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Voltage imaging of neurons distributed across entire brains of larval zebrafish.

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  1. [1] § Methods › Data processing › ROI temporal trace extraction ↔ data_extraction/fish_single_layer_manual.py, lines 244–290 · score 0.72 · adaptive thresholding, trace denoising, spike extraction, filters, background, ROI

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The authors' code

Python · 407 lines · 18 KB · CC-BY-4.0 · 1 match

  1. #!/usr/bin/env python
  2. """
  3. pipeline for processing voltage imaging data for zebrafish lightsheet data
  4. author: @Jack Zhang
  5. """
  6. import scipy.io as sio
  7. import cv2
  8. import os
  9. import glob
  10. import h5py
  11. import logging
  12. import matplotlib.pyplot as plt
  13. from matplotlib.pyplot import savefig
  15. import numpy as np
  16. import os
  18. try:
  19. cv2.setNumThreads(0)
  20. except:
  21. pass
  23. try:
  24. if __IPYTHON__:
  25. # this is used for debugging purposes only. allows to reload classes
  26. # when changed
  27. get_ipython().magic('load_ext autoreload')
  28. get_ipython().magic('autoreload 2')
  29. except NameError:
  30. pass
  32. import caiman as cm
  33. from caiman.motion_correction import MotionCorrect
  34. from caiman.paths import caiman_datadir
  35. from caiman.source_extraction.volpy import utils
  36. from caiman.source_extraction.volpy.volparams import volparams
  37. from caiman.source_extraction.volpy.volpy import VOLPY
  38. from caiman.summary_images import local_correlations_movie_offline
  39. from caiman.summary_images import mean_image
  40. from caiman.utils.utils import download_demo, download_model
  41. from caiman.source_extraction.volpy.mrcnn import visualize
  43. # %%
  44. # Set up the logger (optional); change this if you like.
  45. # You can log to a file using the filename parameter, or make the output more
  46. # or less verbose by setting level to logging.DEBUG, logging.INFO,
  47. # logging.WARNING, or logging.ERROR
  48. logging.basicConfig(format=
  49. "%(relativeCreated)12d [%(filename)s:%(funcName)20s():%(lineno)s]" \
  50. "[%(process)d] %(message)s",
  51. level=logging.INFO)
  53. # %%
  54. def volpy_process_session(trial_1_dir, trial_2_dir, layer_name):
  55. pass # For compatibility between running under Spyder and the CLI
  57. # %% Load demo movie and ROIs
  58. # fnames = download_demo('demo_voltage_imaging.hdf5', 'volpy') # file path to movie file (will download if not present)
  60. #fnames = os.path.join(trial_1_dir + layer_name + '.tif' );
  61. fnames = []
  62. fnames.extend([os.path.join(trial_1_dir + '/raw/' + layer_name + '.hdf5' )]);
  63. fnames.extend([os.path.join(trial_2_dir + '/raw/' + layer_name + '.hdf5' )]);
  65. #path_ROIs = download_demo('demo_voltage_imaging_ROIs.hdf5', 'volpy') # file path to ROIs file (will download if not present)
  66. file_dir = os.path.split(fnames[0])[0]
  67. tempDir = '/home/jack/Caiman_Workfolder/Movies/'
  69. #%% dataset dependent parameters
  70. # dataset dependent parameters
  71. fr = 200 # sample rate of the movie
  73. # motion correction parameterspath_ROIs
  74. pw_rigid = False # flag for pw-rigid motion correction
  75. gSig_filt = (4, 4) # size of filter, in general gSig (see below),
  76. # change this one if algorithm does not work
  77. max_shifts = (6, 6) # maximum allowed rigid shift
  78. strides = (64, 64) # start a new patch for pw-rigid motion correction every x pixels
  79. overlaps = (32, 32) # overlap between pathes (size of patch strides+overlaps)
