MOTIVATION LETTER The BRAIN Initiative Exploratory Research Opportunities programme seeks to advance understanding of the human brain through invasive neural recording and stimulation technologies. My independent research on the Conjunctive Consolidation Threshold model provides a mathematically specified framework for predicting how reward-circuit activity encodes drug-associated memories. The CCT model, validated through ODE/RK45 simulations and Bayesian MCMC methods across five pre-registered hypotheses, demonstrates an 85.8 percent reduction in encoding probability under tripartite pharmacological intervention. This framework can be tested against existing human intracranial datasets from epilepsy patients undergoing reward-circuit monitoring, offering a direct path to neural-level validation without requiring new surgical procedures. I hold a B.Pharm from the University of Ibadan with a CGPA of 5.1 out of 7.0, equivalent to a German 1.9, and am a PCN-licensed pharmacist. My computational tools include 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. These platforms were built independently in Lagos, Nigeria, where I currently conduct research without institutional affiliation. My CCT work has received 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 New York University. A provisional patent on the CCT core architecture is scheduled for Q3 2026. A review article is under review at Neuroscience and Biobehavioral Reviews, and a co-authored paper is under review at Alcohol. Three sole-authored preprints are deposited on OSF and Zenodo with DOIs. This application proposes a feasibility study using open-access human intracranial electroencephalography data to identify neural signatures of conjunctive consolidation in reward-circuit structures. The R61 mechanism is appropriate for this exploratory phase, as the work establishes proof-of-concept for a computational framework that can later guide closed-loop stimulation protocols. My background in computational pharmacology, Bayesian statistics, and neural simulation positions me to execute this analysis independently. The BRAIN Initiative mission to accelerate the development of neural technologies aligns with my goal of translating the CCT model from computational pharmacology into human neural intervention. RESEARCH STATEMENT The Conjunctive Consolidation Threshold model proposes that drug-associated memories are encoded only when dopamine, glutamate, and norepinephrine signals converge above a specific threshold during reward learning. This tripartite framework was formally specified in my preprint on OSF and validated through ODE/RK45 numerical integration and Bayesian MCMC estimation. The model predicts that simultaneous sub-threshold antagonism of D1, NMDA, and alpha-1 receptors reduces encoding probability from 0.855 to 0.122, a reduction of 85.8 percentage points, with super-additivity of 12.8 percentage points beyond additive predictions. All five pre-registered hypotheses H1 through H5 were confirmed. This project will test whether the same conjunctive dynamics can be observed in human intracranial recordings from reward-circuit structures. I will analyze existing open-access datasets from epilepsy patients with depth electrodes placed in the nucleus accumbens, ventral tegmental area, and orbitofrontal cortex during reward-based tasks. The analysis pipeline will extract local field potential and single-unit activity aligned to reward delivery and drug-cue presentation. I will apply time-frequency decomposition and phase-amplitude coupling measures to identify neural signatures that correspond to the CCT model's predicted conjunctive state. The specific aims are three. First, to identify whether reward-related neural activity in human accumbens and VTA shows conjunctive patterns consistent with the CCT model's threshold dynamics. Second, to determine whether pharmacological modulation of dopamine, glutamate, and norepinephrine systems in existing human studies produces neural changes that match the model's super-additivity predictions. Third, to develop a computational biomarker from intracranial data that can predict encoding probability under different pharmacological conditions. My computational tools directly support this analysis. TOPOLOGIX uses persistent homology to detect topological features in neural time series that may correspond to conjunctive states. IMPRINT provides a screening framework for addiction liability that can be calibrated against neural data. GATE evaluates safety parameters for any future stimulation protocols derived from this work. I will implement the analysis in Python using scipy, numpy, and PyMC for Bayesian hierarchical modeling, with NEURON and Brian2 for biophysical validation of observed neural patterns. The expected outcomes include a validated computational biomarker for conjunctive consolidation in human reward circuits, a published methodological paper in a peer-reviewed journal, and a pre-registered replication protocol for a subsequent R01 application. This work establishes the neural grounding necessary to translate the CCT model into closed-loop deep brain stimulation protocols for addiction treatment. COLLABORATION AND TRAINING PLAN My research network provides the scientific mentorship necessary for this project. Kent Berridge at the University of Michigan has endorsed the CCT framework and agreed to consult on the interpretation of reward-circuit neural data. Samuel Gershman at Harvard provided my arXiv endorsement and will advise on the Bayesian computational methods applied to neural time series. Nathaniel Daw at Princeton and Marcelo Mattar at New York University have reviewed the model architecture and offered feedback on the reinforcement learning components. These relationships were established through direct correspondence and preprint sharing, not through formal institutional affiliation. The training component addresses three gaps in my current expertise. First, I require formal instruction in human intracranial electrophysiology analysis, specifically in preprocessing and artifact removal for depth electrode recordings. I will complete the online course Intracranial EEG Analysis offered by the University of California San Francisco through Coursera, followed by a two-week intensive workshop at the MGH/HST Martinos Center if funded. Second, I need supervised practice in closed-loop stimulation protocol design. I will arrange a virtual rotation with the Berridge laboratory at Michigan for three months, focusing on translating rodent optogenetic findings to human intracranial parameters. Third, I will strengthen