Untangling Alzheimer's Disease Clinicoanatomical Heterogeneity Through Selective Network Vulnerability - An Effort to Understand a Complex Disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Alzheimer's disease (AD) is a clinically, anatomically and biologically heterogeneous disorder encompassing a wide spectrum of cognitive profiles, ranging from the typical amnestic syndrome to visuospatial changes in posterior cortical atrophy, language deficits in primary progressive aphasia and behavioural/executive dysfunctions in anterior variants. With the emergence of functional imaging and neural network analysis using graph theory for instance, some authors have hypothesized that this phenotypic variability is produced by the differential involvement of large-scale neural networks - a model called 'molecular nexopathy'. At the moment, however, the hypothesized mechanisms underlying AD's divergent network degeneration remain speculative and mostly involve selective premorbid network vulnerability. Herein we present an overview of AD's clinicoanatomical variability, outline functional imaging and graph theory contributions to our understanding of the disease and discuss ongoing debates regarding the biological roots of its heterogeneity. We finally discuss the clinical promises of statistical signal processing disciplines (graph theory and information theory) in predicting the trajectory of AD variants. This paper aims to raise awareness about AD clinicoanatomical heterogeneity and outline how statistical signal processing methods could lead to a better understanding, diagnosis and treatment of AD variants in the future.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it