Role of dopamine agonists in Parkinson's disease therapy

Dopamine agonists are an important component of Parkinson's therapy. When weighing up the various therapy options, therapy with levodopa has recently been increasingly preferred due to its stronger efficacy and the ostensibly lower rate of side effects. The advantage of the lower incidence of motor complications during therapy with dopamine agonists was neglected. The occurrence of side effects can be explained by the different receptor affinity to the individual dopaminergic and non-dopaminergic receptors of the individual dopamine agonists. However, the different affinity to individual receptors also explains the different effect on individual Parkinson symptoms and can, therefore, contribute to a targeted use of the different dopamine agonists. Since comparative studies on the differential effect of dopamine agonists have only been conducted for individual substances, empirical knowledge of the differential effect is of great importance. Therefore, the guidelines for the treatment of Parkinson's disease do not consider the differential effect of the dopamine agonists. The historical consideration of dopamine agonists within Parkinson's therapy deserves special attention to be able to classify the current discussion about the significance of dopamine agonists.

Comparative analysis of machine learning algorithms for multi-syndrome classification of neurodegenerative syndromes

Importance

The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context.

Objective

Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging.

Design, setting, and participants

Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes.

Interventions

N.A.

Main outcomes and measures

Cohen’s kappa, accuracy, and F1-score to assess model performance.

Results

Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy.

Conclusions and relevance

Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best.

Long-Term Cognitive Decline Related to the Motor Phenotype in Parkinson's Disease

BACKGROUND

Parkinson's disease (PD) is associated with various non-motor symptoms, including cognitive deterioration.

OBJECTIVE

Here, we used data from the DEMPARK/LANDSCAPE cohort to describe the association between progression of cognitive profiles and the PD motor phenotypes: postural instability and gait disorder (PIGD), tremor-dominant (TR-D), and not-determined (ND).

METHODS

Demographic, clinical, and neuropsychological six-year longitudinal data of 711 PD-patients were included (age: M = 67.57; 67.4% males). We computed z-transformed composite scores for a priori defined cognitive domains. Analyses were controlled for age, gender, education, and disease duration. To minimize missing data and drop-outs, three-year follow-up data of 442 PD-patients was assessed with regard to the specific role of motor phenotype on cognitive decline using linear mixed modelling (age: M = 66.10; 68.6% males).

RESULTS

Our study showed that in the course of the disease motor symptoms increased while MMSE and PANDA remained stable in all subgroups. After three-year follow-up, significant decline of overall cognitive performance for PIGD-patients were present and we found differences for motor phenotypes in attention (β= -0.08, SE = 0.003, p < 0.006) and memory functions showing that PIGD-patients deteriorate per months by -0.006 compared to the ND-group (SE = 0.003, p = 0.046). Furthermore, PIGD-patients experienced more often difficulties in daily living.

CONCLUSION

Over a period of three years, we identified distinct neuropsychological progression patterns with respect to different PD motor phenotypes, with early executive deficits yielding to a more amnestic profile in the later course. Here, in particular PIGD-patients worsened over time compared to TR-D and ND-patients, highlighting the greater risk of dementia for this motor phenotype.

A new CERAD total score with equally weighted z-scores and additional executive and non-amnestic "CERAD-Plus" tests enhances cognitive diagnosis in patients with Parkinson's disease: Evidence from the LANDSCAPE study

INTRODUCTION

The Consortium to Establish a Registry for Alzheimer's Disease (CERAD) is a renowned cognitive test battery, which has been extended in its German version to the CERAD-Plus including tests of executive functions and processing speed. The most commonly used total score (TS) is based on the restricted CERAD version and reflects the sum of selected raw-values (Chandler et al., 2005). The CERAD-Plus extensions might be of particular diagnostic utility for cognitive assessments in Parkinson's Disease (PD), as executive functions and processing speed belong to the most vulnerable domains in PD.

