Functional Connectivity Alterations in Spinocerebellar Ataxia Type 10: Insights from Gray Matter Atrophy
Bibliographic record
Abstract
Abstract Spinocerebellar ataxia type 10 (SCA10) is a rare, inherited neurological disorder caused by an expansion of the non-coding ATTCT pentanucleotide repeat in the ATAXIN 10 gene. It is characterized by cerebellar ataxia and epilepsy. Previous research has demonstrated extensive white and gray matter degeneration, particularly in the cerebellum. However, the impact of the SCA10 mutation on functional connectivity (FC) remains unexplored. This study aimed to characterize intrinsic FC changes in SCA10 patients and their relationship to clinical manifestations. Structural and resting-state MRIs were obtained from 26 SCA10 patients and 26 control subjects. Voxel-based morphometry (VBM) and seed-ROI and Independent Components Analysis (ICA) were performed to identify cerebral atrophy and FC changes respectively. Additionally, correlation analyses were conducted between FC changes and scores from the Scale for the Assessment and Rating of Ataxia (SARA) and the Montreal Cognitive Assessment (MoCA). In SCA10 patients, VBM analysis revealed extensive gray matter loss in motor cortices and the cerebellum. FC analysis identified significant FC changes originating from seed-ROIs in the right cerebellar VI and left precentral gyrus. Furthermore, group comparison using ICA components showed that SCA10 patients exhibited higher FC in the sensorimotor and cerebellar functional networks. Moreover, the average BOLD signal within the cerebellar network negatively correlated with MoCA scores. In summary, SCA10 patients exhibited enhanced FC in brain regions that displayed gray matter atrophy, underscoring the impact of SCA10 degeneration on resting state networks and induction of potential maladaptive FC compensatory mechanisms.
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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 | 0.000 |
| 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".