Cognitive Decline in Parkinson's Disease: Understanding the Pathophysiological Differences
Bibliographic record
Abstract
Parkinson’s disease (PD) is the second most common neurodegenerative disorder, and the accompanying cognitive decline sequelae are one of its most pressing and least understood non-motor symptoms. It is estimated that between 20 to 40% of PD patients have some form of cognitive decline at the time of diagnosis, and over 80% of PD patients eventually convert to dementia within 20 years of diagnosis. PD patients can experience deficits in executive function, memory, attention, language and visuospatial function. PD has been linked to many different forms of pathology, ranging from brain atrophy and functional dysregulation, toxic protein deposition, and neurotransmitter system dysfunction. However, it is still not well understood why some PD patients experience rapid cognitive decline while others remain cognitively stable for years. Brain imaging techniques can be used to help elucidate the pathophysiological changes that occur which separate cognitive decline from cognitive sparing, providing insight into the compensatory changes the brain undergoes to maintain healthy cognition. The general aim of this thesis was to uncover the brain differences between PD patients with and without cognitive decline using multimodal imaging techniques and machine learning assisted modelling. First, using a coordinate-based meta-analysis, we found that key hub regions in three different important brain networks were affected by structural atrophy and functional hypometabolism in PD patients with cognitive decline: the bilateral insula, the bilateral dorsolateral prefrontal cortex, and left angular gyrus. Second, we used graph theory analysis to uncover that cognitively spared PD patients had more efficient brain networks than PD patients with mild cognitive impairment, suggesting compensatory network reorganization was occurring to maintain healthy cognition in the face of increasing disease burden. Finally, we found evidence that region beta-amyloid deposition affects cognitive decline in PD, with some brain regions being more vulnerable to the burden than others. Taken together, this thesis posits various ways the brain experiences both compensatory help and pathological harm due to PD cognitive decline pathology, providing a better understanding of this complex symptom umbrella.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".