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Neural network simulation with hyperploid neurons

AuthorsBarrio-Alonso, Estíbaliz; Valero, Manuel; Fontana, Bérénice; Frade López, José María
KeywordsNeuronal cell cycle reentry
SV40 large T antigen
Neuron hypertrophy
Neurite retraction
Synaptic dysfunction
Neural network modelling
Synaptic firing rate
Oscillatory patterns
Issue Date31-Jan-2020
CitationBarrio-Alonso, Estíbaliz ; Fontana, Bérénice ; Valero, Manuel ; Frade, José María; 2020; “Neural network simulation with hyperploid neurons [Dataset]”; DIGITAL.CSIC; http://dx.doi.org/10.20350/digitalCSIC/10541
AbstractWhen subjected to stress, terminally-differentiated neurons are susceptible to reactivate the cell cycle and become hyperploid. This process is well documented in Alzheimer’s disease (AD), where it may participate in the etiology of the disease. However, despite its potential importance, the effects of neuronal hyperploidy (NH) on brain function and its relationship with AD remains obscure. An important step forward in our understanding of the pathological effect of NH has been the development of transgenic mice with neuronal expression of oncogenes as model systems of AD. The analysis of these mice has demonstrated that forced cell cycle reentry in neurons results in most hallmarks of AD, including neurofibrillary tangles, Abeta peptide deposits, gliosis, cognitive loss, and neuronal death. Nevertheless, in contrast to the pathological situation, where a relatively small proportion of neurons become hyperploid, neuronal cell cycle reentry in these mice is generalized. We have recently developed an in vitro system in which cell cycle is induced in a reduced proportion of differentiated neurons, mimicking the in vivo situation. This manipulation reveals that NH correlates with synaptic dysfunction, and that membrane depolarization facilitates the survival of hyperploid neurons. This suggests that the integration of synaptically-silent, hyperploid neurons in electrically-active neural networks allows their survival while perturbing the normal functioning of the network itself, a hypothesis that we have tested in silico. To this aim, an `Integrate-and-fire´ simulation of neural networks containing hyperploid neurons was implemented using the Python-based Brian 2 simulator.
DescriptionThis data set comprises Comma Separated Values (.CSV) files containing the results of the simulated spiking neuronal network, MATLAB variable files (.MAT) and PNG figure files. All data was obtained by the Brian2 neural network simulator. CSV file can be visualized by any text editor. MATLAB files require MATLAB or Octave. We’ve created a neuronal network composed of 4,000 neurons of three types, with different proportion of hyperploid neurons – namely, condition (from 1% to 80%) - in each of these types: (i) excitatory neurons (EXC); (ii) leading neurons (LEAD), a subtype of excitatory neurons that constitute relevant hubs of the circuit; and (iii) interneurons, with inhibitory capacity (INH). Control networks (CONTROL) did not contain hyperploid neurons. Five repetitions of each condition were simulated. All data is organized hierarchically regarding condition and repetition, and experimental folder names contains date of creation (October 2018). Estibaliz Barrio-Alonso and Manuel Valero coded the simulation and the analysis scripts. All simulations were performed under a Windows 10 machine, with Python 2.7 and MATLAB R2018a.
ReferencesBarrio-Alonso, Estíbaliz; Fontana, Bérénice; Valero, Manuel; Frade López, José María (2020) Pathological Aspects of Neuronal Hyperploidization in Alzheimer’s Disease Evidenced by Computer Simulation. Front. Genet. 11: 287. https://doi.org/10.3389/fgene.2020.00287. http://hdl.handle.net/10261/205699
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File Description SizeFormat 
TetAnalysis.m6,34 kBUnknownView/Open
TetDatabase_13-Nov-2018.mat299,58 kBUnknownView/Open
TetExample.m6,54 kBUnknownView/Open
TetStatistics.m23,42 kBUnknownView/Open
Raw Data Index.docx18,18 kBMicrosoft Word XMLView/Open
ALL.rarHyperploidy in all neurons9,2 GBUnknownView/Open
CONTROL.rarNo hyperploid neurons9,8 GBUnknownView/Open
EXC.rarHyperploidy in excitatory neurons9,85 GBUnknownView/Open
INH.rarHyperploidy in inhibitory neurons9,73 GBUnknownView/Open
LEAD.rarHyperploidy in leading neurons10,14 GBUnknownView/Open
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