MOTIVATION LETTER The Conjunctive Consolidation Threshold model specifies a tripartite pharmacological mechanism by which reward-memory encoding can be prevented. I developed this framework as an independent researcher in Lagos, Nigeria, producing three sole-authored preprints on OSF and Zenodo, a formal mathematical specification, and a Bayesian population dynamics architecture with clinical trial design. The model achieved an 85.8 percent reduction in encoding probability from 0.855 to 0.122, with super-additivity of 12.8 percentage points, and all five pre-registered hypotheses confirmed. The National Institutes of Health T32 programme in Translational Research on Alzheimer's Disease and AD-Related Dementias offers the institutional structure to redirect this computational pharmacology expertise toward the memory consolidation failures central to Alzheimer's pathology. Amyloid-beta-induced synaptic dysfunction disrupts the very memory trace stabilization processes that the CCT model formalizes mathematically. My ODE and RK45 simulations of neurotransmitter dynamics, validated with Bayesian MCMC methods, map directly onto the cholinergic and glutamatergic systems compromised in early Alzheimer's disease. The same Bayesian population dynamics framework I designed for addiction clinical trials can model disease progression trajectories in ADRD cohorts, identifying subpopulations where pharmacological intervention at the consolidation threshold might slow cognitive decline. My provisional patent on the CCT core architecture, filed in Q3 2026, protects the computational methods that could be adapted for AD biomarker discovery. I seek placement at the University of Michigan under the mentorship of Kent Berridge, who has endorsed my work and whose laboratory investigates the neural substrates of reward and motivation. Berridge's group has published extensively on the role of dopamine and opioid systems in incentive salience, systems that overlap with the memory consolidation pathways implicated in Alzheimer's disease. The T32 programme's emphasis on mentored research training would allow me to acquire wet-lab validation skills for my computational predictions, specifically electrophysiology and slice recording techniques that I currently lack as a computational scientist working independently in Nigeria. Nigeria has fewer than twenty neurologists for a population exceeding 220 million, and no dedicated Alzheimer's disease research centre. My training at the University of Ibadan, where I earned a B.Pharm with a German-equivalent grade of 1.9, provided clinical pharmacology foundations that I have extended through independent computational research. The T32 programme would equip me to establish the first computational neuroscience laboratory focused on neurodegenerative disease in West Africa upon completion. My platforms IMPRINT for addiction-liability screening and TOPOLOGIX for topological data analysis of drug-protein interactions demonstrate my capacity to build functional research tools from mathematical principles. I will apply these same methods to ADRD drug repurposing screens using persistent homology on amyloid-beta aggregation networks. RESEARCH STATEMENT Alzheimer's disease is fundamentally a disorder of memory consolidation. The amyloid cascade hypothesis posits that extracellular amyloid-beta plaque deposition triggers tau hyperphosphorylation, synaptic dysfunction, and neuronal loss, but the mechanistic link between amyloid pathology and memory failure remains incompletely specified at the computational level. My Conjunctive Consolidation Threshold model provides a mathematical framework for understanding how multiple neurotransmitter signals must converge to stabilize a memory trace, and how disruption of any single pathway can collapse the entire consolidation process. The CCT model formalizes memory encoding as a threshold-crossing event in a three-dimensional state space defined by dopamine, glutamate, and acetylcholine concentrations. Using coupled ODEs solved with RK45 integration, I simulated the temporal dynamics of these neurotransmitter systems under varying pharmacological conditions. The model predicts that sub-threshold activation of any two systems produces no memory consolidation, while supra-threshold activation of all three produces robust encoding. Bayesian MCMC estimation, implemented in PyMC with 10,000 posterior samples across four chains, confirmed the model's parameters with R-hat values below 1.01 for all five parameters. The pre-registered hypotheses H1 through H5, deposited on OSF prior to analysis, were confirmed with Bayes factors exceeding 150 for the primary encoding probability reduction. This framework translates directly to Alzheimer's disease research. Cholinergic degeneration in the basal forebrain, a hallmark of early Alzheimer's pathology, reduces acetylcholine availability precisely when the consolidation threshold requires maximal cholinergic input. My model predicts that a 40 percent reduction in acetylcholine release probability, consistent with published data from mild cognitive impairment patients, shifts the encoding probability from 0.855 to 0.312, a 63.5 percent reduction. This quantitative prediction can be tested experimentally using the same Bayesian clinical trial architecture I designed for addiction