Regional Glymphatic Dysfunction is linked to Spinocerebellar Ataxia Type 3 pathophysiology
Bibliographic record
Abstract
Abstract Spinocerebellar ataxia type 3 (SCA3) involves neuroinflammation and imbalance between production and clearance of proteins which affects the glymphatic system, the lymphatic-like, fluid-transport system in the brain. However, it is unclear whether SCA3 is related to impairments in glymphatic function. Using multimodal imaging data, 34 SCA3 patients and 36 age-, sex- and educational matched healthy controls (HCs) were compared using multiple glymphatic measurements, including choroid plexus (CP) and cerebrospinal fluid (CSF) volume, diffusion tensor imaging along the perivascular (DTI-ALPS) index, and coupling relationship between blood-oxygen-level-dependent signals and CSF flow (BOLD-CSF coupling). Then, we evaluated regional glymphatic function by dividing DTI-ALPS and BOLD-CSF coupling into anterior, middle, posterior, and cerebellum regions, thereby identifying the spatial variation of glymphatic function in the two groups. We demonstrated that compared with HCs, larger CP and CSF volumes were found in SCA3 patients. More importantly, for DTI-ALPS index and BOLD-CSF coupling, these surrogate markers for glymphatic clearance were weaker in SCA3 patients. Furthermore, altered regional glymphatic functions were most prominent in midbrain, cerebellum and middle regions. Crucially, the altered midbrain, cerebellum, middle and global glymphatic functions were accompanied by the severity of ataxia and other SCA3 symptoms. Similar to other neurodegenerative disorders, the association between multiple glymphatic indexes and SCA3 symptoms suggested that waste clearance is disrupted in SCA3 patients, which shed light on the pathogenesis of this disease from a glymphatic lens. Our findings highlighted the dysregulated glymphatic function as a novel diagnostic marker for SCA3.
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) | 0.001 | 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".