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  • 1
    Online Resource
    Online Resource
    Long-Term Cognitive Decline Related to the Motor Phenotype in Parkinson’s Disease
    IOS Press ; 2022
    In:  Journal of Parkinson's Disease Vol. 12, No. 3 ( 2022-04-05), p. 905-916
    Details
    In: Journal of Parkinson's Disease, IOS Press, Vol. 12, No. 3 ( 2022-04-05), p. 905-916
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1877-7171 , 1877-718X
    URL: Article
    DOI: 10.3233/JPD-212787
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2022
    detail.hit.zdb_id: 2599550-9
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  • 2
    Online Resource
    Online Resource
    Cognitive Performance and Learning Parameters Predict Response to Working Memory Training in Parkinson’s Disease
    IOS Press ; 2022
    In:  Journal of Parkinson's Disease Vol. 12, No. 7 ( 2022-10-14), p. 2235-2247
    Details
    In: Journal of Parkinson's Disease, IOS Press, Vol. 12, No. 7 ( 2022-10-14), p. 2235-2247
    Abstract: Background: Working memory (WM) training (WMT) is a popular intervention approach against cognitive decline in patients with Parkinson’s disease (PD). However, heterogeneity in WM responsiveness suggests that WMT may not be equally efficient for all patients. Objective: The present study aims to evaluate a multivariate model to predict post-intervention verbal WM in patients with PD using a supervised machine learning approach. We test the predictive potential of novel learning parameters derived from the WMT and compare their predictiveness to other more commonly used domains including demographic, clinical, and cognitive data. Methods: 37 patients with PD (age: 64.09±8.56, 48.6% female, 94.7% Hoehn & Yahr stage 2) participated in a 5-week WMT. Four random forest regression models including 1) cognitive variables only, 2) learning parameters only, 3) both cognitive and learning variables, and 4) the entire set of variables (with additional demographic and clinical data, ‘all’ model), were built to predict immediate and 3-month-follow-up WM. Result: The ‘all’ model predicted verbal WM with the lowest root mean square error (RMSE) compared to the other models, at both immediate (RMSE = 0.184; 95% -CI=[0.184;0.185] ) and 3-month follow-up (RMSE = 0.216; 95% -CI=[0.215;0.217]). Cognitive baseline parameters were among the most important predictors in the ‘all’ model. The model combining cognitive and learning parameters significantly outperformed the model solely based on cognitive variables. Conclusion: Commonly assessed demographic, clinical, and cognitive variables provide robust prediction of response to WMT. Nonetheless, inclusion of training-inherent learning parameters further boosts precision of prediction models which in turn may augment training benefits following cognitive interventions in patients with PD.
    Type of Medium: Online Resource
    ISSN: 1877-7171 , 1877-718X
    URL: Article
    DOI: 10.3233/JPD-223448
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2022
    detail.hit.zdb_id: 2599550-9
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
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