Computational Decomposition of New Memory Failure in Alzheimers Disease Through a Hippocampal Cortical Consolidation Bottleneck Model
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Objective
Alzheimers disease (AD) is characterised by difficulty retaining newly learned information, but routine memory scores often conflate poor initial encoding with impaired post-encoding stabilisation. This study aimed to develop an interpretable computational phenotype that separates new-memory failure during progression from mild cognitive impairment (MCI) to AD.
Methods
We proposed a Hippocampal–Cortical Consolidation Bottleneck (HCCB) model, representing newly learned information as a rapidly formed hippocampal trace and a slowly stabilised cortical trace. The model predicts a residual bottleneck when delayed recall is lower than expected from immediate recall. This prediction was operationalised as the Consolidation Bottleneck Index ∗ (CBI ∗ ), a cognitively normal reference-normalised residual index. CBI ∗ was evaluated in ADNI participants spanning normal cognition, MCI nonconversion, MCI conversion and AD, using cognitive and MRI data. Independent neurodynamic support was examined using OpenNeuro resting-state EEG.
Results
Simulations showed new-memory vulnerability when hippocampal vulnerability exceeded cortical vulnerability. In ADNI, CBI ∗ increased across the clinical spectrum and reached AD-like levels in MCI converters. Higher CBI ∗ was associated with hippocampal atrophy, supporting its anatomical relevance. CBI ∗ added limited discrimination beyond established clinical and structural predictors, indicating that it captured a mechanistic phenotype rather than serving as a replacement prognostic model. OpenNeuro EEG further showed increased neurodynamic rigidity in AD.
Conclusions
The HCCB framework quantifies failed stabilisation of newly encoded information and links this phenotype to hippocampal degeneration and altered neurodynamics.
Significance
This study provides an interpretable computational framework for characterising consolidation failure in AD progression.
