Longitudinal electrophysiological dataset from cortical organoids, recorded weekly using planar multi-electrode arrays over 10 months. Please cite DOI of this repository when using the data. Thanks! Data associated with the following publication: Cleber A. Trujillo, Richard Gao, Priscilla D. Negraes, Jing Gu, Justin Buchanan, Sebastian Preissl, Allen Wang, Wei Wu, Gabriel G. Haddad, Isaac A. Chaim, Alain Domissy, Matthieu Vandenberghe, Anna Devor, Gene W. Yeo, Bradley Voytek, Alysson R. Muotri, Complex Oscillatory Waves Emerging from Cortical Organoids Model Early Human Brain Network Development, Cell Stem Cell,
Volume 25, Issue 4,
2019,
Pages 558-569.e7,
ISSN 1934-5909,
https://doi.org/10.1016/j.stem.2019.08.002.
(https://www.sciencedirect.com/science/article/pii/S1934590919303376) --- Instructions: /CTC_YYMMDD/LFP_Sp_YYMMDD.mat files contain preprocessed data for recording on a given date YY-MM-DD. Each .mat file contains the following variables: (explanation given in python and with 0-indexing) - LFP: broadband recording downsampled to 1000Hz; 12-element cell array, one for each well of the MEA. In each cell is a 2D matrix, [time x 64 channels]. - t_ds: downsampled timestamps at 1000Hz (just a continuously increasing vector, 1ms apart) - t_s: original timestamps at 12.5kHz (same idea as t_ds) - spikes: [12 wells x 64 channel] cell array; each cell contains a 1D array denoting multi-unit spike times in sample number (at 12.5kHz), use t_s to find the corresponding time in seconds. e.g., spikes[5, 32] contains the spiketrain from well 5 (of 12), channel 32 (of 64). Briefly, spikes were detected after bandpass filtering (300-3000Hz) and as threshold crossings adaptively set to 5.5 standard deviations. Note that the spikes were not sorted, and thus represent multi-unit activity. Refer to manuscript for details. - spike_cnt: [12 wells x 64 channel] total spike count at each well and channel. Just a count over the channels of the ‘spikes’ variable. - spike_shape: cell array, [12 wells x 64 channel] each cell contains a 2D array [101 samples x N spikes], containing 101-samples of the spike waveform for each of the N detected spike, centered at time 0 of spike, i.e., detected spike peak/trough. Empty arrays denote no spikes detected. - spike_avg: [12 wells x 64 channel x 101 timepoints] 3D array average spike waveform detected at that well and channel, 101-samples (at 12.5kHz), i.e., averaged over all N spikes in each element of the ‘spike_shape” variable. ———————— Note that wells 1-4 are empty, all data analyzed in the paper are from wells 5-12. To load the data: in MATLAB: just open the .mat file and do as you please. in python: from scipy import io data = io.loadmat(‘CTC_YYMMDD/LFP_Sp_YYMMDD.mat’, squeeze_me = True) LFP = data[‘LFP’]

Longitudinal electrophysiological dataset from cortical organoids, recorded weekly using planar multi-electrode arrays over 10 months. Please cite DOI of this repository when using the data. Thanks! Data associated with the following publication: Cleber A. Trujillo, Richard Gao, Priscilla D. Negraes, Jing Gu, Justin Buchanan, Sebastian Preissl, Allen Wang, Wei Wu, Gabriel G. Haddad, Isaac A. Chaim, Alain Domissy, Matthieu Vandenberghe, Anna Devor, Gene W. Yeo, Bradley Voytek, Alysson R. Muotri, Complex Oscillatory Waves Emerging from Cortical Organoids Model Early Human Brain Network Development, Cell Stem Cell,
Volume 25, Issue 4,
2019,
Pages 558-569.e7,
ISSN 1934-5909,
https://doi.org/10.1016/j.stem.2019.08.002.
(https://www.sciencedirect.com/science/article/pii/S1934590919303376) --- Instructions: /CTC_YYMMDD/LFP_Sp_YYMMDD.mat files contain preprocessed data for recording on a given date YY-MM-DD. Each .mat file contains the following variables: (explanation given in python and with 0-indexing) - LFP: broadband recording downsampled to 1000Hz; 12-element cell array, one for each well of the MEA. In each cell is a 2D matrix, [time x 64 channels]. - t_ds: downsampled timestamps at 1000Hz (just a continuously increasing vector, 1ms apart) - t_s: original timestamps at 12.5kHz (same idea as t_ds) - spikes: [12 wells x 64 channel] cell array; each cell contains a 1D array denoting multi-unit spike times in sample number (at 12.5kHz), use t_s to find the corresponding time in seconds. e.g., spikes[5, 32] contains the spiketrain from well 5 (of 12), channel 32 (of 64). Briefly, spikes were detected after bandpass filtering (300-3000Hz) and as threshold crossings adaptively set to 5.5 standard deviations. Note that the spikes were not sorted, and thus represent multi-unit activity. Refer to manuscript for details. - spike_cnt: [12 wells x 64 channel] total spike count at each well and channel. Just a count over the channels of the ‘spikes’ variable. - spike_shape: cell array, [12 wells x 64 channel] each cell contains a 2D array [101 samples x N spikes], containing 101-samples of the spike waveform for each of the N detected spike, centered at time 0 of spike, i.e., detected spike peak/trough. Empty arrays denote no spikes detected. - spike_avg: [12 wells x 64 channel x 101 timepoints] 3D array average spike waveform detected at that well and channel, 101-samples (at 12.5kHz), i.e., averaged over all N spikes in each element of the ‘spike_shape” variable. ———————— Note that wells 1-4 are empty, all data analyzed in the paper are from wells 5-12. To load the data: in MATLAB: just open the .mat file and do as you please. in python: from scipy import io data = io.loadmat(‘CTC_YYMMDD/LFP_Sp_YYMMDD.mat’, squeeze_me = True) LFP = data[‘LFP’]