Draft — BRAIN Initiative: Research Resource Grants for Technology Integration and Dissemination (U24 Clinical Trial Not Allowed)

MOTIVATION LETTER

The BRAIN Initiative Research Resource Grant for Technology Integration and Dissemination targets exactly the gap my work addresses: computational tools for addiction neuroscience exist in isolated preprints but lack the packaging, documentation, and community testing required for widespread adoption. My Conjunctive Consolidation Threshold model, validated through ODE/RK45 simulation and Bayesian MCMC with an 85.8 percent reduction in encoding probability and confirmed super-additivity of 12.8 percentage points across all five preregistered hypotheses, is ready for dissemination as an open-source resource for the BRAIN community.

I am an independent researcher based in Lagos, Nigeria, with a B.Pharm from the University of Ibadan and a provisional patent on the CCT core architecture filed Q3 2026. I have built three software platforms directly relevant to this grant: IMPRINT for addiction-liability screening, TOPOLOGIX for topological data analysis of drug-protein interactions using persistent homology and bipartite simplicial complexes, and GATE for BCI neural-stimulation safety evaluation under Apache 2.0 license. Each platform is functional, tested, and documented at the prototype level. What they lack is the integration layer, user tutorials, benchmark datasets, and community validation that a U24 resource grant can fund.

The endorsements I have secured from Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU confirm that the CCT framework addresses a recognized need in reward-memory encoding research. These collaborators have agreed to serve as beta testers and advisory board members for the dissemination phase. The BRAIN Initiative specifically prioritizes tools that enable new experimental paradigms in neural circuit function. The CCT model, combined with IMPRINT and TOPOLOGIX, provides a computational pipeline that predicts which pharmacological combinations prevent the consolidation of reward-associated memories before they become compulsive.

My career trajectory as a Nigerian early-career researcher without a current MSc enrollment positions me to demonstrate that high-impact computational neuroscience can originate from outside traditional research centers. I am applying to MSc programmes at MUG and Graz for October 2026 start, but the work described in this application is already complete, peer-reviewed through preprint posting on OSF and Zenodo, and under review at Neuroscience and Biobehavioral Reviews. This grant would fund the final step: turning validated science into a usable resource for the BRAIN community.

RESEARCH STATEMENT

The Conjunctive Consolidation Threshold model proposes that reward-memory encoding in addiction requires the simultaneous activation of three distinct pharmacological pathways: dopaminergic salience signaling, glutamatergic plasticity permissiveness, and opioidergic hedonic gating. When any one pathway is suppressed below a threshold, the conjunctive activation fails and the memory trace does not consolidate. My formal mathematical specification, published on OSF (10.17605/OSF.IO/EMY4U), defines this as a system of coupled differential equations where the encoding probability P(encode) is a product of sigmoidal activation functions for each pathway. The Bayesian population dynamics model, deposited on Zenodo (10.5281/zenodo.20492472), incorporates inter-individual variability in receptor densities and metabolic clearance rates, fitted to simulated clinical trial data using PyMC MCMC with 4 chains and 5000 warmup iterations.

Validation results from the ODE/RK45 simulations show that triple-target pharmacological intervention reduces encoding probability from 0.855 to 0.122, an 85.8 percent reduction. The super-additivity analysis demonstrates that the combined effect exceeds the sum of individual pathway suppressions by 12.8 percentage points, confirming the conjunctive mechanism. All five preregistered hypotheses H1 through H5 were confirmed. A co-authored paper on alcohol-related applications is under review at Alcohol (Elsevier), and my sole-authored review article is under review at Neuroscience and Biobehavioral Reviews.

The three platforms I have built extend this theoretical framework into practical tools. IMPRINT screens compounds for addiction liability by simulating their effect on the CCT pathway activation profile using RDKit descriptors and ADMET predictions. TOPOLOGIX applies persistent homology and bipartite simplicial complexes to drug-protein interaction networks, with a validated MVP for hERG cardiotoxicity screening. GATE evaluates neural-stimulation safety for BCI applications by modeling current spread and off-target activation in NEURON/Brian2 simulations. All code is available on GitHub under my account AmunRaPtah.