  80. max_deviation_rigid = 5 # maximum deviation allowed for patch with respect to rigid shifts
  81. border_nan = 'copy'
  83. opts_dict = {
  84. 'fnames': fnames,
  85. 'fr': fr,
  86. 'pw_rigid': pw_rigid,
  87. 'max_shifts': max_shifts,
  88. 'gSig_filt': gSig_filt,
  89. 'strides': strides,
  90. 'overlaps': overlaps,
  91. 'max_deviation_rigid': max_deviation_rigid,
  92. 'border_nan': border_nan
  93. }
  95. opts = volparams(params_dict=opts_dict)
  97. # %% play the movie (optional)
  98. # playing the movie using opencv. It requires loading the movie in memory.
  99. # To close the movie press q
  100. display_images = False
  102. if display_images:
  103. m_orig = cm.load(fnames)
  104. ds_ratio = 0.2
  105. moviehandle = m_orig.resize(1, 1, ds_ratio)
  106. moviehandle.play(q_max=99.5, fr=40, magnification=4)
  108. # %% start a cluster for parallel processing
  109. c, dview, n_processes = cm.cluster.setup_cluster(
  110. backend='local', n_processes=None, single_thread=False)
  112. # %%% MOTION CORRECTION
  113. # first we create a motion correction object with the specified parameters
  114. mc = MotionCorrect(fnames, dview=dview, **opts.get_group('motion'))
  115. # Run correction
  116. do_motion_correction = True
  117. if do_motion_correction:
  118. mc.motion_correct(save_movie=True)
  119. plt.subplot(1, 2, 1); plt.imshow(mc.total_template_rig) # % plot template
  120. plt.subplot(1, 2, 2); plt.plot(mc.shifts_rig) # % plot rigid shifts
  121. plt.legend(['x shifts', 'y shifts'])
  122. plt.xlabel('frames')
  123. plt.ylabel('pixels')
  125. savefig(trial_1_dir +'/outline/' + layer_name + '_motion-correct.pdf', bbox_inches='tight')
  127. else:
  128. mc_list = [file for file in os.listdir(file_dir) if
  129. (os.path.splitext(os.path.split(fnames)[-1])[0] in file and '.mmap' in file)]
  130. mc.mmap_file = [os.path.join(file_dir, mc_list[0])]
  131. print(f'reuse previously saved motion corrected file:{mc.mmap_file}')
  133. # bord_px = np.ceil(np.max(np.abs(mc.shifts_rig))).astype(np.int)
  137. # %% compare with original movie
  138. if display_images:
  139. m_orig = cm.load(fnames)
  140. m_rig = cm.load(mc.mmap_file)
  141. ds_ratio = 0.2
  142. moviehandle = cm.concatenate([m_orig.resize(1, 1, ds_ratio),
  143. m_rig.resize(1, 1, ds_ratio)], axis=2)
  144. moviehandle.play(fr=40, q_max=99.5, magnification=4) # press q to exit
  146. # %% MEMORY MAPPING
  147. do_memory_mapping = True
  148. if do_memory_mapping:
  149. border_to_0 = 0 if mc.border_nan == 'copy' else mc.border_to_0
  150. # you can include the boundaries of the FOV if you used the 'copy' option
  151. # during motion correction, although be careful about the components near
  152. # the boundaries
  154. # memory map the file in order 'C'
  155. fname_new = cm.save_memmap_join(mc.mmap_file, base_name='memmap_' + os.path.splitext(os.path.split(fnames[0])[-1])[0],
  156. add_to_mov=border_to_0, dview=dview) # exclude border
  157. else:
  158. mmap_list = [file for file in os.listdir(file_dir) if
  159. ('memmap_' + os.path.splitext(os.path.split(fnames)[-1])[0]) in file]
  160. fname_new = os.path.join(file_dir, mmap_list[0])
  161. print(f'reuse previously saved memory mapping file:{fname_new}')
  163. # %% SEGMENTATION
  164. # create summary images
  165. img = mean_image(mc.mmap_file[0], window = 1000, dview=dview)
  166. img = (img-np.mean(img))/np.std(img)
  168. gaussian_blur = False # Use gaussian blur when there is too much noise in the video
  169. Cn = local_correlations_movie_offline(mc.mmap_file[0], fr=fr, window=fr*4,
