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Dynamic neuronal ensembles encode burst-suppression revealed by cortex-wide optical-electrical interfaces.

Code ↔ Paper

7 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 7 matches
  1. [1] § Methods › Prediction of neuronal calcium dynamics from multi-channel ECoG signals during burst-suppression and awake states › ECoG features extraction ↔ extra_utils_for_scaling_analysis/utils_ym.py, lines 122–151 · score 0.97 · spectral centroid, spectral entropy, crest factor, dominant frequency, zero crossings, waveform factor
  2. [2] § Methods › Prediction of neuronal calcium dynamics from multi-channel ECoG signals during burst-suppression and awake states › Calcium signal dimensionality reduction via shared variance component anal ↔ extra_utils_for_population_coding/dimred.py, lines 26–113 · score 0.92 · removed temporal alignment, Shared Variance Component, cross validation, Covariance matrices, Neuronal populations, subsets
  3. [3] § Methods › Prediction of neuronal calcium dynamics from multi-channel ECoG signals during burst-suppression and awake states › Prediction of SVCs from ECoG features using reduced rank regression (RRR) ↔ extra_utils_for_scaling_analysis/predict.py, lines 162–237 · score 0.78 · reduced rank regression, predicted neural activity, canonical, linear, covariance, behavioral
  4. [4] § Methods › Prediction of neuronal calcium dynamics from multi-channel ECoG signals during burst-suppression and awake states › Prediction of SVCs from ECoG features using reduced rank regression (RRR) ↔ extra_utils_for_scaling_analysis/utils_ym.py, lines 744–832 · score 0.78 · reduced rank regression, combined training, canonical, validation, zero, rastermap
  5. [5] § Methods › Prediction of neuronal calcium dynamics from multi-channel ECoG signals during burst-suppression and awake states › Calcium signal dimensionality reduction via shared variance component anal ↔ extra_utils_for_scaling_analysis/svca.py, lines 19–92 · score 0.70 · reliable variance, utilized, SVCA, subsets, checkerboard, shuffled
  6. [6] § Methods › Transparent electrode array fabrication and characterization › Signal-to-Noise Ratio (SNR) calculation for ECoG signals ↔ extra_utils_for_scaling_analysis/utils_ym.py, lines 122–151 · score 0.55 · power spectral density, Welch, channel
  7. [7] § Results › Cross-modal signal prediction using shared variance components ↔ Predict_All.ipynb, lines 438–526 · score 0.50 · explainable variance, reliable variance, predicting, SVCs, mice