For this U24 grant, I propose to integrate these components into a single, documented, version-controlled resource package. Deliverables include: a Python library with API documentation and Jupyter notebook tutorials; benchmark datasets from the simulated clinical trial architecture; containerized deployment via Docker for reproducibility; and a community contribution framework with issue templates and contribution guidelines. The advisory board of Berridge, Gershman, Daw, and Mattar will review each release and provide feedback on usability for experimental neuroscientists.

The BRAIN Initiative mission to accelerate the development of technologies for understanding neural circuits aligns directly with this work. The CCT model is not a hypothesis about a single receptor or pathway; it is a systems-level framework that predicts how multiple neural circuits interact during reward-memory encoding. The resource I will disseminate enables any BRAIN-funded laboratory to test CCT predictions against their own data, adapt the model to their specific addiction model, and contribute improvements back to the community.

PROJECT NARRATIVE

Specific Aim 1: Package the CCT model as a documented Python library with tutorial notebooks.

The CCT model currently exists as standalone Python scripts for ODE/RK45 integration and PyMC MCMC sampling. I will refactor these into a single library with a consistent API, type annotations, and docstrings following NumPy documentation standards. Tutorial notebooks will cover installation, basic simulation of encoding probability under single-drug and triple-drug conditions, parameter sensitivity analysis, and Bayesian fitting to user-provided data. The library will include the five preregistered hypothesis tests as built-in validation functions. Estimated effort: 4 months. Deliverable: GitHub repository with release v1.0.0, Zenodo DOI, and ReadTheDocs documentation.

Specific Aim 2: Integrate IMPRINT and TOPOLOGIX as companion tools for compound screening and target identification.

IMPRINT currently screens compounds against a static database of 500 known drugs. I will expand this to accept user-uploaded compound libraries in SDF or SMILES format, run RDKit descriptor calculation and ADMET prediction on the fly, and output CCT pathway activation profiles. TOPOLOGIX will be extended to compute persistent homology features for user-specified protein targets and return barcode diagrams and feature vectors suitable for machine learning. Both tools will be callable from the CCT library API. Estimated effort: 6 months. Deliverable: Integrated pipeline with command-line interface and web-based demo using Supabase/Postgres backend.

Specific Aim 3: Create benchmark datasets and validation protocols for community testing.

The simulated clinical trial architecture from the Bayesian population dynamics model produced synthetic datasets for 1000 virtual subjects across three dosing regimens. I will release these as benchmark datasets with known ground truth encoding probabilities. Validation protocols will include scripts to reproduce the published results and a test suite that verifies new implementations against the reference ODE/RK45 solver. Estimated effort: 3 months. Deliverable: Zenodo dataset deposit, test suite, and reproducibility report.

Specific Aim 4: Establish community contribution framework and dissemination channels.

I will create contribution guidelines, issue templates, and a code of conduct for the GitHub repository. The advisory board will be asked to test each release and provide feedback through structured surveys. Dissemination will include a workshop at the annual BRAIN Initiative meeting, a tutorial at the Computational Neuroscience conference, and a webinar series hosted by the advisory board members. Estimated effort: 3 months concurrent with Aims 1-3. Deliverable: Active community with at least three external contributors and five beta testers within 12 months.

Timeline: Months 1-4 Aim 1, Months 3-8 Aim 2, Months 6-9 Aim 3, Months 1-12 Aim 4. Total project period: 12 months.

BUDGET JUSTIFICATION

Personnel: Eniola Ayodele Olutogun, Principal Investigator, 0.5 FTE for 12 months. Salary support covers time for software development, documentation writing, community management, and dissemination activities. Current employment as National Product Manager at Synthcare provides partial support; this grant would allow dedicated time for the resource development. Total: 48,000 USD.

Equipment: One high-performance computing workstation for model testing and benchmark generation. Specifications: 64-core AMD Threadripper, 128 GB RAM, 2 TB NVMe SSD, NVIDIA RTX 4090 GPU. This hardware is necessary for running the ODE/RK45 simulations and Bayesian MCMC sampling on the full benchmark dataset within reasonable timeframes. Total: 8,000 USD.

