F4‐04‐03: Brain Networks as Targets of Neurodegeneration in Pd and Ad
Bibliographic record
Abstract
Our main objective is to uncover mechanisms of disease initiation and propagation in Alzheimer’s Disease (AD) and Parkinson’s Disease (PD). Human functional brain imaging has shown that the brain is organized in large-scale networks. These have been identified mainly from resting state functional MRI (rsfMRI) and diffusion tractography (DTI). Also, recent evidence suggests that neurodegenerative diseases may be the result of prion-like spreading of misfolded proteins. A misfolded protein is thought to spread via neuronal projections and then induce further misfolding and neurodegeneration in target regions. Transneuronal spread has been demonstrated in animal models for tau and beta-amyloid (implicated in AD) and alpha-synuclein (PD). Accordingly, MRI studies in AD and fronto-temporal dementia (FTD) show that the patterns of atrophy in different syndromes demonstrate overlap with intrinsic brain networks found in healthy individuals. AD appears to target the default mode network, a set of interconnected brain areas implicated in monitoring and memory retrieval. Conversely, variants of FTD target different networks; for example, the behavioral variant of FTD, which is characterized by impulsivity and disordered emotional regulation, targets a brain network implicated in reward and motivation. We used the Parkinson’s Progression Markers Initiative (PPMI), a large open-source database of imaging and clinical data in de novo PD patients. We performed deformation based morphometry and independent component analysis to identify areas showing atrophy in PD patients compared to control subjects. Striatum, basal forebrain, amygdala, hippocampus, insula and anterior cingulate cortex demonstrated atrophy in proportion to clinical disease severity, consistent with the scheme proposed by Braak from postmortem data. We also show that these regions form a connected intrinsic network. Moreover, we show that disease patterns follow brain connectomics, compatible with an epicenter in the substantia nigra. Finally we find that cognitive impairment at visit 2 is associated with progression of atrophy in the entorhinal cortex.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".