README: Pandas DataFrame to load: import pickle pickle_filename = 'YOUR_DATA_PATH/df_name.pkl' # change accordingly with open(pickle_filename, 'rb') as pickle_in: df_name = pickle.load(pickle_in) Motorneuron data: Fish 3 Trial 1 and Fish 5 Trial 2 for Figure 3. Fish 5 Trial 2 for figure 4. Columns: - Fish: fish index - Trial: trial index - fluo: fluorescence traces [n_cells x n_timesteps] - fluo_type: 'dff' or 'f_smooth', respectively before and after smoothing procedure - n_cells: number of cells in the plane (only those kept for analysis, "bad" cells removed) - mid: middle cell, to split left vs right neurons (left until index mid-1, right from index mid and on) - cell_centers: x and y position of the cell center [n_cells x 2] - multivariate: boolean to indicate bivariate (False) or multivariate (True) GC - GC: Granger causality matrix results [n_cells x n_cells] - GC_sig: Granger causality matrix results, significant with original threshold (where Fstat > threshold_F) [n_cells x n_cells] - GC_sig_new_thresh: Granger causality matrix results, significant with new threshold (where Fstat > new_threshold_F) [n_cells x n_cells] - Fstat: F-statistics matrix [n_cells x n_cells] - threshold_F: original threshold for the F-statistics significance - new_threshold_F: new threshold for the F-statistics after the whole pipeline is applied Hindbrain data Fish 6 Trial 07 Columns: - fluo: fluorescence traces [n_cells x n_timesteps] - cell_centers: x and y position of the cell center [n_cells x 2] - background: plane background for plotting [249 x 512] - n_cells: number of cells in the plane - tail_angle: array of angle of the tail [75000,] - 75000 timesteps: higher frequency than calcium imaging recording - tail_angle_regressor: tail angle convolved to calcium decay function [75000,] - is_swim: boolean array whether each cell in correlated to swim activity (True if pearson correlation between cell's fluorescence trace and tail_angle_regressor > 0.6) [n_cells,] - swim_neurons: indices of swim-correlated neurons [n_swim_cells,] - medial_neurons: indices of swim-correlated neurons [n_medial_cells,] - SNR: signal-to-noise ratio for each cell [n_cells,] - BV_GC_medial: original bivariate (BV) Granger causality results matrix [n_medial_cells,n_medial_cells] - BV_Fstat_medial: original BV F-statistics matrix [n_medial_cells,n_medial_cells] - BV_threshold_F_ori: original threshold for the BV F-statistics significance - BV_threshold_F_new_mat_medial: new threshold customized for each pair of neurons (BV) [n_medial_cells,n_medial_cells] - BV_Fstat_normalized_medial: new BV F-statistics matrix normalized by customized threshold [n_medial_cells,n_medial_cells] - BV_GC_normalized_medial: new BV GC results matrix normalized by customized threshold [n_medial_cells,n_medial_cells] - MV_GC_medial: original multivariate (MV) Granger causality results matrix [n_medial_cells,n_medial_cells] - MV_Fstat_medial: original MV F-statistics matrix [n_medial_cells,n_medial_cells] - MV_threshold_F_ori_medial: original threshold for the MV F-statistics significance - MV_threshold_F_new_mat_medial: new MV F-statistics matrix normalized by customized threshold [n_medial_cells,n_medial_cells] - MV_Fstat_normalized_medial: new MV F-statistics matrix normalized by customized threshold [n_medial_cells,n_medial_cells] - MV_GC_normalized_medial: new MV GC results matrix normalized by customized threshold [n_medial_cells,n_medial_cells]

{"references": ["Chen, Ginoux, Mora, Walczak, Wyart. Granger causality analysis for calcium transients in neuronal networks: challenges and improvements. https://doi.org/10.1101/2022.06.27.497721"]}