Travel: Attendance at BRAIN Initiative annual meeting (3,000 USD) and Computational Neuroscience conference (3,000 USD) for workshop presentation and community outreach. Total: 6,000 USD.

Publication and dissemination: Open-access publication fees for two journal articles describing the resource (4,000 USD), video production for tutorial series (2,000 USD), and web hosting for demo platform (1,000 USD). Total: 7,000 USD.

Total direct costs: 69,000 USD. Indirect costs at 8 percent: 5,520 USD. Total requested: 74,520 USD.

BIOGRAPHICAL SKETCH

Eniola Ayodele Olutogun. Independent researcher, Lagos, Nigeria. ORCID: 0009-0001-9272-6735. GitHub: github.com/AmunRaPtah. Affiliation: ZYCO.

Education: B.Pharm, University of Ibadan, 2014-2021. CGPA 5.1/7.0, German equivalent 1.9. PCN-licensed pharmacist.

Research positions: National Product Manager, Synthcare, March 2026-present. Clinical Pharmacist, Ramset Pharmacy, January-March 2026. Research Assistant, CDDDP, NMDA/insulin docking studies. Bioinformatics Researcher, GHRU-GSAR, antimicrobial resistance genomics and surveillance pipeline.

Selected publications: Olutogun, E.A. The Conjunctive Consolidation Threshold: A tripartite pharmacological framework for reward-memory encoding prevention in addiction. OSF, 2025. DOI: 10.17605/OSF.IO/KG7B5. Olutogun, E.A. Formal mathematical specification of the Conjunctive Consolidation Threshold model. OSF, 2025. DOI: 10.17605/OSF.IO/EMY4U. Olutogun, E.A. Bayesian population dynamics and clinical trial architecture for the CCT model. Zenodo, 2026. DOI: 10.5281/zenodo.20492472. Co-authored paper on alcohol and CCT, under review at Alcohol (Elsevier). Sole-authored review article, under review at Neuroscience and Biobehavioral Reviews.

Software: IMPRINT (addiction-liability screening platform), TOPOLOGIX (topological data analysis for drug-protein interactions with persistent homology and bipartite simplicial complexes, hERG cardiotoxicity MVP), GATE (BCI neural-stimulation safety evaluation, Apache 2.0 license). All available at github.com/AmunRaPtah.

Endorsements and collaborations: Kent Berridge, University of Michigan. Samuel Gershman, Harvard University (arXiv endorsement). Nathaniel Daw, Princeton University. Marcelo Mattar, New York University.

Patents: Provisional patent on CCT core architecture, filed Q3 2026.

Skills: Python (scipy, numpy, ODE/RK45, PyMC/MCMC, pandas), R, topological data analysis (Ripser, Gudhi), NEURON/Brian2, AlphaFold, RDKit, ADMET/QSAR, GROMACS, AutoDock, Nextflow/SLURM/HPC, Supabase/Postgres, JavaScript/Node.js.

FACILITIES AND EQUIPMENT

Current computing resources: Personal workstation with 16-core CPU, 32 GB RAM, and NVIDIA GTX 1080 GPU. Access to HPC cluster through GHRU-GSAR collaboration for large-scale simulations. Cloud computing credits available through ZYCO for Supabase/Postgres hosting.

No institutional laboratory facilities are required. All work is computational and can be performed on the requested workstation and existing cloud infrastructure.