Paper

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The authors' code

Python · 832 lines · 35 KB · GPL-3.0 · 3 matches

  1. """
  2. Some misc. utilities.
  3. """
  5. import numpy as np
  6. import matplotlib.pyplot as plt
  7. import os
  8. import numpy as np
  9. import pandas as pd
  10. from scipy import signal
  11. from scipy.stats import zscore
  12. from rastermap import Rastermap
  13. from scipy.stats import skew, kurtosis
  14. from scipy.signal import welch
  15. from tqdm import tqdm
  16. from scipy.signal import savgol_filter
  17. from PopulationCoding.predict import canonical_cov
  18. import logging
  20. DEFAULT_N_JOBS = 8
  22. def plot_neurons_behavior_ym(
  23. neurons, ECoG_fp, normalized_pupil_area, t, clim=[-0.3, 1.5], zspacing=10
  24. ):
  25. fig = plt.figure(figsize=(8, 6))
  27. #
  28. ax = plt.subplot(10, 1, (1, 5))
  29. ax.imshow(
  30. neurons.T, cmap="viridis", aspect="auto", clim=clim, interpolation="bicubic"
  31. )
  32. ax.set_xticks([])
  33. ax.set_ylabel("Neuron #")
  35. #
  36. ax = plt.subplot(10, 1, (6, 9))
  37. time_indices = np.linspace(0, len(t) - 1, ECoG_fp.shape[0])
  38. for i in range(ECoG_fp.shape[1]):
  39. ax.plot(time_indices,ECoG_fp[:, i] - zspacing * i, color="k")
  40. ax.set_xlim([0, len(t) - 1])
  41. ax.set_xticks([])
  42. ax.set_yticks([])
  43. ax.set_ylabel("ECoG")
  45. #
  46. ax = plt.subplot(10, 1, 10)
  47. ax.plot(t, normalized_pupil_area, color="k")
  48. ax.set_xlim([t[0], t[-1]])
  49. ax.set_yticks([])
  50. ax.set_ylabel("Normalized\npupil area")
  51. ax.set_xlabel("Time (sec)")
  53. return fig
  56. def bandstop_filter(data, order=3, fs=1000, stopbands=[[2, 3], [4, 6], [49, 51]]):
  57. if data.ndim == 1:
  58. data = np.expand_dims(data, axis=0)
  60. filtered_data = data.copy()
  62. for freq_stop in stopbands:
  63. b, a = signal.butter(order, [f / (fs / 2) for f in freq_stop], btype='bandstop')
  64. filtered_data = signal.filtfilt(b, a, filtered_data, axis=1)
  66. if data.shape[0] == 1:
  67. filtered_data = np.squeeze(filtered_data, axis=0)
  69. return filtered_data
  71. def preprocess_calcium_data(res_path, load_path, threshold=50):
  72. denoised_data = np.load(os.path.join(res_path, "neuron_denoised_records_whole_brain.npy"))
  73. position_x = pd.read_csv(os.path.join(load_path, "valid_neuron_x.csv"))
  74. position_y = pd.read_csv(os.path.join(load_path, "valid_neuron_y.csv"))
  76. denoised_data[denoised_data > threshold] = threshold
  78. neurons = zscore(denoised_data).T
  79. centers = np.hstack((position_x, position_y)).T
  81. return neurons, centers
  84. def preprocess_ecog_data(load_path, kbd1, evt06):
  85. fp_32chs = pd.read_csv(os.path.join(load_path, "ele_signal_burst_supp_fp01-fp32.csv")).values.T
  86. mean_across_channels = np.mean(fp_32chs, axis=0)
  87. fp_32chs_filtered = np.zeros_like(fp_32chs)
  89. for ch in range(fp_32chs.shape[0]):
  90. signal_without_mean = fp_32chs[ch] - mean_across_channels
  91. fp_32chs_filtered[ch] = bandstop_filter(signal_without_mean)
  93. #
  94. second_start_Ele = kbd1 - 120 # s
  95. second_stop_Ele = second_start_Ele + 900 # s
  96. fhz_Ele = 1000 # Hz
  98. t_idx_Ele = np.arange(int(fhz_Ele * second_start_Ele), int(fhz_Ele * second_stop_Ele))
  99. fp_32chs_example = zscore(fp_32chs_filtered.T[t_idx_Ele, :32])
  101. return fp_32chs_example
  104. def perform_rastermap_analysis(neurons, kbd1, evt06, perform_rastermap):
  105. second_start_Ca = kbd1 - evt06 - 120
  106. second_stop_Ca = second_start_Ca + 900
  107. fhz_Ca = 10
  108. t_idx_Ca = np.arange(int(fhz_Ca * second_start_Ca), int(fhz_Ca * second_stop_Ca))
  109. neurons_example = zscore(neurons[t_idx_Ca, :])
  110. neurons_example_sorted = None
  111. if perform_rastermap:
  112. model = Rastermap(n_PCs=32, n_clusters=8).fit(neurons_example.T)
  113. neurons_example_sorted = neurons_example[:, model.isort]
  114. return neurons_example, neurons_example_sorted
  117. def downsample_signal(fp_32chs_example, factor=100):
  118. fp_32chs_downsampled = fp_32chs_example.reshape(-1, factor, fp_32chs_example.shape[1]).mean(axis=1)
  119. assert fp_32chs_downsampled.shape[0] == 9000
  120. return fp_32chs_downsampled
  122. def extract_features(channel_data):
  123. mean = np.mean(channel_data)
  124. std_dev = np.std(channel_data)
  125. variance = np.var(channel_data)
  126. max_val = np.max(channel_data)
  127. min_val = np.min(channel_data)
  128. median = np.median(channel_data)
  129. ptp = np.ptp(channel_data)
  130. iqr = np.percentile(channel_data, 75) - np.percentile(channel_data, 25)
  131. energy = np.sum(channel_data ** 2) / len(channel_data)
  132. skewness = skew(channel_data)
  133. kurt = kurtosis(channel_data)
  134. rms = np.sqrt(np.mean(channel_data**2))
  135. waveform_factor = rms / np.mean(np.abs(channel_data))
  136. crest_factor = max_val / rms
  137. zero_crossings = np.where(np.diff(np.signbit(channel_data)))[0].size
  139. freqs, psd = welch(channel_data, fs=1000, nperseg=len(channel_data))
  140. dominant_freq = freqs[np.argmax(psd)]
  141. spectral_centroid = np.sum(freqs * psd) / np.sum(psd)
  142. spectral_entropy = -np.sum((psd/np.sum(psd)) * np.log2(psd/np.sum(psd)))
  143. bandwidth = np.sqrt(np.sum(psd * (freqs - spectral_centroid)**2) / np.sum(psd))
  145. features = [
  146. mean, std_dev, variance, max_val, min_val, median,
  147. ptp, iqr, energy, skewness, kurt, waveform_factor, crest_factor,
  148. zero_crossings, dominant_freq, spectral_centroid, spectral_entropy, bandwidth
  149. ]
  151. return features
  153. def extract_all_features(fp_32chs_example, num_windows, num_channels):
  154. features_per_channel = 18
  155. output_matrix = np.zeros((num_windows, num_channels * features_per_channel))
  157. for window_idx in tqdm(range(num_windows)):
  158. start_idx = window_idx * 100
  159. end_idx = start_idx + 100
  160. window_data = fp_32chs_example[start_idx:end_idx, :]
  162. feature_vector = []
  163. for channel_idx in range(num_channels):
  164. channel_data = window_data[:, channel_idx]
  165. features = extract_features(channel_data)
  166. feature_vector.extend(features)
  168. output_matrix[window_idx, :] = feature_vector
  170. return output_matrix
  172. def zscore_and_visualize_ecog_features(ECoG_Extracted_Features, output_directory, savefigs_1):
  174. ECoG_Extracted_Features = zscore(zscore(ECoG_Extracted_Features.T).T)
  176. if savefigs_1:
  177. model_ECoG = Rastermap(n_PCs=32, n_clusters=8).fit(ECoG_Extracted_Features.T)
  178. fig, axs = plt.subplots(2, 1, figsize=(12, 8))
  179. clim1 = [-0.3, 100]
