# A Scalable Method for Mapping Microconnectivity in Transcriptomically Distinct Neuron Types

> **NIH NIH F32** · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · 2024 · $6,201

## Abstract

Project Summary
 Identifying the cell types that compose each brain region and the patterns of connectivity that link them is
key to understanding how neural circuits give rise to all perception, cognition, and behavior. Large-scale projects
enabled by next-generation sequencing technologies are revealing that the brain contains thousands of cell
types, each with unique molecular features, axonal targets, and roles in brain function. However, the synaptic
connections between these cell types is currently determined using low throughput methods in which connectivity
between pairs of cells is tested one-by-one. Data describing connectivity at the cellular level have become a
essential for theoretical models of brain function, and necessitate the development of larger scale and higher
throughput methods. In remarkable proof of concept experiments, genetically encoded voltage indicators
(GEVIs) have been employed to visualize activity and infer the connectivity of cells within the brain. I propose to
leverage this advance to develop SYNMAP, an efficient all-optical method for measuring connectivity between
the thousands of genetically defined cell types that make up the mammalian brain. In SYNMAP, neural activity
will be both controlled and observed with light. Gene expression will be visualized across the same cells with
highly multiplexed fluorescence in situ hybridization in situ. Using SYNMAP, synaptic connectivity can be
assayed across molecularly defined cell types with 100X higher throughout than currently possible, allowing us
to test important hypotheses about neural circuit architecture across systems neuroscience. I will apply SYNMAP
to determine whether parallel thalamocortical pathways relay information from the basal ganglia and cerebellum
to discrete subcircuits in the motor cortex, taking us one step further towards understanding how motor actions
are planned and executed by motor systems spanning multiple brain regions. Optical physiology is being quickly
adopted by neurophysiology labs, promising the widespread application of SYNMAP across neuroscience.
Successful development of SYNMAP will be transformative, allowing us to study the structure and dynamics of
any neural circuit and its component cell types.

## Key facts

- **NIH application ID:** 10906159
- **Project number:** 5F32MH129149-03
- **Recipient organization:** BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
- **Principal Investigator:** Maria Victoria Moya
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $6,201
- **Award type:** 5
- **Project period:** 2022-09-01 → 2024-09-02

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10906159

## Citation

> US National Institutes of Health, RePORTER application 10906159, A Scalable Method for Mapping Microconnectivity in Transcriptomically Distinct Neuron Types (5F32MH129149-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10906159. Licensed CC0.

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