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

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

Brain age and Alzheimer's-like atrophy are domain-specific predictors of cognitive impairment in Parkinson's disease

Recently, it was shown that patients with Parkinson's disease (PD) who exhibit an Alzheimer's disease (AD)-like pattern of brain atrophy are at greater risk for future cognitive decline. This study aimed to investigate whether this association is domain-specific and whether atrophy associated with brain aging also relates to cognitive impairment in PD. SPARE-AD, an MRI index capturing AD-like atrophy, and atrophy-based estimates of brain age were computed from longitudinal structural imaging data of 178 PD patients and 84 healthy subjects from the LANDSCAPE cohort. All patients underwent an extensive neuropsychological test battery. Patients diagnosed with mild cognitive impairment or dementia were found to have higher SPARE-AD scores as compared to patients with normal cognition and healthy controls. All patient groups showed increased brain age. SPARE-AD predicted impairment in memory, language and executive functions, whereas advanced brain age was associated with deficits in attention and working memory. Data suggest that SPARE-AD and brain age are differentially related to domain-specific cognitive decline in PD. The underlying pathomechanisms remain to be determined. (c) 2021 Elsevier Inc. All rights reserved

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

BackgroundApathy 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.ObjectiveTo facilitate the application of the AES in PD, we reduced the AES to two-thirds its length and validated this abbreviated version.DesignData 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.ResultsThe 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.ConclusionResults 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

sj-docx-1-eso-10.1177_23969873241242239 – Supplemental material for Efficacy and safety of oral factor Xa inhibitors versus vitamin-K antagonists in the early phase after acute ischemic stroke or TIA in the real-world setting: The PRODAST study

Supplemental material, sj-docx-1-eso-10.1177_23969873241242239 for Efficacy and safety of oral factor Xa inhibitors versus vitamin-K antagonists in the early phase after acute ischemic stroke or TIA in the real-world setting: The PRODAST study by Hans-Christoph Diener, Gerrit M Grosse, Anika Hüsing, Andreas Stang, Nils Kuklik, Marcus Brinkmann, Gabriele D Maurer, Hassan Soda, Carsten Pohlmann, Rüdiger Hilker-Roggendorf, Nikola Popovic, Peter Kraft, Bruno-Marcel Mackert, Christoph C Eschenfelder and Christian Weimar in European Stroke Journal</p

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

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

Cognitive profiles of patients with mild cognitive impairment due to Alzheimer's versus Parkinson's disease defined using a base rate approach: Implications for neuropsychological assessments

INTRODUCTION: Large studies on cognitive profiles of patients with mild cognitive impairment (MCI) due to Alzheimer's disease (AD-MCI) compared to Parkinson's disease (PD-MCI) are rare. METHODS: Data from two multicenter cohort studies in AD and PD were merged using a unified base rate approach for the MCI diagnosis. Cognitive profiles were compared using scores derived from the Consortium to Establish a Registry for Alzheimer's Disease battery. RESULTS: Patients with AD-MCI showed lower standardized scores on all memory test scores and a language test. Patients with PD-MCI showed lower standardized scores in a set-shifting measure as an executive task. A cross-validated logistic regression with test scores as predictors was able to classify 72% of patients correctly to AD-MCI versus PD-MCI. DISCUSSION: The applied test battery successfully discriminated between AD-MCI and PD-MCI. Neuropsychological test batteries in clinical practice should always include a broad spectrum of cognitive domains to capture any cognitive changes

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 followup 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 was present and we found differences for motor phenotypes in attention (beta = -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