Topological Latent Manifold Model v6.6: Causal Personalized Viability Forecasting with Hierarchical Bayesian Cohort Adaptation

This release presents TLMM v6.6 (Topological Latent Manifold Model v6.6), a causal and personalized extension of the TLMM framework for viability forecasting in neurodegenerative disease.

Building upon the causal foundations introduced in TLMM v6.5, this version advances the framework toward individualized forecasting, cross-cohort adaptation, and intervention-aware prediction. The central contribution of v6.6 is the integration of hierarchical Bayesian cohort adaptation, enabling transfer of viability models from a large reference cohort (ADNI) to smaller external cohorts (AIBL and OASIS) through Bayesian partial pooling while preserving cohort-specific characteristics.

TLMM v6.6 further introduces individualized Viability Risk Index (VRI) estimation with explicit decomposition of epistemic and aleatoric uncertainty, allowing personalized characterization of prediction confidence. A new Personalized Predictive Viability Horizon (PVH) module provides individualized time-to-threshold forecasting with posterior credible intervals, supporting longitudinal viability assessment under uncertainty.

To extend viability forecasting into the causal domain, TLMM v6.6 introduces the Individualized Causal Viability Effect (ICVE), a counterfactual estimand designed to quantify the potential impact of intervention strategies on future viability trajectories. The framework additionally includes conditional subgroup causal analyses, allowing exploration of effect heterogeneity across age groups, baseline risk levels, disease severity categories, and network integrity states.

A new Intervention Response Model (IRM) is introduced to characterize heterogeneous response patterns following intervention and to update posterior predictive beliefs using observed deviations from expected trajectories. These components are integrated into a unified personalized decision-support dashboard that combines current risk estimation, predictive horizons, causal effect estimates, uncertainty decomposition, and intervention-response characterization within a single framework.

TLMM v6.6 also extends the framework's scientific rigor through an expanded falsifiability architecture (C1–C5), including a new criterion for hierarchical adaptation failure and cross-cohort transportability assessment. Finally, the release outlines the roadmap toward TLMM v7.0, which is planned to incorporate Amyloid–Blood Flow (ABF) cascade modeling, multiscale biological fidelity enhancement, mechanistic constraints, and cross-modal data fusion.

All figures, numerical values, and experimental outputs included in this release are illustrative synthetic examples generated for methodological demonstration. No real patient data are used. TLMM v6.6 is intended as a research framework for viability forecasting, uncertainty quantification, causal reasoning, and methodological exploration, and should not be interpreted as a diagnostic device, clinical decision system, or validated medical tool.