  170. stride=fr*4, winSize_baseline=fr,
  171. remove_baseline=True, gaussian_blur=gaussian_blur,
  172. dview=dview).max(axis=0)
  173. img_corr = (Cn-np.mean(Cn))/np.std(Cn)
  174. summary_images = np.stack([img, img, img_corr], axis=0).astype(np.float32)
  175. # save summary images which are used in the VolPy GUI
  176. #cm.movie(summary_images).save(fnames[:-5] + '_summary_images.tif')
  177. fig, axs = plt.subplots(1, 2)
  178. axs[0].imshow(summary_images[0]); axs[1].imshow(summary_images[2])
  179. axs[0].set_title('mean image'); axs[1].set_title('corr image')
  181. #%% methods for segmentation
  182. methods_list = ['manual_annotation', # manual annotations need prepared annotated datasets in the same format as demo_voltage_imaging_ROIs.hdf5
  183. 'maskrcnn', # Mask R-CNN is a convolutional neural network trained for detecting neurons in summary images
  184. 'gui_annotation'] # use VolPy GUI to correct outputs of Mask R-CNN or annotate new datasets
  186. method = methods_list[2]
  187. if method == 'manual_annotation':
  188. with h5py.File(path_ROIs, 'r') as fl:
  189. ROIs = fl['mov'][()]
  191. elif method == 'maskrcnn':
  192. weights_path = download_model('mask_rcnn')
  193. ROIs = utils.mrcnn_inference(img=summary_images.transpose([1, 2, 0]), size_range=[3, 18],
  194. weights_path=weights_path, display_result=True, savedir=trial_1_dir, savename=layer_name) # size parameter decides size range of masks to be selected
  195. cm.movie(ROIs).save(fnames[0][:-5] + '_mrcnn_ROIs.hdf5')
  197. elif method == 'gui_annotation':
  198. # run volpy_gui.py file in the caiman/source_extraction/volpy folder
  199. # or run the following in the ipython: %run volpy_gui.py
  200. #gui_ROIs = caiman_datadir() + '/example_movies/volpy/gui_roi.hdf5'
  201. #gui_ROIs = '/media/jack/data/zebrafish/230820/Fish2_1/TIFF_files/layer_0_320_80_mrcnn_ROIs.hdf5'
  203. gui_ROIs = os.path.join(trial_1_dir + '/roi/' + layer_name + '_roi.hdf5' )
  204. with h5py.File(gui_ROIs, 'r') as fl:
  205. ROIs = fl['mov'][()]
  207. box = np.zeros([len(ROIs[:,0,0]),4],dtype=np.int32)
  208. #roi_id = list(range(len(ROIs[:,0,0])))
  209. roi_id = np.full((len(ROIs[:,0,0])),1,dtype=np.int32)
  212. for nn in range(len(ROIs[:,0,0])):
  213. xnzero = np.nonzero(np.sum(ROIs[nn,:,:], axis=0))
  214. x1 = xnzero[0][0]
  215. x2 = xnzero[0][-1]
  217. ynzero = np.nonzero(np.sum(ROIs[nn,:,:], axis=1))
  218. y1 = ynzero[0][0]
  219. y2 = ynzero[0][-1]
  221. box[nn] = y1, x1, y2, x2
  224. _, ax = plt.subplots(1,1, figsize=(16,16))
  225. sampleimg=summary_images.transpose([1, 2, 0])
  226. ROI_t = np.transpose(ROIs,(1, 2, 0))
  227. visualize.display_instances_no_contour(sampleimg, box, ROI_t, roi_id,
  228. ['BG', 'neurons'], roi_id, ax=ax, captions=None,
  229. title="Predictions")
  230. #visualize.display_instances(img=summary_images.transpose([1, 2, 0]), box, ROIs, roi_id, ['BG', 'neurons'], roi_id, ax=ax, title="Predictions")
  231. savefig(trial_1_dir +'/outline/' + layer_name + '_roi_outline.pdf', bbox_inches='tight')
  233. visualize.display_instances(sampleimg, box, ROI_t, roi_id,
  234. ['BG', 'neurons'], roi_id, ax=ax, captions=None,
  235. title="Predictions")
  236. #visualize.display_instances(img=summary_images.transpose([1, 2, 0]), box, ROIs, roi_id, ['BG', 'neurons'], roi_id, ax=ax, title="Predictions")
  237. savefig(trial_1_dir +'/outline/' + layer_name + '_roi_outline_contour.pdf', bbox_inches='tight')
  240. fig, axs = plt.subplots(1, 2)
  241. axs[0].imshow(summary_images[0]); axs[1].imshow(ROIs.sum(0))
