Draft — Forecast to Publish a Funding Opportunity Announcement for Copy of Integrative Neuroscience Initiative on Alcoholism (INIA) Consortia Research Resource (U24) (Clinical Trial Optional)

MOTIVATION LETTER

The Integrative Neuroscience Initiative on Alcoholism (INIA) consortia have spent two decades building the foundational datasets and multi-scale models that define modern alcohol research. My independent work on the Conjunctive Consolidation Threshold (CCT) model addresses a gap these consortia have not yet filled: a formal, pharmacologically grounded framework for predicting when reward-memory encoding crosses a consolidation threshold and how to prevent that encoding pharmacologically. I am writing to offer my CCT model, my computational platforms IMPRINT and TOPOLOGIX, and my Bayesian population dynamics architecture as a novel analytical resource that could be integrated into an existing INIA consortium research resource (U24) application.

I am a 29-year-old Nigerian independent researcher with a B.Pharm from the University of Ibadan (CGPA 5.1/7.0, German equivalent 1.9) and a PCN-licensed pharmacist. Since 2025, I have produced three sole-authored preprints on the CCT framework, hosted on OSF and Zenodo with DOIs. The foundational paper (OSF 10.17605/OSF.IO/KG7B5) defines the tripartite model. The formal mathematical specification (OSF 10.17605/OSF.IO/EMY4U) provides the ODE/RK45 system. The Bayesian population dynamics and clinical trial architecture paper (Zenodo 10.5281/zenodo.20492472) demonstrates that the model reduces encoding probability from 0.855 to 0.122, an 85.8% reduction, with super-additivity of +12.8 percentage points. All five pre-registered hypotheses H1 through H5 were confirmed. A review article is under review at Neuroscience and Biobehavioral Reviews. A co-authored paper is under review at Alcohol (Elsevier).

I have built three open-source computational platforms. IMPRINT screens addiction liability using the CCT framework. TOPOLOGIX applies topological data analysis, persistent homology, and bipartite simplicial complexes to drug-protein interaction networks, with a validated MVP for hERG cardiotoxicity screening. GATE evaluates BCI neural-stimulation safety under Apache 2.0. My technical stack includes Python (scipy, numpy, ODE/RK45, PyMC/MCMC, pandas), R, TDA libraries (Ripser, Gudhi), NEURON/Brian2, AlphaFold, RDKit, ADMET/QSAR, GROMACS, AutoDock, and Nextflow/SLURM/HPC. I maintain a Supabase/Postgres backend and JavaScript/Node.js frontend for IMPRINT.

I have secured endorsements from Kent Berridge at the University of Michigan, Samuel Gershman at Harvard (who provided my arXiv endorsement), Nathaniel Daw at Princeton, and Marcelo Mattar at NYU. A provisional patent on the CCT core architecture is scheduled for Q3 2026.

I am not eligible to lead a U24 application because I am not affiliated with a U.S. institution. I am seeking a U.S.-based INIA principal investigator who would include me as a foreign collaborator or subcontractor. My CCT model and computational tools could serve as a novel analytical resource for the INIA consortia, providing a formal framework for predicting and preventing reward-memory encoding in alcohol use disorder. I can contribute Bayesian population dynamics simulations, TDA-based drug-protein interaction screening, and addiction liability profiling using IMPRINT. I am available for virtual collaboration and can travel to U.S. partner institutions.

I am currently applying for MSc programs starting October 2026 at the Medical University of Graz and the University of Graz, Austria. I am eligible for early-career, pre-PhD, LMIC-track, and independent researcher funding mechanisms.

RESEARCH STATEMENT

The INIA consortia have generated extensive multi-omics, electrophysiological, and behavioral datasets on alcohol use disorder. What these datasets lack is a unified, mathematically specified framework that predicts the precise moment when a reward memory becomes consolidated and identifies the pharmacological intervention that can prevent that consolidation. My CCT model fills this gap.

The CCT model posits that reward-memory encoding proceeds through three conjunctive phases: a dopaminergic salience signal, a glutamatergic plasticity gate, and a cholinergic consolidation trigger. Each phase has a distinct threshold. Encoding occurs only when all three thresholds are crossed simultaneously. The model is specified as a system of ordinary differential equations solved via RK45 integration. I validated the model using Bayesian Markov Chain Monte Carlo methods with PyMC, fitting the ODE system to simulated population data. The results: encoding probability dropped from 0.855 to 0.122, an 85.8% reduction. The combination of three pharmacological agents targeting each phase produced super-additivity of +12.8 percentage points, meaning the triple combination outperformed the sum of individual effects. All five pre-registered hypotheses were confirmed.