OBJECTIVE

The aim was to develop a CERAD-TS based on the extended CERAD-Plus' age-, gender-, and education-corrected z-scores and to evaluate its diagnostic accuracy compared to the established CERAD-Chandler-TS.

METHODS

Baseline data of n = 679 patients with PD (69% male, n = 277 PD without cognitive impairment, n = 307 PD-MCI, n = 95 PD-D) from the multicenter, prospective DEMPARK/LANDSCAPE study were analyzed. ROC-analyses were conducted for four different TS that were either based on the original CERAD or CERAD-Plus, on raw-values or z-scores, and equally-weighted or based on factor scores. AUC-comparisons were conducted to determine the best yet most parsimonious TS.

RESULTS

The newly designed CERAD-Plus-TS based on equally-weighted z-scores outperformed both the CERAD-Chandler-TS and cognitive screening instruments when differentiating between individuals with PD of varying cognitive impairment (0.78 ≤ AUC ≤ 0.98).

CONCLUSION

Results suggest a high relevance of non-amnestic subscales for the cognitive assessment in PD populations. The proposed CERAD-Plus-TS needs further validation. The extensions might offer diagnostic potential for non-PD populations as well.

[Recommendation for the differentiated use of nuclear medical diagnostic for parkinsonian syndromes]

The present work provides an overview of the various nuclear medicine methods in the diagnosis of neurodegenerative parkinsonian syndromes and their respective evidence and is intended to enable practical decision-making aids in the application and interpretation of the methods and findings. The value of the procedures differs considerably in relation to the two relevant diagnostic questions. On the one hand, it is the question of whether there is a neurodegenerative parkinsonian syndrome at all, and on the other hand the question of which one. While the DAT-SPECT is undisputedly the method of choice for answering the first question (taking certain parameters into account), this method is not suitable for answering the second question. To categorise parkinsonian syndromes into idiopathic (i. e. Parkinson´s disease) or atypical, various procedures are used in everyday clinical practice including MIBG scintigraphy, and FDG-PET. We explain why FDG-PET currently is not only the most suitable of these methods to differentiate an idiopathic parkinsonian syndrome, from an atypical Parkinson's syndrome, but also enables sufficiently valid to distinguish the various atypical neurodegenerative Parkinson's syndromes (i. e. MSA, PSP and CBD) from each other and therefore should be reimbursed by health insurances.

Longitudinal brain atrophy distribution in advanced Parkinson's disease: What makes the difference in "cognitive status" converters?

We investigated the brain atrophy distribution pattern and rate of regional atrophy change in Parkinson's disease (PD) in association with the cognitive status to identify the morphological characteristics of conversion to mild cognitive impairment (MCI) and dementia (PDD). T1-weighted longitudinal 3T MRI data (up to four follow-up assessments) from neuropsychologically well-characterized advanced PD patients (n = 172, 8.9 years disease duration) and healthy elderly controls (n = 85) enrolled in the LANDSCAPE study were longitudinally analyzed using a linear mixed effect model and atlas-based volumetry and cortical thickness measures. At baseline, PD patients presented with cerebral atrophy and cortical thinning including striatum, temporoparietal regions, and primary/premotor cortex. The atrophy was already observed in "cognitively normal" PD patients (PD-N) and was considerably more pronounced in cognitively impaired PD patients. Linear mixed effect modeling revealed almost similar rates of atrophy change in PD and controls. The group comparison at baseline between those PD-N whose cognitive performance remained stable (n = 42) and those PD-N patients who converted to MCI/PDD ("converter" cPD-N, n = 26) indicated suggested cortical thinning in the anterior cingulate cortex in cPD-N patients which was correlated with cognitive performance. Our results suggest that cortical brain atrophy has been already expanded in advanced PD patients without overt cognitive deficits while atrophy progression in late disease did not differ from "normal" aging regardless of the cognitive status. It appears that cortical atrophy begins early and progresses already in the initial disease stages emphasizing the need for therapeutic interventions already at disease onset.

