# A Shared Neuroscience Platform for National Dissemination and Training  in Brain Organogenesis, Behavioral and Brain Disease Models, Viral Vectors, and Imaging Technologies

> **NIH NIH U24** · STANFORD UNIVERSITY · 2022 · $1,358,699

## Abstract

Advances in neuroscience depend on robust in vivo and in vitro models with innovative technologies to carry out
functional and mechanistic studies accompanied by advanced imaging techniques. The Human Brain
Organogenesis Program (HBOP), Behavioral and Functional Neuroscience Laboratory (BFNL), Gene Vector
and Virus Core (GVVC), and Neuroscience Microscopy Services (NMS) make up a platform, the Stanford
Neuroscience Research Center (SNRC), for centralization and dissemination of innovative neuroscience
models, reagents and methods. The vision of SNRC is to provide an integrated platform in which users can
expand their research to areas outside their expertise or engage multiple modalities in their research, such as
applying viral vector approaches and neuroimaging approaches to behavioral models of disease or human
organoid cultures. SNRC outreach will approach Neuroscience departments nationally with a call for applications
for 6 fully-funded merit-based pilot studies designed to engage diverse SNRC resources. We will also offer
comprehensive workshops on techniques and teach participants how to apply and integrate novel approaches
into their current research programs. SNRC is strategically equipped with resources to provide critical support to
a range of national research projects and has supported over 500 labs nationwide with over 200 peer-reviewed
publications in the last decade. SNRC has a growing national user base from institutions including Yale, Harvard,
University of Missouri, Cornell, Princeton, Columbia, University of Pennsylvania, University of Texas, MIT, and
many more. Investigators anywhere in the world can request a viral vector, phenotype rodent lines, or have an
in vivo stroke study or an efficacy study in a model of neurodegenerative disease run remotely. Anyone can
attend a workshop for training in behavioral models, 3D imaging of whole brains, or methods for 3D human
cellular models (organoids and assembloids). SNRC supports many small and large biotech companies in proof
of concept efficacy studies of clinical drug candidates. This contribution has supported the advancement of these
projects to human clinical studies. Through SNRC, academic users will have access to these same industry-
standard efficacy studies. All SNRC activities will be supervised by a steering committee consisting of external,
internal, and NIH members. Under this U24 grant, we will disseminate essential and cutting-edge resources. We
will expand our external user network to share emerging technologies with the Pilot Study program and Annual
Workshops. BFNL will expand its automated testing and data processing capabilities, including the Digilab cloud-
based automated behavioral phenotyping system. GVVC will set up large-scale viral production as well as
higher-level purification technology. NMS will include training classes in STARmap genomic imaging and array
tomography proteomic imaging. Under this program, SNRC will engage with the national neurosc...

## Key facts

- **NIH application ID:** 10439384
- **Project number:** 1U24NS124026-01A1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** William T Newsome
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $1,358,699
- **Award type:** 1
- **Project period:** 2022-06-16 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10439384

## Citation

> US National Institutes of Health, RePORTER application 10439384, A Shared Neuroscience Platform for National Dissemination and Training  in Brain Organogenesis, Behavioral and Brain Disease Models, Viral Vectors, and Imaging Technologies (1U24NS124026-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10439384. Licensed CC0.

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