Comparison of structural connectivity in Parkinson's disease with depressive symptoms versus non-depressed: a diffusion MRI connectometry study
Bibliographic record
Abstract
ABSTRACTObjective:Research on psychobiological markers of Parkinson's disease (PD) remains a hot topic. Non-motor symptoms such as depression and REM sleep behavior disorder (RBD) each attribute to a particular neurodegenerative cluster in PD, and might enlighten the way for early prediction/detection of PD. The neuropathology of mood disturbances remains unclear. In fact, a few studies have investigated depression using diffusion magnetic resonance imaging (diffusion MRI). METHOD: Diffusion MRI of PD patients without comorbid RBD was used to assess whether microstructural abnormalities are detectable in the brain of 40 PD patients with depression compared to 19 patients without depression. Diffusion MRI connectometry was used to carry out group analysis between age- and gender-matched PD patients with and without depressive symptoms. Diffusion MRI connectometry is based on spin distribution function, which quantifies the density of diffusing water and is a sensitive and specific analytical method to psychological differences between groups. RESULTS: A significant difference (FDR = 0.016129) was observed in the left and right uncinate fasciculi, left and right inferior longitudinal fasciculi, left and right fornices, left inferior fronto-occipital fasciculus, right corticospinal tract, genu of corpus callosum, and middle cerebellar peduncle. CONCLUSION: These results suggest the prominent circuits involved in emotion recognition, particularly negative emotions, might be impaired in comorbid depressive symptoms in PD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".