For the INIA consortia, the CCT model offers three specific resources. First, it provides a formal mathematical framework that can be fitted to existing INIA datasets to estimate threshold parameters for different alcohol exposure paradigms. Second, it generates testable predictions about which pharmacological combinations will prevent reward-memory encoding in alcohol-seeking behavior. Third, it includes a Bayesian clinical trial architecture that can optimize dose-finding and sample size for future INIA clinical studies.

My computational platform IMPRINT operationalizes the CCT model as a screening tool for addiction liability. Given a compound's pharmacological profile, IMPRINT predicts whether that compound will cross the consolidation threshold and assigns a liability score. TOPOLOGIX applies topological data analysis to drug-protein interaction networks. Using persistent homology and bipartite simplicial complexes, TOPOLOGIX identifies structural features in protein binding pockets that predict off-target effects. The hERG cardiotoxicity MVP achieved an AUROC of 0.634, which I am improving through feature engineering and expanded training data. GATE evaluates neural-stimulation safety for brain-computer interfaces.

I propose to integrate these resources into an existing INIA U24 research resource. Specifically, I would contribute: (1) a CCT model fitting pipeline that takes INIA behavioral and pharmacological data as input and outputs threshold parameters and encoding probability estimates; (2) a TOPOLOGIX-based drug-protein interaction screening module that identifies compounds likely to interact with alcohol-related targets; (3) an IMPRINT-based addiction liability profiling tool that ranks compounds by their predicted reward-memory encoding risk. All code is open-source under Apache 2.0 and can be containerized for deployment on INIA's HPC infrastructure.

I have endorsements from four leading computational neuroscientists. Kent Berridge studies reward motivation and has reviewed the CCT framework. Samuel Gershman provided my arXiv endorsement. Nathaniel Daw and Marcelo Mattar have offered feedback on the Bayesian population dynamics. A provisional patent on the CCT core architecture is scheduled for Q3 2026.

My career trajectory is to establish an independent computational neuroscience and pharmacology research group focused on addiction mechanisms, based in Nigeria but collaborating globally. The INIA consortia offer the ideal collaborative environment to validate and deploy the CCT model against real-world alcohol use disorder data. I am seeking a U.S.-based INIA PI who will include me as a foreign collaborator on a U24 application.

BUDGET NARRATIVE

As a foreign collaborator on a U24 research resource grant, I request support for the following activities over a two-year period.

Personnel: 0.25 FTE for myself as the independent researcher leading the CCT model integration and computational platform development. This covers my time to adapt the CCT ODE/RK45 system to INIA datasets, build the TOPOLOGIX drug-protein interaction screening module, and deploy IMPRINT as a web-accessible screening tool. Salary request: $25,000 per year for two years, totaling $50,000.

Equipment: One high-performance computing workstation for running Bayesian MCMC simulations and TDA computations. Estimated cost: $5,000. Cloud computing credits for GPU-accelerated AlphaFold and GROMACS simulations: $3,000 per year for two years, totaling $6,000.

Travel: Two trips per year to INIA consortium meetings and partner institutions for in-person collaboration and data integration workshops. Estimated cost: $4,000 per trip, eight trips over two years, totaling $32,000. This includes airfare from Lagos, Nigeria, accommodation, and per diem.

Materials and supplies: Open-access publication fees for two papers describing the CCT model integration with INIA data. Estimated cost: $4,000. Software licenses for RDKit and PyMC: $1,000 per year for two years, totaling $2,000.

Total direct costs: $99,000 over two years. Indirect costs at the foreign institution rate of 8%: $7,920. Total requested: $106,920.

This budget is modest relative to the U24 mechanism and reflects the low-overhead nature of my independent research operation. I have no institutional salary support and currently work as a National Product Manager at Synthcare in Lagos. The requested funds would allow me to dedicate significant time to the INIA collaboration while maintaining my independent research program.

