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Probing inter-areal computations with a two-photon holographic mesoscope.

Code ↔ Paper

8 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 8 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Visual stimulation and retinotopy ↔ matlab/rig/scanimage/MesoRetino2p_1channel_facingright_withjsondata_new.m, lines 421–567 · score 0.74 · preference maps, sign map, elevation, azimuth, gradients, smoothed
  2. [2] § Methods › SLM stimulation FOV ↔ matlab/rig/holo_computer/function_loadparameters2.m, the whole file · a weak match · score 0.58 · focal length, SLM pixel, wavelength
  3. [3] § Methods › Offline analysis of mesoscopic 2p calcium data ↔ python/suite2p_pipeline/parallels2p_210617_59LA.py, lines 1–86 · score 0.58 · neuropil coefficient, suite2p, baseline, connectivity, mesoscale, frames
  4. [4] § Methods › Offline analysis of mesoscopic 2p calcium data ↔ python/suite2p_pipeline/online_analysis_mesoholo.m, lines 312–434 · score 0.56 · Fluorescence traces, faster, scored, neuropil, median, baseline
  5. [5] § Methods › Holographic stimulation of functionally defined ensembles ↔ python/suite2p_pipeline/parallels2p_210617_59LA.py, lines 1–86 · score 0.53 · functional channel, suite2p, PMTs, ROIs, frames
  6. [6] § Methods › 2p-RAM mesoscope with a temporally focused holographic path ↔ python/suite2p_pipeline/+ScanImageTiffReader/ScanImageTiffReader.m, lines 1–34 · score 0.53 · Vidrio Technologies, ScanImage
  7. [7] § Methods › Rapidly interleaved photostimulation of remote ensembles ↔ matlab/rig/daq/newStimParams_group_LA_HoloOnly_GALVOS_FAST.m, lines 29–89 · score 0.51 · galvo speed, buffer, interleaved, ms, pulses, cells
  8. [8] § Methods › Holographic stimulation of functionally defined ensembles ↔ python/suite2p_pipeline/online_analysis_mesoholo.m, lines 144–215 · score 0.50 · inter trial intervals, static, orientation, gratings

