An exploratory study of the diffusion tensor imaging analysis along perivascular spaces (DTI-ALPS) index combined with quantitative analysis of choroid plexus volume and perivascular spaces in different cognitive stages of cerebral small vessel disease
Bibliographic record
Abstract
Background: Cerebral small vessel disease (CSVD) is a major contributor to cognitive impairment and dementia. Growing evidence suggests that impaired perivascular clearance plays a pivotal role in CSVD pathogenesis, yet non-invasive biomarkers for early cognitive decline remain limited. This study aimed to explore the diagnostic value of the diffusion tensor imaging analysis along perivascular spaces (DTI-ALPS) index, enlarged perivascular spaces (EPVS) numbers/volume, and choroid plexus volume (CPV) across different cognitive stages of CSVD. Methods: We retrospectively analyzed data from 102 CSVD patients [33 CSVD-cognitive normal (CSVD-CN); 39 CSVD-mild cognitive impairment (CSVD-MCI); 30 vascular dementia (VaD)] and 29 normal controls (NCs). Quantitative measurements of the DTI-ALPS index, EPVS numbers/volume, and CPV were obtained. Correlations with Montreal Cognitive Assessment (MoCA) scores and diagnostic performance were also evaluated. Results: NCs/CSVD-CN: AUC =0.900, 95% CI: 0.847-0.952. Conclusions: Combining the DTI-ALPS index, BG-EPVS numbers, and CPV may enhance early diagnosis and subtype differentiation in CSVD-related cognitive impairment, supporting targeted interventions.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".