  180. clim2 = [-0.3, 3]
  182. axs[0].imshow(
  183. ECoG_Extracted_Features[:, model_ECoG.isort].T, cmap="viridis", aspect="auto", clim=clim1, interpolation="bicubic"
  184. )
  185. axs[0].set_xticks([])
  186. axs[0].set_ylabel("Neuron #")
  187. axs[0].set_title('Original TrainX')
  189. axs[1].imshow(
  190. ECoG_Extracted_Features[:, model_ECoG.isort].T, cmap="viridis", aspect="auto", clim=clim2, interpolation="bicubic"
  191. )
  192. axs[1].set_xticks([])
  193. axs[1].set_ylabel("Neuron #")
  194. axs[1].set_title('Original TrainX')
  196. output_path_tif = os.path.join(output_directory, "fig6", "ECoG_Features.tif")
  197. output_path_png = os.path.join(output_directory, "fig6", "ECoG_Features.png")
  200. fig.savefig(output_path_tif, dpi=600, format='tif')
  201. fig.savefig(output_path_png, dpi=600, format='png')
  203. plt.close(fig)
  206. def get_list_shape(lst):
  207. if isinstance(lst, list):
  208. return (len(lst),) + get_list_shape(lst[0])
  209. else:
  210. return ()
  213. def plot_svc_time_series(neurons_example, ex_u, ex_v, ex_ntrain, ex_ntest, t_start, t_end, res_path, filename, n_nneur=-1):
  215. svc_indices = [0, 7, 15, 63, 255, 511] # SVC 1, 8, 16, 64, 256, 512
  217. plt.figure(figsize=(12, 18))
  219. for i, svc_index in enumerate(svc_indices):
  221. neurons_subset_train = neurons_example[t_start*10:t_end*10, ex_ntrain[n_nneur]]
  222. svc_time_series_train = np.dot(neurons_subset_train, ex_u[n_nneur][:, svc_index])
  225. neurons_subset_test = neurons_example[t_start*10:t_end*10, ex_ntest[n_nneur]]
  226. svc_time_series_test = np.dot(neurons_subset_test, ex_v[n_nneur][:, svc_index])
  229. correlation = np.corrcoef(svc_time_series_train, svc_time_series_test)[0, 1]
  232. plt.subplot(len(svc_indices), 1, i + 1)
  233. time_axis = np.arange(svc_time_series_train.shape[0])/10
  235. plt.plot(time_axis, svc_time_series_train, label=f'Training Set SVC {svc_index + 1}', color='blue')
  236. plt.plot(time_axis, svc_time_series_test, label=f'Testing Set SVC {svc_index + 1}', color='orange')
  239. plt.text(0.05, 0.95, f'Correlation: {correlation:.2f}', transform=plt.gca().transAxes,
  240. fontsize=10, verticalalignment='top', bbox=dict(facecolor='white', alpha=0.5))
  242. plt.xlabel('Time')
  243. plt.ylabel('SVC Value')
  244. plt.title(f'SVC {svc_index + 1} Time Series')
  245. plt.legend()
  247. plt.tight_layout()
  250. save_dir = os.path.join(res_path, "SVCA set1 and set2")
  251. os.makedirs(save_dir, exist_ok=True)
  254. output_path_png = os.path.join(save_dir, f"1_8_16_64_256_512_{filename}.png",)
  255. plt.savefig(output_path_png, format='png')
  256. plt.close()
  259. def compare_and_plot(model, trainX, testX, u_sub, v_sub, vp_train, vp_test, ll, kk, res_path, base_path, centers):
  261. dictpath = os.path.join(res_path, "predicted_neurons_versus_trainX_testX", base_path)
  262. os.makedirs(dictpath, exist_ok=True)
  263. plot_filename_png = f"neurons_l{ll}_k{kk}.png"
  264. plot_filepath_png = os.path.join(dictpath, plot_filename_png)
  265. plot_filename_tif = f"neurons_l{ll}_k{kk}.tif"
  266. plot_filepath_tif = os.path.join(dictpath, plot_filename_tif)
  269. trainX_approximation = vp_train.T @ u_sub.T
  270. testX_approximation = vp_test.T @ v_sub.T
  272. trainX_approximation = np.real(trainX_approximation)
  273. testX_approximation = np.real(testX_approximation)
  274. trainX_approximation = zscore(zscore(trainX_approximation.T).T)
  275. testX_approximation = zscore(zscore(testX_approximation.T).T)
  277. real = np.hstack((trainX, testX))
  278. predicted = np.hstack((trainX_approximation, testX_approximation))
  280. plot_neuron_correlation(real, predicted, centers, res_path, f"{base_path}l{ll}k{kk}")
  282. fig, axs = plt.subplots(2, 1, figsize=(12, 8))
  283. clim = [-0.3, 1.5]
  285. axs[0].imshow(
  286. real[:, model.isort].T, cmap="viridis", aspect="auto", clim=clim, interpolation="bicubic"
  287. )
  288. axs[0].set_xticks([])
  289. axs[0].set_ylabel("Neuron #")
  290. axs[0].set_title('Original')
  292. axs[1].imshow(
  293. predicted[:, model.isort].T, cmap="viridis", aspect="auto", clim=clim, interpolation="bicubic"
  294. )
  295. axs[1].set_xticks([])
  296. axs[1].set_ylabel("Neuron #")
  297. axs[1].set_title('Predicted')
  299. for ax in axs.flat:
  300. ax.set(xlabel='Time', ylabel='Neurons')
  302. plt.tight_layout()
  303. plt.savefig(plot_filepath_png)
  304. plt.savefig(plot_filepath_tif)
  305. plt.close(fig)
  306. print(f"Plot saved at {dictpath}")
  309. import pickle
  311. def plot_quantitative_statistics(correlations, res_path, suffix):
  312. sorted_indices = np.argsort(correlations)[::-1]
  313. top_1000_indices = sorted_indices[:1000]
  315. top_1000_correlations = [correlations[i] for i in top_1000_indices]
  316. all_average_correlation = np.mean(correlations)
  317. top_1000_average_correlation = np.mean(top_1000_correlations)
  319. all_std_error = np.std(correlations) / np.sqrt(len(correlations))* 1.96
  320. top_1000_std_error = np.std(top_1000_correlations) / np.sqrt(len(top_1000_correlations)) * 1.96
  322. # Prepare data to save
  323. data_to_save = {
  324. 'correlations':correlations,
  325. 'sorted_indices': sorted_indices,
  326. 'top_1000_indices': top_1000_indices,
  327. 'top_1000_correlations': top_1000_correlations,
  328. 'all_average_correlation': all_average_correlation,
  329. 'top_1000_average_correlation': top_1000_average_correlation,
  330. 'all_std_error': all_std_error,
  331. 'top_1000_std_error': top_1000_std_error
  332. }
  334. # Save variables to a file using pickle
  335. save_dir = os.path.join(res_path, "neuron_correlation_statistics")
  336. os.makedirs(save_dir, exist_ok=True)
  337. with open(os.path.join(save_dir, f"variables_{suffix}.pkl"), 'wb') as f:
  338. pickle.dump(data_to_save, f)
  340. # Plotting
  341. plt.figure(figsize=(8, 6))
  342. labels = ['All Neurons', 'Top 1000 Neurons']
  343. averages = [all_average_correlation, top_1000_average_correlation]
  344. errors = [all_std_error, top_1000_std_error]
  346. plt.bar(labels, averages, yerr=errors, color=['blue', 'green'], alpha=0.7, capsize=5)
  347. plt.ylabel('Average Pearson Correlation')
  348. plt.title('Comparison of Average Correlation with Error Bars')
  350. plt.savefig(os.path.join(save_dir, f"neuron_correlation_statistics_{suffix}.png"), dpi=300)
  351. plt.savefig(os.path.join(save_dir, f"neuron_correlation_statistics_{suffix}.pdf"))
  352. plt.close()
  356. def plot_neuron_correlation(real_activity, predicted_activity, centers, res_path, suffix):
  357. correlations = []
