MOTIVATION LETTER The Conjunctive Consolidation Threshold model specifies a tripartite pharmacological mechanism by which reward-memory encoding can be prevented during addiction. Three sole-authored preprints on OSF and Zenodo formalize this framework: a foundational paper (OSF 10.17605/OSF.IO/KG7B5), a mathematical specification (OSF 10.17605/OSF.IO/EMY4U), and a Bayesian population dynamics study with clinical trial architecture (Zenodo 10.5281/zenodo.20492472). ODE/RK45 and Bayesian MCMC validation demonstrated an encoding probability reduction from 0.855 to 0.122, an 85.8 percent reduction, with super-additivity of 12.8 percentage points. All five pre-registered hypotheses H1 through H5 were confirmed. This application to the BRAIN Initiative Research Opportunities Using Invasive Neural Recording and Stimulating Technologies in the Human Brain targets a specific gap: no existing closed-loop neuromodulation protocol uses a pharmacologically grounded, mathematically specified model of reward-memory consolidation to guide stimulation timing and parameters. The CCT model provides exactly that computational architecture. I propose to partner with a U.S. institution, specifically the University of Michigan or Harvard University, where I would serve as a co-investigator providing the computational framework, Bayesian validation pipeline, and closed-loop algorithm design. The lead PI at the partner institution would manage human subjects recruitment, IRB approval, and invasive neural recording and stimulation procedures. My collaborators include Kent Berridge at the University of Michigan, Samuel Gershman at Harvard who endorsed my arXiv submission, Nathaniel Daw at Princeton, and Marcelo Mattar at New York University. These relationships provide direct access to the theoretical neuroscience expertise and human research infrastructure required for this U01 mechanism. A provisional patent on the CCT core architecture is filed for Q3 2026. The CCT model is not a theoretical exercise. It is a deployable computational pharmacology framework that can be implemented in real-time for closed-loop deep brain stimulation or cortical recording paradigms. The Bayesian population dynamics study already specifies the clinical trial architecture, including dosing schedules, stimulation windows, and outcome measures. This application seeks to translate that architecture into a human neural recording and stimulation protocol. I am a Nigerian independent researcher, 29 years old, with a B.Pharm from the University of Ibadan and a German equivalent grade of 1.9. I have built three platforms relevant to this work: IMPRINT for addiction-liability screening, TOPOLOGIX for topological data analysis of drug-protein interactions, and GATE for BCI neural-stimulation safety evaluation under Apache 2.0. My computational skills include Python with ODE/RK45 and PyMC for MCMC, NEURON and Brian2 for neural simulation, and Nextflow for HPC pipeline management. The BRAIN Initiative mission to accelerate the development of neural recording and stimulation technologies aligns directly with the CCT model's requirement for precise, temporally gated intervention in human reward circuitry. This application proposes a concrete, testable computational framework ready for integration with invasive human neuroscience methods. RESEARCH STATEMENT The Conjunctive Consolidation Threshold model addresses a fundamental question in addiction neuroscience: can reward-memory encoding be prevented pharmacologically and neuromodulatorily at the moment of consolidation? The model specifies three concurrent conditions that must be met for a reward-memory to be consolidated: sufficient dopamine D1 receptor activation, sufficient glutamate NMDA receptor activation, and a specific temporal window of beta-gamma oscillatory coherence between the ventral tegmental area and the hippocampus. If any one of these three conditions falls below threshold, consolidation fails and the memory trace is not encoded. The formal mathematical specification (OSF 10.17605/OSF.IO/EMY4U) expresses these conditions as a system of coupled ordinary differential equations with state-dependent thresholds. The Bayesian population dynamics study (Zenodo 10.5281/zenodo.20492472) used Hamiltonian Monte Carlo with 4 chains of 2000 samples each to estimate posterior distributions over the three threshold parameters. The encoding probability under baseline conditions was 0.855. Under the CCT intervention protocol, which combines a sub-anesthetic dose of ketamine for NMDA modulation with a D1 partial agonist and phase-locked hippocampal stimulation, the encoding probability dropped to 0.122. The super-additivity effect of 12.8 percentage points indicates that the three interventions interact non-additively, consistent with the conjunctive threshold mechanism. For the BRAIN Initiative U01 mechanism, I propose to adapt this framework for invasive neural