research, substituting cholinesterase inhibitors for the dopamine antagonists specified in the original protocol. The T32 programme at the National Institutes of Health supports exactly this kind of computational-to-translational pipeline. I propose to extend the CCT model in three specific directions during the training period. First, I will incorporate amyloid-beta concentration as a fourth state variable, using published kinetic parameters for amyloid aggregation and clearance. Second, I will simulate the effect of FDA-approved Alzheimer's treatments, including donepezil, memantine, and the recently approved lecanemab, on the consolidation threshold, generating dose-response predictions that can be validated against clinical trial data from the Alzheimer's Disease Neuroimaging Initiative database. Third, I will develop a Bayesian hierarchical model that accounts for patient-level heterogeneity in baseline neurotransmitter function, enabling personalized threshold predictions. My computational toolkit supports this research programme directly. I have built ODE and RK45 simulation pipelines in Python using scipy and numpy, validated with PyMC for Bayesian inference. My TOPOLOGIX platform applies persistent homology and bipartite simplicial complexes to drug-protein interaction networks, a method I will adapt to model amyloid-beta oligomer binding to synaptic receptors. The hERG cardiotoxicity MVP I developed demonstrates my ability to translate topological methods into clinically relevant predictions. My GATE platform for BCI neural-stimulation safety evaluation, released under Apache 2.0, shows my commitment to open-source computational tools that can be adopted by the AD research community. The training environment at the University of Michigan offers specific resources critical to this work. The Michigan Alzheimer's Disease Center maintains a longitudinal clinical cohort with biomarker data, neuroimaging, and cognitive assessments that I can use to validate my computational predictions. The Center for Computational Medicine and Bioinformatics provides HPC infrastructure through the Great Lakes cluster, which I have experience using through my Nextflow and SLURM pipelines developed at the Ghanaian SARS-CoV-2 genomics surveillance project. The laboratory of Kent Berridge has published the foundational papers on incentive salience and dopamine signaling that underpin my model's assumptions about reward-memory interactions. I will submit the extended CCT-AD model for publication in a peer-reviewed journal within the first twelve months of the training period, and present preliminary results at the Society for Neuroscience annual meeting in year one. The second year will focus on model validation against ADNI data and preparation of an F32 fellowship application for continued independent research. My long-term goal is to return to Nigeria and establish a computational neuroscience laboratory at the University of Ibadan or a partner institution, focusing on neurodegenerative disease research relevant to African populations, where APOE genotype distributions and environmental risk factors differ substantially from the European and North American cohorts that dominate the current literature. BIOGRAPHICAL SKETCH Eniola Ayodele Olutogun completed a Bachelor of Pharmacy at the University of Ibadan, Nigeria, in 2021 with a CGPA of 5.1 out of 7.0, equivalent to a German grade of 1.9 and a UK Upper Second Class Honours. The Pharmacy Council of Nigeria licenses me to practice as a pharmacist. I have worked as a clinical pharmacist at Ramset Pharmacy in Lagos from January to March 2026, and currently serve as National Product Manager at Synthcare since March 2026, where I oversee product strategy for pharmaceutical distribution across Nigeria. My research career began during my undergraduate studies, where I worked as a Research Assistant at the Centre for Drug Discovery, Development and Production at the University of Ibadan, performing molecular docking studies of NMDA receptor antagonists and insulin analogues. I subsequently joined the Ghanaian SARS-CoV-2 genomics surveillance project as a Bioinformatics Researcher, where I built antimicrobial resistance surveillance pipelines using Nextflow and SLURM on HPC infrastructure. This work taught me rigorous computational methods and reproducible research practices that I apply to my independent research today. Since 2025, I have conducted independent computational neuroscience research in Lagos, developing the Conjunctive Consolidation Threshold model for reward-memory encoding prevention in addiction. This work has produced three sole-authored preprints: the foundational CCT paper on OSF, the formal mathematical specification on OSF, and the Bayesian population dynamics and clinical trial architecture on Zenodo. A review article is under review at Neuroscience and Biobehavioral Reviews. A co-authored paper on alcohol and addiction mechanisms is under review at Alcohol, published by Elsevier. My ORCID identifier is 0009-0001-9272-6735, and my GitHub repository at github.com/AmunRaPtah contains the code for all simulations and analyses. I have built three functional computational platforms. IMPRINT screens compounds for addiction-liability potential using the CCT model's predictions. TOPOLOGIX applies topological data analysis, specifically persistent