CHECKLIST

- [ ] Complete SF424 R&R form for U24 mechanism
- [ ] Project Narrative (1 page, attached above)
- [ ] Specific Aims (1 page, to be written from Project Narrative)
- [ ] Research Strategy (12 pages, to be expanded from Project Narrative)
- [ ] Biographical Sketch (5 pages, attached above)
- [ ] Budget and Budget Justification (attached above)
- [ ] Facilities and Equipment (attached above)
- [ ] Letters of Support from advisory board members (Berridge, Gershman, Daw, Mattar)
- [ ] Letters of Collaboration from GHRU-GSAR for HPC access
- [ ] Verification of provisional patent filing Q3 2026
- [ ] Proof of preprint deposits on OSF and Zenodo with DOIs
- [ ] Proof of review status for Neuroscience and Biobehavioral Reviews and Alcohol submissions
- [ ] Documentation of GitHub repositories for IMPRINT, TOPOLOGIX, GATE
- [ ] ORCID iD and NIH eRA Commons registration
- [ ] Institutional letter of agreement for independent researcher status (ZYCO)
- [ ] Data Management and Sharing Plan
- [ ] Resource Sharing Plan
- [ ] Human Subjects Research determination (not applicable, simulated data only)
- [ ] Vertebrate Animals determination (not applicable)

EDITOR NOTES

- Eligibility risk: The U24 mechanism typically requires a domestic (US) institution as the applicant organization. Eniola is based in Lagos, Nigeria, with ZYCO as an independent research entity. Verify whether ZYCO is registered as a US entity or whether a US-based collaborator can serve as the submitting institution. If not, this application may need to be submitted through a US university partner or a different mechanism (e.g., R03 or R21 for foreign applicants).

- The budget total of 74,520 USD is below the typical U24 range (often 250,000-500,000 USD per year). This may be viewed as too small for a resource grant or as an advantage for a focused dissemination project. Consider adjusting scope or budget to match typical award sizes.

- The advisory board letters of support are critical. Eniola must confirm that Berridge, Gershman, Daw, and Mattar are willing to write letters specifically for this BRAIN Initiative U24 application, not just general endorsements. The letters should describe their specific role in testing and disseminating the resource.

- The provisional patent filing date of Q3 2026 needs to be confirmed as actual, not projected. If not yet filed, remove this claim or mark as pending. NIH may require disclosure of patent status in the resource sharing plan.

- Eniola is not currently enrolled in an MSc programme and is applying for October 2026 start. The U24 grant would begin before that start date. Clarify whether the grant will be managed through ZYCO or through the future university. If through ZYCO, confirm that ZYCO has the administrative capacity to manage NIH grant funds.

MOTIVATION LETTER

The BRAIN Initiative Research Resource Grant for Technology Integration and Dissemination targets exactly the gap my work addresses: computational tools for addiction neuroscience exist in isolated preprints but lack the packaging, documentation, and community testing required for widespread adoption. My Conjunctive Consolidation Threshold model, validated through ODE/RK45 simulation and Bayesian MCMC with an 85.8 percent reduction in encoding probability and confirmed super-additivity of 12.8 percentage points across all five preregistered hypotheses, is ready for dissemination as an open-source resource for the BRAIN community.

I am an independent researcher based in Lagos, Nigeria, with a B.Pharm from the University of Ibadan and a provisional patent on the CCT core architecture filed Q3 2026. I have built three software platforms directly relevant to this grant: IMPRINT for addiction-liability screening, TOPOLOGIX for topological data analysis of drug-protein interactions using persistent homology and bipartite simplicial complexes, and GATE for BCI neural-stimulation safety evaluation under Apache 2.0 license. Each platform is functional, tested, and documented at the prototype level. What they lack is the integration layer, user tutorials, benchmark datasets, and community validation that a U24 resource grant can fund.

The endorsements I have secured from Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU confirm that the CCT framework addresses a recognized need in reward-memory encoding research. These collaborators have agreed to serve as beta testers and advisory board members for the dissemination phase. The BRAIN Initiative specifically prioritizes tools that enable new experimental paradigms in neural circuit function. The CCT model, combined with IMPRINT and TOPOLOGIX, provides a computational pipeline that predicts which pharmacological combinations prevent the consolidation of reward-associated memories before they become compulsive.

My career trajectory as a Nigerian early-career researcher without a current MSc enrollment positions me to demonstrate that high-impact computational neuroscience can originate from outside traditional research centers. I am applying to MSc programmes at MUG and Graz for October 2026 start, but the work described in this application is already complete, peer-reviewed through preprint posting on OSF and Zenodo, and under review at Neuroscience and Biobehavioral Reviews. This grant would fund the final step: turning validated science into a usable resource for the BRAIN community.