recording and stimulation in human subjects with substance use disorder. The specific aims are: Aim 1: Identify the beta-gamma coherence threshold in human ventral tegmental area and hippocampus during reward-cue presentation using stereotactic EEG recordings in patients undergoing deep brain stimulation for treatment-resistant addiction. Aim 2: Develop a closed-loop algorithm that detects when the three CCT conditions are simultaneously met and triggers a precisely timed stimulation pulse to disrupt consolidation. Aim 3: Test the algorithm in a within-subject, sham-controlled design with 12 participants, measuring cue-induced craving and implicit memory bias as primary outcomes. The Bayesian MCMC pipeline already developed for the CCT model can be directly applied to human neural data. The posterior distributions over threshold parameters from the preclinical study provide informative priors for the human experiments. The ODE/RK45 solver runs in under 50 milliseconds on a standard CPU, making real-time closed-loop implementation feasible. My role in this partnership would be to deliver the computational framework, the Bayesian analysis pipeline, and the closed-loop algorithm code. The lead PI at the partner U.S. institution would handle all human subjects aspects. I have already established the necessary collaborations: Kent Berridge at Michigan has agreed to advise on the dopamine D1 component, and Samuel Gershman at Harvard has endorsed my arXiv submission and offered computational neuroscience guidance. The provisional patent on the CCT core architecture, filed Q3 2026, covers the tripartite threshold detection algorithm and the closed-loop stimulation triggering method. This intellectual property position strengthens the translational potential of the proposal. BUDGET JUSTIFICATION The requested funds support a two-year project period with the following categories: Personnel: 50,000 USD for my salary as a co-investigator at 0.5 FTE. This covers my time to develop the closed-loop algorithm, run the Bayesian analysis pipeline, and coordinate with the U.S. partner institution. No salary is requested for the lead PI, who is supported by their home institution. Equipment: 15,000 USD for a dedicated workstation with GPU for real-time neural data processing and MCMC computation. The ODE solver and Bayesian inference require parallel processing capability that exceeds standard laptop specifications. Travel: 10,000 USD for two visits to the partner U.S. institution per year, including airfare from Lagos, accommodation, and per diem. These visits are essential for hands-on collaboration with the neural recording team and for data analysis sessions. Publication and dissemination: 5,000 USD for open-access publication fees in Neuroscience and Biobehavioral Reviews or equivalent journal, and for conference travel to the Society for Neuroscience annual meeting. Indirect costs: 20,000 USD at the partner institution's negotiated rate. Total direct costs: 80,000 USD. Total indirect costs: 20,000 USD. Total requested: 100,000 USD. This budget is modest for a U01 mechanism because the major infrastructure costs for human neural recording and stimulation are borne by the partner institution through their existing NIH-funded programs. My contribution is the computational framework, which requires minimal equipment beyond a GPU workstation. BIOGRAPHICAL SKETCH Eniola Ayodele Olutogun Independent Researcher, Lagos / ZYCO ORCID: 0009-0001-9272-6735 GitHub: github.com/AmunRaPtah Website: zyco.org Education: B.Pharm, University of Ibadan, 2014-2021. CGPA 5.1/7.0, German equivalent 1.9. PCN-licensed pharmacist. Research Experience: Independent Researcher, ZYCO, Lagos, 2025-2026. Developed the Conjunctive Consolidation Threshold model for reward-memory encoding prevention in addiction. Three sole-authored preprints on OSF and Zenodo. Review article under review at Neuroscience and Biobehavioral Reviews. Co-authored paper in Alcohol (Elsevier, under review). Research Assistant, Center for Drug Discovery, Development and Production (CDDDP), University of Ibadan. Conducted NMDA and insulin receptor docking studies using AutoDock Vina. Bioinformatics Researcher, Ghanaian-Swedish Antimicrobial Resistance Research Group (GHRU-GSAR). Built AMR surveillance pipeline using Nextflow and SLURM on HPC cluster. Employment: National Product Manager, Synthcare, Lagos, March 2026-present. Clinical Pharmacist, Ramset Pharmacy, Lagos, January-March 2026. Selected Platforms Built: IMPRINT: Addiction-liability screening platform. TOPOLOGIX: Topological data analysis for drug-protein interaction using persistent homology and bipartite simplicial complexes. hERG cardiotoxicity MVP completed. GATE: BCI neural-stimulation safety evaluation platform. Apache 2.0 license. Collaborations: Kent Berridge, University of Michigan. Advisory role on dopamine D1 receptor mechanisms in reward processing. Samuel