my statistical expertise in hierarchical Bayesian models for neural data through the Statistical Methods for Neuroscience workshop at the Cold Spring Harbor Laboratory. My application to the MSc programme at the Medical University of Graz in Austria for October 2026 start will provide formal academic structure for this work. The Graz neuroscience programme offers coursework in computational neuroscience and neural engineering that directly supports the BRAIN Initiative project. I will use the R61 funding period to complete the MSc thesis on the neural validation of the CCT model, with the Graz faculty providing institutional oversight and data access agreements. The timeline spans 24 months. Months one through three involve dataset acquisition, ethics review, and preprocessing pipeline development. Months four through nine focus on Aim 1 neural signature identification. Months ten through fifteen address Aim 2 pharmacological modulation analysis. Months sixteen through twenty develop the computational biomarker for Aim 3. Months twenty-one through twenty-four are dedicated to manuscript preparation, code release, and the R01 application. BUDGET JUSTIFICATION The requested budget supports an independent researcher based in Lagos, Nigeria, with no institutional overhead. Personnel costs cover 12 months of salary at 4,000 USD per month for the principal investigator, totaling 48,000 USD. This rate is calculated based on the current salary for a National Product Manager at Synthcare in Nigeria, adjusted upward to account for the full-time research commitment required by this project. No additional personnel are requested. Equipment costs total 8,500 USD. A workstation with 64 GB RAM, NVIDIA RTX 4090 GPU, and 4 TB SSD storage costs 4,500 USD and is necessary for running Bayesian MCMC chains on neural time series data. A backup external storage system with 10 TB capacity costs 500 USD. A portable EEG amplifier and electrode kit for pilot validation experiments costs 3,500 USD. Travel costs total 12,000 USD. Round-trip airfare from Lagos to Boston for the Martinos Center workshop costs 2,500 USD. Accommodation and per diem for two weeks costs 3,500 USD. Round-trip airfare from Lagos to Ann Arbor for the Berridge laboratory rotation costs 2,500 USD. Accommodation and per diem for three months costs 3,500 USD. These travel costs are essential for the training plan and collaboration components. Software and computing costs total 6,000 USD. Cloud computing credits for AWS or Google Cloud for large-scale Bayesian sampling cost 3,000 USD. Software licenses for MATLAB and the FieldTrip toolbox cost 2,000 USD. Publication fees for open-access journals cost 1,000 USD. Materials and supplies total 3,500 USD. Consumables for pilot EEG validation experiments cost 1,500 USD. Books and reference materials for computational neuroscience training cost 1,000 USD. Printing and shipping costs for conference posters and materials cost 1,000 USD. Total direct costs are 78,000 USD. No indirect costs are requested as the applicant is an independent researcher without a federally negotiated indirect cost rate. The total requested budget is 78,000 USD for the 24-month project period. CHECKLIST - [ ] Complete SF-424 R&R form with Eniola Ayodele Olutogun as Principal Investigator - [ ] Upload Research Strategy document (12-page limit, PDF format) - [ ] Upload Bibliography and References Cited - [ ] Upload Biographical Sketch for Eniola Ayodele Olutogun (NIH format, 5-page limit) - [ ] Upload Budget and Budget Justification (SF-424 R&R Budget Form) - [ ] Upload Facilities and Other Resources statement (describe independent research setup in Lagos) - [ ] Upload Equipment list with specifications and vendor quotes - [ ] Upload Data Management and Sharing Plan (2-page limit) - [ ] Upload Authentication of Key Biological and/or Chemical Resources plan - [ ] Obtain three letters of support: Kent Berridge (Michigan), Samuel Gershman (Harvard), Nathaniel Daw (Princeton) - [ ] Obtain letter of collaboration from Medical University of Graz MSc programme - [ ] Verify ORCID iD 0009-0001-9272-6735 is linked to NIH eRA Commons profile - [ ] Register for NIH eRA Commons as independent researcher (no institution required) - [ ] Submit through Grants.gov by 02/11/2027 at 5:00 PM local time of applicant organization - [ ] Confirm eligibility as Nigerian national working independently (no US institution affiliation required) - [ ] Verify that Clinical Trial Not Allowed designation permits analysis of existing human datasets - [ ] Prepare institutional letter from ZYCO as host organization for indirect cost waiver EDITOR NOTES - Eligibility risk: The R61 mechanism requires the applicant to be affiliated with an eligible institution. As an independent researcher, Eniola must either register ZYCO as a domestic or foreign organization in NIH systems or find a US-based institutional sponsor. Verify that ZYCO can serve as the applicant organization with a valid DUNS number or UEI. If not, identify a Nigerian university or research institute willing to serve as the applicant institution. - Dataset access: The proposal assumes access to open-access human intracranial datasets from epilepsy patients. Verify that specific datasets exist with depth electrode recordings in nucleus accumbens, VTA, and orbitofrontal cortex during reward tasks. The Human Brain Project, iEEG.org, and the MNI Open iEEG Atlas are potential sources. Confirm data use agreements allow analysis by an independent researcher in Nigeria. - Training plan feasibility: The virtual rotation with Berridge laboratory and the Martinos Center workshop require confirmation from those institutions that they accept independent researchers without formal affiliation. Draft letters of support should explicitly state the training arrangement and any fees involved. - Budget justification: The 4,000 USD per month salary for an independent researcher in Lagos may exceed typical NIH salary caps for foreign researchers. Verify that the NIH allows salary requests at local market rates rather than US-based caps for foreign applicants. The total budget of 78,000 USD is below the typical R61 cap of 275,000 USD per year, but confirm that the R61 mechanism has no minimum budget requirement. - Missing personal detail: The profile does not specify Eniola's date of birth or citizenship status beyond Nigerian nationality. The NIH requires citizenship or permanent residency information for certain eligibility determinations. Insert the applicant's birth date and confirm that Nigerian citizenship does not disqualify them from NIH funding as a foreign applicant.