The extent to which Alzheimer neuropathology, particularly the accumulation of misfolded beta-amyloid, contributes to cognitive decline and dementia in Parkinson's disease (PD) is unresolved. Here, we used Florbetaben PET imaging to test for any association between cerebral amyloid deposition and cognitive impairment in PD, in a sample enriched for cases with mild cognitive impairment. This cross-sectional study used Movement Disorders Society level II criteria to classify 115 participants with PD as having normal cognition (PDN, n = 23), mild cognitive impairment (PD-MCI, n = 76), or dementia (PDD, n = 16). We acquired 18F-Florbetaben (FBB) amyloid PET and structural MRI. Amyloid deposition was assessed between the three cognitive groups, and also across the whole sample using continuous measures of both global cognitive status and average performance in memory domain tests. Outcomes were cortical FBB uptake, expressed in centiloids and as standardized uptake value ratios (SUVR) using the Centiloid Project whole cerebellum region as a reference, and regional SUVR measurements. FBB binding was higher in PDD, but this difference did not survive adjustment for the older age of the PDD group. We established a suitable centiloid cut-off for amyloid positivity in Parkinson's disease (31.3), but there was no association of FBB binding with global cognitive or memory scores. The failure to find an association between PET amyloid deposition and cognitive impairment in a moderately large sample, particularly given that it was enriched with PD-MCI patients at risk of dementia, suggests that amyloid pathology is not the primary driver of cognitive impairment and dementia in most patients with PD.

Background Neuropsychiatric symptoms in Parkinson's disease (PD) may increase dementia (PDD) risk. The predictive value of these symptoms, however, has not been compared to clinical and demographic predictors of future PDD. Objectives Determine if neuropsychiatric symptoms are useful markers of PDD risk. Methods 328 PD participants completed baseline neuropsychiatric and MDS‐Task Force‐Level II assessments. Of these, 202 non‐demented individuals were followed‐up over a four‐years period to detect conversion to PDD; 51 developed PDD. ROC analysis tested associations between baseline neuropsychiatric symptoms and future PDD. The probability of developing PDD was also modeled as a function of neuropsychiatric inventory (NPI)‐total score, PD Questionnaire (PDQ)‐hallucinations, PDQ‐anxiety, and contrasted to cognitive ability, age, and motor function. Leave‐one‐out information criterion was used to evaluate which models provided useful information when predicting future PDD. Results The PDD group experienced greater levels of neuropsychiatric symptoms compared to the non‐PDD groups at baseline. Few differences were found between the PD‐MCI and PD‐N groups. Six neuropsychiatric measures were significantly, but weakly, associated with future PDD. The strongest was NPI‐total score: AUC = 0.66 [0.57–0.75]. There was, however, no evidence it contained useful out‐of‐sample predictive information of future PDD (delta ELPD = 1.8 (SD 2.5)); Similar results held for PDQ‐hallucinations and PDQ‐anxiety. In contrast, cognitive ability (delta ELPD = 36 (SD 8)) and age (delta ELPD = 11 (SD 5)) provided useful predictive information of future PDD. Conclusions Cognitive ability and age strongly out‐performed neuropsychiatric measures as markers of developing PDD within 4 years. Therefore, neuropsychiatric symptoms do not appear to be useful markers of PDD risk.

Parkinson’s disease affects millions worldwide with a large rise in expected burden over the coming decades. More easily accessible tools and techniques to diagnose and monitor Parkinson’s disease can improve the quality of life of patients. With the advent of new wearable technologies such as smart rings and watches, this is within reach. However, it is unclear what method for these new technologies may provide the best opportunity to capture the patient-specific severity. This study investigates which locations on the hand can be used to capture and monitor maximal movement/tremor severity. Using a Leap Motion device and custom-made software the volume, velocity, acceleration, and frequency of Parkinson’s (n = 55, all right-handed, majority right-sided onset) patients’ hand locations (25 joints inclusive of all fingers/thumb and the wrist) were captured simultaneously. Distal locations of the right hand, i.e., the ends of fingers and the wrist showed significant trends (p < 0.05) towards having the largest movement velocities and accelerations. The right hand, compared with the left hand, showed significantly greater volumes, velocities, and accelerations (p < 0.01). Supplementary analysis showed that the volumes, acceleration, and velocities had significant correlations (p < 0.001) with clinical MDS-UPDRS scores, indicating the potential suitability of using these metrics for monitoring disease progression. Maximal movements at the distal hand and wrist area indicate that these locations are best suited to capture hand tremor movements and monitor Parkinson’s disease.

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Background: Neuropsychiatric symptoms in Parkinson's disease (PD) may increase dementia (PDD) risk. The predictive value of these symptoms, however, has not been compared to clinical and demographic predictors of future PDD. Methods: 325 PD participants completed baseline neuropsychiatric and MDS-Task Force Level II assessments. Of these, 195 non-demented individuals were followed-up over a four-year period to detect conversion to PDD; 51 developed PDD. ROC analysis tested associations between baseline neuropsychiatric symptoms and conversion to PDD. The probability of developing PDD was also modelled as a function of neuropsychiatric inventory (NPI) total score, PD Questionnaire (PDQ) hallucinations, PDQ anxiety and contrasted to cognitive ability, age and motor function. Leave-one-out information criterion was used to evaluate which models provided useful information when predicting future PDD. Results: The PDD group experienced greater levels of neuropsychiatric symptoms compared to the PD-MCI and PD-N groups at baseline. Few differences were found between the PD-MCI and PD-N groups. Five neuropsychiatric measures were significantly, but weakly, associated with future PDD. The strongest was NPI total score: AUC=0.66 [0.55-0.76]. There was, however, no evidence that it contained useful out-of-sample predictive information of future PDD (delta ELPD=1.6 (SD 2.4)); Similar results held for PDQ hallucinations and PDQ anxiety. In contrast, cognitive ability (delta ELPD=35 (SD 8)) and age (delta ELPD=11 (SD 5)) provided useful predictive information of future PDD. Conclusions: Cognitive ability and age strongly out-performed neuropsychiatric measures as markers of developing PDD within four years. Therefore, neuropsychiatric symptoms do not appear to be useful markers of PDD risk.