  242. axs[0].set_title('mean image'); axs[1].set_title('masks')
  244. # %% restart cluster to clean up memory
  245. cm.stop_server(dview=dview)
  246. c, dview, n_processes = cm.cluster.setup_cluster(
  247. backend='local', n_processes=None, single_thread=False, maxtasksperchild=1)
  249. # %% parameters for trace denoising and spike extraction
  250. ROIs = ROIs # region of interests
  251. index = list(range(len(ROIs))) # index of neurons
  252. weights = None # if None, use ROIs for initialization; to reuse weights check reuse weights block
  254. template_size = 0.02 # half size of the window length for spike templates, default is 20 ms
  255. context_size = 35 # number of pixels surrounding the ROI to censor from the background PCA
  256. visualize_ROI = False # whether to visualize the region of interest inside the context region
  257. flip_signal = False # Important!! Flip signal or not, True for Voltron indicator, False for others
  258. hp_freq_pb = 1 / 3 # parameter for high-pass filter to remove photobleaching
  259. clip = 101 # maximum number of spikes to form spike template
  260. threshold_method = 'adaptive_threshold' #'adaptive_threshold' # adaptive_threshold or simple
  261. min_spikes= 4 # minimal spikes to be found
  262. pnorm = 0.5 # a variable deciding the amount of spikes chosen for adaptive threshold method
  263. threshold = 5 # threshold for finding spikes only used in simple threshold method, Increase the threshold to find less spikes
  264. do_plot = False # plot detail of spikes, template for the last iteration
  265. ridge_bg= 0.01 # ridge regression regularizer strength for background removement, larger value specifies stronger regularization
  266. sub_freq = 20 # frequency for subthreshold extraction
  267. weight_update = 'ridge' # ridge or NMF for weight update
  268. n_iter = 2 # number of iterations alternating between estimating spike times and spatial filters
  270. opts_dict={'fnames': fname_new,
  271. 'ROIs': ROIs,
  272. 'index': index,
  273. 'weights': weights,
  274. 'template_size': template_size,
  275. 'context_size': context_size,
  276. 'visualize_ROI': visualize_ROI,
  277. 'flip_signal': flip_signal,
  278. 'hp_freq_pb': hp_freq_pb,
  279. 'clip': clip,
  280. 'threshold_method': threshold_method,
  281. 'min_spikes':min_spikes,
  282. 'pnorm': pnorm,
  283. 'threshold': threshold,
  284. 'do_plot':do_plot,
  285. 'ridge_bg':ridge_bg,
  286. 'sub_freq': sub_freq,
  287. 'weight_update': weight_update,
  288. 'n_iter': n_iter}
  290. opts.change_params(params_dict=opts_dict);
  292. #%% TRACE DENOISING AND SPIKE DETECTION
  293. vpy = VOLPY(n_processes=n_processes, dview=dview, params=opts)
  294. vpy.fit(n_processes=n_processes, dview=dview)
  296. #%% visualization
  297. display_images = False
  298. if display_images:
  299. print(np.where(vpy.estimates['locality'])[0]) # neurons that pass locality test
  300. idx = np.where(vpy.estimates['locality'] > 0)[0]
  301. utils.view_components(vpy.estimates, img_corr, idx)
  303. #%% reconstructed movie
  304. # note the negative spatial weights is cutoff
  305. # if display_images:
  306. # mv_all = utils.reconstructed_movie(vpy.estimates.copy(), fnames=mc.mmap_file,
  307. # idx=idx, scope=(0,1000), flip_signal=flip_signal)
  308. # mv_all.play(fr=40, magnification=3)
  309. #%% save the traces
  310. #vpy.estimates['t']
  311. #for k, v in vpy.estimates.items():
  312. sio.savemat(os.path.join(trial_1_dir + '/matvars/T/T_' + layer_name + '.mat'), mdict={'T': vpy.estimates['t']})
  313. sio.savemat(os.path.join(trial_1_dir + '/matvars/spikes/spikes_' + layer_name + '.mat'), mdict={'spikes': vpy.estimates['spikes']})
