MEA recordings and data analysis

All recordings were performed using the 24-well MEA system (Multichannel Systems, MCS GmbH, Reutlingen, Germany). MEA devices are composed by 24 independent wells with embedded microelectrodes (i.e., 12 electrodes/well, 80 µm in diameter and spaced 300 µm apart). Spontaneous electrophysiological activity of iPS cell-derived neuronal network grown on MEAs was recorded for 20 min. During the recording, the temperature was maintained constant at 37 °C, and the evaporation and pH changes of the medium was prevented by inflating a constant, slow flow of humidified gas (5% CO₂ and 95% O₂) onto the MEA plate (with lid on). The signal was sampled at 10 kHz, filtered with a high-pass filter (i.e., Butterworth, 100 Hz cutoff frequency) and the noise threshold was set at ±4.5 standard deviations.

Data analysis was performed off-line by using Multiwell Analyzer (i.e., software from the 24-well MEA system that allows the extraction of the spike trains) and a custom software package named SPYCODE developed in MATLAB (The Mathworks, Natick, MA, USA) that allows the extraction of parameters describing the network activity⁶².

The mean firing rate (MFR) of the network was obtained by computing the firing rate of each channel averaged among all the active electrodes of the MEA. Burst detection: bursts were detected using a Burst Detection algorithm. The algorithm is based on the computation of the logarithmic inter-spike interval histogram in which inter-burst activity (i.e., between bursts and/or outside bursts) and intra-burst activity (i.e., within burst) for each recording channel can be easily identified, and then a threshold for detecting spikes belonging to the same burst is automatically defined. From the burst detection, the number of bursting channels (above threshold 0.4 burst/s and at least 5 spikes in burst with a minimal inter-spike-interval of 100 ms) was determined. Network burst detection: synchronous events were detected by looking for sequences of closely spaced single-channels bursts. A network burst was defined as burst that occurs in >80% of the network active channels. The distributions of NBDʼs and network inter-burst interval (NIBI, interval between two consecutive network bursts) were computed using bins of 100 ms and 1 s respectively. Network burst irregularity: irregularity was estimated by computing the CV of the NIBI, which is the standard deviation divided by the mean of the NIBI. Discriminant function analysis with canonical discriminant functions and reclassification of group membership based on parameters describing neuronal network activity were performed in SPSS.