Application of nonlinear analysis for the assessment of gait in patients with Parkinson’s disease

BACKGROUND: Gait can be affected by diseases such as Parkinson’s disease (PD), which lead to alterations like shuffle gait or loss of balance. PD diagnosis is based on subjective measures to generate a score using the Unified Parkinson’s Disease Rating Scale (UPDRS). To improve clinical assessment accuracy, gait analysis can utilise linear and nonlinear methods. A nonlinear method called the Lyapunov exponent (LE) is being used to identify chaos in dynamic systems. This article presents an application of LE for diagnosing PD. OBJECTIVE: The objectives were to use the largest Lyapunov exponents (LaLyEx), sample entropy (SampEn) and root mean square (RMS) to assess the gait of subjects diagnosed with PD; to verify the applicability of these parameters to distinguish between people with PD and healthy controls (CO); and to differentiate subjects within the PD group according to the UPDRS assessment. METHODS: The subjects were divided into the CO group (n= 12) and the PD group (n= 14). The PD group was also divided according to the UPDRS score: UPDRS 0 (n= 7) and UPDRS 1 (n= 7). Kinematic data of lower limbs were measured using inertial measurement units (IMU) and nonlinear parameters (LaLyEx, SampEn and RMS) were calculated. RESULTS: There were significant differences between the CO and PD groups for RMS, SampEn and the LaLyEx. After dividing the PD group according to the UPDRS score, there were significant differences in LaLyEx and RMS. CONCLUSIONS: The selected parameters can be used to distinguish people with PD from CO subjects, and separate people with PD according to the UPDRS score.

The association between frailty and Parkinson’s disease in the ReSPOnD trial

Background: Frailty and Parkinson’s disease (PD) are both highly prevalent in older people, but few studies have studied frailty in people with Parkinson’s. Identifying frailty in this population is vital, to target new interventions to those who would most benefit. Methods: Data were collected as part of the double-blind randomised controlled rivastigmine to stabilise gait ReSPonD trial in 130 people with Hoehn and Yahr 2-3, idiopathic PD who had fallen in the year prior to enrolment. Individuals were assessed at baseline and followed up at 8 months, including determination of frailty status. Results: 120 patients attended for follow-up. At follow-up, the mean (SD) age was 70.2 years (8.0) and MDS-UPDRS total score 91.5 (29.1). Median disease duration was 9.2 years (IQR 4.6 to 13.1), Geriatric Depression Score 4 (IQR 2 to 6). Using the Fried frailty criteria, 31 (26%) were frail and 70 (58%) pre-frail. In univariable analysis, being female, higher depression score and MDS-UPDRS score was associated with greater frailty. Using ordinal regression, in the multivariable model, being female (Odds ratio [OR] 3.10, 95%CI 1.53 to 6.26, p=0.002), higher total MDS-UPDRS score (OR 2.02, 95%CI 1.42 to 2.87, p<0.0001) and higher depression (OR 1.47, 95%CI 1.05 to 2.06, p=0.03) were associated with higher number of frailty markers. Conclusion: There was a high prevalence (84%) of pre-frail and frail individuals in patients participating in this RCT. Future research should determine the optimum tool to assess frailty in this at-risk population and delineate the association between Parkinson’s, frailty and health outcomes.

Bilateral MR-Guided Focused Ultrasound Pallidothalamic Tractotomy for Parkinson's Disease With 1-Year Follow-Up