CHECKLIST

- [ ] Confirm that the U24 FOA explicitly allows foreign collaborators or subcontractors. If not, identify an alternative mechanism such as an R01 with a foreign component.
- [ ] Identify a U.S.-based INIA PI who is willing to include me as a foreign collaborator. Contact Kent Berridge (Michigan), Samuel Gershman (Harvard), Nathaniel Daw (Princeton), or Marcelo Mattar (NYU) for introductions.
- [ ] Prepare a one-page letter of collaboration from the U.S. PI describing the scope of work and resource integration plan.
- [ ] Update my ORCID profile (0009-0001-9272-6735) with all three preprint DOIs and the review article under review.
- [ ] Update my GitHub repository (github.com/AmunRaPtah) with documentation for IMPRINT, TOPOLOGIX, and GATE, including installation instructions and example notebooks.
- [ ] Prepare a two-page CV formatted to NIH biosketch standards, including publications, preprints, computational platforms, and endorsements.
- [ ] Obtain a letter of support from a Nigerian institution or research center to demonstrate local infrastructure and commitment to LMIC-track research.
- [ ] Verify the deadline on the grants.gov page (URL: https://www.grants.gov/search-results-detail/358826) and confirm that the FOA has been published.
- [ ] Prepare a data management and sharing plan compliant with NIH policy, describing how CCT model code, TOPOLOGIX outputs, and IMPRINT screening results will be shared through Zenodo and GitHub.
- [ ] Prepare a training plan describing how I will build capacity in computational neuroscience and pharmacology in Nigeria through workshops and open-source contributions.

EDITOR NOTES

- Eligibility risk: The U24 mechanism is restricted to U.S. institutions. The applicant cannot apply as a lead. The strategy of seeking a U.S. PI collaborator is correct, but the applicant must confirm that the specific FOA allows foreign subcontractors. If not, the applicant should target an R01 with a foreign component or a different mechanism entirely.
- Verification needed: The AUROC of 0.634 for the hERG cardiotoxicity MVP is low for a production tool. The applicant should either explain that this is an early-stage MVP and describe planned improvements, or omit the specific number and describe the methodology instead.
- Gap in profile: The applicant has no published peer-reviewed papers yet. The review article under review at Neuroscience and Biobehavioral Reviews and the co-authored paper under review at Alcohol are both in review, not accepted. The applicant should emphasize the preprint DOIs and the confirmed pre-registered hypotheses as evidence of rigor, but should be transparent about the publication status.
- Gap in profile: The applicant has no MSc or PhD. The U24 mechanism typically funds established investigators. The applicant should emphasize the endorsements from senior researchers (Berridge, Gershman, Daw, Mattar) as evidence of credibility and mentorship support.
- Missing detail: The applicant should specify which INIA consortium (e.g., INIA-Stress, INIA-Neuroimmune, INIA-South) is the best fit for the CCT model. The applicant should research the specific INIA consortia and name one or two in the application materials.

MOTIVATION LETTER

The Integrative Neuroscience Initiative on Alcoholism (INIA) consortia have spent two decades building the foundational datasets and multi-scale models that define modern alcohol research. My independent work on the Conjunctive Consolidation Threshold (CCT) model addresses a gap these consortia have not yet filled: a formal, pharmacologically grounded framework for predicting when reward-memory encoding crosses a consolidation threshold and how to prevent that encoding pharmacologically. I am writing to offer my CCT model, my computational platforms IMPRINT and TOPOLOGIX, and my Bayesian population dynamics architecture as a novel analytical resource that could be integrated into an existing INIA consortium research resource (U24) application.

I am a 29-year-old Nigerian independent researcher with a B.Pharm from the University of Ibadan (CGPA 5.1/7.0, German equivalent 1.9) and a PCN-licensed pharmacist. Since 2025, I have produced three sole-authored preprints on the CCT framework, hosted on OSF and Zenodo with DOIs. The foundational paper (OSF 10.17605/OSF.IO/KG7B5) defines the tripartite model. The formal mathematical specification (OSF 10.17605/OSF.IO/EMY4U) provides the ODE/RK45 system. The Bayesian population dynamics and clinical trial architecture paper (Zenodo 10.5281/zenodo.20492472) demonstrates that the model reduces encoding probability from 0.855 to 0.122, an 85.8% reduction, with super-additivity of +12.8 percentage points. All five pre-registered hypotheses H1 through H5 were confirmed. A review article is under review at Neuroscience and Biobehavioral Reviews. A co-authored paper is under review at Alcohol (Elsevier).

I have built three open-source computational platforms. IMPRINT screens addiction liability using the CCT framework. TOPOLOGIX applies topological data analysis, persistent homology, and bipartite simplicial complexes to drug-protein interaction networks, with a validated MVP for hERG cardiotoxicity screening. GATE evaluates BCI neural-stimulation safety under Apache 2.0. My technical stack includes Python (scipy, numpy, ODE/RK45, PyMC/MCMC, pandas), R, TDA libraries (Ripser, Gudhi), NEURON/Brian2, AlphaFold, RDKit, ADMET/QSAR, GROMACS, AutoDock, and Nextflow/SLURM/HPC. I maintain a Supabase/Postgres backend and JavaScript/Node.js frontend for IMPRINT.

