward designated targets, without explicit knowledge of the underlying connectivity or dynamics. We test REACH-Ctrl in macaque prefrontal cortex, demonstrating high control accuracy, robust across sessions and stimulation parameters. Geometric analyses showed that multi-pulse sequences traverse a well-defined reachable manifold with substantial, but incomplete, overlap with the intrinsic neural activity manifold, revealing both on- and off-manifold components of control. Encoding models further revealed that, in our weak-stimulation regime, population responses to multi-electrode sequences are well approximated by the linear sum of localized "stimulation fields" with modest history dependence, explaining the success of our linear control approach. These results demonstrate precise, sample-efficient control of cortical population activity with clinically relevant microstimulation hardware, and provide a general blueprint for designing perturbations for sparsely observed neural circuits.

neuroscience↗

bioRxiv2025

Structural Connectome Dimension Shapes Brain Dynamics in Health and Disease

The structural connectome serves as the foundation for neural information signaling, playing a primary constraint on brain functionality. Yet, its influence on emergent dynamical properties is not fully understood. Generally, a key measure of a systems structural impact on dynamical phenomena is its dimension. By tracking the temporal evolution of diffusive perturbations, we estimate a scale-dependent measure of dimension of empirical connectomes. At the local scale, it is highly heterogeneous and follows a gradient from sensory-motor to high-cognitive areas. At the global scale, it encapsulates mesoscale topological information related to the balance between segregation and integration. Furthermore, by comparing connectomes from stroke patients, we find that dimension captures the local effects of lesions and, at a global level, is linked to impaired critical patterns and decreased cognitive performance. Overall, the dimension of the connectome may serve as a powerful descriptor for bridging the gap between structure and function in the human brain.

neuroscience↗

bioRxiv2023

EEG microstate transition cost correlates with task demands

The ability to solve complex tasks relies on the adaptive changes occurring in the spatio-temporal organization of brain activity under different conditions. Altered flexibility in these dynamics can lead to impaired cognitive performance, manifesting for instance as difficulties in attention regulation, distraction inhibition, and behavioral adaptation. Such impairments result in decreased efficiency and increased effort in accomplishing goal-directed tasks. Therefore, developing quantitative measures that can directly assess the effort involved in these transitions using neural data is of paramount importance. In this study, we propose a framework to associate cognitive effort during the performance of tasks with electroencephalography (EEG) activation patterns. The methodology relies on the identification of discrete dynamical states (EEG microstates) and optimal transport theory. To validate the effectiveness of this framework, we apply it to a dataset collected during a spatial version of the Stroop task. The Stroop task is a cognitive test where participants must respond to one aspect of a stimulus while ignoring another, often conflicting, aspect. Our findings reveal an increased cost linked to cognitive effort, thus confirming the frameworks effectiveness in capturing and quantifying cognitive transitions. By utilizing a fully data-driven method, this research opens up fresh perspectives for physiologically describing cognitive effort within the brain. Author SummaryIn our daily lives, our brains manage various tasks with different mental demands. Yet, quantifying how much mental effort each task demands is not always straightforward. To tackle this challenge, we developed a way to measure how much cognitive effort our brains use during tasks directly from electroencephalography (EEG) data, which is one of the most used tools to non-invasively measure brain activity. Our approach involved the identification of distinct patterns of synchronized neural activity across the brain, named EEG microstates. By employing optimal transport theory, we established a framework to quantify the cost associated with cognitive transitions based on modifications in EEG microstates. This allowed us to link changes in brain activity patterns to the cognitive effort required for task performance. To validate our framework, we applied it to EEG data collected during a commonly employed cognitive task known as the Stroop task. This task is recognized for challenging us with varying levels of cognitive demand. Our analysis revealed that as the task became more demanding, there were discernible shifts in the EEG microstates. Importantly, these shifts in neural activity patterns corresponded to higher costs associated with cognitive transitions. Our approach offers a promising methodology to assess cognitive effort using neural data, contributing to our comprehension of how the brain manages and adapts to varying cognitive challenges.

neuroscience↗

bioRxiv2023

Prenatal experience with language shapes the brain

Human infants acquire language with striking ease compared to adults, but the neural basis of their remarkable brain plasticity for language remains little understood. Applying a scaling analysis of neural oscillations for the first time to address this question, we show that newborns electrophysiological activity exhibits increased long-range temporal correlations after stimulation with speech, particularly in the prenatally heard language, indicating the early emergence of brain specialization for the native language.

neuroscience↗

bioRxiv2022

Criticality and network structure drive emergent oscillations in a stochastic whole-brain model

Understanding the relation between the structure of brain networks and its functions is a fundamental open question. Simple models of neural activity based on real anatomical networks have proven to be effective in describing features of whole-brain spontaneous activity when tuned at their critical point. In this work, we show that indeed structural networks are a crucial ingredient in the emergence of collective oscillations in a whole-brain stochastic model at criticality. We study analytically a stochastic Greenberg-Hastings cellular automaton in the mean-field limit, showing that it undergoes an abrupt phase transition with a bistable region. In particular, no global oscillations emerge in this limit. Then, we show that by introducing a network structure in the homeostatic normalization regime, the bistability may be disrupted, and the transition may become smooth. Concomitantly, through an interplay between the network topology and weights, a large peak in the power spectrum appears around the transition point, signaling the emergence of collective oscillations. Hence, both the structure of brain networks and criticality are fundamental in driving the collective responses of whole-brain stochastic models.

neuroscience↗