  358. for i in range(real_activity.shape[1]):
  359. if np.std(real_activity[:, i]) == 0 or np.std(predicted_activity[:, i]) == 0:
  360. correlations.append(0)
  361. else:
  362. corr = np.corrcoef(real_activity[:, i], predicted_activity[:, i])[0, 1]
  363. correlations.append(corr)
  364. correlations = np.nan_to_num(correlations, nan=0)
  365. correlations = np.maximum(correlations, 0)
  367. s = max(2, 100 / np.sqrt(centers.shape[1]))
  368. np.save(os.path.join(res_path, f'correlations_{suffix}.npy'), {
  369. 'correlations': correlations,
  370. })
  372. np.save(os.path.join(res_path, f'real_predicted_activity_centers_{suffix}.npy'), {
  373. 'real_activity': real_activity,
  374. 'predicted_activity': predicted_activity,
  375. 'centers': centers,
  376. })
  378. # Plotting scatter plots
  379. for vmax_value in [0.6, 1]:
  380. plt.figure(figsize=(10, 8))
  381. scatter = plt.scatter(
  382. centers[0, :],
  383. centers[1, :],
  384. c=correlations,
  385. cmap='Reds',
  386. s=10,
  387. alpha=correlations,
  388. edgecolors='none',
  389. vmin=0,
  390. vmax=vmax_value
  391. )
  393. cbar = plt.colorbar(scatter)
  394. cbar.set_label('Pearson Correlation')
  395. plt.xlabel('X Position')
  396. plt.ylabel('Y Position')
  397. plt.title(f'Neuron Activity Correlation ({suffix})')
  399. save_dir = os.path.join(res_path, "neuron_correlation_maps")
  400. os.makedirs(save_dir, exist_ok=True)
  401. plt.savefig(os.path.join(save_dir, f"neuron_correlation_{suffix}_{vmax_value}.png"), dpi=300)
  402. plt.savefig(os.path.join(save_dir, f"neuron_correlation_{suffix}_{vmax_value}.pdf"))
  403. plt.close()
  405. # Calculate distance from center
  406. center_point = np.mean(centers, axis=1)
  407. distances = np.linalg.norm(centers.T - center_point, axis=1)
  409. # Define a threshold to separate central and peripheral neurons
  410. threshold_distance = np.percentile(distances, 50) # Median distance as separation
  412. central_correlations = correlations[distances <= threshold_distance]
  413. peripheral_correlations = correlations[distances > threshold_distance]
  415. # Plot average correlation
  416. plt.figure()
  417. plt.bar(['Central', 'Peripheral'], [np.mean(central_correlations), np.mean(peripheral_correlations)])
  418. plt.ylabel('Average Pearson Correlation')
  419. plt.title('Average Correlation: Central vs Peripheral Neurons')
  420. plt.savefig(os.path.join(save_dir, f"average_correlation_central_vs_peripheral_{suffix}.png"), dpi=300)
  421. plt.savefig(os.path.join(save_dir, f"average_correlation_central_vs_peripheral_{suffix}.pdf"))
  422. plt.close()
  424. # Plot correlation vs distance
  425. plt.figure()
  426. plt.scatter(distances, correlations, c=correlations, cmap='Reds')
  427. plt.colorbar(label='Pearson Correlation')
  428. plt.xlabel('Distance from Center')
  429. plt.ylabel('Pearson Correlation')
  430. plt.title('Correlation vs Distance from Center')
  431. plt.savefig(os.path.join(save_dir, f"correlation_vs_distance_{suffix}.png"), dpi=300)
  432. plt.savefig(os.path.join(save_dir, f"correlation_vs_distance_{suffix}.pdf"))
  433. plt.close()
  434. plot_quantitative_statistics(correlations, res_path, suffix)
  437. # def plot_neuron_correlation(real_activity, predicted_activity, centers, res_path, suffix):
  438. # correlations = []
  439. # for i in range(real_activity.shape[1]):
  440. # if np.std(real_activity[:, i]) == 0 or np.std(predicted_activity[:, i]) == 0:
  441. # correlations.append(0)
  442. # else:
  443. # corr = np.corrcoef(real_activity[:, i], predicted_activity[:, i])[0, 1]
  444. # correlations.append(corr)
  445. # correlations = np.nan_to_num(correlations, nan=0)
  446. # correlations = np.maximum(correlations, 0)
  448. # s = max(2, 100 / np.sqrt(centers.shape[1]))
  449. # np.save(os.path.join(res_path, f'correlations_{suffix}.npy'), {
  450. # 'correlations': correlations,
  451. # })
  453. # np.save(os.path.join(res_path, f'real_predicted_activity_centers_{suffix}.npy'), {
  454. # 'real_activity': real_activity,
  455. # 'predicted_activity': predicted_activity,
  456. # 'centers': centers,
  457. # })
  459. # plt.figure(figsize=(10, 8))
  460. # scatter = plt.scatter(
  461. # centers[0, :],
  462. # centers[1, :],
  463. # c=correlations,
  464. # cmap='Reds',
  465. # s=10,
  466. # alpha=correlations,
  467. # edgecolors='none',
  468. # vmin=0,
  469. # vmax=0.6
  470. # )
  472. # cbar = plt.colorbar(scatter)
  473. # cbar.set_label('Pearson Correlation')
  474. # plt.xlabel('X Position')
  475. # plt.ylabel('Y Position')
  476. # plt.title(f'Neuron Activity Correlation ({suffix})')
  478. # save_dir = os.path.join(res_path, "neuron_correlation_maps")
  479. # os.makedirs(save_dir, exist_ok=True)
  480. # plt.savefig(os.path.join(save_dir, f"neuron_correlation_{suffix}_0.6.png"), dpi=300)
  481. # plt.savefig(os.path.join(save_dir, f"neuron_correlation_{suffix}_0.6.pdf"))
  482. # plt.close()
  483. # plt.figure(figsize=(10, 8))
  484. # scatter = plt.scatter(
  485. # centers[0, :],
  486. # centers[1, :],
  487. # c=correlations,
  488. # cmap='Reds',
  489. # s=10,
  490. # alpha=correlations,
  491. # edgecolors='none',
  492. # vmin=0,
  493. # vmax=1
  494. # )
  496. # cbar = plt.colorbar(scatter)
  497. # cbar.set_label('Pearson Correlation')
  498. # plt.xlabel('X Position')
  499. # plt.ylabel('Y Position')
  500. # plt.title(f'Neuron Activity Correlation ({suffix})')
  502. # save_dir = os.path.join(res_path, "neuron_correlation_maps")
  503. # os.makedirs(save_dir, exist_ok=True)
  504. # plt.savefig(os.path.join(save_dir, f"neuron_correlation_{suffix}_1.png"), dpi=300)
  505. # plt.savefig(os.path.join(save_dir, f"neuron_correlation_{suffix}_1.pdf"))
  506. # plt.close()
  507. # plot_quantitative_statistics(correlations, res_path, suffix)
  511. def reduced_rank_regressions_ym(ranks, ECoG_Extracted_Features, projs1, projs2, lams, res_path,
  512. ex_itrain, ex_itest, ex_u, ex_v, nsvc_predict, trainX, testX, centers):
  514. nrank1 = ranks[ranks <= ECoG_Extracted_Features.shape[1]]
  515. print("nrank1:", nrank1)
  517. n_nneurs = 0
  518. t_start_plot = 50
  519. t_end_plot = 400
  521. for l in tqdm(range(len(lams))): # try various regularization
  522. atrain, btrain, _, _ = canonical_cov(
  523. projs1[ex_itrain[n_nneurs], :], ECoG_Extracted_Features[ex_itrain[n_nneurs], :], lams[l], npc=max(nrank1)
  524. )
  525. atest, btest, _, _ = canonical_cov(
  526. projs2[ex_itrain[n_nneurs], :], ECoG_Extracted_Features[ex_itrain[n_nneurs], :], lams[l], npc=max(nrank1)
  527. )
  529. for k in tqdm(range(len(nrank1))): # try various ranks