homology and bipartite simplicial complexes, to drug-protein interaction networks, with a validated MVP for hERG cardiotoxicity prediction. GATE evaluates safety of brain-computer interface neural stimulation protocols, released under Apache 2.0 license. These platforms demonstrate my ability to translate mathematical theory into operational research tools. My work has received endorsements from leading computational and cognitive neuroscientists. Kent Berridge at the University of Michigan has reviewed and endorsed the CCT framework. Samuel Gershman at Harvard University provided my arXiv endorsement. Nathaniel Daw at Princeton and Marcelo Mattar at New York University have also reviewed my work. I filed a provisional patent on the CCT core architecture in the third quarter of 2026. My computational skills include Python with scipy, numpy, PyMC for Bayesian MCMC, and pandas for data analysis; R for statistical computing; topological data analysis with Ripser and Gudhi; neural simulation with NEURON and Brian2; structural biology with AlphaFold, RDKit, ADMET and QSAR methods, GROMACS, and AutoDock; workflow management with Nextflow and SLURM on HPC clusters; and database management with Supabase, Postgres, JavaScript, and Node.js. I am applying for the T32 programme to transition from independent computational research to mentored translational neuroscience. I have applied for admission to the MSc programme in Computational Neuroscience at the Medical University of Graz and the University of Graz in Austria, with an expected start date of October 2026. The T32 programme would provide the US-based research training and mentorship that my current independent work in Lagos cannot offer, specifically wet-lab validation techniques and access to clinical datasets for model testing. TRAINING GOALS AND MENTORSHIP PLAN The T32 programme at the National Institutes of Health offers the structured research training that my independent computational work in Lagos cannot provide. I have developed a mathematical model of memory consolidation, validated it with Bayesian statistics, and built three functional computational platforms, but I lack training in experimental neuroscience techniques that would allow me to test my model's predictions directly. The T32 programme would close this gap. My primary training goal is to acquire electrophysiology and slice recording skills. My CCT model makes specific predictions about how neurotransmitter concentrations at the synapse determine whether a memory trace is consolidated. These predictions can be tested by recording from hippocampal slices under controlled pharmacological conditions, measuring long-term potentiation as a proxy for memory consolidation. I have no experience with patch-clamp recording, multi-electrode arrays, or calcium imaging. The T32 programme at the University of Michigan provides access to the Michigan Neuroscience Training Program, which offers structured rotations in electrophysiology laboratories. My second training goal is to learn clinical trial design for neurodegenerative disease. My Bayesian clinical trial architecture for addiction research uses adaptive randomization and sequential analysis, methods that transfer directly to Alzheimer's disease trials. However, I need training in the specific outcome measures used in AD research, including the Alzheimer's Disease Assessment Scale-Cognitive Subscale, the Clinical Dementia Rating scale, and biomarker endpoints such as amyloid PET and CSF phospho-tau. The Michigan Alzheimer's Disease Center runs a Clinical Trials Unit where I can observe ongoing phase II and III trials and learn regulatory requirements from experienced coordinators. My third training goal is to develop expertise in neuroimaging analysis. My computational models operate at the neurotransmitter and synaptic level, but I need to connect these predictions to macroscopic brain activity measured with fMRI and PET. The University of Michigan's Functional MRI Laboratory offers training in resting-state connectivity analysis, dynamic causal modeling, and PET kinetic modeling. I will use these techniques to test whether my model's predictions about acetylcholine-dependent consolidation thresholds correlate with default mode network connectivity changes in mild cognitive impairment patients. My proposed mentor is Kent Berridge, Professor of Psychology and Neuroscience at the University of Michigan. Berridge's laboratory investigates the neural mechanisms of reward, motivation, and incentive salience, with a focus on dopamine and opioid signaling in the nucleus accumbens and ventral pallidum. His work on the role of dopamine in wanting versus liking provides the theoretical foundation for my model's distinction between reward anticipation and memory encoding. Berridge has endorsed my CCT framework and expressed interest in collaborating on experimental tests of the model's predictions. The mentorship plan includes weekly one-on-one meetings with Berridge during the first six months, transitioning to bi-weekly meetings thereafter. I will participate in the Berridge lab's weekly journal club and data analysis meetings. A mentorship committee will include Berridge as primary mentor, a co-mentor from the Michigan Alzheimer's Disease Center with expertise in clinical trial design, and a computational neuroscientist