Objective: Bilateral stereotactic neurosurgery for advanced Parkinson's disease (PD) has a long history beginning in the late 1940s. In view of improved lesioning accuracy and reduced bleeding risk and in spite of long-standing caveats about bilateral approaches, there is a need to investigate bilateral MR-guided focused ultrasound (MRgFUS) interventions. We hereby present the clinical results of bilateral pallidothalamic tractotomy (PTT), i.e., targeting of pallidal efferent fibers below the thalamus at the level of Forel's field H1, followed for 1 year after operation of the second side.Methods: Ten patients suffering from chronic and therapy-resistant PD having received bilateral PTT were followed for 1 year after operation of the second side. The primary endpoints included the Unified Parkinson's Disease Rating Scale (UPDRS) scores in on- and off-medication states, dyskinesias, dystonia, sleep disturbances, pain, reduction in drug intake, and assessment by the patient of her/his global symptom relief as well as tremor control.Results: The time frame between baseline UPDRS score and 1 year after the second side was 36 ± 15 months. The total UPDRS score off-medication at 1 year after the second PTT was reduced by 52% compared to that at baseline on-medication (p &lt; 0.007). Percentage reductions of the mean scores comparing 1 year off- with baseline on-medication examinations were 91% for tremor (p = 0.006), 67% for distal rigidity (p = 0.006), and 54% for distal hypobradykinesia (p = 0.01). Gait and postural instability were globally unchanged to baseline (13% improvement of the mean, p = 0.67, and 5.3% mean reduction, p = 0.83). Speech difficulties, namely, hypophonia, tachyphemia, and initiation of speech, were increased by 58% (p = 0.06). Dyskinesias were suppressed in four over four, dystonia in four over five, and sleep disorders in three over four patients. There was 89% pain reduction. Mean L-Dopa intake was reduced from 690 ± 250 to 110 ± 190.Conclusions: Our results suggest an efficiency of bilateral PTT in controlling tremor, distal rigidity, distal hypobradykinesia, dyskinesias, dystonia, and pain when compared to best medical treatment at baseline. Larger series are of course needed.

Long-Term Improvement of Parkinson Disease Motor Symptoms Derived From Lesions of Prelemniscal Fiber Tract Components

BACKGROUND Prelemniscal radiations (Raprl) are composed of different fiber tracts, connecting the brain stem and cerebellum with basal ganglia and cerebral cortex. In Parkinson disease (PD), lesions in Raprl induce improvement of tremor, rigidity, and bradykinesia in some patients, while others show improvement of only 1 or 2 symptoms, suggesting different fiber tracts mediate different symptoms. OBJECTIVE To search for correlations between improvements of specific symptoms with surgical lesions of specific fiber tract components of Raprl in patients with PD. METHODS A total of 10 patients were treated with unilateral radiofrequency lesions directed to Raprl. The improvement for tremor, rigidity, bradykinesia, posture, and gait was evaluated at 24 to 33 mo after operation through the Unified Parkinson's Disease Rating Scale (UPDRS) score, and the precise location and extension of lesions through structural magnetic resonance imaging and probabilistic tractography at 6 to 8 mo postsurgery. Correlation between percentage of fiber tract involvement and percentage of UPDRS-III score improvement was evaluated through Spearman's correlation coefficient. RESULTS Group average improvement was 86% for tremor, 62% for rigidity, 56% for bradykinesia, and 45% for gait and posture. Improvement in global UPDRS score correlated with extent of lesions in fibers connecting with contralateral cerebellar cortex and improvement of posture and gait with fibers connecting with contralateral deep cerebellar nuclei. Lesion of fibers connecting the globus pallidum with pedunculopontine nucleus induced improvement of gait and posture over other symptoms. CONCLUSION Partial lesion of Raprl fibers resulted in symptom improvement at 2-yr follow-up. Lesions of selective fiber components may result in selective improvement of specific symptoms.

