Dataset: Hierarchical Bayesian estimation of motor-evoked potential recruitment curves yields accurate and robust estimates

This repository contains data used in the manuscript: Hierarchical Bayesian estimation of motor-evoked potential recruitment curves yields accurate and robust estimates It includes three datasets consisting of recruitment curves: rat_scs.csv - Rat Spinal Cord Stimulation (SCS) Intensity column: pulse_amplitude Feature columns: participant, compound_position Response columns (AUC): LADM, LBiceps, LDeltoid, LECR, LFCR, LTriceps human_tms.csv — Human Transcranial Magnetic Stimulation (TMS) Intensity column: TMSInt Feature columns: participant, participant_condition Response columns (peak-to-peak): PKPK_ADM, PKPK_APB, PKPK_Biceps, PKPK_ECR, PKPK_FCR, PKPK_Triceps human_scs.csv — Human Spinal Cord Stimulation (SCS) Intensity column: sc_current Feature columns: participant, sc_laterality Response columns (AUC): ADM, APB, Biceps, Triceps Visualization A Python script is provided below to generate recruitment curve plots for all datasets. It uses pandas, matplotlib and seaborn. Logic: For each dataset, we group the data by the feature columns (e.g., participant and compound_position for rat_scs.csv). For each group, we plot recruitment curves by placing the intensity on the x-axis and the response on the y-axis. To run the script, simply update the DATA_DIR constant on line 11 to point to the directory containing the .csv files on your system. The generated plots are saved in the same DATA_DIR directory. import os import pandas as pd import matplotlib.pyplot as plt from matplotlib.figure import Figure from matplotlib.backends.backend_pdf import PdfPages import seaborn as sns # Path to directory containing the CSV files # This should be updated to the path on your system DATA_DIR = "/home/vishu/hbmep-data" def make_pdf(figures: list[Figure], output_path: str): """ Save a list of matplotlib figures to a multi-page PDF. Args: figures (List[Figure]): List of figures to save. output_path (str): Path to the output PDF file. """ with PdfPages(output_path) as pdf: for fig in figures: pdf.savefig(fig, bbox_inches='tight') plt.close(fig) print(f"Saved to {output_path}") return def plot( data_path: str, intensity: str, features: list[str], response: list[str], output_path: str ): """ Plot response vs. intensity for each unique feature combination and save as PDF. Each page of the PDF contains scatter plots arranged in a grid: - Rows: unique combinations of features - Columns: response variables Args: data_path (str): Path to the input CSV file. intensity (str): Column name for stimulation intensity. features (list[str]): Feature columns to define unique groups. response (list[str]): List of response variable column names. output_path (str): Path to save the output PDF file. """ num_rows_per_page = 10 num_columns_per_page = len(response) colors = sns.color_palette("tab10", num_columns_per_page) df = pd.read_csv(data_path) df_features = df[features].apply(tuple, axis=1) combinations = sorted(df_features.unique().tolist()) num_combinations = len(combinations) num_pages = num_combinations // num_rows_per_page if num_combinations % num_rows_per_page: num_pages += 1 figures = [] counter = 0 print(f"Making {output_path} ...") for page_idx in range(num_pages): print(f"Page {page_idx + 1}/{num_pages} ...") num_current_page_rows = min( num_rows_per_page, num_combinations - page_idx * num_rows_per_page ) figsize = (8, (11.69 / num_rows_per_page) * num_current_page_rows) fig, axes = plt.subplots( *(num_current_page_rows, num_columns_per_page), figsize=figsize, squeeze=False, constrained_layout=True ) for i in range(num_current_page_rows): combination = combinations[counter] idx = df_features.isin([combination]) ccdf = df[idx].reset_index(drop=True).copy() for j, response_name in enumerate(response): ax = axes[i, j] x = ccdf[intensity] y = ccdf[response_name] color = colors[j] sns.scatterplot(x=x, y=y, ax=ax, s=14, color=color) ax.set_xlabel("") ax.set_ylabel("") if not i: ax.set_title(response_name, fontsize=8) if not j: ax.set_ylabel("\n".join(combination), fontsize=8) counter += 1 for i in range(num_current_page_rows): for j in range(num_columns_per_page): ax = axes[i, j] ax.spines[['top', 'right']].set_visible(False) ax.tick_params(axis='both', labelsize=6) fig.suptitle(f"Page {page_idx + 1}/{num_pages}") figures.append(fig) make_pdf(figures, output_path) return def main(): """ Generate plots for all datasets and save them as PDFs. This function loads three datasets (Rat SCS, Human TMS, Human SCS), specifies their intensity, features, and response variables, and calls `plot` to generate and save the figures. """ # Rat SCS data_path = os.path.join(DATA_DIR, "rat_scs.csv") intensity = 'pulse_amplitude' features = ['participant', 'compound_position'] response = ['LADM', 'LBiceps', 'LDeltoid', 'LECR', 'LFCR', 'LTriceps'] output_path = os.path.join(DATA_DIR, "rat_scs.pdf") plot(data_path, intensity, features, response, output_path) # Human TMS data_path = os.path.join(DATA_DIR, "human_tms.csv") intensity = 'TMSInt' features = ['participant', 'participant_condition'] response = ['PKPK_ADM', 'PKPK_APB', 'PKPK_Biceps', 'PKPK_ECR', 'PKPK_FCR', 'PKPK_Triceps'] output_path = os.path.join(DATA_DIR, "human_tms.pdf") plot(data_path, intensity, features, response, output_path) # Human SCS data_path = os.path.join(DATA_DIR, "human_scs.csv") intensity = 'sc_current' features = ['participant', 'sc_laterality'] response = ['ADM', 'APB', 'Biceps', 'Triceps'] output_path = os.path.join(DATA_DIR, "human_scs.pdf") plot(data_path, intensity, features, response, output_path) return if __name__ == "__main__": main()