# Next generation axonal quantification and classification using AI

> **NIH NIH R44** · MICROBRIGHTFIELD, LLC · 2024 · $850,421

## Abstract

Abstract
This Phase II project describes the commercial development of HyperAxon™, highly innovative software for
performing automated segmentation, tracing, reconstruction and quantitative analysis of all axonal fibers (with
and without signs of acute axonal injury) visible in two- and three-dimensional (2D and 3D) microscopy 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 and 2D microscopy images of CNS tissue of non-transgenic
and transgenic animal models as well as in human post mortem CNS tissue promises to enable researchers to
gain 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. During Phase I we created
HyperAxon prototype software by leveraging the original, lab-built technology Learning-based Tracing of Dense
Axonal Fibers created at MIT Lincoln Laboratory (MIT LL) (Lexington, MA) and extending this technology with
several new, specialized deep neural networks. Furthermore, we validated that our approach will be successful
in research applications. All specific aims of Phase I were fully completed, demonstrating feasibility of
successfully developing HyperAxon. The game-changing innovation in HyperAxon is the ability to automatically
(i) segment, trace and reconstruct all axonal fibers visible in 3D and 2D microscopy images of CNS areas with
high axonal fiber density, (ii) identify axonal branch points, (iii) resolve axonal fibers of passage in fiber tracts
from those in axonal terminal fields, (iv) identify axonal fibers showing acute axonal injury and (v) precisely
quantify alterations in number and density of axonal fibers in CNS tissue. For widespread dissemination of this
important new technology we plan to commercialize the HyperAxon software at the end of Phase II as both a
cloud-based “software as a service” running on Amazon Web Services (AWS) and traditional software
application running on local institutional computers. 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 demonstrated feasibility of this novel technology by developing prototype
software; work in Phase II will focus on...

## Key facts

- **NIH application ID:** 10868467
- **Project number:** 5R44MH128076-03
- **Recipient organization:** MICROBRIGHTFIELD, LLC
- **Principal Investigator:** JACOB R GLASER
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $850,421
- **Award type:** 5
- **Project period:** 2021-08-19 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10868467

## Citation

> US National Institutes of Health, RePORTER application 10868467, Next generation axonal quantification and classification using AI (5R44MH128076-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10868467. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*