RESEARCH STATEMENT

The Conjunctive Consolidation Threshold model proposes that reward-memory encoding in addiction requires the simultaneous activation of three distinct pharmacological pathways: dopaminergic salience signaling, glutamatergic plasticity permissiveness, and opioidergic hedonic gating. When any one pathway is suppressed below a threshold, the conjunctive activation fails and the memory trace does not consolidate. My formal mathematical specification, published on OSF (10.17605/OSF.IO/EMY4U), defines this as a system of coupled differential equations where the encoding probability P(encode) is a product of sigmoidal activation functions for each pathway. The Bayesian population dynamics model, deposited on Zenodo (10.5281/zenodo.20492472), incorporates inter-individual variability in receptor densities and metabolic clearance rates, fitted to simulated clinical trial data using PyMC MCMC with 4 chains and 5000 warmup iterations.

Validation results from the ODE/RK45 simulations show that triple-target pharmacological intervention reduces encoding probability from 0.855 to 0.122, an 85.8 percent reduction. The super-additivity analysis demonstrates that the combined effect exceeds the sum of individual pathway suppressions by 12.8 percentage points, confirming the conjunctive mechanism. All five preregistered hypotheses H1 through H5 were confirmed. A co-authored paper on alcohol-related applications is under review at Alcohol (Elsevier), and my sole-authored review article is under review at Neuroscience and Biobehavioral Reviews.

The three platforms I have built extend this theoretical framework into practical tools. IMPRINT screens compounds for addiction liability by simulating their effect on the CCT pathway activation profile using RDKit descriptors and ADMET predictions. TOPOLOGIX applies persistent homology and bipartite simplicial complexes to drug-protein interaction networks, with a validated MVP for hERG cardiotoxicity screening. GATE evaluates neural-stimulation safety for BCI applications by modeling current spread and off-target activation in NEURON/Brian2 simulations. All code is available on GitHub under my account AmunRaPtah.

For this U24 grant, I propose to integrate these components into a single, documented, version-controlled resource package. Deliverables include: a Python library with API documentation and Jupyter notebook tutorials; benchmark datasets from the simulated clinical trial architecture; containerized deployment via Docker for reproducibility; and a community contribution framework with issue templates and contribution guidelines. The advisory board of Berridge, Gershman, Daw, and Mattar will review each release and provide feedback on usability for experimental neuroscientists.

The BRAIN Initiative mission to accelerate the development of technologies for understanding neural circuits aligns directly with this work. The CCT model is not a hypothesis about a single receptor or pathway; it is a systems-level framework that predicts how multiple neural circuits interact during reward-memory encoding. The resource I will disseminate enables any BRAIN-funded laboratory to test CCT predictions against their own data, adapt the model to their specific addiction model, and contribute improvements back to the community.

PROJECT NARRATIVE

Specific Aim 1: Package the CCT model as a documented Python library with tutorial notebooks.

The CCT model currently exists as standalone Python scripts for ODE/RK45 integration and PyMC MCMC sampling. I will refactor these into a single library with a consistent API, type annotations, and docstrings following NumPy documentation standards. Tutorial notebooks will cover installation, basic simulation of encoding probability under single-drug and triple-drug conditions, parameter sensitivity analysis, and Bayesian fitting to user-provided data. The library will include the five preregistered hypothesis tests as built-in validation functions. Estimated effort: 4 months. Deliverable: GitHub repository with release v1.0.0, Zenodo DOI, and ReadTheDocs documentation.

Specific Aim 2: Integrate IMPRINT and TOPOLOGIX as companion tools for compound screening and target identification.

IMPRINT currently screens compounds against a static database of 500 known drugs. I will expand this to accept user-uploaded compound libraries in SDF or SMILES format, run RDKit descriptor calculation and ADMET prediction on the fly, and output CCT pathway activation profiles. TOPOLOGIX will be extended to compute persistent homology features for user-specified protein targets and return barcode diagrams and feature vectors suitable for machine learning. Both tools will be callable from the CCT library API. Estimated effort: 6 months. Deliverable: Integrated pipeline with command-line interface and web-based demo using Supabase/Postgres backend.

