This summary details the research presented by Frank Morales Aguilera, which establishes a mathematical isomorphism between fMRISTAT (2002) and TOPO-2026 (2026). Core Principle The fundamental principle shared by both methods is: "Fix a sparse reference, let the rest adapt". This approach separates stable structures from noise in hierarchical data (fMRISTAT) and prevents catastrophic forgetting in artificial neural networks (TOPO-2026). fMRISTAT (2002) Context: Developed to address the problem of low degrees of freedom in random effects analyses of fMRI data. Mechanism: It uses a fixed effects variance image as a sparse reference to regularize the noisy random effects variance. This regularization increased effective degrees of freedom from 3 to over 100. TOPO-2026 (2026) Context: A universal solution to catastrophic forgetting in Large Language Models (LLMs). Mechanism: It anchors six specific embedding rows at prime indices $\{2, 3, 5, 7, 11, 13\}$. These anchor rows are fixed and protected from gradient updates, while all other parameters adapt during training. Performance: This method achieves $O(1)$ memory complexity with an average of 0.25% forgetting across 122 billion parameters. Mathematical Basis: The choice of these six primes is critical, as they capture 97.85% of all spectral weight, creating a spectral trap at $\sigma=0.5$. Mathematical Isomorphism The paper formalizes the isomorphism between the two methods, defining a mapping that preserves the structure of the reference frame, the noisy estimate, and the regularization operator. The two methods are separated by 26 years and different domains, but they share identical structural and mathematical properties. Validation Patterns fMRISTAT: Validated on real fMRI pain perception data, where regularization significantly lowered the T-statistic threshold for inference. TOPO-2026: Validated across five distinct production architectures (GPT-OSS-20B, Sarvam-30B, Mixtral-8x7B, DeepSeek-V2-Lite, and GLM-4.6V-Flash) spanning three continents. Personal Lineage and Tribute The research serves as a personal tribute to the author's late mentors, Keith Worsley and Alan Evans, who brought the author from Cuba to Canada in 1998. The author credits these mentors with teaching him the philosophical and empirical foundations that led to the development of TOPO-2026.