Comparing Montreal Cognitive Assessment Performance in Parkinson’s Disease Patients: Age- and Education-Adjusted Cutoffs vs. Machine Learning

Objective@#The Montreal Cognitive Assessment (MoCA) is recommended for general cognitive evaluation in Parkinson’s disease (PD) patients. However, age- and education-adjusted cutoffs specifically for PD have not been developed or systematically validated across PD cohorts with diverse education levels. @*Methods@#In this retrospective analysis, we utilized data from 1,293 Korean patients with PD whose cognitive diagnoses were determined through comprehensive neuropsychological assessments. Age- and education-adjusted cutoffs were formulated based on 1,202 patients with PD. To identify the optimal machine learning model, clinical parameters and MoCA domain scores from 416 patients with PD were used. Comparative analyses between machine learning methods and different cutoff criteria were conducted on an additional 91 consecutive patients with PD. @*Results@#The cutoffs for cognitive impairment decrease with increasing age within the same education level. Similarly, lower education levels within the same age group correspond to lower cutoffs. For individuals aged 60–80 years, cutoffs were set as follows: 25 or 24 years for those with more than 12 years of education, 23 or 22 years for 10–12 years, and 21 or 20 years for 7–9 years. Comparisons between age- and education-adjusted cutoffs and the machine learning method showed comparable accuracies. The cutoff method resulted in a higher sensitivity (0.8627), whereas machine learning yielded higher specificity (0.8250). @*Conclusion@#Both the age- and education-adjusted cutoff methods and machine learning methods demonstrated high effectiveness in detecting cognitive impairment in PD patients. This study highlights the necessity of tailored cutoffs and suggests the potential of machine learning to improve cognitive assessment in PD patients.

The Protein-Protein Interaction Network of Hereditary Parkinsonism Genes Is a Hierarchical Scale-Free Network

Purpose@#Hereditary parkinsonism genes consist of causative genes of familial Parkinson’s disease (PD) with a locus symbol prefix (PARK genes) and hereditary atypical parkinsonian disorders that present atypical features and limited responsiveness to levodopa (non-PARK genes). Although studies have shown that hereditary parkinsonism genes are related to idiopathic PD at the phenotypic, gene expression, and genomic levels, no study has systematically investigated connectivity among the proteins encoded by these genes at the protein-protein interaction (PPI) level. @*Materials and Methods@#Topological measurements and physical interaction enrichment were performed to assess PPI networks constructed using some or all the proteins encoded by hereditary parkinsonism genes (n=96), which were curated using the Online Mendelian Inheritance in Man database and literature. @*Results@#Non-PARK and PARK genes were involved in common functional modules related to autophagy, mitochondrial or lysosomal organization, catecholamine metabolic process, chemical synapse transmission, response to oxidative stress, neuronal apoptosis, regulation of cellular protein catabolic process, and vesicle-mediated transport in synapse. The hereditary parkinsonism proteins formed a single large network comprising 51 nodes, 83 edges, and three PPI pairs. The probability of degree distribution followed a power-law scaling behavior, with a degree exponent of 1.24 and a correlation coefficient of 0.92. LRRK2 was identified as a hub gene with the highest degree of betweenness centrality; its physical interaction enrichment score was 1.28, which was highly significant. @*Conclusion@#Both PARK and non-PARK genes show high connectivity at the PPI and biological functional levels.

Accuracy of Machine Learning Using the Montreal Cognitive Assessment for the Diagnosis of Cognitive Impairment in Parkinson’s Disease

Objective@#The Montreal Cognitive Assessment (MoCA) is recommended for assessing general cognition in Parkinson’s disease (PD). Several cutoffs of MoCA scores for diagnosing PD with cognitive impairment (PD-CI) have been proposed, with varying sensitivity and specificity. This study investigated the utility of machine learning algorithms using MoCA cognitive domain scores for improving diagnostic performance for PD-CI. @*Methods@#In total, 2,069 MoCA results were obtained from 397 patients with PD enrolled in the Parkinson’s Progression Markers Initiative database with a diagnosis of cognitive status based on comprehensive neuropsychological assessments. Using the same number of MoCA results randomly sampled from patients with PD with normal cognition or PD-CI, discriminant validity was compared between machine learning (logistic regression, support vector machine, or random forest) with domain scores and a cutoff method. @*Results@#Based on cognitive status classification using a dataset that permitted sampling of MoCA results from the same individual (n = 221 per group), no difference was observed in accuracy between the cutoff value method (0.74 ± 0.03) and machine learning (0.78 ± 0.03). Using a more stringent dataset that excluded MoCA results (n = 101 per group) from the same patients, the accuracy of the cutoff method (0.66 ± 0.05), but not that of machine learning (0.74 ± 0.07), was significantly reduced. Inclusion of cognitive complaints as an additional variable improved the accuracy of classification using the machine learning method (0.87–0.89). @*Conclusion@#Machine learning analysis using MoCA domain scores is a valid method for screening cognitive impairment in PD.