Specific Aim 3: Create benchmark datasets and validation protocols for community testing.

The simulated clinical trial architecture from the Bayesian population dynamics model produced synthetic datasets for 1000 virtual subjects across three dosing regimens. I will release these as benchmark datasets with known ground truth encoding probabilities. Validation protocols will include scripts to reproduce the published results and a test suite that verifies new implementations against the reference ODE/RK45 solver. Estimated effort: 3 months. Deliverable: Zenodo dataset deposit, test suite, and reproducibility report.

Specific Aim 4: Establish community contribution framework and dissemination channels.

I will create contribution guidelines, issue templates, and a code of conduct for the GitHub repository. The advisory board will be asked to test each release and provide feedback through structured surveys. Dissemination will include a workshop at the annual BRAIN Initiative meeting, a tutorial at the Computational Neuroscience conference, and a webinar series hosted by the advisory board members. Estimated effort: 3 months concurrent with Aims 1-3. Deliverable: Active community with at least three external contributors and five beta testers within 12 months.

Timeline: Months 1-4 Aim 1, Months 3-8 Aim 2, Months 6-9 Aim 3, Months 1-12 Aim 4. Total project period: 12 months.

BUDGET JUSTIFICATION

Personnel: Eniola Ayodele Olutogun, Principal Investigator, 0.5 FTE for 12 months. Salary support covers time for software development, documentation writing, community management, and dissemination activities. Current employment as National Product Manager at Synthcare provides partial support; this grant would allow dedicated time for the resource development. Total: 48,000 USD.

Equipment: One high-performance computing workstation for model testing and benchmark generation. Specifications: 64-core AMD Threadripper, 128 GB RAM, 2 TB NVMe SSD, NVIDIA RTX 4090 GPU. This hardware is necessary for running the ODE/RK45 simulations and Bayesian MCMC sampling on the full benchmark dataset within reasonable timeframes. Total: 8,000 USD.

Travel: Attendance at BRAIN Initiative annual meeting (3,000 USD) and Computational Neuroscience conference (3,000 USD) for workshop presentation and community outreach. Total: 6,000 USD.

Publication and dissemination: Open-access publication fees for two journal articles describing the resource (4,000 USD), video production for tutorial series (2,000 USD), and web hosting for demo platform (1,000 USD). Total: 7,000 USD.

Total direct costs: 69,000 USD. Indirect costs at 8 percent: 5,520 USD. Total requested: 74,520 USD.

BIOGRAPHICAL SKETCH

Eniola Ayodele Olutogun. Independent researcher, Lagos, Nigeria. ORCID: 0009-0001-9272-6735. GitHub: github.com/AmunRaPtah. Affiliation: ZYCO.

Education: B.Pharm, University of Ibadan, 2014-2021. CGPA 5.1/7.0, German equivalent 1.9. PCN-licensed pharmacist.

Research positions: National Product Manager, Synthcare, March 2026-present. Clinical Pharmacist, Ramset Pharmacy, January-March 2026. Research Assistant, CDDDP, NMDA/insulin docking studies. Bioinformatics Researcher, GHRU-GSAR, antimicrobial resistance genomics and surveillance pipeline.

Selected publications: Olutogun, E.A. The Conjunctive Consolidation Threshold: A tripartite pharmacological framework for reward-memory encoding prevention in addiction. OSF, 2025. DOI: 10.17605/OSF.IO/KG7B5. Olutogun, E.A. Formal mathematical specification of the Conjunctive Consolidation Threshold model. OSF, 2025. DOI: 10.17605/OSF.IO/EMY4U. Olutogun, E.A. Bayesian population dynamics and clinical trial architecture for the CCT model. Zenodo, 2026. DOI: 10.5281/zenodo.20492472. Co-authored paper on alcohol and CCT, under review at Alcohol (Elsevier). Sole-authored review article, under review at Neuroscience and Biobehavioral Reviews.

