Locus Coeruleus Integrity from <scp>7 T MRI</scp> Relates to Apathy and Cognition in Parkinsonian Disorders
Bibliographic record
Abstract
BACKGROUND: Neurodegeneration in the locus coeruleus (LC) contributes to neuropsychiatric symptoms in both Parkinson's disease (PD) and progressive supranuclear palsy (PSP). Spatial precision of LC imaging is improved with ultrahigh field 7 T magnetic resonance imaging. OBJECTIVES: This study aimed to characterize the spatial patterns of LC pathological change in PD and PSP and the transdiagnostic relationship between LC signals and neuropsychiatric symptoms. METHODS: ) 7 T-magnetization-transfer imaging to measure LC integrity in vivo. Spatial patterns of LC change were obtained using subregional mean contrast ratios and significant LC clusters; we further correlated the LC contrast with measures of apathy and cognition, using both mixed-effect models and voxelwise analyses. RESULTS: PSP and PD groups showed significant LC degeneration in the caudal subregion relative to controls. Mixed-effect models revealed a significant interaction between disease-group and apathy-related correlations with LC degeneration (β = 0.46, SE [standard error] = 0.17, F(1, 35) = 7.46, P = 0.01), driven by a strong correlation in PSP (β = -0.58, SE = 0.21, t(35) = -2.76, P = 0.009). Across both disease groups, voxelwise analyses indicated that lower LC integrity was associated with worse cognition and higher apathy scores. CONCLUSIONS: The relationship between LC and nonmotor symptoms highlights a role for noradrenergic dysfunction across both PD and PSP, confirming the potential for noradrenergic therapeutic strategies to address transdiagnostic cognitive and behavioral features in neurodegenerative disease. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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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 | 0.001 | 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 | 0.001 | 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".