Biology subjects

Zonca, L.

Publications and source records attributed to Zonca, L..

6 recordsLinked to original sources

bioRxiv2026

Long-projection astrocytes challenge canonical territorial organization in the sleep-promoting VLPO

The ventrolateral preoptic nucleus (VLPO) is a key hypothalamic hub for non-rapid eye movement sleep, yet the glial architecture supporting its circuits remains poorly understood. Here, combining genetic labeling, high-resolution imaging and calcium imaging, we uncover unexpected astrocyte diversity in the VLPO. In addition to classical protoplasmic astrocytes, we identify paired "doublet" astrocytes associated with high local proliferative activity, as revealed by EdU incorporation. We further describe a population of long-projection astrocytes extending processes far beyond canonical astrocytic territories and contacting distant cells. These projections challenge the classical territorial organization of astrocytes and resemble morphologies previously thought to be restricted to hominid brains. Notably, VLPO astrocytes display robust spontaneous Ca{superscript 2} activity and a highly functionally connected network compared to astrocytes in the cortex and hippocampus. Together, these findings reveal specialized astrocyte architectures and enhanced glial network integration within a sleep-promoting nucleus. Reporting summaryBellier et al. identify three astrocyte subtypes in the sleep-promoting VLPO, including long-projection astrocytes with hominid-like morphology. They uncover marked postnatal gliogenesis, distinctive spontaneous Ca{superscript 2} dynamics, and tightly interconnected astrocytic networks, revealing region-specific astrocyte specialization and enhanced glial communication.

neuroscience↗

bioRxiv2024

Unveiling the Functional Connectivity of Astrocytic Networks with AstroNet, a Graph Reconstruction Algorithm Coupled to Image Processing

Astrocytes form extended intercellular networks, displaying complex calcium activity. However, the specific organization of these astrocytic networks and the precise extent of their functional connectivity in different brain areas remain unexplored. To unveil the functional architecture of astrocytic networks, we developed, using a data-driven methodology, a novel algorithm called AstroNet that uses two-photon calcium imaging to map temporal correlations in activation events among neighboring astro-cytes. Our approach involves reconstructing functional astrocytic networks by organizing individual astrocyte activation events chronologically. This chronological order creates activity paths that enable the extraction of local astrocyte functional correlations. Ultimately, by tallying the occurrences of direct co-activations between pairs of cells along these pathways, we construct a graph that mirrors the underlying astrocyte functional network. By applying this method to two distinct brain regions (CA1 hippocampus and motor cortex), we identified notable differences in local network organizations in sub-regions of around 20-40 astrocytes. Specifically, the cortex exhibited a lower connectivity, while astrocytes in the hippocampus displayed stronger connections. Moreover, we found that in both regions, astrocytic networks consist of smaller, tightly connected sub-networks embedded within a larger, more loosely connected one. Altogether, our innovative method enables the identification of activation paths among astrocytes, facilitates the characterization of local network functional connectivity, and quantifies distinct connectivity patterns among astrocytes from different brain regions. This approach sheds light on the heterogeneous functional organization of astrocytic networks within the brain, pointing to region-specific astrocyte connectivity.

neuroscience↗

bioRxiv2024

Modeling disorders of consciousness at the patient level reveals the network's influence on the diagnosis vs the local node parameters role in prognosis

The study of disorders of consciousness (DoC) is very complex because patients suffer from a wide variety of lesions, affected brain mechanisms, different severity of symptoms, and are unable to communicate. Combining neuroimaging data and mathematical modeling can help us quantify and better describe some of these alterations. The goal of this study is to provide a new analysis and modeling pipeline for fMRI data leading to new diagnosis and prognosis biomarkers at the individual patient level. To do so, we project patients fMRI data into a low-dimension latent-space. We define the latent spaces dimension as the smallest dimension able to maintain the complexity, non-linearities, and information carried by the data, according to different criteria that we detail in the first part. This dimensionality reduction procedure then allows us to build biologically inspired latent whole-brain models that can be calibrated at the single-patient level. In particular, we propose a new model inspired by the regulation of neuronal activity by astrocytes in the brain. This modeling procedure leads to two types of model-based biomarkers (MBBs) that provide novel insight at different levels: (1) the connectivity matrices bring us information about the severity of the patients diagnosis, and, (2) the local node parameters correlate to the patients etiology, age and prognosis. Altogether, this study offers a new data processing framework for resting-state fMRI which provides crucial information regarding DoC patients diagnosis and prognosis. Finally, this analysis pipeline could be applied to other neurological conditions.