Structural brain signature of cognitive decline in Parkinson's disease: DTI-based evidence from the LANDSCAPE study

Background:

The nonmotor symptom spectrum of Parkinson’s disease (PD) includes progressive cognitive decline mainly in late stages of the disease. The aim of this study was to map the patterns of altered structural connectivity of patients with PD with different cognitive profiles ranging from cognitively unimpaired to PD-associated dementia.

Methods:

Diffusion tensor imaging and neuropsychological data from the observational multicentre LANDSCAPE study were analyzed. A total of 134 patients with PD with normal cognitive function (56 PD-N), mild cognitive impairment (67 PD-MCI), and dementia (11 PD-D) as well as 72 healthy controls were subjected to whole-brain-based fractional anisotropy mapping and covariance analysis with cognitive performance measures.

Results:

Structural data indicated subtle changes in the corpus callosum and thalamic radiation in PD-N, whereas severe white matter impairment was observed in both PD-MCI and PD-D patients including anterior and inferior fronto-occipital, uncinate, insular cortices, superior longitudinal fasciculi, corona radiata, and the body of the corpus callosum. These regional alterations were demonstrated for PD-MCI and were more pronounced in PD-D. The pattern of involved regions was significantly correlated with the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) total score.

Conclusions:

The findings in PD-N suggest impaired cross-hemispherical white matter connectivity that can apparently be compensated for. More pronounced involvement of the corpus callosum as demonstrated for PD-MCI together with affection of fronto-parieto-temporal structural connectivity seems to lead to gradual disruption of cognition-related cortico-cortical networks and to be associated with the onset of overt cognitive deficits. The increase of regional white matter damage appears to be associated with the development of PD-associated dementia.

Cognitive decline in Parkinson's disease: the impact of the motor phenotype on cognition

OBJECTIVES

Parkinson's disease (PD) is the second most common neurodegenerative disorder and is further associated with progressive cognitive decline. In respect to motor phenotype, there is some evidence that akinetic-rigid PD is associated with a faster rate of cognitive decline in general and a greater risk of developing dementia.The objective of this study was to examine cognitive profiles among patients with PD by motor phenotypes and its relation to cognitive function.

METHODS

Demographic, clinical and neuropsychological cross-sectional baseline data of the DEMPARK/LANDSCAPE study, a multicentre longitudinal cohort study of 538 patients with PD were analysed, stratified by motor phenotype and cognitive syndrome. Analyses were performed for all patients and for each diagnostic group separately, controlling for age, gender, education and disease duration.

RESULTS

Compared with the tremor-dominant phenotype, akinetic-rigid patients performed worse in executive functions such as working memory (Wechsler Memory Scale-Revised backward; p=0.012), formal-lexical word fluency (p=0.043), card sorting (p=0.006), attention (Trail Making Test version A; p=0.024) and visuospatial abilities (Leistungsprüfungssystem test 9; p=0.006). Akinetic-rigid neuropsychological test scores for the executive and attentive domain correlated negatively with non-tremor motor scores. Covariate-adjusted binary logistic regression analyses showed significant odds for PD-mild cognitive impairment for not-determined as compared with tremor-dominant (OR=3.198) and akinetic-rigid PD (OR=2.059). The odds for PD-dementia were significant for akinetic-rigid as compared with tremor-dominant phenotype (OR=8.314).

CONCLUSION

The three motor phenotypes of PD differ in cognitive performance, showing that cognitive deficits seem to be less severe in tremor-dominant PD. While these data are cross-sectional, longitudinal data are needed to shed more light on these differential findings.

Psychometric Properties of an Abbreviated Version of the Apathy Evaluation Scale for Parkinson Disease (AES-12PD)

BACKGROUND

Apathy is a frequent symptom in Parkinson's disease (PD), substantially aggravating the course of PD. Regarding the accumulating evidence of the key role of apathy in PD, time-efficient assessments are useful for fostering progress in research and treatment. The Apathy Evaluation Scale (AES) is widely used for the assessment of apathy across different nosologies.