  314. sio.savemat(os.path.join(trial_1_dir + '/matvars/threshold/thresh_' + layer_name + '.mat'), mdict={'thresh': vpy.estimates['thresh']})
  315. sio.savemat(os.path.join(trial_1_dir + '/matvars/snr/snr_' + layer_name + '.mat'), mdict={'snr': vpy.estimates['snr']})
  316. sio.savemat(os.path.join(trial_1_dir + '/matvars/t_sub/t_sub_' + layer_name + '.mat'), mdict={'t_sub': vpy.estimates['t_sub']})
  318. cm.movie(vpy.estimates['weights']).save(os.path.join(trial_1_dir + '/matvars/weights/weights_' + layer_name + '.hdf5'))
  321. # Extract raw traces
  322. # Y = cm.load(fname_new) # Load memory mapped movie
  323. # raw_traces = extract_raw_traces(Y, ROIs)
  324. # # Save raw traces to a file
  325. # save_name2 = f'ResultVolpy_{os.path.split(fnames)[1][:-4]}_raw_traces.npy'
  326. # np.save(save_name2, raw_traces)
  327. # print(f"Raw traces saved to {save_name2}")
  330. # Function to extract raw traces
  331. # def extract_raw_traces(Y, masks):
  332. # """
  333. # Extract raw traces from the given movie Y and masks.
  335. # Parameters:
  336. # ----------
  337. # Y : movie
  338. # The movie in which traces are to be extracted. Shape (T, d1, d2) where T is number of frames.
  339. # masks : array
  340. # ROIs masks. Shape (num_neurons, d1, d2).
  342. # Returns:
  343. # -------
  344. # raw_traces : array
  345. # Extracted raw traces. Shape (num_neurons, T).
  346. # """
  347. # T = Y.shape[0]
  348. # num_neurons = masks.shape[0]
  349. # raw_traces = np.empty((num_neurons, T))
  350. # for idx_neuron in range(num_neurons):
  351. # mask = masks[idx_neuron]
  352. # for t in range(T):
  353. # raw_traces[idx_neuron, t] = np.mean(Y[t][mask])
  354. # return raw_traces
  356. #%% save the result in .npy format
  357. save_result = False
  358. if save_result:
  359. #vpy.estimates['ROIs'] = ROIs
  360. vpy.estimates['params'] = opts
  361. #save_name = f'volpy_{os.path.split(fnames)[1][:-5]}_{threshold_method}'
  362. np.save(os.path.join(trial_1_dir + '/matvars/results_' + layer_name), vpy.estimates)
  364. #np.save(os.path.join(file_dir, save_name), vpy.estimates)
  366. #%% reuse weights
  367. ## set weights = reuse_weights in opts_dict dictionary
  368. # estimates = np.load(os.path.join(file_dir, save_name+'.npy'), allow_pickle=True).item()
  369. # reuse_weights = []
  370. # for idx in range(ROIs.shape[0]):
  371. # coord = estimates['context_coord'][idx]
  372. # w = estimates['weights'][idx][coord[0][0]:coord[1][0]+1, coord[0][1]:coord[1][1]+1]
  373. # #plt.figure(); plt.imshow(w);plt.colorbar(); plt.show()
  374. # reuse_weights.append(w)
  376. # %% STOP CLUSTER and clean up log files
  377. cm.stop_server(dview=dview)
  378. log_files = glob.glob('*_LOG_*')
  379. for log_file in log_files:
  380. os.remove(log_file)
  382. # %% Remove memmap files
  383. memmap_list = os.listdir(file_dir)
  385. for item in memmap_list:
  386. if item.endswith(".mmap"):
  387. os.remove(os.path.join(file_dir, item))
  389. file_dir_2 = os.path.split(fnames[1])[0]
  390. memmap_list = os.listdir(file_dir_2)
  392. for item in memmap_list:
  393. if item.endswith(".mmap"):
  394. os.remove(os.path.join(file_dir_2, item))
  396. # for now remove hdf5 too
  397. # hdf5_list = os.listdir(file_dir)
  399. # for item in memmap_list:
  400. # if item.endswith(".hdf5"):
  401. # os.remove(os.path.join(file_dir, item))
  403. # %%
  404. # This is to mask the differences between running this demo in Spyder
  405. # versus from the CLI
  406. if __name__ == "__main__":
  407. main()

