Biology subjects

Centeno, E. G. Z.

Publications and source records attributed to Centeno, E. G. Z..

4 recordsLinked to original sources

bioRxiv2025

A lobule-specific neuronal representation of song temporal structure in the songbird cerebellum

The cerebellum is involved in the acquisition and production of speech as revealed by clinical evidence and imaging studies, but its specific role however remains unclear. Songbirds provide a unique model to study the neural mechanisms of speech learning and production. Recent evidence highlights a contribution of the cerebellum to syllable duration in songbirds. Here, we aim at better understanding how and which cerebellar circuits may contribute to the tight control of syllable duration in zebra finches. We first confirmed that lesions in the lateral cerebellum affect syllable duration. We then recorded neuronal activity in the various cerebellar lobules in response to song playback and during singing with or without auditory feedback perturbation. We found that many cerebellar lobules receive non-selective auditory information locked to syllable boundaries during song playback, both in anaesthetized and awake behaving birds. During singing, cerebellar neurons in several lobules display song-locked responses with varying degrees of alignment to their playback responses and no alteration during auditory feedback perturbation. Singing-related activity tends to correlate with the fluctuations in syllable duration rather than any acoustic song feature. Importantly, neurons in lobule IV of the cerebellum are largely unaffected by auditory stimulation but display syllable-locked firing rate modulations during singing that precisely encode syllable boundaries with a sharp and tight increase in firing at syllable onsets and offsets. Altogether, our findings reveal cerebellar signals that may contribute to the control of the duration of vocal elements during singing, possibly serving as a forward model of song temporal features.

neuroscience↗

bioRxiv2024

Neural population dynamics during sleep in a songbird vocal circuit resemble sharp wave ripple activity

Similar to motor skill learning in mammals, vocal learning in songbirds requires a set of interconnected brain areas that make up an analogous basal ganglia-thalamocortical circuit known as the anterior forebrain pathway (AFP). Although neural activity in the AFP has been extensively investigated during awake singing, very little is known about its neural activity patterns during other behavioral states. Here, we used chronically implanted Neuropixels probes to investigate spontaneous neural activity in the AFP during natural sleep and awake periods in male zebra finches. We found that during sleep, neuron populations in the pallial region LMAN (lateral magnocellular nucleus of the nidopallium) spontaneously exhibited synchronized bursts that were characterized by a negative sharp deflection in the local field potential (LFP) and a transient increase in gamma power. LMAN population bursts occurred primarily during non-rapid eye movement (NREM) sleep and were highly reminiscent of sharp-wave ripple (SWR) activity observed in rodent hippocampus. We also examined the functional connectivity within the AFP by calculating the pairwise LFP coherence. As expected, delta and theta band coherence within LMAN and Area X was higher during sleep compared to awake periods. Contrary to our expectations, we did not observe strong coherence between LMAN and Area X during sleep, suggesting that the input from LMAN into Area X is spatially restricted. Overall, these results provide the first description of spontaneous neural dynamics within the AFP across behavioral states. Significance statementAlthough cortical and basal ganglia circuits are known to be required for learning in both mammals and birds, little is known about the ongoing spontaneous activity patterns within these circuits, or how they are modulated by behavioral state. Here we prove the first description of cortical-basal ganglia network activity during sleep and awake periods in birds. Within the pallial area LMAN, we observed population-wide bursting events that were highly reminiscent of hippocampal sharp-wave ripple (SWR) activity, suggesting that large-scale population events have diverse functions across vertebrates.

neuroscience↗

bioRxiv2023

Emergence of High-Order Functional Hubs in the Human Brain

Network theory is often based on pairwise relationships between nodes, which is not necessarily realistic for modeling complex systems. Importantly, it does not accurately capture non-pairwise interactions in the human brain, often considered one of the most complex systems. In this work, we develop a multivariate signal processing pipeline to build high-order networks from time series and apply it to resting-state functional magnetic resonance imaging (fMRI) signals to characterize high-order communication between brain regions. We also propose connectivity and signal processing rules for building uniform hypergraphs and argue that each multivariate interdependence metric could define weights in a hypergraph. As a proof of concept, we investigate the most relevant three-point interactions in the human brain by searching for high-order "hubs" in a cohort of 100 individuals from the Human Connectome Project. We find that, for each choice of multivariate interdependence, the high-order hubs are compatible with distinct systems in the brain. Additionally, the high-order functional brain networks exhibit simultaneous integration and segregation patterns qualitatively observable from their high-order hubs. Our work hereby introduces a promising heuristic route for hypergraph representation of brain activity and opens up exciting avenues for further research in high-order network neuroscience and complex systems.

neuroscience↗

bioRxiv2021

A hands-on tutorial on network and topological neuroscience

The brain is an extraordinarily complex system that facilitates the efficient integration of information from different regions to execute its functions. With the recent advances in technology, researchers can now collect enormous amounts of data from the brain using neuroimaging at different scales and from numerous modalities. With that comes the need for sophisticated tools for analysis. The field of network neuroscience has been trying to tackle these challenges, and graph theory has been one of its essential branches through the investigation of brain networks. Recently, topological data analysis has gained more attention as an alternative framework by providing a set of metrics that go beyond pair-wise connections and offer improved robustness against noise. In this hands-on tutorial, our goal is to provide the computational tools to explore neuroimaging data using these frameworks and to facilitate their accessibility, data visualisation, and comprehension for newcomers to the field. We will start by giving a concise (and by no means complete) overview of the field to introduce the two frameworks, and then explain how to compute both well-established and newer metrics on resting-state functional magnetic resonance imaging. We use an open-source language (Python) and provide an accompanying publicly available Jupyter Notebook that uses data from the 1000 Functional Connectomes Project. Moreover, we would like to highlight one part of our notebook that is solely dedicated to realistic visualisation of high order interactions in brain networks. This pipeline provides three-dimensional (3-D) plots of pair-wise and higher-order interactions projected in a brain atlas, a new feature tailor-made for network neuroscience.

neuroscience↗