Explainable Artificial Intelligence for Decoding and Modulating Neural Circuit Activity Linked to Behavior (R01 Clinical Trial Optional) | PAR 19 344

Opportunity Information: Apply for PAR 19 344

The National Institutes of Health (NIH) funding opportunity PAR 19-344, titled "Explainable Artificial Intelligence for Decoding and Modulating Neural Circuit Activity Linked to Behavior (R01 Clinical Trial Optional)," supports research projects that combine modern machine learning with clear, mechanistic explanations aimed at understanding the brain. The central idea is not just to build models that predict neural or behavioral outcomes, but to develop explainable artificial intelligence (XAI) approaches that can also clarify why those outcomes occur and how specific neural circuit features relate to behavior. The FOA is aimed at advancing neuroscience questions about how neural circuits encode information, how neural activity can be decoded to infer internal states or behavioral variables, and how neural activity can be modulated through experimental interventions in ways that change behavior.

A key emphasis of this announcement is that successful applications should move beyond black-box prediction and instead deliver interpretable, causal, and biologically meaningful accounts of neural function. The NIH is looking for machine learning algorithms and analytical frameworks that can generate hierarchical, unbiased explanations of complex relationships across multiple levels of data, for example linking experimental manipulations to changes in neural population dynamics and then to measurable behavioral shifts. The focus on "mechanistically explain" signals that proposals should be able to connect model outputs to plausible neural circuit mechanisms, rather than relying only on post hoc interpretation that is disconnected from experimental reality. The FOA is also interested in approaches that explicitly help explain how experimental manipulations might improve cognitive, affective, or social processing in either humans or animal models, which places the work at the intersection of computational modeling, systems neuroscience, and behavioral science.

The opportunity strongly encourages collaborations between teams with complementary strengths, particularly computational researchers who build XAI methods and experimental neuroscientists who generate or work with neural and behavioral datasets and can test mechanistic predictions. In practice, a competitive project under this FOA would often include a loop where explainable models are trained on rich neural and behavioral recordings, the model provides testable hypotheses about circuit-level mechanisms or intervention targets, and experiments then validate or refine those hypotheses. Proof-of-concept projects are also considered responsive when they push the current technology forward, especially when they demonstrate that XAI methods can reveal causal structure in complicated datasets rather than simply summarizing correlations.

Administratively, this is an NIH discretionary grant opportunity using the R01 mechanism, meaning it is intended for full-scale research projects rather than small pilots. The activity category is Health, and the CFDA listing is 93.242. The "Clinical Trial Optional" label means applicants may propose studies that include a clinical trial if appropriate, but a clinical trial is not required; both trial and non-trial projects can fit as long as they align with the scientific goals around explainable AI for neural circuit decoding and modulation. The original closing date listed in the source information is March 10, 2022, and the FOA was created on August 14, 2019; no award ceiling or expected number of awards is specified in the provided source data.

Eligibility is broad and includes many types of organizations. Standard eligible applicants include state, county, and local governments; special district governments; independent school districts; public and private institutions of higher education; public or Indian housing authorities; federally recognized tribal governments; tribal organizations that are not federally recognized; nonprofits with or without 501(c)(3) status (excluding higher education institutions when applicable); for-profit organizations other than small businesses; and small businesses. The FOA also explicitly highlights additional eligible applicant categories such as Alaska Native and Native Hawaiian Serving Institutions, Asian American Native American Pacific Islander Serving Institutions (AANAPISIs), Hispanic-serving Institutions, Historically Black Colleges and Universities (HBCUs), Tribally Controlled Colleges and Universities (TCCUs), faith-based or community-based organizations, eligible federal agencies, U.S. territories or possessions, regional organizations, and even non-U.S. entities (foreign organizations). Overall, the program is structured to draw in interdisciplinary teams and diverse institutions capable of developing explainable machine learning tools that can meaningfully connect neural circuit activity, experimental interventions, and behavioral outcomes.

• The National Institutes of Health in the health sector is offering a public funding opportunity titled "Explainable Artificial Intelligence for Decoding and Modulating Neural Circuit Activity Linked to Behavior (R01 Clinical Trial Optional)" and is now available to receive applicants.
• Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 93.242.
• This funding opportunity was created on 2019-08-14.
• Applicants must submit their applications by 2022-03-10. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
• Eligible applicants include: State governments, County governments, City or township governments, Special district governments, Independent school districts, Public and State controlled institutions of higher education, Native American tribal governments (Federally recognized), Public housing authorities/Indian housing authorities, Native American tribal organizations (other than Federally recognized tribal governments), Nonprofits having a 501 (c) (3) status with the IRS, other than institutions of higher education, Nonprofits that do not have a 501 (c) (3) status with the IRS, other than institutions of higher education, Private institutions of higher education, For-profit organizations other than small businesses, Small businesses, Others.

Apply for PAR 19 344

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