Microstructural White Matter Characteristics in Parkinson's Disease With Depression: A Diffusion Tensor Imaging Replication Study
Bibliographic record
Abstract
Background Clarifying the neuropathology of depression as a symptom of Parkinson's disease (PD) has been the goal of recent neuroimaging studies; however, results have been conflicting and lack replication. The purpose of the current study was to replicate recent methods that have used diffusion tensor imaging (DTI) to compare individuals with PD with and without depression and to extend previous findings to allow for a better understanding of the results. Methods Thirty-seven participants with de novo PD were retrieved from the Parkinson's Progression Marker's Initiative (PPMI) and were separated into a depressed PD group (dPD) or a non-depressed PD group (ndPD). Groups were determined based on scores on the Geriatric Depression Scale Short Form (GDS-15). Initially, a replicated cut off score of ≥ 5 for dPD and <5 for ndPD was applied. To better understand the results, we secondarily applied a more extreme group analysis with ≥ 9 for dPD and 0 for ndPD. White matter integrity between groups was compared between groups using tract-based spatial statistics. Results & Conclusion The current study did not reveal significant differences in white matter microstructure between dPD and ndPD groups at the whole brain level or in specific regions of interest. The extreme group results were consistent. These findings did not replicate previous work that found reduced white matter integrity in limbic prefrontal regions in dPD relative to ndPD. The current study highlights the need for more replications of neuroimaging research.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".