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A cloud-based miniscope for neurosurveillance of brain health and disease in freely behaving animals.

Overview

Authors: Janaka Senarathna1, Darren Yang2, Julia Brill3, Subhrajit Das4, Shruthi Bare2, Yunke Ren5, Devorah VanNess3, Vu Dinh1, Irfaan Karim6,7, Amit K Banerjee8, Nitish V Thakor4,5, Mingyao Ying6,7, David J Linden3, Arvind P Pathak1,4,5,9
ORCID iDs: Janaka Senarathna, Darren Yang, Subhrajit Das, Shruthi Bare, Arvind P Pathak
  1. The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD USA
  2. Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD USA
  3. The Solomon H. Snyder Department of Neuroscience, The Johns Hopkins University School of Medicine, Baltimore, MD USA
  4. Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD USA
  5. Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD USA
  6. Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD USA
  7. The Kennedy Krieger Institute, Baltimore, MD USA
  8. Applied Physics Lab, Johns Hopkins University, Baltimore, MD USA
  9. The Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD USA
Institutions: Johns Hopkins University (United States); Johns Hopkins Medicine (United States); Kennedy Krieger Institute (United States); Sidney Kimmel Comprehensive Cancer Center (United States)
Journal: Nature methods, volume 23, issue 7, pages 1424-1436
Dates: received 2 October 2024; accepted 13 April 2026; published online 22 June 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41592-026-03111-z · PMID 42332086 · PMCID PMC13345946 · OpenAlex W7165554536
Open access: hybrid, a free copy (OpenAlex)
Status: code found, not verified yet
Categories: optical imaging (calcium, voltage, 2-photon) (modality), mouse (organism), other condition (population), epilepsy (population)
Methods: Connectivity, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Graphs, Machine learning, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Neuro-vascular interactions, Cancer imaging, Cancer microenvironment, Neurophysiology, Preclinical research
MeSH: Behavior, Animal*, Brain*, Cloud Computing*, Neuroimaging*, Animals, Brain Neoplasms, Disease Models, Animal, Mice, Seizures (* major topic)
Topic: Cell Image Analysis Techniques (Biophysics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) (1R21NS138938-01); U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) (5R01CA196701-08, 5R01CA237597-05)
Citations: not cited yet (Europe PMC); 54 references in the paper

Abstract

Miniaturized microscopes or ‘miniscopes’ for neuroimaging in freely behaving animals mostly operate over short durations (<2 h) and image either neuronal activity or cerebral hemodynamics. In contrast, central nervous system (CNS) disease models involving seizures, brain tumors etc. necessitate long-term (>24 h) imaging, remote operation and simultaneous characterization of multiple neurophysiological variables such as neuronal activity, blood flow, blood volume, oxygenation and cellular dynamics (a capability that we call ‘neurosurveillance’). Thus, we developed the ‘CloudScope’, a cloud-based multicontrast miniscope for autonomous neurosurveillance in freely behaving animals. Its cloud-based architecture enables global remote operation and continuous acquisition of multicontrast images over CNS disease model life cycles. We demonstrate CloudScope’s neurosurveillance capabilities in predicting behavior from 24-h neuroimaging data with deep learning (DL), characterizing neurovascular changes during natural behavior, seizure-induced neurovascular disruptions, and in vivo cellular and microvascular phenotyping of brain tumor microenvironments. Finally, CloudScope’s architecture enables ‘time-shared’ imaging, which potentially reduces animal use. Collectively, CloudScope’s neurosurveillance capabilities in conjunction with CNS disease models establish a new paradigm for characterizing their etiology and evolution.

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

Code

No file of the authors' code could be read here: it is described below, and read at its source.

codeocean:4182092

License: none: the authors keep all their rights
State: cannot be verified, verified on 27 September 2026
Evidence: found in the paper
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: cannot be verified
  • 27 September 2026: cannot be verified
At the source:

Code availability

Image-processing scripts central to the findings of this study, for example pre-processing of images and visualizations for Figs. 1 and 3 are included in the CodeOcean repository at 10.24433/CO.4182092.v1. Output data were manually uploaded and included for processing steps that were not executable within the CodeOcean repository due to its resource constraints. Code reuse is permitted under the GNU General Public License v.3 (https://opensource.org/license/gpl-3.0).

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;
  • 0 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

The 24-h neuroimaging data from a representative animal are available via the CodeOcean repository at 10.24433/CO.4182092.v1. Data are provided as 24-h time-series of 64 × 64-pixel images for each contrast channel. STL files for 3D printing CloudScope parts are also included in the same repository.

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, 14 authors, 5 keywords, 9 MeSH terms, 2 funders, 51 references.

Cite

This paper

Senarathna, J., Yang, D., Brill, J., Das, S., Bare, S., Ren, Y., VanNess, D., Dinh, V., Karim, I., Banerjee, A. K., Thakor, N. V., Ying, M., Linden, D. J., & Pathak, A. P. (2026). A cloud-based miniscope for neurosurveillance of brain health and disease in freely behaving animals. Nature methods, 23(7), 1424-1436. https://doi.org/10.1038/s41592-026-03111-z

BibTeX

@article{senarathna2026cloud,
author = {Senarathna, Janaka and Yang, Darren and Brill, Julia and Das, Subhrajit and Bare, Shruthi and Ren, Yunke and VanNess, Devorah and Dinh, Vu and Karim, Irfaan and Banerjee, Amit K and Thakor, Nitish V and Ying, Mingyao and Linden, David J and Pathak, Arvind P},
title = {{A cloud-based miniscope for neurosurveillance of brain health and disease in freely behaving animals}},
journal = {Nature methods},
year = {2026},
month = jun,
volume = {23},
number = {7},
pages = {1424--1436},
publisher = {Nature Portfolio},
issn = {1548-7091},
doi = {10.1038/s41592-026-03111-z},
url = {https://doi.org/10.1038/s41592-026-03111-z},
pmid = {42332086},
pmcid = {PMC13345946}
}

RIS

TY - JOUR
AU - Senarathna, Janaka
AU - Yang, Darren
AU - Brill, Julia
AU - Das, Subhrajit
AU - Bare, Shruthi
AU - Ren, Yunke
AU - VanNess, Devorah
AU - Dinh, Vu
AU - Karim, Irfaan
AU - Banerjee, Amit K
AU - Thakor, Nitish V
AU - Ying, Mingyao
AU - Linden, David J
AU - Pathak, Arvind P
TI - A cloud-based miniscope for neurosurveillance of brain health and disease in freely behaving animals
T2 - Nature methods
J2 - Nat Methods
PY - 2026
DA - 2026/06/22
VL - 23
IS - 7
SP - 1424
EP - 1436
SN - 1548-7091
PB - Nature Portfolio
DO - 10.1038/s41592-026-03111-z
UR - https://doi.org/10.1038/s41592-026-03111-z
LA - en
ER -

CSL-JSON

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