Cross vendor test‐retest validation of diffusion tensor analysis along the perivascular space (DTI‐ALPS) method for evaluating glymphatic system function in vascular cognitive impairment and dementia (VCID)
Bibliographic record
Abstract
Abstract Background Glymphatic system (GS) is a recently discovered brain‐wide perivascular fluid transport system in the cerebral nervous system (CNS)(Benveniste H et al., 2019) The impairment of glymphatic transport has been demonstrated to be associated with several neurological diseases, in particular the vascular cognitive impairment and dementia (VCID)(Tang J et al., 2022). The diffusion tensor analysis along the perivascular space (DTI‐ALPS) was proposed to non‐invasively evaluate the GS clearance dysfunction which has been used to investigate the GS in various pathologies(Taoka T et al., 2017, Hsu JL et al., 2022). However, studies on the cross‐vendor and test‐retest reliability of DTI‐ALPS method are lacking. The current study aimed to perform cross‐vendor, inter‐rater and test‐retest validations of DTI‐ALPS method by using a cohort from the MarkVCID consortium. Method Fifty participants’ DTI data from the MarkVCID consortium (consists of 7 sites) were included in this study: 15 participants were scanned on four MRI scanners; 35 participants underwent two MR scans within 14 days. The DTI data were acquired used a single shell, b = 1000s/mm2, 40‐direction with a voxel size of 2.02.02.0mm3 and six b = 0 s/mm2 on 3T MR scanners from seven sites, including two Siemens systems, one Philips system and one GE system. Two pipelines by using DSI studio and FSL software were developed for data processing and ALPS index calculation (Figure 1). The mean ALPS (mALPS) index was obtained by the average of bilateral ALPS index and was used for testing the inter‐rater, cross‐vendor and test‐retest reliability. The association between mALPS index and Montreal Cognitive assessment (MoCA) scores were evaluated using a general linear model with age and gender as covariate. Result The Bland‐Altman plot and scatterplot illustrated the validation results of the mALPS index analyzed using DSI studio and FSL pipelines (Figure 2). The mALPS index demonstrated favorable inter‐scanner reproducibility (ICC = 0.77 to 0.95, P< 0.001), inter‐rater reliability (ICC = 0.96 to 1, P< 0.001) and test‐retest repeatability (ICC = 0.89 to 0.95, P< 0.001). A significantly positive correlation was observed between mALPS index and MoCA scores (r = 0.502, P< 0.01) (Figure 3). Conclusion The mALPS index offers a robust potential biomarker for evaluating GS clearance function in VCID.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".