Early prediction of developing spontaneous activity in cultured neuronal networks

Abstract

Synchronization and bursting activity are intrinsic electrophysiological properties of in vivo and in vitro neural networks. During early development, cortical cultures exhibit a wide repertoire of synchronous bursting dynamics whose characterization may help to understand the parameters governing the transition from immature to mature networks. Here we used machine learning techniques to characterize and predict the developing spontaneous activity in mouse cortical neurons on microelectrode arrays (MEAs) during the first three weeks in vitro. Network activity at three stages of early development was defined by 18 electrophysiological features of spikes, bursts, synchrony, and connectivity. The variability of neuronal network activity during early development was investigated by applying k-means and self-organizing map (SOM) clustering analysis to features of bursts and synchrony. These electrophysiological features were predicted at the third week in vitro with high accuracy from those at earlier times using three machine learning models: Multivariate Adaptive Regression Splines, Support Vector Machines, and Random Forest. Our results indicate that initial patterns of electrical activity during the first week in vitro may already predetermine the final development of the neuronal network activity. The methodological approach used here may be applied to explore the biological mechanisms underlying the complex dynamics of spontaneous activity in developing neuronal cultures.

Homophilic wiring principles underpin neuronal network topologyin vitro

“…We also apply a recently published transfer entropy algorithm to show that the main results of this study translate to other measures of (effective) connectivity 75 . In line with previous research 76,77 , we find that overall STTC values increased with development (F(3,12)=11.82, p=6.77×10 -4 , sparse cultures; Figure 1i ), as did the network density of inferred functional connectivity graphs (F(3,12)=11.08, p=8.97×10 -4 , n=6 sparse PC networks; Supplementary Figure 2a ). The probability of inferred STTC connections decayed with inter-neuronal distance ( Supplemental Figure 2b ).…”

Spontaneous Activity Predicts Survival of Developing Cortical Neurons

“…For this purpose, we applied machine learning, which is a consolidated approach in neuroscience (for review, see Vu et al, 2018 ) and allows to identify nonlinear dependencies in multidimensional neurophysiological datasets (for review, see Cunningham and Yu, 2014 ). Moreover, this approach has been successfully used for prediction of spontaneous activity during early development on MEAs ( Cabrera-Garcia et al, 2021 ). For each of the 1874 neurons, we included all parameters measured at DIV 9 describing neuronal firing, cluster spatial organization and network functional connectivity, and assigned a label according to its cell fate at DIV 12.…”

“…This likely reflects differences in neuronal subtypes or maturational stages across individual neurons and indicates the multiplicity of factors regulating cell death. To identify these and reveal predictive features of survival or cell death of individual neurons, we applied machine learning, which use as diagnostic and prognostic tool is recently emerging to assess brain development based on EEG recordings of preterm infants ( Wei L et al, 2020 ; for review, see Tataranno et al, 2021 ) and for early prediction of spontaneous activity in cortical networks in vitro ( Cabrera-Garcia et al, 2021 ). The overall good performance of the applied classifiers demonstrated how the survival fate of immature neurons is predictable based on activity features from single neuron, cluster and network level.…”

Calpain-Independent Intracellular Protease Activity Is Elevated in Excitotoxic Cortical Neurons Prior to Delayed Calcium Deregulation and Mitochondrial Dysfunction

“…Additionally, these results suggest that the cell-to-cell baseline cleavage rate variability may be, at least in part, reflective of variability in the endogenous level of neuronal activity (i.e., basal glutamate signaling). This is because the spontaneous electrical activity exhibited by mature cortical neurons in culture is expected to alter the basal levels of intracellular Ca 2+ [ 29 ].…”