# Long-range neuronal projections: circuit blueprint or stochastic targeting?  Rigorous classification of brain-wide axonal reconstructions

> **NIH NIH RF1** · GEORGE MASON UNIVERSITY · 2021 · $1,287,090

## Abstract

ABSTRACT (PROJECT SUMMARY)
The classification of neurons in the mammalian brain has long been a focus of intensive investigation in
neuroscience. Neurons are widely recognized as the fundamental computational elements of the nervous
system, and the broad diversity of their morphological, physiological, and molecular properties may provide
crucial insights into their function and involvement in disease. Long-range axonal projections, in particular, are
the quintessential determinants of network connectivity, providing a key nexus between cellular organization
and circuit architecture. Converging technological breakthroughs in microscopic imaging, genetic labeling, and
algorithmic development have only recently enabled the high-throughput collection of large-scale, whole-brain
axonal reconstructions under the BRAIN initiative. Using a principled statistical strategy, the proposed project
leverages such information-rich resources to rigorously identify, from each brain region, all “projection neuron
types” with objectively distinct patterns of anatomical targeting. This application will thus directly test the
seminal hypothesis that the axonal trajectories of individual neurons follow specific coordination plans as
opposed to aiming randomly within the constraints of regional connections. While this data-driven classification
necessarily depends on the existing digital tracings, we will deploy our full analysis workflow as an automated
pipeline on public cloud servers, allowing not only free community access, but also continuous refinement of
the resulting knowledge as more datasets become available. Moreover, our approach allows the quantitative
estimation of the population size of every separate neuron class as well as its unique distribution of path
distances from the soma to each target, defining the basic temporal dynamics of information transmission. We
will also determine if different projection neuron types vary in their dendritic morphology, providing an important
clue as to whether input processing is specifically tuned for the intended outputs. Furthermore, we will extend
this innovative methodology by applying it to complementary datasets obtained by stochastic nucleic acid
barcoding, laser capture microdissection, and sequencing, yielding far greater sample sizes in exchange for
lower anatomical resolution. Last but not least, we will model the discovered axonal projection patterns into a
novel artificial neural network design (“projectron nets”) to systematically explore their possible selective
advantages in learning and memory robustness and performance. Achieving these goals will thus quantify, for
the first time, the relevant single-neuron motifs to outline the functional blueprint of the mammalian central
nervous system and related impairments for the long-lasting benefit of public health.

## Key facts

- **NIH application ID:** 10360723
- **Project number:** 1RF1MH128693-01
- **Recipient organization:** GEORGE MASON UNIVERSITY
- **Principal Investigator:** GIORGIO A ASCOLI
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,287,090
- **Award type:** 1
- **Project period:** 2021-09-15 → 2025-09-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10360723

## Citation

> US National Institutes of Health, RePORTER application 10360723, Long-range neuronal projections: circuit blueprint or stochastic targeting?  Rigorous classification of brain-wide axonal reconstructions (1RF1MH128693-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10360723. Licensed CC0.

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