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.

Functional connectivity between cortical areas may appear as correlated time behavior of neural activity. It has been suggested that merging of separate features into a single percept (''binding'') is associated with coherent gamma band activity across the cortical areas involved. Therefore, it would be of utmost interest to image cortico-cortical coherence in the working human brain. The frequency specificity and transient nature of these interactions requires time-sensitive tools such as magneto-or electroencephalography (MEG͞EEG). Coherence between signals of sensors covering different scalp areas is commonly taken as a measure of functional coupling. However, this approach provides vague information on the actual cortical areas involved, owing to the complex relation between the active brain areas and the sensor recordings. We propose a solution to the crucial issue of proceeding beyond the MEG sensor level to estimate coherences between cortical areas. Dynamic imaging of coherent sources (DICS) uses a spatial filter to localize coherent brain regions and provides the time courses of their activity. Reference points for the computation of neural coupling may be based on brain areas of maximum power or other physiologically meaningful information, or they may be estimated starting from sensor coherences. The performance of DICS is evaluated with simulated data and illustrated with recordings of spontaneous activity in a healthy subject and a parkinsonian patient. Methods for estimating functional connectivities between brain areas will facilitate characterization of cortical networks involved in sensory, motor, or cognitive tasks and will allow investigation of pathological connectivities in neurological disorders.oscillations ͉ functional connectivity ͉ coherence ͉ magnetoencephalography ͉ synchronization T he hypothesis that relevant information in the brain is coded by accurate timing of neuronal discharges has received strong support from recent reports of synchronization of neuronal firing within and across areas of the cat visual cortex (1). The synchronization of neural activity, which was modulated by gammaband oscillations, was shown to depend on stimulus properties like continuity, vicinity, and common motion, and on receptive field constellations (for review, see ref.2). This and similar findings seem to support the concept that synchronized rhythmic neural firing has a role in solving the binding problem, i.e., the integration of distributed information into a unified representation (1-4).To investigate cortico-cortical synchrony noninvasively in the human brain, new analysis tools must be developed. In functional magnetic resonance imaging (fMRI) studies, structural equation models have been used to estimate connectivities between brain areas (5, 6). Although this is a very promising approach, it lacks the temporal resolution required to measure oscillatory activity and to observe the expected transient formation of neuronal assemblies (7).Magnetoencephalography (MEG) and electroencephalography (...

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...

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The huge number of neurons in the human brain are connected to form functionally specialized assemblies. The brain's amazing processing capabilities rest on local communication within and long-range communication between these assemblies. Even simple sensory, motor and cognitive tasks depend on the precise coordination of many brain areas. Recent improvements in the methods of studying long-range communication have allowed us to address several important questions. What are the common mechanisms that govern local and long-range communication and how do they relate to the structure of the brain? How does oscillatory synchronization subserve neural communication? And what are the consequences of abnormal synchronization?

Bilateral pallidal neurostimulation for 3 months was more effective than sham stimulation in patients with primary generalized or segmental dystonia. (ClinicalTrials.gov number, NCT00142259 [ClinicalTrials.gov].).

SummaryBackground Deep brain stimulation (DBS) of the subthalamic nucleus (STN) reduces motor symptoms in patients with Parkinson's disease (PD) and improves their quality of life; however, the eff ect of DBS on cognitive functions and its psychiatric side-eff ects are still controversial. To assess the neuropsychiatric consequences of DBS in patients with PD we did an ancillary protocol as part of a randomised study that compared DBS with the best medical treatment.

Because of attentional limitations, the human visual system can process for awareness and response only a fraction of the input received. Lesion and functional imaging studies have identified frontal, temporal, and parietal areas as playing a major role in the attentional control of visual processing, but very little is known about how these areas interact to form a dynamic attentional network. We hypothesized that the network communicates by means of neural phase synchronization, and we used magnetoencephalography to study transient long-range interarea phase coupling in a well studied attentionally taxing dual-target task (attentional blink). Our results reveal that communication within the fronto-parieto-temporal attentional network proceeds via transient long-range phase synchronization in the beta band. Changes in synchronization reflect changes in the attentional demands of the task and are directly related to behavioral performance. Thus, we show how attentional limitations arise from the way in which the subsystems of the attentional network interact. T he human brain faces an inestimable task of reducing a potentially overloading amount of input into a manageable flow of information that reflects both the current needs of the organism and the external demands placed on it. This task is accomplished via a ubiquitous construct known as ''attention,'' whose mechanism, although well characterized behaviorally, is far from understood at the neurophysiological level. Whereas attempts to identify particular neural structures involved in the operation of attention have met with considerable success (1-5) and have resulted in the identification of frontal, parietal, and temporal regions, far less is known about the interaction among these structures in a way that can account for the task-dependent successes and failures of attention. The goal of the present research was, thus, to unravel the means by which the subsystems making up the human attentional network communicate and to relate the temporal dynamics of their communication to observed attentional limitations in humans.A prime candidate for communication among distributed systems in the human brain is neural synchronization (for review, see ref. 6). Indeed, a number of studies provide converging evidence that long-range interarea communication is related to synchronized oscillatory activity (refs. 7-14; for review, see ref. 15). To determine whether neural synchronization plays a role in attentional control, we placed humans in an attentionally demanding task and used magnetoencephalography (MEG) to track interarea communication by means of neural synchronization.In particular, we presented 10 healthy subjects with two visual target letters embedded in streams of 13 distractor letters, appearing at a rate of seven per second. The targets were separated in time by a single distractor. This condition leads to the ''attentional blink'' (AB), a well studied dual-task phenomenon showing the reduced ability to report the second of two targets when an interval Ͻ5...