Quantitative differences in developmental profiles of spontaneous activity in cortical and hippocampal cultures

Abstract

Background: Neural circuits can spontaneously generate complex spatiotemporal firing patterns during development. This spontaneous activity is thought to help guide development of the nervous system. In this study, we had two aims. First, to characterise the changes in spontaneous activity in cultures of developing networks of either hippocampal or cortical neurons dissociated from mouse. Second, to assess whether there are any functional differences in the patterns of activity in hippocampal and cortical networks. Results: We used multielectrode arrays to record the development of spontaneous activity in cultured networks of either hippocampal or cortical neurons every 2 or 3 days for the first month after plating. Within a few days of culturing, networks exhibited spontaneous activity. This activity strengthened and then stabilised typically around 21 days in vitro. We quantified the activity patterns in hippocampal and cortical networks using 11 features. Three out of 11 features showed striking differences in activity between hippocampal and cortical networks: (1) interburst intervals are less variable in spike trains from hippocampal cultures; (2) hippocampal networks have higher correlations and (3) hippocampal networks generate more robust theta-bursting patterns. Machine-learning techniques confirmed that these differences in patterning are sufficient to classify recordings reliably at any given age as either hippocampal or cortical networks. Conclusions: Although cultured networks of hippocampal and cortical networks both generate spontaneous activity that changes over time, at any given time we can reliably detect differences in the activity patterns. We anticipate that this quantitative framework could have applications in many areas, including neurotoxicity testing and for characterising the phenotype of different mutant mice. All code and data relating to this report are freely available for others to use.

Distinct Regulation of Bioenergetics and Translation by Group I mGluR and NMDAR

“…The neurons fired bursts of matured action potential spontaneously detected both in the current-clamp and voltage-clamp mode ( Figure 1D and 1E ) at day in-vitro 15. The action potential properties calculated through injecting a series of 500ms depolarizing current steps (−40pA to +540pA) were comparable to previously published reports ( Figure EV 1H, 1J, and 1K ) [33,34,36,37]. The presence of robust mEPSCs recorded at the baseline supports the presence of spontaneous excitatory neurotransmission ( Figure 1F and 1G ) [35].…”

Burst Detection Methods

“…Based on these assessments, four burst detectors, namely the MI, logISI, PS and CMA methods, were chosen as the best performing burst detection methods, and used to analyse bursting activity in novel recordings of networks of human induced pluripotent stem cell (hiPSC)-derived neuronal networks over several months of development. This analysis showed a slight increase in the proportion of bursting activity observed in these networks as they mature, although this increase was far lower than that which has been observed in developing rodent neuronal networks [14,19,95].…”

“…Analysis of patterns of bursting activity can thus be used as a proxy for studying the underlying physiological processes and structural features of neuronal networks. A common method of studying bursting activity in vitro involves the use of MEA recordings of spontaneous or evoked neuronal network activity [50,14,69,93]. This approach has been employed to study changes in spontaneous network activity over development [95], and the effect of pharmacological or genetic manipulations [23,15].…”

“…Instead, a large variety of burst detection methods have been proposed, many of which have been developed and assessed using specific data sets and single experimental conditions. As most studies of bursting activity have been performed on experimental data from recordings of rodent neuronal networks [14,55], this type of data has most often been used to assess the performance of burst detec-tion techniques [19,55,30].…”

Emergence of the small-world architecture in neural networks by activity dependent growth

“…Spontaneous neuronal activity refers to the ability of an individual neuron to be electro-physiologically active without receiving external stimulation. During development, many parts of the nervous system generate patterns of spontaneous activity [27] and these patterns of activity are thought to be instructive in the assembly of neural connectivity. It is shown that neurons generated from pluripotent stem cells (PSCs) generate random spontaneous spikes that are independent of external stimulation [28].…”