May Bradykinesia Features Aid in Distinguishing Parkinson's Disease, Essential Tremor, And Healthy Elderly Individuals?

Abstract

Background: Bradykinesia is the hallmark feature of Parkinson's disease (PD); however, it can manifest in other conditions, including essential tremor (ET), and in healthy elderly individuals.

Objective: Here we assessed whether bradykinesia features aid in distinguishing PD, ET, and healthy elderly individuals.

Methods: We conducted simultaneous video and kinematic recordings of finger tapping in 44 PD patients, 69 ET patients, and 77 healthy elderly individuals. Videos were evaluated blindly by expert neurologists. Kinematic recordings were blindly analyzed. We calculated the inter-raters agreement and compared data among groups. Density plots assessed the overlapping in the distribution of kinematic data. Regression analyses and receiver operating characteristic curves determined how the kinematics influenced the likelihood of belonging to a clinical score category and diagnostic group.

Results: The inter-rater agreement was fair (Fleiss K = 0.32). Rater found the highest clinical scores in PD, and higher scores in ET than healthy elderly individuals (p < 0.001). In regard to kinematic analysis, the groups showed variations in movement velocity, with PD presenting the slowest values and ET displaying less velocity than healthy elderly individuals (all ps < 0.001). Additionally, PD patients showed irregular rhythm and sequence effect. However, kinematic data significantly overlapped. Regression analyses showed that kinematic analysis had high specificity in differentiating between PD and healthy elderly individuals. Nonetheless, accuracy decreased when evaluating subjects with intermediate kinematic values, i.e., ET patients.

Conclusion: Despite a considerable degree of overlap, bradykinesia features vary to some extent in PD, ET, and healthy elderly individuals. Our findings have implications for defining bradykinesia and categorizing patients.

Keywords: Bradykinesia; Parkinson’s disease; essential tremor; finger tapping; kinematic analysis; mild parkinsonian signs.

Fig. 1

Blinded video evaluation results. A) Finger tapping blinded video rating scores in healthy controls (HC), patients with Parkinson’s disease (PD), and essential tremor (ET), according to the Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Horizontal lines denote median values. Asterisks indicate p < 0.05 in the post hoc comparisons. B) Number of subjects (percentage are in brackets) within the HC, ET, and PD groups who obtained specific clinical scores (ranging from 0 to 4) at the blinded video evaluations. C) Percentage of subjects within the HC, ET, and PD groups showing specific movement abnormalities at the blinded video evaluation, including reduced velocity (RV), altered rhythm (AR), and sequence effect (SE) (PD group: RV vs. AR: p = 0.58; AR vs. SE: p = 0.026; RV vs. SE: p = 0.026; ET group: RV vs. AR: p = 0.043; AR vs. SE: p = 0.19; RV vs. SE: p = 0.003; HC group: RV vs. AR: p = 0.23; AR vs. SE: p = 0.43; RV vs. SE: p = 0.42). Note that movement slowness was the most prominent abnormality and it differed between PD and ET, being much lower in HC (RV: PD vs. ET: p = 0.012, PD vs. HC: p = 0.002; ET vs. HC: p < 0.001). Irregular movement rhythm differentiates controls from patients, but not PD from ET (AR: PD vs. ET: p = 0.55, PD vs. HC: p = 0.03; ET vs. HC: p = 0.03). The SE was more prevalent in PD than in ET and HC, but it did not differ between ET and HC (SE: PD vs. ET: p < 0.001, PD vs. HC: p < 0.001; ET vs. HC: p = 0.15).

Fig. 2

Stratified density plots. Upper part: Density plots were used to evaluate the overlapping in the distribution of the kinematic data based on the median score (ranging from 0 to 4) given by the 7 raters. Note the marked overlapping between data curves, indicating that a given clinical score reflected a wide range of kinematic values in participants. Velocity is expressed as degrees/s (Vel^K). The coefficient of variation (CV), computed by the standard deviation/mean value of the inter-tap intervals, expresses movement rhythm (with higher CV values representing a lower regularity of repetitive movements) (Rhythm^K). Amplitude slope is expressed in (degrees/s)/n.mov (Seq_Eff^K). Lower part: distribution of kinematic parameters among the three groups of participants (HC: healthy controls, ET: essential tremor, PD: Parkinson’s disease). Note that the PD group had the lowest Vel^K values (left-hand graph). However, Vel^K greatly overlapped in ET and HC. The overlapping of kinematic data between the three groups was even greater for the Rhythm^K and Seq_Eff^K (middle and right-hand graph).

Fig. 3

Ordinal and multinomial logistic regression analysis results. A) The figure depicts the probability of belonging to a specific clinical score category (0, 1, 2, or 3-4) based on individual kinematic parameters, i.e., movement velocity (Vel^K), coefficient of variation (Rhythm^K), and amplitude slope (Seq_Eff^K), and on the combined kinematic score (CKS). Note that subjects with high CKS, i.e., greater than 0.6, had a high probability of being classified as bradykinetic, i.e., with Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) clinical scores higher than 2. The opposite occurred for subjects with low CKS score (<than 0.2), which had a high probability of being classified as normal (MDS-UPDRS clinical scores = 0). Intermediate CKS values, however, correspond to a low probability of being correctly classified as bradykinetic or not bradykinetic. B) The figure depicts the probability of belonging to a specific diagnostic group (HC, healthy controls; ET, essential tremor; PD, Parkinson’s disease) based on individual kinematic parameters, and on the CKS. Note that subjects with CKS values greater than 0.7 had a high probability of belonging to the PD group, and a very low probability of belonging to the HC group. The opposite was observed for subjects with low CKS score values (lower than 0.2), which had a high probability of being normal subjects and a very low probability of belonging to the PD group. At intermediate CKS values corresponded comparable probabilities of belonging to the PD, ET, and HC groups.

Fig. 4

Receiver operating characteristic (ROC) curves. ROC curves were used to graphically represent and to determine the capacity of the kinematic variables and of the combined kinematic score (CKS) to evaluate the clinical score category and the diagnostic group in participants. The value of area under the ROC curve (AUC) was considered to measure for how well the model can discriminate between subjects.