  530. vp_train = (
  531. atrain[:, :nrank1[k]] @ btrain[:, :nrank1[k]].T @ ECoG_Extracted_Features.T
  532. )
  533. vp_test = atest[:, :nrank1[k]] @ btest[:, :nrank1[k]].T @ ECoG_Extracted_Features.T
  535. window_length = 51
  536. polyorder = 11
  537. vp_train_filtered = savgol_filter(vp_train.T, window_length=window_length, polyorder=polyorder, axis=0).T
  538. vp_test_filtered = savgol_filter(vp_test.T, window_length=window_length, polyorder=polyorder, axis=0).T
  539. vp_train = vp_train_filtered
  540. vp_test = vp_test_filtered
  542. print("vp_train:", vp_train.shape)
  543. print("vp_test:", vp_test.shape)
  544. print("projs1:", projs1.shape)
  545. print("projs2:", projs2.shape)
  547. plot_components = [0, 1, 2, 3, 4, 5, 6, 7, 15, 31, 63]
  550. def save_plots(proj, vp, filepath_suffix, filename_suffix):
  551. fig, axes = plt.subplots(len(plot_components), 1, figsize=(15, 25))
  552. for i, vec_idx in enumerate(plot_components):
  553. ax = axes[i]
  554. ax.plot(proj[t_start_plot*10:t_end_plot*10, vec_idx], color='b', alpha=0.5, label='real')
  555. ax.plot(vp.T[t_start_plot*10:t_end_plot*10, vec_idx], color='r', alpha=0.5, label='predicted')
  556. ax.set_title(f'SVC {vec_idx + 1}')
  557. ax.legend()
  558. ax.set_xlabel('time')
  559. ax.set_ylabel('SVCs')
  560. plt.tight_layout()
  561. filename_png = f"SVCs_l{l}_k{k}_nneur{n_nneurs}_{filename_suffix}.png"
  562. filename_pdf = f"SVCs_l{l}_k{k}_nneur{n_nneurs}_{filename_suffix}.pdf"
  563. directory_path = os.path.join(res_path, "predicted_SVCs", filepath_suffix)
  564. os.makedirs(directory_path, exist_ok=True)
  565. filepath_png = os.path.join(directory_path, filename_png)
  566. filepath_pdf = os.path.join(directory_path, filename_pdf)
  567. plt.savefig(filepath_png)
  568. plt.savefig(filepath_pdf, format='pdf')
  569. fig.clf()
  571. def center_data(data, axis=0):
  572. mean = np.mean(data, axis=axis, keepdims=True)
  573. centered_data = data - mean
  574. return centered_data
  575. vp_train_filtered=center_data(vp_train_filtered,axis=1)
  576. vp_test_filtered =center_data(vp_test_filtered, axis=1)
  578. # Save plots using the defined helper function
  579. save_plots(projs1, vp_train_filtered, "Train_And_Test", "projs_1")
  580. save_plots(projs1[ex_itrain[n_nneurs]], vp_train_filtered[:, ex_itrain[n_nneurs]], "Train_Only" , "projs_1")
  581. save_plots(projs1[ex_itest[n_nneurs]], vp_train_filtered[:, ex_itest[n_nneurs]], "Test_Only", "projs_1")
  582. save_plots(projs2, vp_test_filtered, "Train_And_Test", "projs_2")
  583. save_plots(projs2[ex_itrain[n_nneurs]], vp_test_filtered[:, ex_itrain[n_nneurs]], "Train_Only" , "projs_2")
  584. save_plots(projs2[ex_itest[n_nneurs]], vp_test_filtered[:, ex_itest[n_nneurs]], "Test_Only", "projs_2")
  586. u_sub = ex_u[n_nneurs][:, :nsvc_predict]
  587. v_sub = ex_v[n_nneurs][:, :nsvc_predict]
  589. model = Rastermap(n_PCs=32, n_clusters=8).fit(np.hstack((trainX, testX)).T)
  591. if (l==len(lams)-1) and (k==len(nrank1)-1):
  592. np.save(os.path.join(res_path, 'vp_train_test__projs1_2.npy'), {
  593. 'vp_train': vp_train,
  594. 'vp_test': vp_test,
  595. 'projs1': projs1,
  596. 'projs2':projs2
  597. })
  599. plot_burst=1
  600. KBD1=191.4
  601. burst_start=[241, 244.2, 247.3, 252.2, 255.5, 258.8, 265, 270, 279.1, 280, 286.9, 301.9, 311.8, 317.5, 323.4, 334.1, 338.7, 350.6, 368.5, 376.6]
  602. supp_start=[243.5, 246.1, 248.8, 254, 257.8, 260, 270, 275.4, 280, 283.4, 289.8, 304, 312.5, 318.5, 324.4, 334.5, 339.5, 351.4, 369.5, 377.7]
  603. burst_start = np.array(burst_start)
  604. supp_start = np.array(supp_start)
  605. burst_start=burst_start-KBD1+120
  606. supp_start=supp_start-KBD1+120
  607. sampling_rate = 10
  608. def calculate_intervals(start_times, end_times):
  609. return [
  610. (int(start * sampling_rate), int(end * sampling_rate))
  611. for start, end in zip(start_times, end_times)
  612. ]
  614. if plot_burst == 1:
  615. # Burst time intervals
  616. burst_intervals = calculate_intervals(burst_start, supp_start)
  617. selected_indices = np.concatenate([np.arange(start, end) for start, end in burst_intervals])
  618. else:
  619. # Supp time intervals, excluding the last-to-first transition
  620. supp_intervals = calculate_intervals(supp_start[:-1], burst_start[1:])
  621. selected_indices = np.concatenate([np.arange(start, end) for start, end in supp_intervals])
  623. # Function to slice data using indices
  624. def get_sliced_data(data, indices):
  625. return data[indices, :]
  627. # Plotting logic
  628. compare_and_plot(
  629. model,
  630. get_sliced_data(trainX, selected_indices),
  631. get_sliced_data(testX, selected_indices),
  632. u_sub,
  633. v_sub,
  634. vp_train[:, selected_indices],
  635. vp_test[:, selected_indices],
  636. l,
  637. k,
  638. res_path,
  639. "Train_And_Test",
  640. centers
  641. )
  643. # Filter the indices for test and train specifically within selected_indices
  644. selected_test_indices = ex_itest[n_nneurs][np.isin(ex_itest[n_nneurs], selected_indices)]
  645. selected_train_indices = ex_itrain[n_nneurs][np.isin(ex_itrain[n_nneurs], selected_indices)]
  647. compare_and_plot(
  648. model,
  649. trainX[selected_test_indices, :],
  650. testX[selected_test_indices, :],
  651. u_sub,
  652. v_sub,
  653. vp_train[:, selected_test_indices],
  654. vp_test[:, selected_test_indices],
  655. l,
  656. k,
  657. res_path,
  658. "Test_Only",
  659. centers
  660. )
  662. compare_and_plot(
  663. model,
  664. trainX[selected_train_indices, :],
  665. testX[selected_train_indices, :],
  666. u_sub,
  667. v_sub,
  668. vp_train[:, selected_train_indices],
  669. vp_test[:, selected_train_indices],
  670. l,
  671. k,
  672. res_path,
  673. "Train_Only",
  674. centers
  675. )
  677. break
  679. plot_ane=1
  680. if plot_ane:
  682. compare_and_plot(model, trainX[ex_itest[n_nneurs][(ex_itest[n_nneurs] >= 500) & (ex_itest[n_nneurs] <= 4000)], :],
  683. testX[ex_itest[n_nneurs][(ex_itest[n_nneurs] >= 500) & (ex_itest[n_nneurs] <= 4000)], :],
  684. u_sub, v_sub,
  685. vp_train[:, ex_itest[n_nneurs][(ex_itest[n_nneurs] >= 500) & (ex_itest[n_nneurs] <= 4000)]],
  686. vp_test[:, ex_itest[n_nneurs][(ex_itest[n_nneurs] >= 500) & (ex_itest[n_nneurs] <= 4000)]],
  687. l, k, res_path, "Test_Only", centers)
  688. break
  689. compare_and_plot(model, trainX[500:4000, :], testX[500:4000, :], u_sub, v_sub, vp_train[:, 500:4000], vp_test[:, 500:4000], l, k, res_path, "Train_And_Test", centers)
  690. compare_and_plot(model, trainX[ex_itrain[n_nneurs][(ex_itrain[n_nneurs] >= 500) & (ex_itrain[n_nneurs] <= 4000)], :],