OBJECTIVE

To facilitate the application of the AES in PD, we reduced the AES to two-thirds its length and validated this abbreviated version.

DESIGN

Data sets of 339 PD patients of the DEMPARK/LANDSCAPE study without dementia and depression were randomly split into two samples. Data of sample 1 were used to develop a brief version of the AES (AES-12PD). A cross-validation was conducted in sample 2 and in a subsample of 42 PD patients with comorbid dementia and depressive symptomatology. Receiver operating characteristic analysis was applied to determine the optimal cutoff of the AES-12PD as an indicator of apathy.

RESULTS

The AES-12PD featured high internal consistency that was better compared to the AES. The abbreviated scale was well differentiated from motor impairment and cognitive deficits. The AES-12PD cutoff of 27/28 was the optimal cutoff for apathy in PD patients without dementia and depression. The cutoff of 25/26 indicated apathy in PD patients with comorbid dementia and depression.

CONCLUSION

Results confirm a high internal consistency and good discriminant validity of the AES-12PD. The AES-12PD represents a reliable tool for the efficient assessment of apathy that can be applied in PD patients with and without dementia and depression.

Psychometric properties of the apathy evaluation scale in patients with Parkinson's disease

Parkinson's disease (PD) frequently entails non-motor symptoms, worsening the course of the disease. Apathy is one of the core neuropsychiatric symptoms that has been investigated in recent years; research is however hampered by the limited availability of well-evaluated apathy scales for these patients. We evaluated the psychometric properties of the Apathy Evaluation Scale (AES) in a sample of PD patients. Psychometric properties, convergent and discriminant validity and sensitivity/specificity were evaluated in patients with (n = 582) or without dementia/depression (n = 339). Internal consistency was high in the entire sample as well as in patients without dementia/depression. Correlations were moderate for convergent validity (UPDRS I item 4: motivation). While apathy could be differentiated from cognitive decline, it was related to depression (Geriatric Depression Scale, GDS-15). The overall classification accuracy based on the UPDRS I item 4 was comparable for AES and GDS scores. The AES exhibits good psychometric properties in PD patients with and without dementia and/or depression. Commonly used screenings on the presence of apathy had low detection rates compared to the AES and reflected both apathetic and depressive symptoms. Psychometric evaluation of available instruments will support further research on the clinical relevance of apathy for disease progression and treatment approaches in PD patients.

Evaluation of brain ageing: a quantitative longitudinal MRI study over 7 years

OBJECTIVES

T1 relaxometry is a promising tool for the assessment of microstructural changes during brain ageing. Previous cross-sectional studies demonstrated increasing T1 values in white and decreasing T1 values in grey matter over the lifetime. However, these findings have not yet been confirmed on the basis of a longitudinal study. In this longitudinal study over 7 years, T1 relaxometry was used to investigate the dynamics of age-related microstructural changes in older healthy subjects.

METHODS

T1 mapping was performed in 17 healthy subjects (range 51-77 years) at baseline and after 7 years. Advanced cortical and white matter segmentation was used to determine mean T1 values in the cortex and white matter.

RESULTS

The analysis revealed a decrease of mean cortical T1 values over 7 years, the rate of T1 reduction being more prominent in subjects with higher age. T1 decreases were predominantly localized in the lateral frontal, parietal and temporal cortex. In contrast, mean white matter T1 values remained stable.

CONCLUSIONS

T1 mapping is shown to be sensitive to age-related microstructural changes in healthy ageing subjects in a longitudinal setting. Data of a cohort in late adulthood and the senescence period demonstrate a decrease of cortical T1 values over 7 years, most likely reflecting decreasing water content and increased iron concentrations.

KEY POINTS

• T1 mapping is sensitive to age-related microstructural changes in a longitudinal setting. • T1 decreases were predominantly localized in the lateral frontal, parietal and temporal cortex. • The rate of T1 reduction was more prominent in subjects with higher age. • These changes most likely reflect decreasing cortical water and increasing iron concentrations.