Brain tissue pulsatility is related to clinical features of Parkinson's disease
Bibliographic record
Abstract
Introduction This study investigated whether brain tissue pulsatility is associated with features of disease severity in Parkinson's disease (PD). Methods Data were extracted from the Parkinson's Progression Markers Initiative among 81 adults with PD (confirmed with DATSCAN™). Brain tissue pulsatility was computed using resting state blood oxygenation level dependent (BOLD) MRI in white matter (WM), referred to as BOLD TP . Motor impairment was assessed using the Movement Disorders Society unified Parkinson's disease rating scale. Factor analysis generated composite scores for cognition and vascular risk burden. A linear regression model examined the association of BOLD TP with age, sex, motor impairment, cognition, vascular risk burden and PD duration. In addition, we investigated whether BOLD TP relates to WM hyperintensity (WMH) volume, WM fractional anisotropy (WM-FA) and striatal binding ratio (SBR) of dopamine transporter. Results Motor impairment ( t = 2.3, p = .02), vascular burden ( t = 2.4, p = .02) and male sex ( t = 3.0, p = .003) were independently associated with BOLD TP (r 2 = 0.40, p < .001). BOLD TP was correlated with WMH volume ( r = 0.22, p = .05) but not WM-FA nor SBR ( p > .1). In addition, BOLD TP ( t = 2.76, p = .008) and SBR ( t = −2.04, p = .04) were independently related to motor impairment (r 2 = 0.18, p = .006). Conclusion Our findings show that brain tissue pulsatility from BOLD images in WM is related to neurological and vascular features in PD. BOLD TP may be useful in PD to study small vessel alterations that appear distinct from WM structural changes.
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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 | 0.002 |
| 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) | 0.002 | 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".