CaltechAUTHORS
Published December 2025 | Version Supplemental material
Journal Article Open

An implantable CMOS deep-brain fluorescence imager with single-neuron resolution

Creators

  • 1. ROR icon Columbia University
  • 2. ROR icon Stevens Institute of Technology
  • 3. ROR icon California Institute of Technology

Abstract

Optical imaging offers a number of advantages over electrophysiology including cell-type specificity. However, its application has been limited to the investigation of shallow brain regions (less than 2 mm) because of the light scattering property of brain tissue. Passive optical conduits, such as graded-index lenses and waveguides, have permitted access to deeper locales but with restricted resolution and field of view, while creating massive lesions along the inserted path. Here we report an implantable complementary metal–oxide–semiconductor fluorescence imager with single-neuron resolution. The imager has a 512-pixel silicon image sensor post-processed into a 4.1-mm-long, 120-μm-wide shank with a collinear fibre for illumination. It can record transient fluorescent signals in deep brain regions at 400 frames per second. We show that the system can offer single-neuron resolution in functional imaging of GCaMP6s-expressing neurons at a frame rate of 400 frames per second.

Copyright and License

© The Author(s), under exclusive licence to Springer Nature Limited 2025.

Acknowledgement

This work was supported by the Defense Advanced Research Projects Agency under contract no. N66001-17-C-4012 (K.L.S.) and by the National Science Foundation under grant no. 1706207 (K.L.S.). We gratefully acknowledge TSMC for chip fabrication and their support in the use of experimental SPAD devices.

Data Availability

All measurement data relevant to the figures presented in this paper are available on GitHub at https://github.com/klshepard/acus with a version available via Zenodo at https://doi.org/10.5281/zenodo.17017395 (ref. 60). All other relevant data are available from the corresponding authors upon reasonable request. No data exclusions were made throughout the paper. Source data are provided with this paper.

Code Availability

All scripts used for data analysis are available on GitHub at https://github.com/klshepard/acus with a version available via Zenodo at https://doi.org/10.5281/zenodo.17017395 (ref. 60). All other relevant codes are available from the corresponding authors upon reasonable request.

Supplemental Material

Supplementary Information

Supplementary Information includes Figs. 1–20, Table 1 and sections 1–5.

Reporting Summary

Supplementary Video 1

Confocal microscope video of the moving bead serving as ground truth for the Supplementary Videos .

Supplementary Video 2

Change of total photon count detected by Acus in time.

Supplementary Video 3

Raw data detected by Acus.

Supplementary Video 4

Video output of the 3D localization through blind source separation algorithm at 400 frames/sec.

Supplementary Video 5

Video output of the 3D localization through blind source separation algorithm at 40 frames/sec.

Supplementary Video 6

Video output of the 3D localization through blind source separation algorithm at 20 frames/sec.

Supplementary Video 7

In vivo structural imaging of eGFP-expressing mouse brain at 40 frames/sec.

Supplementary Video 8

In vivo structural imaging of eGFP-expressing mouse brain at 400 frames/sec.

Supplementary Video 9

In vivo functional imaging of GCaMP6s-expressing mouse brain at 400 frames/sec.

Source data:

Source Data Fig. 2

Source data for Fig. 2c–e.

Source Data Fig. 3

Source data for Fig. 3a–f.

Source Data Fig. 4

Source data for Fig. 4b.

Source Data Fig. 5

Source data for Fig. 5b,e.

Source Data Extended Data Fig. 1

Source data for Extended Data Fig. 1.

Source Data Extended Data Fig. 2

Source data for Extended Data Fig. 2.

 

Additional Information

Extended Data Fig. 1 Analysis of in-vivo GCaMP6s imaging data

Extended Data Fig. 2 Detection of population activity in-vivo with GCaMP6f

Files

41928_2025_1487_MOESM1_ESM.pdf

Files (292.0 MB)

Name Size
md5:fe5a1f86bf376318106e449f4a5fa153
28.6 MB Download
md5:c45e4ddc19efd5735934da9df72350e1
25.0 MB Download
md5:5f044c87d74006978a1154fb887ebb12
17.6 kB Download
md5:adaf3bf75ce7964a4be57fead7331b3a
162.7 kB Download
md5:d10b74fafa7f16c0c36079c48cb5cdd1
12.3 kB Download
md5:1e3051411d7ab96b6f5e05a6cc7541e8
1.5 MB Download
md5:caedb319e6252a4dac9a16f509f9debd
2.7 MB Download
md5:a6f6ab053c538665f3f5cc24d2936987
1.9 MB Download
md5:a14c9d501f1a3913a3eb70d4d0520ada
19.0 MB Preview Download
md5:e2228efcd74851c7b6f70989d01f6d92
53.0 MB Download
md5:bedcaae79c8a518e6092210d126674c1
24.6 MB Download
md5:85de231d2e8b9f39169f7a811f0792f7
49.8 MB Download
md5:c93a43ff7cd75e36c368cf7e4331a1e1
22.6 MB Download
md5:1eb0f7e6df798ad0a95780a68e4112d0
22.2 MB Download
md5:5fd4edfab1d5eae8f2f263fd64236d99
21.9 MB Download
md5:f48d7ac77f8bb7fe67752191f6e5617a
19.1 MB Download

Additional details

Related works

Describes
Journal Article: https://rdcu.be/eYVbc (ReadCube)
Is new version of
Discussion Paper: 10.1101/2025.06.03.657675 (DOI)
Is supplemented by
Dataset: https://github.com/klshepard/acus (URL)
Dataset: 10.5281/zenodo.17017395 (DOI)

Funding

Defense Advanced Research Projects Agency
N66001-17-C-4012
National Science Foundation
1706207

Dates

Submitted
2024-06-12
Accepted
2025-09-25
Available
2025-10-27
vers

Caltech Custom Metadata

Caltech groups
Kavli Nanoscience Institute , Division of Physics, Mathematics and Astronomy (PMA) , Physics Department
Publication Status
Published