Disrupted cerebellar structural connectome in spinocerebellar ataxia type 3 and its association with transcriptional profiles
Bibliographic record
Abstract
Abstract Spinocerebellar ataxia type 3 (SCA3) is primarily characterized by progressive cerebellar degeneration, including gray matter atrophy and disrupted anatomical and functional connectivity of the cerebellum. The alterations of topological organization of cerebellar white matter structural network in SCA3 and the underlying neurobiological mechanism remain unknown. Using a cohort of 20 patients with SCA3 and 20 healthy controls, we constructed cerebellar structural networks from diffusion magnetic resonance imaging and investigated alterations of topological organization. Then we mapped the alterations with transcriptome data from the Allen Human Brain Atlas to identify possible biological mechanisms for regional selective vulnerability to white matter damage. Compared with healthy controls, decreased global and nodal efficiency, and widely distributed decreased edge strength were observed in SCA3 patients. The regions with decreased nodal global efficiency were mainly located in cerebellar anterior lobe, and the genes express higher in these regions were significantly enriched in synapse-related biological processes. The regions with decreased nodal local efficiency were mainly located in cerebellar posterior lobe, and the higher gene expression in these regions were significantly enriched in metabolic biological processes. Similar hub distributions were found in two groups of subjects, whereas the strengths of rich-club and feeder connections were lower in SCA3 patients. Moreover, strength of the inter-module connections was lower in SCA3 group and negatively correlated with SARA score, ICARS score, and CAG repeat number. These findings suggest a mechanism of white matter vulnerability and a potential image biomarker for the disease severity, providing insights into neurodegeneration and pathogenesis in this disease.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".