neuroscience↗

bioRxiv2023

Whole-brain modelling of low-dimensional manifold modes reveals organising principle of brain dynamics

The discovery of resting state networks shifted the focus from the role of local regions in cognitive tasks to the ongoing spontaneous dynamics in global networks. Recently, efforts have been invested to reduce the complexity of brain activity recordings through the application of nonlinear dimensionality reduction algorithms. Here, we investigate how the interaction between these networks emerges as an organising principle in human cognition. We combine deep variational auto-encoders with computational modelling to construct a dynamical model of brain networks fitted to the whole-brain dynamics measured with functional magnetic resonance imaging (fMRI). Crucially, this allows us to infer the interaction between these networks in resting state and 7 different cognitive tasks by determining the effective functional connectivity between networks. We found a high flexible reconfiguration of task-driven network interaction patterns and we demonstrate that can be used to classify different cognitive tasks. Importantly, compared to using all the nodes in a parcellation, we obtain better results by modelling the dynamics of interacting networks in both model and classification performance. These findings show the key causal role of manifolds as a fundamental organising principle of brain function, providing evidence that interacting networks are the computational engines brain during cognitive tasks.

neuroscience↗

bioRxiv2022

Astroglial gap junctions strengthen hippocampal network activity by sustaining afterhyperpolarization via KCNQ channels

Throughout the brain, astrocytes form networks mediated by gap-junction channels that promote the activity of neuronal ensembles. Although their inputs on neuronal information processing are well established, how molecularly gap junction channels shape neuronal network patterns remains unclear. Here using astroglial connexin-deficient mice, in which astrocytes are disconnected and neuronal bursting patterns are abnormal, we found that astrocyte networks strengthen bursting activity via dynamic regulation of extracellular potassium levels, independently of glutamate homeostasis or metabolic support. Using a novel facilitation-depression model, we identified neuronal afterhyperpolarization as the key parameter underlying bursting patterns regulation by extracellular potassium in mice with disconnected astrocytes. We confirmed experimentally this prediction, and revealed that astroglial network-control of extracellular potassium sustains neuronal afterhyperpolarization via activation of KCNQ voltage-gated K+ channels. Altogether, these data delineate how astroglial gap-junctions mechanistically strengthen neuronal population bursts, and points to approaches for controlling aberrant activity in neurological diseases.

neuroscience↗

bioRxiv2021

Emergence and fragmentation of the alpha-band driven by neuronal network dynamics

Rhythmic neuronal network activity underlies brain oscillations. To investigate how connected neuronal networks contribute to the emergence of the -band and the regulation of Up and Down states, we study a model based on synaptic short-term depression-facilitation with afterhyperpolarization (AHP). We found that the -band is generated by the network behavior near the attractor of the Up-state. Coupling inhibitory and excitatory networks by reciprocal connections leads to the emergence of a stable -band during the Up states, as reflected in the spectrogram. To better characterize the emergence and stability of thalamocortical oscillations containing and{delta} rhythms during anesthesia, we model the interaction of two excitatory with one inhibitory networks, showing that this minimal network topology leads to a persistent -band in the neuronal voltage characterized by dominant Up over Down states. Finally, we show that the emergence of the -band appears when external inputs are suppressed, while the fragmentation occurs at small synaptic noise or with increasing inhibitory inputs. To conclude, interaction between excitatory neuronal networks with and without AHP seems to be a general principle underlying changes in network oscillations that could apply to other rhythms. Author summaryBrain oscillations recorded from electroencephalograms characterize behaviors such as sleep, wakefulness, brain evoked responses, coma or anesthesia. The underlying rhythms for these oscillations are associated at a neuronal population level to fluctuations of the membrane potential between Up (depolarized) and Down (hyperpolarized) states. During anesthesia with propofol, a dominant alpha-band (8-12Hz) can emerge or disappear, but the underlying mechanisms remain unclear. Using modeling, we report that the alpha-band appears during Up states in neuronal populations driven by short-term synaptic plasticity and noise. Moreover, we show that three connected networks representing the thalamocortical loop reproduce the dynamics of the alpha-band, which emerges following the arrest of excitatory stimulations, but can disappear by increasing inhibitory inputs. To conclude, short-term plasticity in well connected neuronal networks can explain the emergence and fragmentation of the alpha-band.

neuroscience↗