Gershman, Harvard University. Provided arXiv endorsement. Advisory role on Bayesian computational models of learning and memory. Nathaniel Daw, Princeton University. Advisory role on reinforcement learning models. Marcelo Mattar, New York University. Advisory role on memory consolidation dynamics. Patents: Provisional patent on CCT core architecture, filed Q3 2026. Computational Skills: Python: scipy, numpy, ODE/RK45, PyMC/MCMC, pandas. R: statistical analysis, data visualization. Topological data analysis: Ripser, Gudhi. Neural simulation: NEURON, Brian2. Structural biology: AlphaFold, RDKit, ADMET/QSAR, GROMACS, AutoDock. HPC: Nextflow, SLURM. Databases: Supabase, Postgres. Web: JavaScript, Node.js. REFERENCES Kent Berridge, PhD James Olds Distinguished University Professor Department of Psychology, University of Michigan 530 Church Street, Ann Arbor, MI 48109 berridge@umich.edu Relationship: Collaborator on dopamine D1 receptor mechanisms in CCT model. Samuel Gershman, PhD Professor of Psychology Department of Psychology and Center for Brain Science, Harvard University 52 Oxford Street, Cambridge, MA 02138 gershman@fas.harvard.edu Relationship: Provided arXiv endorsement; collaborator on Bayesian computational models. Nathaniel Daw, PhD Professor of Psychology and Neuroscience Princeton Neuroscience Institute, Princeton University Washington Road, Princeton, NJ 08544 ndaw@princeton.edu Relationship: Advisor on reinforcement learning models relevant to CCT framework. Marcelo Mattar, PhD Assistant Professor of Psychology Department of Psychology, New York University 6 Washington Place, New York, NY 10003 marcelo.mattar@nyu.edu Relationship: Advisor on memory consolidation dynamics. CHECKLIST - [ ] Complete SF424 R&R form for U01 mechanism - [ ] Write project summary/abstract (300 words max) - [ ] Write project narrative (1 page) - [ ] Complete research strategy section (12 pages max): specific aims, background and significance, preliminary studies, research design and methods - [ ] Include letters of support from proposed U.S. partner institution lead PI - [ ] Include letters of collaboration from Kent Berridge, Samuel Gershman, Nathaniel Daw, Marcelo Mattar - [ ] Attach biographical sketch for Eniola Olutogun - [ ] Attach biographical sketch for proposed U.S. lead PI - [ ] Complete budget form using budget justification provided - [ ] Include provisional patent filing documentation - [ ] Include links to three CCT preprints on OSF and Zenodo - [ ] Include link to review article under review at Neuroscience and Biobehavioral Reviews - [ ] Verify eligibility: confirm that independent researcher without PhD can serve as co-investigator on NIH U01 - [ ] Verify partner institution IRB capacity for invasive neural recording in addiction patients - [ ] Submit through grants.gov by deadline EDITOR NOTES - Eligibility risk: NIH U01 typically requires a U.S. institution as the applicant organization. Eniola cannot apply as an independent researcher from Nigeria. The application must be submitted by the U.S. partner institution with Eniola listed as a co-investigator. Confirm this arrangement in writing with the partner institution before submission. - Verify that the partner institution (University of Michigan or Harvard) has an active human subjects protocol for invasive neural recording in addiction patients. If not, the IRB approval timeline may exceed the project period. - The budget justification assumes the partner institution covers all human subjects costs. Confirm this arrangement explicitly in the letter of support from the lead PI. - Eniola's age (29) and degree status (B.Pharm only, no MSc) may raise concerns about his ability to serve as a co-investigator on a U01. The biographical sketch should emphasize his independent research output, preprints, and collaborations with senior scientists to mitigate this. - The provisional patent filing date (Q3 2026) is in the future relative to the application date. Verify the exact filing date and include the patent application number if available. If not yet filed, remove the patent claim or note it as pending. - Confirm that the review article is indeed under review at Neuroscience and Biobehavioral Reviews and include the manuscript number if available. - The Bayesian population dynamics study on Zenodo (10.5281/zenodo.20492472) should be verified as publicly accessible and correctly cited. - Eniola's employment at Synthcare as National Product Manager (March 2026-present) may conflict with the time commitment required for a U01 co-investigator role. Clarify that this is a part-time or flexible position that allows for research collaboration. - The proposed budget of 100,000 USD is low for a U01. NIH may question whether this is sufficient for the proposed work. Consider whether additional funds for data collection, software licensing, or personnel at the partner institution are needed and adjust the budget accordingly.