Paper

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The authors' code

Python · 139 lines · 5.8 KB · no license · 2 matches

  1. """Parallel suite2p (LA example). Uses repository-local ``data/sessions`` paths."""
  2. import os
  3. import suite2p
  4. from suite2p import run_s2p
  5. import json
  6. import time
  7. import logging
  8. import multiprocessing
  10. from _mesoholo_paths import DATA_DIR
  12. planes = 5
  13. data_path = str(DATA_DIR / "sessions" / "example_lamiae" / "20220427_pmt" / "tiffs")
  14. out_path = str(DATA_DIR / "sessions" / "example_lamiae" / "20220427_pmt" / "suite2p_out")
  16. tic1 = time.time()
  17. os.chdir(data_path)
  20. ops = {
  21. 'data_path': [data_path],
  22. 'fast_disk': out_path, # used to store temporary binary file, defaults to save_path0 (set as a string NOT a list)
  23. 'save_path0': out_path, # stores results, defaults to first item in data_path
  24. 'delete_bin': True, # whether to delete binary file after processing
  25. 'look_one_level_down': False,
  26. 'input_format': 'mesoscan',
  27. 'h5py': [],
  28. 'h5py_key': 'data',
  30. # main settings
  31. 'nplanes' : 1, # each tiff has these many planes in sequence
  32. 'mesoscan' : 1,
  33. #'nrois' : opsjson['nrois'],
  34. #'dx': opsjson['dx'],
  35. #'dy': opsjson['dy'],
  36. #'lines': opsjson['lines'],
  38. 'nchannels' : 1, # each tiff has these many channels per plane
  39. 'functional_chan' : 1, # this channel is used to extract functional ROIs (1-based)
  40. 'diameter': 10, # this is the main parameter for cell detection, 2-dimensional if Y and X are different (e.g. [6 12])
  41. 'tau': 1.5, # this is the main parameter for deconvolution. 1.5 for GC6s, 0.7 for GC6f
  42. 'fs': opsjson['fs'], # sampling rate (total across planes)
  44. # output settings
  45. 'save_mat': True, # whether to save output as matlab files
  46. 'combined': True, # combine multiple planes into a single result /single canvas for GUI
  48. # parallel settings
  49. 'num_workers': 0, # 0 to select num_cores, -1 to disable parallelism, N to enforce value
  50. 'num_workers_roi': 0, # 0 to select number of planes, -1 to disable parallelism, N to enforce value
  52. # registration settings
  53. 'do_registration': True, # whether to register data
  54. 'nonrigid': False,
  55. 'keep_movie_raw': False,
  57. 'nimg_init': 100, # 300/1000. subsampled frames for finding reference image
  58. 'batch_size': 300, # 500/5000. number of frames per batch.
  59. 'maxregshift': .1, # max allowed registration shift, as a fraction of frame max(width and height)
  60. 'align_by_chan' : 1, # when multi-channel, you can align by non-functional channel (1-based)
  61. 'reg_tif': False, # whether to save registered tiffs
  62. 'reg_tif_chan2': False,
  63. 'subpixel' : 10, # precision of subpixel registration (1/subpixel steps)
  64. 'do_bidiphase': True, #whether to compute bidirectional phase offset
  66. # cell detection settings
  67. 'sparse_mode': False,
  68. 'connected': True, # whether or not to keep ROIs fully connected (set to 0 for dendrites)
  69. # 'navg_frames_svd': 5000, # max number of binned frames for the SVD
  70. # 'nsvd_for_roi': 2000, # changed 12/9/19 from 1k to 3k max number of SVD components to keep for ROI detection
  71. 'max_iterations': 20, # maximum number of iterations to do cell detection
  72. # 'ratio_neuropil': 6., # ratio between neuropil basis size and cell radius
  73. # 'ratio_neuropil_to_cell': 3, # minimum ratio between neuropil radius and cell radius
  74. # 'tile_factor': 1., # use finer (>1) or coarser (<1) tiles for neuropil estimation during cell detection
  75. 'threshold_scaling': 1., # adjust the automatically determined threshold by this scalar multiplier
  76. # 'max_overlap': 0.75, # cells with more overlap than this get removed during triage, before refinement
  77. 'inner_neuropil_radius': 2, # number of pixels to keep between ROI and neuropil donut
  78. # 'outer_neuropil_radius': np.inf, # maximum neuropil radius
  79. 'min_neuropil_pixels': 350, # minimum number of pixels in the neuropil
  80. # deconvolution settings
  81. 'baseline': 'maximin', # baselining mode
  82. 'win_baseline': 60., # window for maximin
  83. 'sig_baseline': 10., # smoothing constant for gaussian filter
  84. 'prctile_baseline': 8.,# optional (whether to use a percentile baseline)
  85. 'neucoeff': .7, # neuropil coefficient
  86. }
  88. #list of files to analyze:
  89. outdir=[0]*planes
  91. #save output data
  92. for i in range(planes):
  93. outdir[i]= out_path + 'plane_'+str(i)+'//'
  95. print(outdir)
  97. # prepare the processes for each plane
  98. db = []
  99. jobs = []
  100. if __name__ == '__main__':
  102. for i in range(planes):
  103. #while the # of planes is less than the max number of cores we want to use (leaving 2 cores for scanimage)
  104. if i<os.cpu_count()-2:
  105. with open('ops_' + str(i) + '.json') as f:
  106. opsjson = json.load(f)
  107. ops['nrois'] = opsjson['nrois']
  108. ops['dx'] = [opsjson['dx']]
  109. ops['dy'] = [opsjson['dy']]
  110. ops['lines'] = opsjson['lines']
  111. ops['fs'] = opsjson['fs']
  113. # Define the dataset
  114. this_db = {
  115. 'save_path0': outdir[i],
  116. 'fast_disk': outdir[i] }
  117. p = multiprocessing.Process(target=run_s2p, args=(ops,this_db))
  118. p.start()
  119. jobs.append(p)
  120. else:
  121. print(f'Hey, do you really want to run all these {i} cores at the same time?')
  124. logging.basicConfig(level=logging.INFO)
  125. logging.info('Starting suite2p parallel processing in a different CPU core for each plane')
  126. #run each plane in parallel
  127. tic = time.time()
  129. if len(jobs)<os.cpu_count()-2:
  130. for job in jobs:
  131. #print ('Parallel processing: ' + str(outname[i]))
  132. job.join()
  133. toc = time.time() - tic
  134. print('All saved,took ' + str(toc) + 'secs')
  135. else:
  136. print(f'Hey, do you really want to run all these {i} cores at the same time?')
  138. toc2 = time.time() - tic1
  139. print('Suite2p took ' + str(toc2/60) + 'mins')

