In vivo and in silico alpha-synuclein propagation dynamics: The role of genotype, epicentre, and connectivity
Bibliographic record
Abstract
Abstract Neurodegeneration observed in synucleinopathies, like Parkinson’s disease (PD), are hypothesized to be a consequence of the progressive accumulation and spread of misfolded alpha-synuclein (aSyn) throughout the brain. Here, we study the generalizability of this hypothesis across multiple biologically-relevant factors including genotype, aSyn species, and regional vulnerability (i.e.: does the introduction of aSyn into different brain regions have a similar impact) using a detailed longitudinal brain (using magnetic resonance imaging; [MRI]) and behavioural approaches. We first examined wild-type and M83 A53T hemizygous transgenic (engineered to over-express human aSyn) C57BL/6 x C3H mice receiving striatal inoculation with human or mouse preformed fibrils (PFFs) (n=89 mice at the last time point; n=687 MRI). Longitudinal analyses demonstrated a time-dependent increase in network-like atrophy and motor deficits, generalized across genotype and PFF species. We further derived latent dimensions relating brain-behaviour relationships revealing a pattern of sex- and genotype-relevant atrophy. Overall, atrophy was most prominent in M83 mice with mouse PFFs, while human PFFs or wild-type hosts showed attenuated effects. Changing the inoculation site to the hippocampus, a major connectivity hub, revealed differential regional vulnerability in the form of localized atrophy. Computational models previously validated in clinical PD further indicated regional vulnerability with increased predictive ability for atrophy patterns associated with striatal compared to hippocampal inoculation. Our findings reveal atrophy resulting from aSyn spreading is generalizable across genotype and PFF species, but not disease epicentres, emphasizing the role of regional vulnerability in disease progression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".