Parkinson disease is the second-most common neurodegenerative disorder that affects 2-3% of the population ≥65 years of age. Neuronal loss in the substantia nigra, which causes striatal dopamine deficiency, and intracellular inclusions containing aggregates of α-synuclein are the neuropathological hallmarks of Parkinson disease. Multiple other cell types throughout the central and peripheral autonomic nervous system are also involved, probably from early disease onwards. Although clinical diagnosis relies on the presence of bradykinesia and other cardinal motor features, Parkinson disease is associated with many non-motor symptoms that add to overall disability. The underlying molecular pathogenesis involves multiple pathways and mechanisms: α-synuclein proteostasis, mitochondrial function, oxidative stress, calcium homeostasis, axonal transport and neuroinflammation. Recent research into diagnostic biomarkers has taken advantage of neuroimaging in which several modalities, including PET, single-photon emission CT (SPECT) and novel MRI techniques, have been shown to aid early and differential diagnosis. Treatment of Parkinson disease is anchored on pharmacological substitution of striatal dopamine, in addition to non-dopaminergic approaches to address both motor and non-motor symptoms and deep brain stimulation for those developing intractable L-DOPA-related motor complications. Experimental therapies have tried to restore striatal dopamine by gene-based and cell-based approaches, and most recently, aggregation and cellular transport of α-synuclein have become therapeutic targets. One of the greatest current challenges is to identify markers for prodromal disease stages, which would allow novel disease-modifying therapies to be started earlier.

BACKGROUND Neurostimulation of the subthalamic nucleus reduces levodopa-related motor complications in advanced Parkinson's disease. We compared this treatment plus medication with medical management. METHODS In this randomized-pairs trial, we enrolled 156 patients with advanced Parkinson's disease and severe motor symptoms. The primary end points were the changes from baseline to six months in the quality of life, as assessed by the Parkinson's Disease Questionnaire (PDQ-39), and the severity of symptoms without medication, according to the Unified Parkinson's Disease Rating Scale, part III (UPDRS-III). RESULTS Pairwise comparisons showed that neurostimulation, as compared with medication alone, caused greater improvements from baseline to six months in the PDQ-39 (50 of 78 pairs, P = 0.02) and the UPDRS-III (55 of 78, P<0.001), with mean improvements of 9.5 and 19.6 points, respectively. Neurostimulation resulted in improvements of 24 to 38 percent in the PDQ-39 subscales for mobility, activities of daily living, emotional well-being, stigma, and bodily discomfort. Serious adverse events were more common with neurostimulation than with medication alone (13 percent vs. 4 percent, P<0.04) and included a fatal intracerebral hemorrhage. The overall frequency of adverse events was higher in the medication group (64 percent vs. 50 percent, P = 0.08). CONCLUSIONS In this six-month study of patients under 75 years of age with severe motor complications of Parkinson's disease, neurostimulation of the subthalamic nucleus was more effective than medical management alone.

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We use the concept of phase synchronization for the analysis of noisy nonstationary bivariate data. Phase synchronization is understood in a statistical sense as an existence of preferred values of the phase difference, and two techniques are proposed for a reliable detection of synchronous epochs. These methods are applied to magnetoencephalograms and records of muscle activity of a Parkinsonian patient. We reveal that the temporal evolution of the peripheral tremor rhythms directly reflects the time course of the synchronization of abnormal activity between cortical motor areas. [S0031-9007(98)07333-5] PACS numbers: 87.22.Jb, 05.45. + b, 87.22.As Irregular, nonstationary, and noisy bivariate data abound in many fields of research. Usually, two simultaneously registered time series are characterized by means of traditional cross-correlation (cross-spectrum) techniques or nonlinear statistical measures like mutual information or maximal correlation [1]. Only very recently a tool of nonlinear dynamics, mutual nonlinear prediction, was used for characterization of dynamical interdependence among systems [2]. In this Letter we use a synchronization approach to the analysis of such bivariate time series and introduce a new method to detect alternating epochs of phase locking from nonstationary data. By doing so we extract information on the interdependence of weakly interacting systems that cannot be obtained by traditional methods.Our technique, based on theoretical studies of phase synchronization of chaotic oscillators [3], can be fruitfully applied, e.g., in neuroscience, where synchronization processes are of crucial importance, e.g., for visual pattern recognition [4] and motor control [5]. Recent animal experiments have led to the conclusion that the control of coordinated movements is based on a synchronization of the firing activity of groups of neurons in the primary and in secondary motor areas [5]. Synchronization is also assumed to be involved in the generation of pathological movements, e.g., resting tremor in Parkinson's disease (PD) [6]. Although experimental studies indicate which parts of the nervous system are engaged in generating tremor activity, the dynamics of this process is not yet understood [7].Here we study synchronization between the activity of remote brain areas in humans by means of noninvasive measurements. This is possible because a group of synchronously firing neurons within a single area generates a magnetic field which can be registered outside the head by means of multichannel magnetoencephalography (MEG) [8]. Accordingly, synchronization of neuronal activity between remote areas is reflected as phase locking between MEG channels. Our analysis reveals phase synchronization (a) between the activity of certain brain areas and (b) between the activity of these areas and the muscle activity detected by electromyography (EMG).In particular, we find that the phase locking between the activity of primary and secondary motor areas is related to the coordination of antagonistic muscle...

Objective The benefit of deep brain stimulation (DBS) for Parkinson disease (PD) may depend on connectivity between the stimulation site and other brain regions, but which regions and whether connectivity can predict outcome in patients remain unknown. Here, we identify the structural and functional connectivity profile of effective DBS to the subthalamic nucleus (STN) and test its ability to predict outcome in an independent cohort. Methods A training dataset of 51 PD patients with STN DBS was combined with publicly available human connectome data (diffusion tractography and resting state functional connectivity) to identify connections reliably associated with clinical improvement (motor score of the Unified Parkinson Disease Rating Scale [UPDRS]). This connectivity profile was then used to predict outcome in an independent cohort of 44 patients from a different center. Results In the training dataset, connectivity between the DBS electrode and a distributed network of brain regions correlated with clinical response including structural connectivity to supplementary motor area and functional anticorrelation to primary motor cortex (p<0.001). This same connectivity profile predicted response in an independent patient cohort (p<0.01). Structural and functional connectivity were independent predictors of clinical improvement (p<0.001) and estimated response in individual patients with an average error of 15% UPDRS improvement. Results were similar using connectome data from normal subjects or a connectome age, sex, and disease matched to our DBS patients. Interpretation Effective STN DBS for PD is associated with a specific connectivity profile that can predict clinical outcome across independent cohorts. This prediction does not require specialized imaging in PD patients themselves.