Biology subjects

Amato, G.

Publications and source records attributed to Amato, G..

3 recordsLinked to original sources

bioRxiv2025

A proper Excitatory/Inhibitory ratio is required to develop synchronized network activity in mouse cortical cultures

Excitatory/inhibitory (E/I) balance is thought to play a key role in cortical activity development. However, the modeling of cortical networks with different E/I ratios is not feasible in vivo. To address this point, we modeled an in vitro cortical network deployed of the inhibitory neurons normally migrating from the ventral telencephalon. Moreover, we implemented striatal cultures and co-cultures with mixed proportions of cortical and striatal neurons. The resulting cultures contained various proportions of inhibitory Parvalbumin (PV)+ neurons, ranging from 7% to 73%. Interestingly, these pure and mixed cortical/striatal cultures exhibited four distinct patterns of spontaneous activity and functional connectivity. Our findings highlighted a critical role for the inhibitory component in developing correlated network activity. Unexpectedly, cortical networks with 7% of PV+ neurons were not able to generate appreciable network burst activity due to the development of a strong network inhibition, despite their lowest E/I ratio. Our observations support the notion that an optimal ratio of PV+ neurons during cortical development is essential for the establishment of local inhibitory networks capable of generating and spreading correlated activity. HighlightsO_LIIn vitro neurogenesis models the development of mouse cortical network devoid of inhibitory neurons C_LIO_LICortical network with low inhibitory neuron ratio develops poor synchronized network activity C_LIO_LIGABA inhibition unmask intrinsic network ability to generate highly synchronized activity C_LIO_LINetwork response to single node stimulus depends on optimal inhibitory neuron ratio C_LIO_LIA proper excitatory/inhibitory ratio is necessary for the development of network burst activity C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=181 HEIGHT=200 SRC="FIGDIR/small/640720v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@3ee4org.highwire.dtl.DTLVardef@210461org.highwire.dtl.DTLVardef@1a693eforg.highwire.dtl.DTLVardef@13a6e4e_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2023

A Comprehensive Atlas of Perineuronal Net Distribution and Colocalization with Parvalbumin in the Adult Mouse Brain

Perineuronal nets (PNNs) surround specific neurons in the brain and are involved in various forms of plasticity and clinical conditions. However, our understanding of the PNN role in these phenomena is limited by the lack of highly quantitative maps of PNN distribution and association with specific cell types. Here, we present the first comprehensive atlas of PNN distribution (in Allen Brain Atlas coordinates) and colocalization with parvalbumin (PV) cells for over 600 regions of the adult mouse brain. Data analysis showed that PV expression is a good predictor of PNN aggregation. In the cortex, PNNs are dramatically enriched in layer 4 of all primary sensory areas in correlation with thalamocortical input density, and their distribution mirrors intracortical connectivity patterns. Gene expression analysis identified many PNN correlated genes. Strikingly, PNN anticorrelated transcripts were enriched in synaptic plasticity genes, generalizing PNN role as circuit stability factors. Overall, this atlas offers novel resources for understanding the organizational principles of the brain extracellular matrix.

neuroscience↗

bioRxiv2021

MEYE: Web-app for translational and real-time pupillometry

Pupil dynamics alterations have been found in patients affected by a variety of neuropsychiatric conditions, including autism. Studies in mouse models have used pupillometry for phenotypic assessment and as a proxy for arousal. Both in mice and humans, pupillometry is non-invasive and allows for longitudinal experiments supporting temporal specificity, however its measure requires dedicated setups. Here, we introduce a Convolutional Neural Network that performs on-line pupillometry in both mice and humans in a web app format. This solution dramatically simplifies the usage of the tool for non-specialist and non-technical operators. Because a modern web browser is the only software requirement, this choice is of great interest given its easy deployment and set-up time reduction. The tested model performances indicate that the tool is sensitive enough to detect both spontaneous and evoked pupillary changes, and its output is comparable with state-of-the-art commercial devices.

neuroscience↗