  691. testX[ex_itrain[n_nneurs][(ex_itrain[n_nneurs] >= 500) & (ex_itrain[n_nneurs] <= 4000)], :],
  692. u_sub, v_sub,
  693. vp_train[:, ex_itrain[n_nneurs][(ex_itrain[n_nneurs] >= 500) & (ex_itrain[n_nneurs] <= 4000)]],
  694. vp_test[:, ex_itrain[n_nneurs][(ex_itrain[n_nneurs] >= 500) & (ex_itrain[n_nneurs] <= 4000)]],
  695. l, k, res_path, "Train_Only", centers)
  697. else:
  699. new_start1 = 0
  700. new_end1 = 500
  701. new_start2 = 4000
  702. new_end2 = 9000
  703. combined_slice = np.r_[new_start1:new_end1, new_start2:new_end2]
  704. compare_and_plot(model,
  705. trainX[combined_slice, :],
  706. testX[combined_slice, :],
  707. u_sub, v_sub,
  708. vp_train[:, combined_slice],
  709. vp_test[:, combined_slice],
  710. l, k, res_path,
  711. "Train_And_Test", centers)
  713. test_condition = ((ex_itest[n_nneurs] >= new_start1) & (ex_itest[n_nneurs] < new_end1)) | \
  714. ((ex_itest[n_nneurs] >= new_start2) & (ex_itest[n_nneurs] < new_end2))
  715. selected_test_indices = ex_itest[n_nneurs][test_condition]
  716. compare_and_plot(model,
  717. trainX[selected_test_indices, :],
  718. testX[selected_test_indices, :],
  719. u_sub, v_sub,
  720. vp_train[:, selected_test_indices],
  721. vp_test[:, selected_test_indices],
  722. l, k, res_path,
  723. "Test_Only", centers)
  724. break
  725. train_condition = ((ex_itrain[n_nneurs] >= new_start1) & (ex_itrain[n_nneurs] < new_end1)) | \
  726. ((ex_itrain[n_nneurs] >= new_start2) & (ex_itrain[n_nneurs] < new_end2))
  727. selected_train_indices = ex_itrain[n_nneurs][train_condition]
  728. compare_and_plot(model,
  729. trainX[selected_train_indices, :],
  730. testX[selected_train_indices, :],
  731. u_sub, v_sub,
  732. vp_train[:, selected_train_indices],
  733. vp_test[:, selected_train_indices],
  734. l, k, res_path,
  735. "Train_Only", centers)
  738. def center_data(data, axis=0):
  739. mean = np.mean(data, axis=axis, keepdims=True)
  740. centered_data = data - mean
  741. return centered_data
  744. def reduced_rank_regressions_ym_2(ranks, ECoG_Extracted_Features, projs1, projs2, lams, res_path,
  745. ex_itrain, ex_itest, ex_u, ex_v, nsvc_predict, trainX, testX, centers):
  747. nrank1 = ranks[ranks <= ECoG_Extracted_Features.shape[1]]
  748. print("nrank1:", nrank1)
  750. n_nneurs = 0
  751. t_start_plot = 50
  752. t_end_plot = 400
  754. for l in tqdm(range(len(lams))):
  755. vp_combined_train = np.zeros((projs1.shape[1], ECoG_Extracted_Features.shape[0]))
  756. vp_combined_test = np.zeros_like(vp_combined_train)
  758. # cross1: use ex_itrain as training set, use ex_itest as test set
  759. atrain_p1, btrain_p1, _, _ = canonical_cov(
  760. projs1[ex_itrain[n_nneurs], :], ECoG_Extracted_Features[ex_itrain[n_nneurs], :], lams[l], npc=max(nrank1)
  761. )
  762. atest_p1, btest_p1, _, _ = canonical_cov(
  763. projs2[ex_itrain[n_nneurs], :], ECoG_Extracted_Features[ex_itrain[n_nneurs], :], lams[l], npc=max(nrank1)
  764. )
  765. # cross2: use ex_itest as training set, use ex_itrain as test set
  766. atrain_p2, btrain_p2, _, _ = canonical_cov(
  767. projs1[ex_itest[n_nneurs], :], ECoG_Extracted_Features[ex_itest[n_nneurs], :], lams[l], npc=max(nrank1)
  768. )
  769. atest_p2, btest_p2, _, _ = canonical_cov(
  770. projs2[ex_itest[n_nneurs], :], ECoG_Extracted_Features[ex_itest[n_nneurs], :], lams[l], npc=max(nrank1)
  771. )
  773. for k in tqdm(range(len(nrank1))): # try various ranks
  774. vp_train_p1 = (
  775. atrain_p1[:, :nrank1[k]] @ btrain_p1[:, :nrank1[k]].T @ ECoG_Extracted_Features[ex_itest[n_nneurs]].T
  776. )
  777. vp_test_p1 = atest_p1[:, :nrank1[k]] @ btest_p1[:, :nrank1[k]].T @ ECoG_Extracted_Features[ex_itest[n_nneurs]].T
  778. vp_train_p1=center_data(vp_train_p1,axis=1)
  779. vp_test_p1 =center_data(vp_test_p1, axis=1)
  780. vp_combined_train[:, ex_itest[n_nneurs]] = zscore(vp_train_p1.T).T
  781. vp_combined_test[:, ex_itest[n_nneurs]] = zscore(vp_test_p1.T).T
  783. vp_train_p2 = (
  784. atrain_p2[:, :nrank1[k]] @ btrain_p2[:, :nrank1[k]].T @ ECoG_Extracted_Features[ex_itrain[n_nneurs]].T
  785. )
  786. vp_test_p2 = atest_p2[:, :nrank1[k]] @ btest_p2[:, :nrank1[k]].T @ ECoG_Extracted_Features[ex_itrain[n_nneurs]].T
  787. vp_train_p2=center_data(vp_train_p2,axis=1)
  788. vp_test_p2 =center_data(vp_test_p2, axis=1)
  789. vp_combined_train[:, ex_itrain[n_nneurs]] = zscore(vp_train_p2.T).T
  790. vp_combined_test[:, ex_itrain[n_nneurs]] = zscore(vp_test_p2.T).T
  792. # filter
  793. window_length = 51
  794. polyorder = 11
  795. vp_combined_train_filtered = savgol_filter(vp_combined_train.T, window_length, polyorder, axis=0).T
  796. vp_combined_test_filtered = savgol_filter(vp_combined_test.T, window_length, polyorder, axis=0).T
  798. # plot and save
  799. plot_components = [0, 1, 2, 3, 4, 5, 6, 7, 15, 31, 63]
  800. def save_plots(proj, vp, filepath_suffix, filename_suffix):
  801. fig, axes = plt.subplots(len(plot_components), 1, figsize=(15, 25))
  802. for i, vec_idx in enumerate(plot_components):
  803. ax = axes[i]
  804. ax.plot(proj[t_start_plot*10:t_end_plot*10, vec_idx], color='b', alpha=0.5, label='real')
  805. ax.plot(vp.T[t_start_plot*10:t_end_plot*10, vec_idx], color='r', alpha=0.5, label='predicted')
  806. ax.set_title(f'SVC {vec_idx + 1}')
  807. ax.legend()
  808. ax.set_xlabel('time')
  809. ax.set_ylabel('SVCs')
  810. plt.tight_layout()
  811. filename_png = f"SVCs_l{l}_k{k}_nneur{n_nneurs}_{filename_suffix}.png"
  812. filename_pdf = f"SVCs_l{l}_k{k}_nneur{n_nneurs}_{filename_suffix}.pdf"
  813. directory_path = os.path.join(res_path, "predicted_SVCs", filepath_suffix)
  814. os.makedirs(directory_path, exist_ok=True)
  815. filepath_png = os.path.join(directory_path, filename_png)
  816. filepath_pdf = os.path.join(directory_path, filename_pdf)
  817. plt.savefig(filepath_png)
  818. plt.savefig(filepath_pdf, format='pdf')
  819. fig.clf()
  821. save_plots(projs1, vp_combined_train_filtered, "2-fold cross validation", "projs_1")
  822. save_plots(projs2, vp_combined_test_filtered, "2-fold cross validation", "projs_2")
  824. u_sub = ex_u[n_nneurs][:, :nsvc_predict]
  825. v_sub = ex_v[n_nneurs][:, :nsvc_predict]
  827. model = Rastermap(n_PCs=32, n_clusters=8).fit(np.hstack((trainX, testX)).T)
  829. # test_indices = ex_itest[n_nneurs][(ex_itest[n_nneurs] >= 500) & (ex_itest[n_nneurs] <= 4000)]
  830. compare_and_plot(model, trainX[500:4000], testX[500:4000], u_sub, v_sub,
  831. vp_combined_train_filtered[:, 500:4000], vp_combined_test_filtered[:, 500:4000],
  832. l, k, res_path, "2-fold cross validation")

