# Next generation axonal quantification and classification using AI

> **NIH NIH R43** · MICROBRIGHTFIELD, LLC · 2022 · $55,000

## Abstract

Abstract
This Lab to Marketplace project describes the development of HyperAxon™, highly innovative software for
performing automated segmentation, tracing, reconstruction and quantitative analysis of all axonal fibers visible
in three-dimensional (3D) microscopic images of central nervous system (CNS) areas, even those with extremely
high axonal fiber density. Accurate and rigorous analysis of all axonal fibers visible in 3D microscopic images of
CNS tissue of non-transgenic and transgenic animal models as well as in human post mortem CNS tissue holds
the promise of novel insights into physiological neural network connectivity patterns as well as into the
neuropathological underpinnings of alterations in connectivity associated with human neuropsychiatric and
neurological disorders. However, this cannot be achieved with contemporary, computer-assisted tracing and
reconstruction methods, which currently are the gold standard for investigating axonal fibers, because these
methods primarily address tracing and reconstruction of only a limited number of individual axonal fibers.
HyperAxon will be based on the highly innovative artificial intelligence technology Learning-based Tracing of
Dense Axonal Fibers (LTDAF) that was recently developed at MIT Lincoln Laboratory (MIT LL) (Lexington, MA).
This project will build upon the original, lab-built LTDAF technology to create commercial software for wide-
spread dissemination of this important new technology. Dissemination of this technology via a Lab to
Marketplace commercial product is consistent with NIMH goals and will result in the technology having a
significant impact on neuroscience research. The game-changing innovation in HyperAxon is the ability to
automatically (i) segment, trace and reconstruct all axonal fibers visible in 3D microscopic images of CNS areas
with high axonal fiber density, (ii) identify axonal branch points, (iii) resolve axonal fibers of passage from axonal
fibers that make presumptive synapses in target regions, (iv) identify axonal fibers showing acute axonal injury
and (v) precisely quantify alterations in number and density of axonal fibers in CNS tissue. Based on published
pilot work performed at MIT LL, we are convinced that HyperAxon will be impactful in the field of neuroscience
research and will enable substantial advancements in research on alterations in CNS circuitry associated with
neurodevelopmental, neuropsychiatric, neurodegenerative and neurological disorders. Ultimately, this will result
in an improved basis for developing novel treatment strategies for a wide spectrum of complex brain diseases.
In Phase I we will demonstrate feasibility of this novel technology by developing prototype software; work in
Phase II will focus on creating the full functionality of HyperAxon for commercial release. We will perform
extensive feasibility studies, product validation and usability studies of HyperAxon in close collaboration with MIT
LL and our academic collaboration...

## Key facts

- **NIH application ID:** 10609151
- **Project number:** 3R43MH128076-01S1
- **Recipient organization:** MICROBRIGHTFIELD, LLC
- **Principal Investigator:** Paul Angstman
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $55,000
- **Award type:** 3
- **Project period:** 2022-04-01 → 2022-08-18

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10609151

## Citation

> US National Institutes of Health, RePORTER application 10609151, Next generation axonal quantification and classification using AI (3R43MH128076-01S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10609151. Licensed CC0.

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