# BRAIN CONNECTS: Rapid and Cost‐effective Connectomics with Intelligent Image Acquisition, Reconstruction, and Querying

> **NIH NIH U01** · HARVARD UNIVERSITY · 2024 · $1,115,065

## Abstract

SUMMARY
High-throughput connectomics is needed to generate the TB-, PB- and EB-scale wiring diagrams of mammalian
brains, but is limited to the few research institutes (e.g., Janelia, Allen, Max Planck) with sufficient infrastructure.
As resource-rich as these institutes are, none are able to do a whole brain at nanometer scale on their own. The
failure to broaden participation to a larger community is an obstacle to scaling connectomics. We propose a new
and more affordable imaging strategy that will allow many more teams to engage in connectomics.
High-speed electron microscopes for connectomics – e.g., multibeam SEMs – are rare and prohibitively ex-
pensive. More common single-beam SEMs have sufficiently high spatial resolution, but are prohibitively slow
for connectomics. We plan to increase the speed of single-beam SEM systems to the speed of multibeam
SEMs without substantially increasing cost. Our strategy adds artificial intelligence to SEM architecture to re-
duce the number and dwell time of pixels that need to be imaged at high-resolution without adversely affecting
“segmentability”. With new software and standard computer hardware, we can turn single-beam SEMs into intel-
ligent, powerful devices at negligible cost. We demonstrated a proof-of-concept of a smart scanning system that
we engineered into a single-beam SEM. The modified SEM acquires a low-resolution/low-dwell time image of a
brain slice at high speed. It then uses ultrafast ML algorithms to extract most of the wiring from these images,
while at the same time identifying in real time those salient pixels that should be rescanned to improve signal-to
noise in the final wiring diagram. We have achieved >10-fold speedup in image acquisition, and plan to increase
the rate significantly more.
A significant scale-up in the rate of connectomics demands comparable improvements in image processing
(stitching, alignment, and segmentation). We have built computationally more efficient methods for aligning and
segmenting connectome datasets. We will integrate these methods into a cloud-based platform that will allow
researchers without significant computational infrastructure or expertise to process connectomics datasets. All
data products and capabilities will be publicly accessible through BossDB.
In summary, this integrated research program will scale connectomics to a much larger neuroscience
community.

## Key facts

- **NIH application ID:** 10912069
- **Project number:** 5U01NS132158-02
- **Recipient organization:** HARVARD UNIVERSITY
- **Principal Investigator:** Jeff W Lichtman
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,115,065
- **Award type:** 5
- **Project period:** 2023-09-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10912069

## Citation

> US National Institutes of Health, RePORTER application 10912069, BRAIN CONNECTS: Rapid and Cost‐effective Connectomics with Intelligent Image Acquisition, Reconstruction, and Querying (5U01NS132158-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10912069. Licensed CC0.

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