Initial clinical manifestations of Parkinson's disease: features and pathophysiological mechanisms

Rodriguez-Oroz, María C.; Jahanshahi, Marjan; Krack, Paul; Litvan, Irene; Macıás, R.; Bézard, Erwan; Obeso, José A.

doi:10.1016/s1474-4422(09)70293-5

Cited by 717 publications

(522 citation statements)

References 120 publications

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“…The error at point x is the difference between the time derivative _ x and the righthand side of model (1). The fitness of a model on a data set, the residual, is the mean of the squared errors at each point x:…”

Section: B Structure Selection Using Genetic Algorithmmentioning

confidence: 99%

“…1 Deprived of their normal dopaminergic inputs, nuclei within the basal ganglia become dysfunctional leading to abnormal neural oscillations and synchronization within multiple basal ganglia-cortical circuits. 2 These circuit disturbances lead to the clinical manifestations of the disease, which include such motor impairments as bradykinesia (slow movements), muscle rigidity, resting tremor, and postural instability.…”

Section: Introductionmentioning

confidence: 99%

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Finger tapping movements of Parkinson’s disease patients automatically rated using nonlinear delay differential equations

Lainscsek

Rowat

Schettino

et al. 2012

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Parkinson's disease is a degenerative condition whose severity is assessed by clinical observations of motor behaviors. These are performed by a neurological specialist through subjective ratings of a variety of movements including 10-s bouts of repetitive finger-tapping movements. We present here an algorithmic rating of these movements which may be beneficial for uniformly assessing the progression of the disease. Finger-tapping movements were digitally recorded from Parkinson's patients and controls, obtaining one time series for every 10 s bout. A nonlinear delay differential equation, whose structure was selected using a genetic algorithm, was fitted to each time series and its coefficients were used as a six-dimensional numerical descriptor. The algorithm was applied to time-series from two different groups of Parkinson's patients and controls. The algorithmic scores compared favorably with the unified Parkinson's disease rating scale scores, at least when the latter adequately matched with ratings from the Hoehn and Yahr scale. Moreover, when the two sets of mean scores for all patients are compared, there is a strong (r ¼ 0.785) and significant (p < 0:0015) correlation between them. Parkinson's disease (PD) is a common disease affecting tens of millions of people worldwide. Its cardinal signs are resting tremor, bradykinesia (slowness clumsiness of movement), rigidity, and loss of postural reflexes. The disease evolves slowly and, to adjust medications to the severity of the disease, there is a need for automatic and objective evaluation of movements. Such objective movement assessments would supplement subjective clinical ratings, which are ordinal rather than metric and often show large inter-rater variability. Rather than using a spectral based technique, we rated dynamical features of each individuals' finger-tapping-one of the items from the unified Parkinson's disease rating scale (UPDRS) used for rating the severity of the disease-by using data models based on nonlinear delay differential equations (DDEs). The coefficients of the DDEs are then used to assess the severity of the disease.

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Section: B Structure Selection Using Genetic Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Finger tapping movements of Parkinson’s disease patients automatically rated using nonlinear delay differential equations

Lainscsek

Rowat

Schettino

et al. 2012

Chaos: An Interdisciplinary Journal of Nonlinear Science

View full text Add to dashboard Cite

show abstract

“…Thus, the untreated and treated states of PD represent a model of opposite pathophysiological states linked to dysfunction in BG-thalamocortical loops (Figure 1) [28,29]. Different surgical targets exist for different parkinsonian signs (Table 2) [30,31].…”

Section: Reviewmentioning

confidence: 99%

Deep brain stimulation in Parkinson disease—what went wrong?

Krack

Hariz

2010

Nat Rev Neurol

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“…[39][40][41] Speculation about the mechanism underlying the sign reversed correlation in the control group is, however, difficult at this stage.…”

Section: Discussionmentioning

confidence: 99%

EEG spectral power and negative symptoms in at-risk individuals predict transition to psychosis

Zimmermann

Gschwandtner²,

Wilhelm

et al. 2010

Schizophrenia Research

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Objective: EEG power in the delta, theta and beta1 bands has been shown to be positively correlated with negative symptoms in first episode psychotic patients. The present study investigates this correlation in an "at risk mental state for psychosis" (ARMS) with the aim to improve prediction of transition to psychosis.Methods: Thirteen ARMS patients with later transition to psychosis (ARMS-T) and fifteen without (follow-up period of at least 4 years) (ARMS-NT) were investigated using spectral resting EEG data (of 8 electrodes over the fronto-central scalp area placed according to the 10 -20 system) and summary score of the Scale for the Assessment of Negative Symptoms (SANS).Linear regressions were used to evaluate the correlation of SANS and EEG power in seven bands (delta, theta, alpha1, alpha2, beta1, beta2, beta3) in both ARMS groups and logistic regressions were used to predict transition to psychosis. Potentially confounding factors were controlled. Results: ARMS-T and ARMS-NT showed differential correlations of EEG power and SANSin delta, theta, and beta1 bands (p <0.05): ARMS-T showed positive and ARMS-NT negative correlations. Logistic regressions showed that neither SANS score nor EEG spectral power alone predicted transition to psychosis. However, SANS score in combination with power in the delta, theta, beta1, and beta2 bands, respectively, predicted transition significantly (p <0.03). Limitations:The number of subjects is relatively small. The difference in age of both study groups might be considered as a further limitation even though age had no effect on the results according to confounder analysis.Conclusions: ARMS-T and ARMS-NT show differential correlations of SANS summary score and EEG power in delta, theta, and beta bands. Prediction of transition to psychosis is possible using combined information from a negative symptom scale and EEG spectral data.

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Initial clinical manifestations of Parkinson's disease: features and pathophysiological mechanisms

Cited by 717 publications

References 120 publications

Finger tapping movements of Parkinson’s disease patients automatically rated using nonlinear delay differential equations

Finger tapping movements of Parkinson’s disease patients automatically rated using nonlinear delay differential equations

Deep brain stimulation in Parkinson disease—what went wrong?

EEG spectral power and negative symptoms in at-risk individuals predict transition to psychosis

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