BRAIN Initiative: Theories, Models and Methods for Analysis of Complex Data from the Brain

Below is a brief summary. Please check the full solicitation before applying (link in resources section).

Executive Summary:

The NIH BRAIN Initiative: Theories, Models and Methods for Analysis of Complex Data from the Brain (RFA-DA-27-004) is a competitive R01 research grant supporting the development of innovative theories, computational models, and analytical tools to advance understanding of brain function from complex neuroscience data. This funding is part of the NIH BRAIN Initiative, aiming to transform neuroscience through quantitative, predictive frameworks. Applications are due as soon as October 6th, 2026.

How much funding would I receive?

Application budgets are not limited, but NIH expects direct costs of approximately $150,000 – $350,000 per year.

Awards are for up to 3 years of support.

NIH anticipates funding multiple awards each cycle, depending on score and available appropriations.

What could I use the funding for?

Theories of brain function

Development of predictive, mathematically-grounded theories explaining how behavior arises from neural structure, circuit dynamics, computation, cognition, and environmental variables. Examples include:

• Theories of embodied computation that anchor the neural representation of sensory, cognitive, and motor variables to an individual/animal’s ongoing interactions with the environment through dynamic, moment-to-moment, circular, and iterative processes.

• Theories that bridge multiple scales of spatial organization (e.g., molecular, synaptic, cellular, circuit, systems) and/or temporal dynamics (e.g., milliseconds to lifetimes) to generate testable predictions of brain-behavior links or cognitive function.

• Theories linking circuit dynamics and function to specific properties of cell types or anatomical connections, identifying general rules, scaling principles, and contributions of specific circuit motifs to computation.

• Theories elucidating fundamental computational principles employed by biological neural networks, potentially drawing inspiration from or contrasting with artificial networks, but firmly grounded in biological constraints (e.g., neuronal/synaptic dynamics, connectivity patterns, metabolic limits, specific cell-type properties, learning rules).

Computational models of neural and behavioral dynamics

Development and validation of quantitative models that are mechanistically grounded, interpretable, predictive, and rigorously tested against neural and behavioral data. Examples include:

• Mechanistic, interpretable, and/or predictive models of neural dynamics, circuit function, or brain-behavior links that integrate biological details with computational principles.

• Models that integrate knowledge across multiple levels (e.g., linking behavior to neural population activity and cellular/circuit properties).

• Models of cognitive processing (e.g., sensory coding, decision-making, motor control, learning, memory) that are mechanistically grounded in identified circuit elements and dynamics, make quantitative predictions, and are rigorously tested against neural and behavioral data, potentially under ecologically relevant or challenging conditions (e.g., limited information, dynamic environments).

• Development and analysis of neural-inspired computational architectures or artificial intelligence/machine learning systems explicitly designed to gain novel insights into brain function.

Methods for complex data analysis

Development of novel computational, statistical, and analytical techniques designed to extract key insights from complex, large-scale neuroscience datasets. Examples include:

• Development of innovative and scalable computational/statistical methods for dimensionality reduction, identifying latent structure, disentangling contributing factors (e.g., sensory, motor, cognitive, state variables), extracting key dynamical features, or characterizing information flow within large, complex neural and behavioral datasets.

• Novel approaches for principled data fusion and assimilation to quantitatively integrate heterogeneous datasets (e.g., linking behavior with multi-regional activity, anatomical connectivity, and cell-type information) to infer new theories of brain function, or to constrain and validate multi-scale computational models.

• Novel statistical/signal processing methods (e.g., component analysis, graphical models, compressed sensing) to track structure in neural data and link to biophysical signals for mechanistic insights across scales.

Are there any additional benefits I would receive?

• Collaboration with NIH program staff and participation in the broader BRAIN Initiative network.

• Tools developed are expected to be shared with the neuroscience community, enhancing visibility and impact.

What is the timeline to apply and when would I receive funding?

Application Due Dates (all by 5:00 PM local time):

• Cycle 2: October 6, 2026

• Cycle 3: October 6, 2027

Expiration of this NOFO: November 9, 2027

Following review, awards generally begin in March–July of the year after submission.

Where does this funding come from?

This funding is provided by the National Institutes of Health (NIH) through multiple participating Institutes and Centers under the NIH BRAIN Initiative, including NIDA, NEI, NIA, NIAAA, NIBIB, NICHD, NIDCD, NIMH, NINDS, and NCCIH.

Who is eligible to apply?

Eligible applicant organizations include:

• Higher Education Institutions

• Nonprofit organizations

• For-profit organizations (including small businesses)

• Local/state governments and tribal governments

• Foreign organizations (with restrictions on foreign subawards)

• Other research or non-profit entities

Eligible individuals are those qualified to lead the proposed research.

What companies and projects are likely to win?

Strong applicants typically:

• Propose novel and rigorous theoretical or computational frameworks.

• Demonstrate deep expertise in neuroscience, modeling, or computational analysis.

• Have clear plans to validate and share tools with the research community.

• Show relevance to BRAIN Initiative goals and the integration of complex datasets.

Are there any restrictions I should know about?

• Clinical trials are not allowed—only research on theory/model/method development.

• Proposed work must go beyond simple data collection and focus on quantitative theories or analytical tools.

• Foreign subawards are not permitted; collaborations must be unfunded or through other compliant mechanisms.

How long will it take me to prepare an application?

Plan 4-5 months minimum for:

• Concept development

• Budget preparation

• Letters of support and team coordination

• Registering with Grants.gov and eRA Commons (if not already completed)

NIH registration processes can take 6+ weeks, so start early.

How can BW&CO help?

BW&CO can assist with:

• Translating your science aims into NIH-ready specific aims.

• Coordinating NIH format and submission requirements.

• Aligning proposal with BRAIN Initiative priorities.

How much would BW&CO Charge?

We have both fractional engagements ($250 an hour) and full engagements ($13,000 + 5%) available.

Additional Resources

Review the solicitation here.