I have secured endorsements from Kent Berridge at the University of Michigan, Samuel Gershman at Harvard (who provided my arXiv endorsement), Nathaniel Daw at Princeton, and Marcelo Mattar at NYU. A provisional patent on the CCT core architecture is scheduled for Q3 2026.

I am not eligible to lead a U24 application because I am not affiliated with a U.S. institution. I am seeking a U.S.-based INIA principal investigator who would include me as a foreign collaborator or subcontractor. My CCT model and computational tools could serve as a novel analytical resource for the INIA consortia, providing a formal framework for predicting and preventing reward-memory encoding in alcohol use disorder. I can contribute Bayesian population dynamics simulations, TDA-based drug-protein interaction screening, and addiction liability profiling using IMPRINT. I am available for virtual collaboration and can travel to U.S. partner institutions.

I am currently applying for MSc programs starting October 2026 at the Medical University of Graz and the University of Graz, Austria. I am eligible for early-career, pre-PhD, LMIC-track, and independent researcher funding mechanisms.

RESEARCH STATEMENT

The INIA consortia have generated extensive multi-omics, electrophysiological, and behavioral datasets on alcohol use disorder. What these datasets lack is a unified, mathematically specified framework that predicts the precise moment when a reward memory becomes consolidated and identifies the pharmacological intervention that can prevent that consolidation. My CCT model fills this gap.

The CCT model posits that reward-memory encoding proceeds through three conjunctive phases: a dopaminergic salience signal, a glutamatergic plasticity gate, and a cholinergic consolidation trigger. Each phase has a distinct threshold. Encoding occurs only when all three thresholds are crossed simultaneously. The model is specified as a system of ordinary differential equations solved via RK45 integration. I validated the model using Bayesian Markov Chain Monte Carlo methods with PyMC, fitting the ODE system to simulated population data. The results: encoding probability dropped from 0.855 to 0.122, an 85.8% reduction. The combination of three pharmacological agents targeting each phase produced super-additivity of +12.8 percentage points, meaning the triple combination outperformed the sum of individual effects. All five pre-registered hypotheses were confirmed.

For the INIA consortia, the CCT model offers three specific resources. First, it provides a formal mathematical framework that can be fitted to existing INIA datasets to estimate threshold parameters for different alcohol exposure paradigms. Second, it generates testable predictions about which pharmacological combinations will prevent reward-memory encoding in alcohol-seeking behavior. Third, it includes a Bayesian clinical trial architecture that can optimize dose-finding and sample size for future INIA clinical studies.

My computational platform IMPRINT operationalizes the CCT model as a screening tool for addiction liability. Given a compound's pharmacological profile, IMPRINT predicts whether that compound will cross the consolidation threshold and assigns a liability score. TOPOLOGIX applies topological data analysis to drug-protein interaction networks. Using persistent homology and bipartite simplicial complexes, TOPOLOGIX identifies structural features in protein binding pockets that predict off-target effects. The hERG cardiotoxicity MVP achieved an AUROC of 0.634, which I am improving through feature engineering and expanded training data. GATE evaluates neural-stimulation safety for brain-computer interfaces.

I propose to integrate these resources into an existing INIA U24 research resource. Specifically, I would contribute: (1) a CCT model fitting pipeline that takes INIA behavioral and pharmacological data as input and outputs threshold parameters and encoding probability estimates; (2) a TOPOLOGIX-based drug-protein interaction screening module that identifies compounds likely to interact with alcohol-related targets; (3) an IMPRINT-based addiction liability profiling tool that ranks compounds by their predicted reward-memory encoding risk. All code is open-source under Apache 2.0 and can be containerized for deployment on INIA's HPC infrastructure.

I have endorsements from four leading computational neuroscientists. Kent Berridge studies reward motivation and has reviewed the CCT framework. Samuel Gershman provided my arXiv endorsement. Nathaniel Daw and Marcelo Mattar have offered feedback on the Bayesian population dynamics. A provisional patent on the CCT core architecture is scheduled for Q3 2026.

My career trajectory is to establish an independent computational neuroscience and pharmacology research group focused on addiction mechanisms, based in Nigeria but collaborating globally. The INIA consortia offer the ideal collaborative environment to validate and deploy the CCT model against real-world alcohol use disorder data. I am seeking a U.S.-based INIA PI who will include me as a foreign collaborator on a U24 application.

BUDGET NARRATIVE

As a foreign collaborator on a U24 research resource grant, I request support for the following activities over a two-year period.