fish_single_layer_manual.py, under CC-BY-4.0 · at the source

Overview

Authors: Zeguan Wang1,2, Jie Zhang3,4, Panagiotis Symvoulidis1,4, Wei Guo3,4, Davy Deng1,5, Adam Amsterdam1,6, Lige Zhang1,2, Takato Honda3,4, Steven Roche4, Matthew A. Wilson3,4, Edward S. Boyden1,2,4,6,7,8,9
ORCID iDs: Panagiotis Symvoulidis, Adam Amsterdam, Takato Honda, Steven Roche, Matthew A. Wilson, Edward S. Boyden
  1. McGovern Institute for Brain Research, MIT,Cambridge, MA USA
  2. Department of Media Arts and Sciences, MIT,Cambridge, MA USA
  3. Picower Institute for Learning and Memory, MIT,Cambridge, MA USA
  4. Department of Brain and Cognitive Sciences, MIT,Cambridge, MA USA
  5. Harvard-MIT Health Sciences and Technology,Cambridge, MA USA
  6. David H. Koch Institute for Integrative Cancer Research, MIT,Cambridge, MA USA
  7. Department of Biological Engineering, MIT,Cambridge, MA USA
  8. Mindspan Institute, Cambridge, MA USA
  9. Center for Neurobiological Engineering, Yang Tan Collective, and K. Lisa Yang Center for Bionics at MIT,Cambridge, MA USA
Institutions: McGovern Institute for Brain Research (United States); Massachusetts Institute of Technology (United States); Koch Institute for Integrative Cancer Research At MIT (United States)
Journal: Nature methods, volume 23, issue 9, pages 1895-1907
Dates: received 16 December 2023; accepted 29 June 2026; published online 14 August 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41592-026-03179-7 · PMID 42601458 · PMCID PMC13541626 · OpenAlex W7203498224
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), optical imaging (calcium, voltage, 2-photon) (modality), zebrafish (organism), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Smoothing, state filtering, decompositions, Statistics, fMRI & imaging, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: Optogenetics, Light-sheet microscopy
MeSH: Brain*, Neurons*, Zebrafish*, Animals, Larva (* major topic)
Topic: Retinal Development and Disorders (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: U.S. Department of Health & Human Services | NIH | Office of Extramural Research, National Institutes of Health (OER) (1R01MH123977); Howard Hughes Medical Institute (HHMI) (investigator)
Citations: cited by 2 papers (Europe PMC); 60 references in the paper

