Retrograde Procedural Memory in Parkinson’s Disease: A Cross-Sectional, Case-Control Study

in Journal of Parkinson's Disease (2022)

Background: The analysis of the procedural memory is particularly relevant in neurodegenerative disorders like Parkinson’s disease, due to the central role of the basal ganglia in procedural memory. It ... [more ▼]

Background: The analysis of the procedural memory is particularly relevant in neurodegenerative disorders like Parkinson’s disease, due to the central role of the basal ganglia in procedural memory. It has been shown that anterograde procedural memory, the ability to learn a new skill, is impaired in Parkinson’s disease. However, retrograde procedural memory, the long-term retention and execution of skills learned in earlier life stages, has not yet been systematically investigated in Parkinson’s disease. Objective: This study aims to investigate retrograde procedural memory in people with Parkinson’s disease.We hypothesized that retrograde procedural memory is impaired in people with Parkinson’s disease compared to an age- and gender-matched control group. Methods: First, we developed the CUPRO evaluation system, an extended evaluation system based on the Cube Copying Test, to distinguish the cube copying procedure, representing functioning of retrograde procedural memory, and the final result, representing the visuo-constructive abilities. Development of the evaluation system included tests of discriminant validity. Results: Comparing people with typical Parkinson’s disease (n = 201) with age- and gender-matched control subjects (n = 201), we identified cube copying performance to be significantly impaired in people with Parkinson’s disease (p = 0.008) No significant correlation was observed between retrograde procedural memory and disease duration. Conclusion: We demonstrated lower cube copying performance in people with Parkinson’s disease compared to control subjects, which suggests an impaired functioning of retrograde procedural memory in Parkinson’s disease. [less ▲]

Age at onset as stratifier in idiopathic Parkinson's disease - effect of ageing and polygenic risk score on clinical phenotypes

in NPJ Parkinson's Disease (2022), 9(8), 102

Several phenotypic differences observed in Parkinson's disease (PD) patients have been linked to age at onset (AAO). We endeavoured to find out whether these differences are due to the ageing process ... [more ▼]

Several phenotypic differences observed in Parkinson's disease (PD) patients have been linked to age at onset (AAO). We endeavoured to find out whether these differences are due to the ageing process itself by using a combined dataset of idiopathic PD (n = 430) and healthy controls (HC; n = 556) excluding carriers of known PD-linked genetic mutations in both groups. We found several significant effects of AAO on motor and non-motor symptoms in PD, but when comparing the effects of age on these symptoms with HC (using age at assessment, AAA), only positive associations of AAA with burden of motor symptoms and cognitive impairment were significantly different between PD vs HC. Furthermore, we explored a potential effect of polygenic risk score (PRS) on clinical phenotype and identified a significant inverse correlation of AAO and PRS in PD. No significant association between PRS and severity of clinical symptoms was found. We conclude that the observed non-motor phenotypic differences in PD based on AAO are largely driven by the ageing process itself and not by a specific profile of neurodegeneration linked to AAO in the idiopathic PD patients. [less ▲]

Ten Quick Tips for Biomarker Discovery and Validation Analyses Using Machine Learning

in PLoS Computational Biology (2022), 18(8), 1010357

High-throughput experimental methods for biosample profiling and growing collections of clinical and health record data provide ample opportunities for biomarker discovery and medical decision support ... [more ▼]

High-throughput experimental methods for biosample profiling and growing collections of clinical and health record data provide ample opportunities for biomarker discovery and medical decision support. However, many of the new data types, including single-cell omics and high-resolution cellular imaging data, also pose particular challenges for data analysis. A high dimensionality of the data in relation to small numbers of available samples, influences of additive and multiplicative noise, large numbers of uninformative or redundant data features, outliers, confounding factors and imbalanced sample group numbers are all common characteristics of current biomedical data collections. While first successes have been achieved in developing clinical decision support tools using multifactorial omics data, there is still an unmet need and great potential for earlier, more accurate and robust diagnostic and prognostic tools for many complex diseases. Here, we provide a set of broadly applicable tips to address some of the most common pitfalls and limitations for biomarker signature development, including supervised and unsupervised machine learning, feature selection and hypothesis testing approaches. In contrast to previous guidelines discussing detailed aspects of quality control, statistics or study reporting, we give a broader overview of the typical challenges and sort the quick tips to address them chronologically by the study phase (starting with study design, then covering consecutive phases of biomarker signature discovery and validation, see also the overview in Fig. 1). While these tips are not comprehensive, they are chosen to cover what we consider as the most frequent, significant, and practically relevant issues and risks in biomarker development. By pointing the reader to further relevant literature on the covered aspects of biomarker discovery and validation, we hope to provide an initial guideline and entry point into the more detailed technical and application-specific aspects of this field. [less ▲]

The retrograde procedural memory in people with Parkinson’s disease with or without freezing of gait – a cross-sectional study

Poster (2021, September 17)

Objective: To investigate the retrograde procedural memory in people with typical Parkinson’s disease (PwP) with or without freezing of gait (FOG). We hypothesized that the retrograde procedural memory is ... [more ▼]