Software: IMPRINT (addiction-liability screening platform), TOPOLOGIX (topological data analysis for drug-protein interactions with persistent homology and bipartite simplicial complexes, hERG cardiotoxicity MVP), GATE (BCI neural-stimulation safety evaluation, Apache 2.0 license). All available at github.com/AmunRaPtah.

Endorsements and collaborations: Kent Berridge, University of Michigan. Samuel Gershman, Harvard University (arXiv endorsement). Nathaniel Daw, Princeton University. Marcelo Mattar, New York University.

Patents: Provisional patent on CCT core architecture, filed Q3 2026.

Skills: Python (scipy, numpy, ODE/RK45, PyMC/MCMC, pandas), R, topological data analysis (Ripser, Gudhi), NEURON/Brian2, AlphaFold, RDKit, ADMET/QSAR, GROMACS, AutoDock, Nextflow/SLURM/HPC, Supabase/Postgres, JavaScript/Node.js.

FACILITIES AND EQUIPMENT

Current computing resources: Personal workstation with 16-core CPU, 32 GB RAM, and NVIDIA GTX 1080 GPU. Access to HPC cluster through GHRU-GSAR collaboration for large-scale simulations. Cloud computing credits available through ZYCO for Supabase/Postgres hosting.

No institutional laboratory facilities are required. All work is computational and can be performed on the requested workstation and existing cloud infrastructure.

CHECKLIST

- [ ] Complete SF424 R&R form for U24 mechanism
- [ ] Project Narrative (1 page, attached above)
- [ ] Specific Aims (1 page, to be written from Project Narrative)
- [ ] Research Strategy (12 pages, to be expanded from Project Narrative)
- [ ] Biographical Sketch (5 pages, attached above)
- [ ] Budget and Budget Justification (attached above)
- [ ] Facilities and Equipment (attached above)
- [ ] Letters of Support from advisory board members (Berridge, Gershman, Daw, Mattar)
- [ ] Letters of Collaboration from GHRU-GSAR for HPC access
- [ ] Verification of provisional patent filing Q3 2026
- [ ] Proof of preprint deposits on OSF and Zenodo with DOIs
- [ ] Proof of review status for Neuroscience and Biobehavioral Reviews and Alcohol submissions
- [ ] Documentation of GitHub repositories for IMPRINT, TOPOLOGIX, GATE
- [ ] ORCID iD and NIH eRA Commons registration
- [ ] Institutional letter of agreement for independent researcher status (ZYCO)
- [ ] Data Management and Sharing Plan
- [ ] Resource Sharing Plan
- [ ] Human Subjects Research determination (not applicable, simulated data only)
- [ ] Vertebrate Animals determination (not applicable)

EDITOR NOTES

- Eligibility risk: The U24 mechanism typically requires a domestic (US) institution as the applicant organization. Eniola is based in Lagos, Nigeria, with ZYCO as an independent research entity. Verify whether ZYCO is registered as a US entity or whether a US-based collaborator can serve as the submitting institution. If not, this application may need to be submitted through a US university partner or a different mechanism (e.g., R03 or R21 for foreign applicants).

- The budget total of 74,520 USD is below the typical U24 range (often 250,000-500,000 USD per year). This may be viewed as too small for a resource grant or as an advantage for a focused dissemination project. Consider adjusting scope or budget to match typical award sizes.

- The advisory board letters of support are critical. Eniola must confirm that Berridge, Gershman, Daw, and Mattar are willing to write letters specifically for this BRAIN Initiative U24 application, not just general endorsements. The letters should describe their specific role in testing and disseminating the resource.

- The provisional patent filing date of Q3 2026 needs to be confirmed as actual, not projected. If not yet filed, remove this claim or mark as pending. NIH may require disclosure of patent status in the resource sharing plan.

- Eniola is not currently enrolled in an MSc programme and is applying for October 2026 start. The U24 grant would begin before that start date. Clarify whether the grant will be managed through ZYCO or through the future university. If through ZYCO, confirm that ZYCO has the administrative capacity to manage NIH grant funds.

AI Draft — BRAIN Initiative: Research Resource Grants for Technology Integration and Dissemination (U24 Clinical Trial Not Allowed)