Kinect-based objective assessment of the acute levodopa challenge test in parkinsonism: a feasibility study.

INTRODUCTION: The acute levodopa challenge test (ALCT) is an important and valuable examination but there are still some shortcomings with it. We aimed to objectively assess ALCT based on a depth camera and filter out the best indicators. METHODS: Fifty-nine individuals with parkinsonism completed ALCT and the improvement rate (IR, which indicates the change in value before and after levodopa administration) of the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) was calculated. The kinematic features of the patients' movements in both the OFF and ON states were collected with an Azure Kinect depth camera. RESULTS: The IR of MDS-UPDRS III was significantly correlated with the IRs of many kinematic features for arising from a chair, pronation-supination movements of the hand, finger tapping, toe tapping, leg agility, and gait (rs = - 0.277 ~ - 0.672, P < 0.05). Moderate to high discriminative values were found in the selected features in identifying a clinically significant response to levodopa with sensitivity, specificity, and area under the curve (AUC) in the range of 50-100%, 47.22%-97.22%, and 0.673-0.915, respectively. The resulting classifier combining kinematic features of toe tapping showed an excellent performance with an AUC of 0.966 (95% CI = 0.922-1.000, P < 0.001). The optimal cut-off value was 21.24% with sensitivity and specificity of 94.44% and 87.18%, respectively. CONCLUSION: This study demonstrated the feasibility of measuring the effect of levodopa and objectively assessing ALCT based on kinematic data derived from an Azure Kinect-based system.

Early-onset and late-onset Parkinson's disease exhibit a different profile of gait and posture features based on the Kinect.

INTRODUCTION: Gait and posture abnormalities are the common disabling motor symptoms in Parkinson's disease (PD). This study aims to investigate the differential characteristics of gait and posture in early-onset PD (EOPD) and late-onset PD (LOPD) using the Kinect depth camera. METHODS: Eighty-eight participants, including two subgroups of 22 PD patients and two subgroups of 22 healthy controls (HC) matched for age, sex, and height, were enrolled. Gait and posture features were quantitatively assessed using a Kinect-based system. A two-way analysis of variance was used to compare the difference between different subgroups. RESULTS: EOPD had a significantly higher Gait score than LOPD (p = 0.031). Specifically, decreased swing phase (p = 0.034) was observed in the EOPD group. Although the Posture score was similar between the two groups, LOPD was characterized by an increased forward flexion angle of the trunk at the thorax (p = 0.042) and a decreased forward flexion angle of the head relative to the trunk (p = 0.009). Additionally, age-independent features were observed in both PD subgroups, and post hoc tests revealed that EOPD generally performed worse gait features. In comparison, LOPD was characterized by worse performance in posture features. CONCLUSIONS: EOPD and LOPD exhibit different profiles of gait and posture features. The phenotype-specific characteristics likely reflect the distinct neurodegenerative processes between them.

Kinect-based objective assessment for early frailty identification in patients with Parkinson's disease.

BACKGROUND: Frailty is common in Parkinson's disease (PD) and increases vulnerability to adverse outcomes. Early detection of this syndrome aids in early intervention. AIMS: To objectively identify frailty at an early stage during routine motor tasks in PD patients using a Kinect-based system. METHODS: PD patients were recruited and assessed with the Fried criteria to determine their frailty status. Each participant was recorded performing the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) extremity tasks with a Kinect-based system. Statistically significant kinematic parameters were selected to discriminate the pre-frail from the non-frail group. RESULTS: Of the fifty-two participants, twenty were non-frail and thirty-two were pre-frail. Decreased frequency in finger tapping (P = 0.005), hand grasping (P = 0.002), toe tapping (P = 0.002), and leg agility (P = 0.019) alongside reduced hand grasping speed (P = 0.030), lifting (P < 0.001) and falling speed (P < 0.001) in leg agility were observed in the pre-frail group. Amplitude in leg agility (P = 0.048) and amplitude decrement rate (P = 0.046) in hand grasping showed marginally significant differences between two groups. Moderate discriminative values were found in frequency and speed of the extremity tasks to identify pre-frailty with sensitivity, specificity, and area under the curve (AUC) in the range of 45.00-85.00%, 68.75-100%, and 0.701-0.836, respectively. The combination of frequency and speed in extremity tasks showed moderate to high discriminatory ability, with AUC of 0.775 (95% CI 0.637-0.913, P < 0.001) for upper limb tasks and 0.909 (95% CI 0.832-0.987, P < 0.001) for lower limb tasks. When combining these features in both upper and lower limb tasks, the AUC increased to 0.942 (95% CI 0.886-0.999, P < 0.001). CONCLUSIONS: Our findings demonstrated the promise of utilizing Kinect-based kinematic data from MDS-UPDRS III tasks as early indicators of frailty in PD patients.