Objective: To investigate the retrograde procedural memory in people with typical Parkinson’s disease (PwP) with or without freezing of gait (FOG). We hypothesized that the retrograde procedural memory is more strongly impaired in patients with FOG (FOG+) than in patients without FOG (FOG-). Background: Given that cognitive functions, like executive control and automaticity, are crucial for mobility, it is of great importance to get a deeper knowledge of the cognitive impairment that may interfere with walking and causing gait disturbances in PwP, i.e. FOG. The integrity of retrograde procedural memory, the ability to execute skills that have been learned in earlier life stages, is essential for a person’s ability to complete routine, procedural activities like walking. As FOG is characterized as a de-automatization disorder, we hypothesized an impairment of the retrograde procedural memory in patients with FOG. Methods: A total of 194 patients from the Luxembourg Parkinson’s study were included into the cross-sectional study. All patients were assigned to the FOG+ / FOG- groups based on a semi-structured interview conducted by a study physician. The extended evaluation system of the cube copying test was applied to evaluate both the cube-drawing procedure, representing the retrograde procedural memory, and the final result, representing the visuo-constructive abilities (Pauly et al., 2020, MDS abstract). We compared the cube copying performance of n=97 FOG+ with n=97 age-, gender- and education-matched FOG-. Results: FOG+ scored lower on the cube copying procedure compared to the FOG- (p=0.027), which is suggestive of an impaired retrograde procedural memory in FOG+. No significant differences in the visuo-constructional abilities were detected (p=0.945). Conclusion: In line with FOG being considered a de-automatization of walking, a skill acquired in earlier life stages, the present results suggest that PwP with FOG have an impaired retrograde procedural memory in comparison to PwP without FOG. The results lend support to the ability of the extended evaluation system of the cube copying test to assess impaired retrograde procedural memory and help improve our understanding of behavioral symptoms in PwP. [less ▲]

Fast cross-validation for multi-penalty ridge regression

in Journal of Computational and Graphical Statistics (2021), 30(4), 835-847

Prediction based on multiple high-dimensional data types needs to account for the potentially strong differences in predictive signal. Ridge regression is a simple, yet versatile and interpretable model ... [more ▼]

Prediction based on multiple high-dimensional data types needs to account for the potentially strong differences in predictive signal. Ridge regression is a simple, yet versatile and interpretable model for high-dimensional data that has challenged the predictive performance of many more complex models and learners, in particular in dense settings. Moreover, it allows using a specific penalty per data type to account for differences between those. Then, the largest challenge for multi-penalty ridge is to optimize these penalties efficiently in a cross-validation (CV) setting, in particular for GLM and Cox ridge regression, which require an additional loop for fitting the model by iterative weighted least squares (IWLS). Our main contribution is a computationally very efficient formula for the multi-penalty, sample-weighted hat-matrix, as used in the IWLS algorithm. As a result, nearly all computations are in the low-dimensional sample space. We show that our approach is several orders of magnitude faster than more naive ones. We developed a very flexible framework that includes prediction of several types of response, allows for unpenalized covariates, can optimize several performance criteria and implements repeated CV. Moreover, extensions to pair data types and to allow a preferential order of data types are included and illustrated on several cancer genomics survival prediction problems. The corresponding R-package, multiridge, serves as a versatile standalone tool, but also as a fast benchmark for other more complex models and multi-view learners. [less ▲]

Predictive and interpretable models via the stacked elastic net

in Bioinformatics (2021), 37(14), 20122016

Motivation: Machine learning in the biomedical sciences should ideally provide predictive and interpretable models. When predicting outcomes from clinical or molecular features, applied researchers often ... [more ▼]

Motivation: Machine learning in the biomedical sciences should ideally provide predictive and interpretable models. When predicting outcomes from clinical or molecular features, applied researchers often want to know which features have effects, whether these effects are positive or negative, and how strong these effects are. Regression analysis includes this information in the coefficients but typically renders less predictive models than more advanced machine learning techniques. Results: Here we propose an interpretable meta-learning approach for high-dimensional regression. The elastic net provides a compromise between estimating weak effects for many features and strong effects for some features. It has a mixing parameter to weight between ridge and lasso regularisation. Instead of selecting one weighting by tuning, we combine multiple weightings by stacking. We do this in a way that increases predictivity without sacrificing interpretability. Availability and Implementation: The R package starnet is available on GitHub: https://github.com/rauschenberger/starnet. [less ▲]

Testing for association between RNA-Seq and high-dimensional data

in BMC Bioinformatics (2016), 17

Background: Testing for association between RNA-Seq and other genomic data is challenging due to high variability of the former and high dimensionality of the latter. Results: Using the negative binomial ... [more ▼]

Background: Testing for association between RNA-Seq and other genomic data is challenging due to high variability of the former and high dimensionality of the latter. Results: Using the negative binomial distribution and a random-effects model, we develop an omnibus test that overcomes both difficulties. It may be conceptualised as a test of overall significance in regression analysis, where the response variable is overdispersed and the number of explanatory variables exceeds the sample size. Conclusions: The proposed test can detect genetic and epigenetic alterations that affect gene expression. It can examine complex regulatory mechanisms of gene expression. The R package globalSeq is available from Bioconductor. [less ▲]