Atrophy in multiple system atrophy relates to mitochondrial and oligodendrocytic processes
Bibliographic record
Abstract
Abstract Objective To investigate the gene expression and neurobiological underpinnings of brain atrophy in multiple system atrophy (MSA) using imaging transcriptomics and PET-based molecular annotation. Methods We derived brain atrophy measurements from the T1-weighted MRI scans of 65 patients with MSA and 181 age- and sex-matched healthy controls. Using postmortem data from the Allen Human Brain Atlas, partial least square (PLS) regression was used to identify gene expression components associated with atrophy. Gene enrichment analyses were performed to investigate the biological processes with enriched genes in regions showing atrophy. Annotation mapping was used to identify the neurochemical systems whose density maps matched atrophy in MSA. Results Atrophy in MSA was found to primarily affect deep brain regions, including the cerebellar white matter, pons, putamen, olive nuclei, and substantia nigra. PLS regression on deep brain region atrophy identified two gene expression latent variables, explaining 27.5% of the covariance. Regions with greater atrophy overexpressed genes related to the mitochondrial respiratory chain, particularly proton transmembrane transport and complex I assembly. In addition, cell type analysis revealed that regions with atrophy overexpressed genes related to oligodendrocytes. These patterns were distinct from those found in Parkinson’s disease. Atrophic regions in MSA also displayed less serotonin and GABA receptors and more acetylcholine and noradrenaline receptors. Interpretation Regions showing atrophy in MSA show specific features, namely overexpression of genes related to mitochondrial function and oligodendrocytes and distinct neurochemical patterns. This study highlights some of the biological and neurochemical mechanisms underlying selective vulnerability of brain regions in MSA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".