Personnel: 0.25 FTE for myself as the independent researcher leading the CCT model integration and computational platform development. This covers my time to adapt the CCT ODE/RK45 system to INIA datasets, build the TOPOLOGIX drug-protein interaction screening module, and deploy IMPRINT as a web-accessible screening tool. Salary request: $25,000 per year for two years, totaling $50,000.

Equipment: One high-performance computing workstation for running Bayesian MCMC simulations and TDA computations. Estimated cost: $5,000. Cloud computing credits for GPU-accelerated AlphaFold and GROMACS simulations: $3,000 per year for two years, totaling $6,000.

Travel: Two trips per year to INIA consortium meetings and partner institutions for in-person collaboration and data integration workshops. Estimated cost: $4,000 per trip, eight trips over two years, totaling $32,000. This includes airfare from Lagos, Nigeria, accommodation, and per diem.

Materials and supplies: Open-access publication fees for two papers describing the CCT model integration with INIA data. Estimated cost: $4,000. Software licenses for RDKit and PyMC: $1,000 per year for two years, totaling $2,000.

Total direct costs: $99,000 over two years. Indirect costs at the foreign institution rate of 8%: $7,920. Total requested: $106,920.

This budget is modest relative to the U24 mechanism and reflects the low-overhead nature of my independent research operation. I have no institutional salary support and currently work as a National Product Manager at Synthcare in Lagos. The requested funds would allow me to dedicate significant time to the INIA collaboration while maintaining my independent research program.

CHECKLIST

- [ ] Confirm that the U24 FOA explicitly allows foreign collaborators or subcontractors. If not, identify an alternative mechanism such as an R01 with a foreign component.
- [ ] Identify a U.S.-based INIA PI who is willing to include me as a foreign collaborator. Contact Kent Berridge (Michigan), Samuel Gershman (Harvard), Nathaniel Daw (Princeton), or Marcelo Mattar (NYU) for introductions.
- [ ] Prepare a one-page letter of collaboration from the U.S. PI describing the scope of work and resource integration plan.
- [ ] Update my ORCID profile (0009-0001-9272-6735) with all three preprint DOIs and the review article under review.
- [ ] Update my GitHub repository (github.com/AmunRaPtah) with documentation for IMPRINT, TOPOLOGIX, and GATE, including installation instructions and example notebooks.
- [ ] Prepare a two-page CV formatted to NIH biosketch standards, including publications, preprints, computational platforms, and endorsements.
- [ ] Obtain a letter of support from a Nigerian institution or research center to demonstrate local infrastructure and commitment to LMIC-track research.
- [ ] Verify the deadline on the grants.gov page (URL: https://www.grants.gov/search-results-detail/358826) and confirm that the FOA has been published.
- [ ] Prepare a data management and sharing plan compliant with NIH policy, describing how CCT model code, TOPOLOGIX outputs, and IMPRINT screening results will be shared through Zenodo and GitHub.
- [ ] Prepare a training plan describing how I will build capacity in computational neuroscience and pharmacology in Nigeria through workshops and open-source contributions.

EDITOR NOTES

- Eligibility risk: The U24 mechanism is restricted to U.S. institutions. The applicant cannot apply as a lead. The strategy of seeking a U.S. PI collaborator is correct, but the applicant must confirm that the specific FOA allows foreign subcontractors. If not, the applicant should target an R01 with a foreign component or a different mechanism entirely.
- Verification needed: The AUROC of 0.634 for the hERG cardiotoxicity MVP is low for a production tool. The applicant should either explain that this is an early-stage MVP and describe planned improvements, or omit the specific number and describe the methodology instead.
- Gap in profile: The applicant has no published peer-reviewed papers yet. The review article under review at Neuroscience and Biobehavioral Reviews and the co-authored paper under review at Alcohol are both in review, not accepted. The applicant should emphasize the preprint DOIs and the confirmed pre-registered hypotheses as evidence of rigor, but should be transparent about the publication status.
- Gap in profile: The applicant has no MSc or PhD. The U24 mechanism typically funds established investigators. The applicant should emphasize the endorsements from senior researchers (Berridge, Gershman, Daw, Mattar) as evidence of credibility and mentorship support.
- Missing detail: The applicant should specify which INIA consortium (e.g., INIA-Stress, INIA-Neuroimmune, INIA-South) is the best fit for the CCT model. The applicant should research the specific INIA consortia and name one or two in the application materials.

AI Draft — Forecast to Publish a Funding Opportunity Announcement for Copy of Integrative Neuroscience Initiative on Alcoholism (INIA) Consortia Research Resource (U24) (Clinical Trial Optional)