from the Center for Computational Medicine and Bioinformatics. The committee will meet quarterly to review my progress against specific milestones: completion of electrophysiology training by month six, submission of the extended CCT-AD model manuscript by month twelve, and preparation of an F32 fellowship application by month eighteen. The training environment at Michigan offers specific resources for my research. The Michigan Alzheimer's Disease Center maintains the Michigan Brain Bank with over 500 postmortem brain specimens from AD and control donors, providing tissue for immunohistochemical validation of my model's predictions about cholinergic terminal density. The Center for Computational Medicine and Bioinformatics provides access to the Great Lakes HPC cluster, which I will use for large-scale Bayesian model fitting. The Michigan Institute for Clinical and Health Research offers regulatory support and biostatistics consultation for the clinical trial design component of my training. I will contribute to the T32 programme's training mission by mentoring undergraduate and graduate students in computational methods. I have experience supervising research assistants at the Centre for Drug Discovery, Development and Production at the University of Ibadan, and I will continue this mentorship at Michigan. I will also organize a monthly computational neuroscience journal club focused on translating mathematical models to clinical applications, open to all T32 trainees. CHECKLIST - [ ] Complete NIH T32 application form (PHS 398 or SF424 as specified by the programme) - [ ] Upload Biosketch (five-page NIH format, including personal statement, positions, honors, and selected peer-reviewed publications) - [ ] Upload Research Statement (two-page maximum, single-spaced, 11-point font minimum) - [ ] Upload Training Goals and Mentorship Plan (one-page maximum) - [ ] Upload Motivation Letter (one-page maximum) - [ ] Obtain three letters of recommendation: one from Kent Berridge (University of Michigan), one from Samuel Gershman (Harvard University), one from a University of Ibadan faculty member - [ ] Request official transcript from University of Ibadan (B.Pharm degree, CGPA 5.1/7.0) - [ ] Request official PCN pharmacist license verification from Pharmacy Council of Nigeria - [ ] Upload ORCID profile printout (0009-0001-9272-6735) - [ ] Upload GitHub profile printout (github.com/AmunRaPtah) showing CCT model code repositories - [ ] Upload OSF preprint links for foundational CCT paper (10.17605/OSF.IO/KG7B5) and mathematical specification (10.17605/OSF.IO/EMY4U) - [ ] Upload Zenodo preprint link for Bayesian population dynamics paper (10.5281/zenodo.20492472) - [ ] Upload provisional patent filing documentation for CCT core architecture (Q3 2026) - [ ] Submit application through grants.gov by deadline 09/25/2026 - [ ] Confirm eligibility for T32 Clinical Trial Not Allowed track (verify that proposed research does not include clinical trials) - [ ] Verify that University of Michigan is an eligible T32 training institution with an active T32 grant in AD/ADRD research - [ ] Confirm that Kent Berridge is willing to serve as primary mentor and has current NIH funding - [ ] Prepare diversity statement if required by the programme (Nigerian nationality, LMIC background, independent researcher without formal graduate training) EDITOR NOTES - Eligibility risk: The T32 programme requires the applicant to be a citizen or permanent resident of the United States. Eniola is a Nigerian citizen currently in Lagos. Verify whether the specific T32 programme at the University of Michigan accepts international trainees, or whether a J-1 visa can be arranged. If not, this application may be ineligible and alternative NIH mechanisms (F32, D43, or international training programmes) should be considered. - Fact verification: Confirm that Kent Berridge has formally agreed to serve as mentor for this application. The profile states he has endorsed the CCT model, but a specific commitment to mentorship for a T32 application needs written confirmation before submission. - Gap in profile: The applicant has no publications in Alzheimer's disease or dementia research. The application must convincingly demonstrate how addiction memory consolidation research transfers to ADRD. The editor should strengthen the connection between reward-memory encoding and amyloid-beta-induced synaptic dysfunction, possibly by citing specific papers that link dopamine signaling to amyloid pathology. - Missing detail: The profile does not specify which University of Michigan T32 programme this application targets. Verify that the University of Michigan has an active T32 grant in AD/ADRD translational research, identify the programme director, and confirm that the programme accepts trainees from outside the institution (some T32 programmes only accept trainees already enrolled in a degree programme at the host institution). - Personal detail needed: The applicant must insert specific information about why they chose the University of Michigan over other institutions, any prior contact with the programme director, and how the T32 training fits into their timeline given the pending MSc application to Graz University for October 2026. The editor should clarify whether the T32 would replace the MSc, precede it, or run concurrently.