Kinect-based objective evaluation of bradykinesia in patients with Parkinson's disease.

Objective: To quantify bradykinesia in Parkinson's disease (PD) with a Kinect depth camera-based motion analysis system and to compare PD and healthy control (HC) subjects. Methods: Fifty PD patients and twenty-five HCs were recruited. The Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) was used to evaluate the motor symptoms of PD. Kinematic features of five bradykinesia-related motor tasks were collected using Kinect depth camera. Then, kinematic features were correlated with the clinical scales and compared between groups. Results: Significant correlations were found between kinematic features and clinical scales (P < 0.05). Compared with HCs, PD patients exhibited a significant decrease in the frequency of finger tapping (P < 0.001), hand movement (P < 0.001), hand pronation-supination movements (P = 0.005), and leg agility (P = 0.003). Meanwhile, PD patients had a significant decrease in the speed of hand movements (P = 0.003) and toe tapping (P < 0.001) compared with HCs. Several kinematic features exhibited potential diagnostic value in distinguishing PD from HCs with area under the curve (AUC) ranging from 0.684-0.894 (P < 0.05). Furthermore, the combination of motor tasks exhibited the best diagnostic value with the highest AUC of 0.955 (95% CI = 0.913-0.997, P < 0.001). Conclusion: The Kinect-based motion analysis system can be applied to evaluate bradykinesia in PD. Kinematic features can be used to differentiate PD patients from HCs and combining kinematic features from different motor tasks can significantly improve the diagnostic value.

Technology-based therapy-response evaluation of axial motor symptoms under daily drug regimen of patients with Parkinson's disease.

Background: Axial disturbances are the most disabling symptoms of Parkinson's disease (PD). Kinect-based objective measures could extract motion characteristics with high reliability and validity. Purpose: The present research aimed to quantify the therapy-response of axial motor symptoms to daily medication regimen and to explore the correlates of the improvement rate (IR) of axial motor symptoms based on a Kinect camera. Materials and methods: We enrolled 44 patients with PD and 21 healthy controls. All 65 participants performed the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III and the Kinect-based kinematic evaluation to assess arising from a chair, gait, posture, and postural stability before and after medication. Spearman's correlation analysis and multiple linear regression model were performed to explore the relationships between motor feature IR and clinical data. Results: All the features arising from a chair (P = 0.001), stride length (P = 0.001), velocity (P < 0.001), the height of foot lift (P < 0.001), and turning time (P = 0.001) improved significantly after a daily drug regimen in patients with PD. In addition, the anterior trunk flexion (lumbar level) exhibited significant improvement (P = 0.004). The IR of the axial motor symptoms score was significantly correlated with the IRs of kinematic features for gait velocity, stride length, foot lift height, and sitting speed (r s = 0.345, P = 0.022; r s = 0.382, P = 0.010; r s = 0.314, P = 0.038; r s = 0.518, P < 0.001, respectively). A multivariable regression analysis showed that the improvement in axial motor symptoms was associated with the IR of gait velocity only (ß = 0.593, 95% CI = 0.023-1.164, P = 0.042). Conclusion: Axial symptoms were not completely drug-resistant, and some kinematic features can be improved after the daily medication regimen of patients with PD.

A summary index derived from Kinect to evaluate postural abnormalities severity in Parkinson's Disease patients.

Postural abnormalities are common disabling motor complications affecting patients with Parkinson's disease (PD). We proposed a summary index for postural abnormalities (IPA) based on Kinect depth camera and explored the clinical value of this indicator. Seventy individuals with PD and thirty age-matched healthy controls (HCs) were enrolled. All participants were tested using a Kinect-based system with IPA automatically obtained by algorithms. Significant correlations were detected between IPA and the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) total score (rs = 0.369, p = 0.002), MDS-UPDRS-III total score (rs = 0.431, p < 0.001), MDS-UPDRS-III 3.13 score (rs = 0.573, p < 0.001), MDS-UPDRS-III-bradykinesia score (rs = 0.311, p = 0.010), the 39-item Parkinson's Disease Questionnaire (PDQ-39) (rs = 0.272, p = 0.0027) and the Berg Balance Scale (BBS) score (rs = -0.350, p = 0.006). The optimal cut-off value of IPA for distinguishing PD from HCs was 12.96 with a sensitivity of 97.14%, specificity of 100.00%, area under the curve (AUC) of 0.999 (0.997-1.002, p < 0.001), and adjusted AUC of 0.998 (0.993-1.000, p < 0.001). The optimal cut-off value of IPA for distinguishing between PD with and without postural abnormalities was 20.14 with a sensitivity, specificity, AUC and adjusted AUC of 77.78%, 73.53%, 0.817 (0.720-0.914, p < 0.001), and 0.783 (0.631-0.900, p < 0.001), respectively. IPA was significantly correlated to the clinical manifestations of PD patients, and could reflect the global severity of postural abnormalities in PD with important value in distinguishing PD from HCs and distinguishing PD with postural abnormalities from those without.