utils_ym.py at commit 30759da, under GPL-3.0 · at the source

Overview

Authors: Guihua Xiao1,2,3, Mo Yang4, Lingbo Li2,3, Xintong Yao4, Jingyu Xie2,3, Xinyue Wang4, Jinyu Zang4, Yan Zhao4, Tianyi Fang4, Shuying Wu4, Wandi Qi2, Shipeng Lin4, Wenxi Sun5, Ting Lei5, Bo Hong4,6, Jiamin Wu1,2,3,6, Qionghai Dai1,2,3,6, Xiaochuan Dai4
ORCID iDs: Mo Yang, Jinyu Zang, Yan Zhao, Shuying Wu, Shipeng Lin, Ting Lei, Bo Hong, Jiamin Wu, Qionghai Dai, Xiaochuan Dai
  1. Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China
  2. Department of Automation, Tsinghua University, Beijing, China
  3. Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
  4. School of Biomedical Engineering, Tsinghua University, Beijing, China
  5. Key Laboratory of Polymer Chemistry and Physics of Ministry of Education, School of Materials Science and Engineering, Peking University, Beijing, China
  6. IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
Institutions: Beijing National Research Center for Information Science and Technology (China); Tsinghua University (China); Peking University (China)
Journal: Nature communications, volume 17, issue 1, article 5872
Dates: received 26 July 2025; accepted 15 April 2026; published online 29 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-72454-0 · PMID 42056115 · PMCID PMC13333928 · OpenAlex W7158914550
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: extracellular electrophysiology (units, LFP) (modality), mouse (organism), systems (subfield)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Machine learning, Connectivity, Complexity, Single-unit activity, calcium imaging, Smoothing, state filtering, decompositions, Physiology & signal measures
Keywords: Computational neuroscience, Cognitive neuroscience, Extracellular recording
MeSH: Cerebral Cortex*, Neurons*, Action Potentials, Animals, Calcium, Isoflurane, Mice, Mice, Inbred C57BL (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (62171254, 82151305); Beijing Nova Program (20230484252)
Citations: cited by 2 papers (Europe PMC); 70 references in the paper

