Hemispheric asymmetry of tau pathology is related to asymmetric amyloid deposition in Alzheimer’s Disease
Bibliographic record
Abstract
The distribution of tau pathology in Alzheimer's disease (AD) shows remarkable inter-individual heterogeneity, including hemispheric asymmetry. However, the factors driving this asymmetry remain poorly understood. Here we explore whether tau asymmetry is linked to i) reduced inter-hemispheric brain connectivity (potentially restricting tau spread), or ii) asymmetry in amyloid-beta (Aβ) distribution (indicating greater hemisphere-specific vulnerability to AD pathology). We include 452 participants from the Swedish BioFINDER-2 cohort with evidence of both Aβ pathology (CSF Aβ42/40 or neocortical Aβ-PET) and tau pathology (temporal tau-PET), categorising them as left asymmetric (n = 102), symmetric (n = 306), or right asymmetric (n = 44) based on temporal lobe tau-PET uptake distribution. We assess edge-wise inter-hemispheric functional (RSfMRI; n = 318) and structural connectivity (dMRI; n = 352) but find no association between tau asymmetry and connectivity. In contrast, we observe a strong association between tau and Aβ laterality patterns based on PET uptake (n = 233; β = 0.632, p < 0.001), which we replicate in three independent cohorts (n = 234; β = 0.535, p < 0.001). In a longitudinal Aβ-positive sample, we show that baseline Aβ asymmetry predicts progression of tau laterality over time (n = 289; β = 0.025, p = 0.028). These findings suggest that tau asymmetry is not associated with a weaker inter-hemispheric connectivity but might reflect hemispheric differences in vulnerability to Aβ pathology, underscoring the role of regional vulnerability in determining the distribution of AD pathology.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".