Universität zu Köln

Ophey, Anja ORCID: 0000-0001-5858-7762, Wenzel, Julian, Paul, Riya, Giehl, Kathrin, Rehberg, Sarah, Eggers, Carsten, Reker, Paul, van Eimeren, Thilo, Kalbe, Elke and Kambeitz-Ilankovic, Lana ORCID: 0000-0002-8218-0425 (2022). Cognitive Performance and Learning Parameters Predict Response to Working Memory Training in Parkinson's Disease. J. Parkinsons Dis., 12 (7). S. 2235 - 2248. AMSTERDAM: IOS PRESS. ISSN 1877-718X

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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 verbalWMwith 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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Ophey, Anja UNSPECIFIED orcid.org/0000-0001-5858-7762 UNSPECIFIED Wenzel, Julian UNSPECIFIED UNSPECIFIED UNSPECIFIED Paul, Riya UNSPECIFIED UNSPECIFIED UNSPECIFIED Giehl, Kathrin UNSPECIFIED UNSPECIFIED UNSPECIFIED Rehberg, Sarah UNSPECIFIED UNSPECIFIED UNSPECIFIED Eggers, Carsten UNSPECIFIED UNSPECIFIED UNSPECIFIED Reker, Paul UNSPECIFIED UNSPECIFIED UNSPECIFIED van Eimeren, Thilo UNSPECIFIED UNSPECIFIED UNSPECIFIED Kalbe, Elke UNSPECIFIED UNSPECIFIED UNSPECIFIED Kambeitz-Ilankovic, Lana UNSPECIFIED orcid.org/0000-0002-8218-0425 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-688899
DOI:	10.3233/JPD-223448
Journal or Publication Title:	J. Parkinsons Dis.
Volume:	12
Number:	7
Page Range:	S. 2235 - 2248
Date:	2022
Publisher:	IOS PRESS
Place of Publication:	AMSTERDAM
ISSN:	1877-718X
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language OLDER-ADULTS; IMPAIRMENT; DIAGNOSIS; LEVODOPA Multiple languages Neurosciences Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/68889