Exploring the Complex Phenotypes of Impaired Finger Dexterity in Mild-to-moderate Stage Parkinson's Disease: A Time-Series Analysis

Abstract

Background: Impaired dexterity is an early motor symptom in Parkinson's disease (PD) that significantly impacts the daily activity of patients; however, what constitutes complex dexterous movements remains controversial.

Objective: To explore the characteristics of finger dexterity in mild-to-moderate stage PD.

Methods: We quantitatively assessed finger dexterity in 48 mild-to-moderate stage PD patients and 49 age-matched controls using a simple alternating two-finger typing test for 15 seconds. Time-series analyses of various kinematic parameters with machine learning were compared between sides and groups.

Results: Both the more and less affected hands of patients with PD had significantly lower typing frequency and slower typing velocity than the non-dominant and the dominant hands of controls (p = 0.019, p = 0.016, p < 0.001, p < 0.001). The slope of the typing velocity decreased with time, indicating a sequence effect in the PD group. A typing duration of 6 seconds was determined sufficient to discriminate PD patients from controls. Typing error, repetition, and repetition rate were significantly higher in the more affected hands of patients with PD than in the non-dominant hand of controls (p < 0.001, p = 0.03, p < 0.001). The error rate was constant, whereas the repetition rate was steep during the initiation of typing. A predictive model of the more affected hand demonstrated an accuracy of 70% in differentiating PD patients from controls.

Conclusion: Our study demonstrated complex components of impaired finger dexterity in mild-to-moderate stage PD, namely bradykinesia with sequence effects, error, and repetition at the initiation of movement, suggesting that multiple neural networks may be involved in dexterity deficits in PD.

Keywords: Finger dexterity; Parkinson’s disease; bradykinesia; keyboard typing test; quantitative analysis.

Fig. 1

Time-series plots of typing frequency (A), typing velocity (B), accumulative error (C), error rate (D), accumulative repetition (E), repetition rate (F), digraph (G), and digraph rate (H) of the less/the more affected hands of Parkinson’s disease (PD) and the dominant (dom)/the non-dominant (non-dom) hands of controls (C).

Fig. 2

Machine learning based approach with decision tree method using accumulative error, error rate, repetition rate and changing rate of keystroke duration or digraph rate to distinguish between Parkinson’s disease (PD) and controls with highest accuracy. Entropy is a measure of disorders or unpredictability in the system of the decision trees method. Lower entropy of the typing parameters including accumulative error, error rate, and repetition rate, but not digraph rate, would suggest higher information gain or the PD category.

Fig. 3

A) The proposed complex components of impaired dexterity in Parkinson’s disease including bradykinesia, error, and repetitive movements of the less and the more affected sides in different disease stages and (B) the proposed neural networks of each component of impaired dexterity in Parkinson’s disease.