Structural Compression and Entorhinal Vulnerability: Linking Tentorial Adjacency to Tau Burden and Dementia Progression
Bibliographic record
Abstract
ABSTRACT Alzheimer’s disease (AD) is a growing public health crisis. The disease is defined neuropathologically by accumulation of amyloid-β plaques and neurofibrillary tangles (NFTs) composed of abnormal tau protein in the brain. Early neurofibrillary degeneration in the entorhinal cortex (EC) is a hallmark of AD and a critical initiating event in the hierarchical pathoanatomical progression. However, the factors triggering initial tau deposition in the EC remain unclear. We propose a novel biomechanical cascade hypothesis, positing that the unique anatomical inferomedial positioning of the EC, including proximity to the tentorial incisura (TI) and other skull base structures, renders it susceptible to very mild yet persistent age-related mechanical stress, analogous to the effects of repetitive mild traumatic brain injury, triggering tau pathology. To test this hypothesis, we developed a method to quantify Entorhinal-Tentorial (EC-TI) proximity and applied it to multimodal imaging data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI; n =47). Based on this neuroanatomical contact coefficient (NCC), participants were heuristically stratified into high ( n =24) and low ( n =23) adjacency groups. When controlling for other risk factors, tau PET signal in the EC predicted conversion from mild cognitive impairment to AD only in the high-adjacency group (LLR p =0.009, tau PET in EC p =0.036). These findings identify EC-TI proximity as a novel and anatomically grounded biomarker of AD progression risk. More broadly, they suggest a previously unrecognized biomechanical contribution to the initiation of tau pathology in aging and sporadic AD, opening new avenues for early detection, risk stratification, and mechanistically targeted prevention strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".