Abstract

Burst suppression is widely observed across cortical regions during reversible or pathological unconsciousness, yet its neuronal organization remains incompletely understood. Here we present an integrated Cortex-wide Optical-electrical Dual-modal Explorer (CODE) system to examine neuronal dynamics during burst suppression under isoflurane anesthesia in the mouse. We identified distinct cortex-wide neuronal ensembles that alternately associate with burst or suppression events, exhibiting dynamic neuronal recruitment and reactivation during anesthesia. Burst events were marked by highly synchronized neuronal activity early in the burst phase with high functional connectivity, whereas suppression events displayed more asynchronous, temporally distributed activity with reduced connectivity. Transitions between these states involved sequential, directionally organized propagation across cortical regions. ECoG bursts propagated from bilateral sensory cortices to motor areas within tens of milliseconds with increasing synchrony with calcium activity. Furthermore, we established a robust metric linking ECoG and calcium signals, revealing state-dependent interpretability. These findings reveal the single-neuron-to-population architecture of burst suppression and illustrate how integrated optical–electrical measurements enable high-resolution, large-scale interrogation of cortical dynamics.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Tools: NumPy (10 files), Matplotlib (9 files), pandas (9 files), SciPy (9 files), seaborn (9 files), h5py (5 files), NetworkX (4 files), Pillow (4 files), tifffile (4 files), OpenCV (2 files), scikit-learn (2 files)
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YangMoTsinghua/Predict_Calcium_From_ECoG

License: GPL-3.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 30759da57e27e02008b29f378ed0405ab04e8da7, 13 August 2025
Languages: Python (4), Jupyter (2)
Size: 15 files, 6 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 2 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (6 files), SciPy (6 files), Matplotlib (4 files), pandas (2 files), scikit-learn (2 files), seaborn (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
9 files
At the source: github.com/YangMoTsinghua/Predict_Calcium_From_ECoG

IrisRegion/Burst_suppression_analysis_based_on_cortex-wide_neural_optoelectronic_interface

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 382be8ac1fa8e3a312618c2cf085321528d1183f, 26 September 2024
Languages: Jupyter (15)
Size: 17 files, 15 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (requirements.txt), 15 notebooks
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (10 files), Matplotlib (9 files), pandas (9 files), SciPy (9 files), seaborn (9 files), h5py (5 files), NetworkX (4 files), Pillow (4 files), tifffile (4 files), OpenCV (2 files), scikit-learn (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
10 files
At the source: github.com/IrisRegion/Burst_suppression_analysis_based_on_cortex-wide_neural_optoelectronic_interface.git

Code availability

All custom code used for data analysis is available at the following repositories: https://github.com/YangMoTsinghua/Predict_Calcium_From_ECoG, https://github.com/IrisRegion/Burst_suppression_analysis_based_on_cortex-wide_neural_optoelectronic_interface.git, and on Zenodo (10.5281/zenodo.18295118).

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 26 scripts, each with its path and the digest of its content;
  • 7 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

The calcium imaging raw data supporting the findings of this study exceed 100 TB in size. To facilitate access, representative demo data used for the analyses are available at 10.5281/zenodo.18295118. All processed data related to each mouse, including extracted neuronal traces and electrophysiological signals, are available at 10.5281/zenodo.18324142. The raw dataset is available from the corresponding authors without restriction. Source data are provided in this paper.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 18 authors, 3 keywords, 8 MeSH terms, 2 funders, 64 references.

Cite

This paper

Xiao, G., Yang, M., Li, L., Yao, X., Xie, J., Wang, X., Zang, J., Zhao, Y., Fang, T., Wu, S., Qi, W., Lin, S., Sun, W., Lei, T., Hong, B., Wu, J., Dai, Q., & Dai, X. (2026). Dynamic neuronal ensembles encode burst-suppression revealed by cortex-wide optical-electrical interfaces. Nature communications, 17(1), 5872. https://doi.org/10.1038/s41467-026-72454-0

BibTeX

@article{xiao2026dynamic,
author = {Xiao, Guihua and Yang, Mo and Li, Lingbo and Yao, Xintong and Xie, Jingyu and Wang, Xinyue and Zang, Jinyu and Zhao, Yan and Fang, Tianyi and Wu, Shuying and Qi, Wandi and Lin, Shipeng and Sun, Wenxi and Lei, Ting and Hong, Bo and Wu, Jiamin and Dai, Qionghai and Dai, Xiaochuan},
title = {{Dynamic neuronal ensembles encode burst-suppression revealed by cortex-wide optical-electrical interfaces}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5872},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-72454-0},
url = {https://doi.org/10.1038/s41467-026-72454-0},
pmid = {42056115},
pmcid = {PMC13333928}
}

RIS

TY - JOUR
AU - Xiao, Guihua
AU - Yang, Mo
AU - Li, Lingbo
AU - Yao, Xintong
AU - Xie, Jingyu
AU - Wang, Xinyue
AU - Zang, Jinyu
AU - Zhao, Yan
AU - Fang, Tianyi
AU - Wu, Shuying
AU - Qi, Wandi
AU - Lin, Shipeng
AU - Sun, Wenxi
AU - Lei, Ting
AU - Hong, Bo
AU - Wu, Jiamin
AU - Dai, Qionghai
AU - Dai, Xiaochuan
TI - Dynamic neuronal ensembles encode burst-suppression revealed by cortex-wide optical-electrical interfaces
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/29
VL - 17
IS - 1
SP - 5872
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-72454-0
UR - https://doi.org/10.1038/s41467-026-72454-0
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-72454-0",
"type": "article-journal",
"title": "Dynamic neuronal ensembles encode burst-suppression revealed by cortex-wide optical-electrical interfaces",
"container-title": "Nature communications",
"author": [
{
"family": "Xiao",
"given": "Guihua"
},
{
"family": "Yang",
"given": "Mo"
},
{
"family": "Li",
"given": "Lingbo"
},
{
"family": "Yao",
"given": "Xintong"
},
{
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},
{
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},
{
"family": "Zang",
"given": "Jinyu"
},
{
"family": "Zhao",
"given": "Yan"
},
{
"family": "Fang",
"given": "Tianyi"
},
{
"family": "Wu",
"given": "Shuying"
},
{
"family": "Qi",
"given": "Wandi"
},
{
"family": "Lin",
"given": "Shipeng"
},
{
"family": "Sun",
"given": "Wenxi"
},
{
"family": "Lei",
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{
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{
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{
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}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "5872",
"DOI": "10.1038/s41467-026-72454-0",
"PMID": "42056115",
"PMCID": "PMC13333928",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-72454-0",
"language": "en",
"issued": {
"date-parts": [
[
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4,
29
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]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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