Predicting Longitudinal Disease Severity for Individuals with Parkinson’s Disease using Functional MRI and Machine Learning Prognostic Models

Parkinson’s disease is the second most common neurodegenerative disorder and is characterized by the loss of ability to control voluntary movements. Predictive biomarkers of progression in Parkinson’s Disease are urgently needed to expedite the development of neuroprotective treatments and facilitate discussions about disease prognosis between clinicians and patients. Resting-state functional magnetic resonance imaging (rs-fMRI) shows promise in predicting progression, with derived measures, including regional homogeneity (ReHo) and fractional amplitude of low frequency fluctuations (fALFF), having been previously been associated with current disease severity. In this work, ReHo and fALFF features from 82 Parkinson’s Disease subjects are used to train machine learning predictors of baseline clinical severity and progression at 1 year, 2 years, and 4 years follow-up as measured by the Movement Disorder Society Unified Depression Rating Scale (MDS-UPDRS) score. This is the first time that rs-fMRI and machine learning have been combined to predict future disease progression. The machine learning models explain up to 30.4% (R2 = 0.304) of the variance in baseline MDS-UPDRS scores, 55.8% (R2 = 0.558) of the variance in year 1 scores, and 47.1% (R2 = 0.471) of the variance in year 2 scores with high statistical significance (p < 0.0001). For distinguishing high- and low-progression individuals (MDS-UPDRS score above or below the median), the models achieve positive predictive values of up to 71% and negative predictive values of up to 84%. The models learn patterns of ReHo and fALFF measures that predict better and worse prognoses. Higher ReHo and fALFF in regions of the default motor network predicted lower current severity and lower future progression. The rs-fMRI features in the temporal lobe, limbic system, and motor cortex were also identified as predictors. These results present a potential neuroimaging biomarker that accurately predicts progression, which may be useful as a clinical decision-making tool and in future trials of neuroprotective treatments.

Caregiver Burden for Patients Diagnosed with Parkinson’s Disease: A Cross-sectional Study from Southern India

Introduction: Parkinson’s Disease (PD) is a progressive neurodegenerative disorder where the individual over time needs more and more assistance from their caregivers to carry on their life and that causes increasing burden on the caregiver. The burden for the caregiver is affecting them physically, mentally and also on a socioeconomic level. Aim: To examine the factors related to caregiver burden in caregivers of Parkinson’s patients. Materials and Methods: This was a cross-sectional study conducted in Parkinson’s clinic of a Tertiary Care Hospital of Kochi, on 100 Parkinsonism patients and their respective caregivers. Patients were assessed using the Unified PD Rating Scale (UPDRS), Hoehn and Yahr Scale (H&Y) and Mini-Mental State Examination (MMSE). Caregivers were assessed using Zerit’s Caregiver Burden inventory (CBI). Semi structured questionnaire was administered for socio-demographic details. Non parametric tests were done to examine the correlation among various variables. Results: Among the patients and caregivers, mean age was 70.65±7.30 and 67.31±8.56, respectively. Among the patient’s majority were males (n=74) while among caregivers, majority were females (n=73). Mean duration of disease was 6.79±2.68 years, mean caregiver burden score was 65.05±21.79, mean UPDRS score was 21.89±8.74 and had significant positive correlation with caregiver burden. Mean MMSE score was 17.19±4.91. The disease duration and UPDRS score had a significant positive correlation with caregiver burden score. MMSE score had significant negative correlation with caregiver burden score. Conclusion: This study concludes that a patient’s Parkinsonism related disability accounts for majority of caregiver burden. An early identification of factors contributing to stress in caregivers will help to avoid its persistency leading to a better insight in the caregiving role and thus in-patient care.

Comparative Study of Regression Techniques in the Estimation of UPDRS Score for Parkinson’s disease

Studies have shown that instances of Parkinson’s disease have been on the rise over the past 30 years. A metric that measures the extremity of Parkinson’s disease in a person is their Unified Parkinson’s Disease Rating Scale (UPDRS) score. Thus, an algorithm that can predict the UPDRS score of a Parkinson’s patient will be effective in determining the severity of the patient’s condition. This paper aims to forecast a patient’s UPDRS score by inferring patterns from historical figures and other independent parameter values that affect the patients’ UPDRS score. Four regression techniques namely multilinear, ridge, robust and LASSO regression are being used to predict the UPDRS scores. This will be done using the R language and through the use of the MASS, glmnet packages.