parallels2p_210617_59LA.py at commit c50063e, no license · at the source

Overview

Authors: Lamiae Abdeladim1,2, Uday K Jagadisan1,2, Hyeyoung Shin1,2,3, Mora B Ogando1,2, Hillel Adesnik1,2,4
ORCID iDs: Lamiae Abdeladim, Hyeyoung Shin, Hillel Adesnik
  1. Department of Neuroscience, University of California, Berkeley, CA USA
  2. Department of Molecular and Cell Biology, University of California, Berkeley, CA USA
  3. School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
  4. The Helen Wills Neuroscience Institute, University of California, Berkeley, CA USA
Institutions: University of California, Berkeley (United States); Seoul National University (South Korea)
Journal: Nature neuroscience, volume 29, issue 8, pages 2023-2035
Dates: received 13 April 2023; accepted 28 May 2026; published online 3 August 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41593-026-02350-9 · PMID 42547821 · PMCID PMC13433287 · OpenAlex W7172284961
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: optical imaging (calcium, voltage, 2-photon) (modality), mouse (organism), systems (subfield)
Methods: Connectivity, Statistics, Machine learning, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Neural circuits, Sensory processing
MeSH: Brain Mapping*, Cerebral Cortex*, Holography*, Nerve Net*, Neurons*, Optogenetics*, Animals, Mice (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) (RO1MH117824); Burroughs Wellcome Fund (BWF) (1243586); NINDS NIH HHS (RF1 NS128772, UF1 NS107574); U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) (UF1NS107574, R01NS128772)
Citations: cited by 4 papers (Europe PMC); 61 references in the paper

Abstract

Brain computation depends on intricately connected yet highly distributed neural networks. Owing to the absence of the requisite technologies, causally testing fundamental hypotheses on inter-areal processing has remained largely out of reach. Here, we developed a two-photon holographic mesoscope capable of simultaneously reading and writing neural activity patterns with near-single-cell resolution across large regions of the mouse cortex. We demonstrate the precise photoactivation of spatial and temporal sequences of neurons in one or multiple cortical areas while reading out the downstream effects in several other regions. Thus, we have established mesoscale two-photon holographic optogenetics as a platform for mapping functional connectivity and causal interactions across distributed cortical areas with high resolution.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

mesoholo/mesoholo

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: c50063ef7f12aba0de98294fe1daca0198ff62ee, 11 May 2026
Languages: MATLAB (88), Python (8), Jupyter (4), Shell (1)
Size: 112 files, 101 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, documentation, 4 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration
Tools: Image Processing Toolbox (19 files), Statistics and Machine Learning Toolbox (9 files), Suite2p (9 files), Psychtoolbox (6 files), h5py (3 files), Parallel Computing Toolbox (3 files), NumPy (3 files), SciPy (3 files), Optimization Toolbox (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
102 files
At the source: github.com/mesoholo/mesoholo

Code availability

Custom MATLAB (v.2021-2024) and Python (v.3.12-3.14) code to run and analyze holographic mesoscale experiments is available at https://github.com/mesoholo/mesoholo.

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

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 101 scripts, each with its path and the digest of its content;
  • 8 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

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

Data availability

Relevant data for this manuscript are available as source data files. 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 3, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 2 keywords, 8 MeSH terms, 4 funders, 57 references.

Cite

This paper

Abdeladim, L., Jagadisan, U. K., Shin, H., Ogando, M. B., & Adesnik, H. (2026). Probing inter-areal computations with a two-photon holographic mesoscope. Nature neuroscience, 29(8), 2023-2035. https://doi.org/10.1038/s41593-026-02350-9

BibTeX

@article{abdeladim2026probing,
author = {Abdeladim, Lamiae and Jagadisan, Uday K and Shin, Hyeyoung and Ogando, Mora B and Adesnik, Hillel},
title = {{Probing inter-areal computations with a two-photon holographic mesoscope}},
journal = {Nature neuroscience},
year = {2026},
month = aug,
volume = {29},
number = {8},
pages = {2023--2035},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02350-9},
url = {https://doi.org/10.1038/s41593-026-02350-9},
pmid = {42547821},
pmcid = {PMC13433287}
}

RIS

TY - JOUR
AU - Abdeladim, Lamiae
AU - Jagadisan, Uday K
AU - Shin, Hyeyoung
AU - Ogando, Mora B
AU - Adesnik, Hillel
TI - Probing inter-areal computations with a two-photon holographic mesoscope
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/08/03
VL - 29
IS - 8
SP - 2023
EP - 2035
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02350-9
UR - https://doi.org/10.1038/s41593-026-02350-9
LA - en
ER -

CSL-JSON

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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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