Changes of T lymphocyte subpopulations and their roles in predicting the risk of Parkinson's disease.

T lymphocytes are involved in the pathogenesis of Parkinson's disease (PD), while the heterogeneity of T-cell subpopulations remains elusive. In this study, we analyzed up to 22 subpopulations of T lymphocytes in 115 PD patients and 60 matched healthy controls (HC) using flow cytometry. We found that PD patients exhibited decreased naïve CD8+ T cells (CD3+ CD8+ CD45RA+ CD45RO-) and increased late-differentiated CD4+ T cells (CD3+ CD4+ CD28- CD27-), compared to HC, which were not affected by anti-parkinsonism medication administration. The proportion of naïve CD8+ T cells in PD patients was positively correlated with their severity of autonomic dysfunction and psychiatric complications, but negatively associated with the severity of rapid eye movement and sleep behavior disorder. The proportion of late-differentiated CD4+ T cells was negatively correlated with the onset age of the disease. We further developed individualized PD risk prediction models with high reliability and accuracy on the base of the T lymphocyte subpopulations. These data suggest that peripheral cellular immunity is disturbed in PD patients, and changes in CD8+ T cells and late-differentiated CD4+ T cells are representative and significant. Therefore, we recommend naïve CD8 + and late-differentiated CD4+ T cells as candidates for multicentric clinical study and pathomechanism study of PD.

High clinical diagnostic accuracy of combined salivary gland and myocardial metaiodobenzylguanidine scintigraphy in the diagnosis of Parkinson's disease.

Background: Decreased myocardial uptake of 131I-metaiodobenzylguanidine (MIBG) is known to be an important feature to diagnose Parkinson's disease (PD). However, the diagnosis accuracy of myocardial MIBG scintigraphy alone is often unsatisfying. Recent studies have found that the MIBG uptake of the major salivary glands was reduced in PD patients as well. Purpose: To evaluate the diagnostic value of major salivary gland MIBG scintigraphy in PD, and explore the potential role of myocardial MIBG scintigraphy combined with salivary gland MIBG scintigraphy in distinguishing PD from non-PD (NPD). Methods: Thirty-seven subjects were performed with 131I-MIBG scintigraphy. They were classified into the PD group (N = 18) and the NPD group (N = 19), based on clinical diagnostic criteria, DAT PET and 18F-FDG PET imaging findings. Images of salivary glands and myocardium were outlined to calculated the MIBG uptake ratios. Results: The combination of left parotid and left submandibular gland early images had a good performance in distinguishing PD from NPD, with sensitivity, specificity, and accuracy of 50.00, 94.74, and 72.37%, respectively. Combining the major salivary gland and myocardial scintigraphy results in the early period showed a good diagnostic value with AUC, sensitivity and specificity of 0.877, 77.78, and 94.74%, respectively. Meanwhile, in the delayed period yield an excellent diagnostic value with AUC, sensitivity and specificity of 0.904, 88.89, and 84.21%, respectively. Conclusion: 131I-MIBG salivary gland scintigraphy assisted in the diagnosis and differential diagnosis of PD. The combination of major salivary gland and myocardial 131I-MIBG scintigraphy further increased the accuracy of PD diagnosis.

Automated and accurate assessment for postural abnormalities in patients with Parkinson's disease based on Kinect and machine learning.

BACKGROUND: Automated and accurate assessment for postural abnormalities is necessary to monitor the clinical progress of Parkinson's disease (PD). The combination of depth camera and machine learning makes this purpose possible. METHODS: Kinect was used to collect the postural images from 70 PD patients. The collected images were processed to extract three-dimensional body joints, which were then converted to two-dimensional body joints to obtain eight quantified coronal and sagittal features (F1-F8) of the trunk. The decision tree classifier was carried out over a data set established by the collected features and the corresponding doctors' MDS-UPDRS-III 3.13 (the 13th item of the third part of Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale) scores. An objective function was implanted to further improve the human-machine consistency. RESULTS: The automated grading of postural abnormalities for PD patients was realized with only six selected features. The intraclass correlation coefficient (ICC) between the machine's and doctors' score was 0.940 (95%CI, 0.905-0.962), meaning the machine was highly consistent with the doctors' judgement. Besides, the decision tree classifier performed outstandingly, reaching 90.0% of accuracy, 95.7% of specificity and 89.1% of sensitivity in rating postural severity. CONCLUSIONS: We developed an intelligent evaluation system to provide accurate and automated assessment of trunk postural abnormalities in PD patients. This study demonstrates the practicability of our proposed method in the clinical scenario to help making the medical decision about PD.