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      Alpha-synuclein oligomers alter the spontaneous firing discharge of cultured midbrain neurons

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          Abstract

          The aim of this work was to monitor the effects of extracellular α-synuclein on the firing activity of midbrain neurons dissociated from substantia nigra TH-GFP mice embryos and cultured on microelectrode arrays (MEA). We monitored the spontaneous firing discharge of the network for 21 days after plating and the role of glutamatergic and GABAergic inputs in regulating burst generation and network synchronism. Addition of GABA A , AMPA and NMDA antagonists did not suppress the spontaneous activity but allowed to identify three types of neurons that exhibited different modalities of firing and response to applied L-DOPA: high-rate (HR) neurons, low-rate pacemaking (LR-p), and low-rate non-pacemaking (LR-np) neurons. Most HR neurons were insensitive to L-DOPA, while the majority of LR-p neurons responded with a decrease of the firing discharge; less defined was the response of LR-np neurons. The effect of exogenous α-synuclein (α-syn) on the firing discharge of midbrain neurons was then studied by varying the exposure time (0–48 h) and the α-syn concentration (0.3–70 μM), while the formation of α-syn oligomers was monitored by means of AFM. Independently of the applied concentration, acute exposure to α-syn monomers did not exert any effect on the spontaneous firing rate of HR, LR-p, and LR-np neurons. On the contrary, after 48 h exposure, the firing activity was drastically altered at late developmental stages (14 days in vitro, DIV, neurons): α-syn oligomers progressively reduced the spontaneous firing discharge (IC 50 = 1.03 μM), impaired burst generation and network synchronism, proportionally to the increased oligomer/monomer ratio. Different effects were found on early-stage developed neurons (9 DIV), whose firing discharge remained unaltered, regardless of the applied α-syn concentration and the exposure time. Our findings unravel, for the first time, the variable effects of exogenous α-syn at different stages of midbrain network development and provide new evidence for the early detection of neuronal function impairment associated to aggregated forms of α-syn.

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          Brain connectivity datasets comprise networks of brain regions connected by anatomical tracts or by functional associations. Complex network analysis-a new multidisciplinary approach to the study of complex systems-aims to characterize these brain networks with a small number of neurobiologically meaningful and easily computable measures. In this article, we discuss construction of brain networks from connectivity data and describe the most commonly used network measures of structural and functional connectivity. We describe measures that variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, characterize patterns of local anatomical circuitry, and test resilience of networks to insult. We discuss the issues surrounding comparison of structural and functional network connectivity, as well as comparison of networks across subjects. Finally, we describe a Matlab toolbox (http://www.brain-connectivity-toolbox.net) accompanying this article and containing a collection of complex network measures and large-scale neuroanatomical connectivity datasets. Copyright (c) 2009 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Journal
                Front Cell Neurosci
                Front Cell Neurosci
                Front. Cell. Neurosci.
                Frontiers in Cellular Neuroscience
                Frontiers Media S.A.
                1662-5102
                20 January 2023
                2023
                : 17
                : 1078550
                Affiliations
                [1] 1Drug Science Department, University of Torino , Turin, Italy
                [2] 2Nanostructured Interfaces and Surfaces Inter-Departmental Research Centre , Turin, Italy
                [3] 3Department of Chemistry and INSTM-UdR Torino , Turin, Italy
                [4] 4Mathematical Biology and Physiology, Department of Electronics and Telecommunications , Turin, Italy
                [5] 5Laboratory Experimental Neurophysiology, IRCCS San Raffaele Rome , Rome, Italy
                [6] 6Neurological Clinic, Fondazione Policlinico Universitario Agostino Gemelli IRCCS , Rome, Italy
                [7] 7Neurology, Department of Neuroscience, Faculty of Medicine, Università Cattolica del “Sacro Cuore,” Rome, Italy
                [8] 8Dipartimento di Scienze Umane e Promozione della Qualitá della Vita, Telematic University San Raffaele Roma , Rome, Italy
                Author notes

                Edited by: Carmen Castro Canavier, Louisiana State University, United States

                Reviewed by: Vincent Seutin, University of Liège, Belgium; Jochen Roeper, Goethe University Frankfurt, Germany

                *Correspondence: Valentina Carabelli, valentina.carabelli@ 123456unito.it

                This article was submitted to Cellular Neurophysiology, a section of the journal Frontiers in Cellular Neuroscience

                Article
                10.3389/fncel.2023.1078550
                9896582
                36744002
                b6a55afa-d9cb-4f15-b0ba-03f54e2ce67b
                Copyright © 2023 Tomagra, Franchino, Cesano, Chiarion, de lure, Carbone, Calabresi, Mesin, Picconi, Marcantoni and Carabelli.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 24 October 2022
                : 06 January 2023
                Page count
                Figures: 11, Tables: 0, Equations: 0, References: 92, Pages: 17, Words: 12385
                Funding
                This project was supported by Compagnia di San Paolo (Progetto Trapezio) and by Italian Miur.
                Categories
                Neuroscience
                Original Research

                Neurosciences
                alpha-synuclein,multi-electrodes arrays (mea),midbrain dopamine neuron,maximum of the absolute value of the cross-correlation (mavcc),spontaneous firing activity

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