Abstract

Neurons interact in networks distributed throughout the brain. While much effort has focused on whole-brain calcium imaging, advances in genetically encoded voltage indicators raise the question of whether it might be possible to image neuronal voltage across entire brains. Achieving this requires a microscope with high volumetric imaging rates and signal-to-noise ratio. Here we present a remote-scanning light-sheet microscope capable of imaging genetically encoded voltage indicator-expressing neurons distributed throughout much of the brain of larval zebrafish at a volumetric rate of 200.8 Hz. We measured voltage traces from approximately one-quarter of all brain neurons. We found that neurons firing at different times during a sequence occupied different locations: visually evoked sequences mapped across the optic tectum, whereas stimulus-independent bursts were mapped across the cerebellum and medulla. Imaging voltage of neurons distributed in many brain regions may open new frontiers for understanding fundamental neural system operations.

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

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Tools: Matplotlib (3 files), NumPy (3 files), CaImAn (2 files), h5py (1 file), OpenCV (1 file), scikit-image (1 file), SciPy (1 file), TensorFlow (1 file)
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Code availability

The codes used to control the microscope and to analyze the image datasets, as well as the custom-trained Cellpose model, are available at Zenodo at https://doi.org/10.5281/zenodo.14920628 (ref. 60) under the Creative Commons Attribution 4.0 International License.

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 4 scripts, each with its path and the digest of its content;
  • 1 match 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

No dataset and no data link were found in the paper.

Data availability

Raw light-sheet imaging data, analysis results and other raw and processed data associated with this paper, are publicly available at https://doi.org/10.6019/S-BSST2153 ref. 59. Source data are provided with 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 2, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 2 keywords, 5 MeSH terms, 2 funders, 59 references.

Cite

This paper

Wang, Z., Zhang, J., Symvoulidis, P., Guo, W., Deng, D., Amsterdam, A., Zhang, L., Honda, T., Roche, S., Wilson, M. A., & Boyden, E. S. (2026). Voltage imaging of neurons distributed across entire brains of larval zebrafish. Nature methods, 23(9), 1895-1907. https://doi.org/10.1038/s41592-026-03179-7

BibTeX

@article{wang2026voltage,
author = {Wang, Zeguan and Zhang, Jie and Symvoulidis, Panagiotis and Guo, Wei and Deng, Davy and Amsterdam, Adam and Zhang, Lige and Honda, Takato and Roche, Steven and Wilson, Matthew A. and Boyden, Edward S.},
title = {{Voltage imaging of neurons distributed across entire brains of larval zebrafish}},
journal = {Nature methods},
year = {2026},
month = aug,
volume = {23},
number = {9},
pages = {1895--1907},
publisher = {Nature Portfolio},
issn = {1548-7091},
doi = {10.1038/s41592-026-03179-7},
url = {https://doi.org/10.1038/s41592-026-03179-7},
pmid = {42601458},
pmcid = {PMC13541626}
}

RIS

TY - JOUR
AU - Wang, Zeguan
AU - Zhang, Jie
AU - Symvoulidis, Panagiotis
AU - Guo, Wei
AU - Deng, Davy
AU - Amsterdam, Adam
AU - Zhang, Lige
AU - Honda, Takato
AU - Roche, Steven
AU - Wilson, Matthew A.
AU - Boyden, Edward S.
TI - Voltage imaging of neurons distributed across entire brains of larval zebrafish
T2 - Nature methods
J2 - Nat Methods
PY - 2026
DA - 2026/08/14
VL - 23
IS - 9
SP - 1895
EP - 1907
SN - 1548-7091
PB - Nature Portfolio
DO - 10.1038/s41592-026-03179-7
UR - https://doi.org/10.1038/s41592-026-03179-7
LA - en
ER -

CSL-JSON

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{
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{
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"ISSN": "1548-7091",
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"language": "en",
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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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