Abstract
Deep brain stimulation (DBS) of the internal pallidal segment (GPi: globus pallidus internus) is gold standard treatment for medically intractable dystonia, but detailed knowledge of mechanisms of action is still not available. There is evidence that stimulation of ventral and dorsal GPi produces opposite motor effects. The aim of this study was to analyse connectivity profiles of ventral and dorsal GPi. Probabilistic tractography was initiated from DBS electrode contacts in 8 patients with focal dystonia and connectivity patterns compared. We found a considerable difference in anterior-posterior distribution of fibres along the mesial cortical sensorimotor areas between the ventral and dorsal GPi connectivity. This finding of distinct GPi connectivity profiles further confirms the clinical evidence that the ventral and dorsal GPi belong to different functional and anatomic motor subsystems. Their involvement could play an important role in promoting clinical DBS effects in dystonia.

Abstract
Patients with medically refractory epilepsy may benefit from surgical resection of the epileptic focus. Subdural electrodes are implanted to accurately locate the seizure onset and locate the eloquent areas to be spared. However, the visualization of the subdural electrodes may be limited by the current methods. The aim of this work was to assist physicians in the localization of subdural electrodes in relation to anatomical landmarks using co-registration methods and by removing the cerebellum from MRI images. Three patients with refractory epilepsy were studied, in whom subdural electrodes were implanted. All electrodes were correctly localized in a 3D view over the cortex and their visualization was improved by the removal of cerebellum. This method promises to be useful in the optimization of the surgical plan.

Abstract
A critical question in tapping behavior is to understand whether the temporal control is exerted on the duration and trajectory of the downward-upward hand movement or on the pause between hand movements. In the present study, we determined the duration of both the movement execution and pauses of monkeys performing a synchronization-continuation task (SCT), using the speed profile of their tapping behavior. We found a linear increase in the variance of pause-duration as a function of interval, while the variance of the motor implementation was relatively constant across intervals. In fact, 96% of the variability of the duration of a complete tapping cycle (pause + movement) was due to the variability of the pause duration. In addition, we performed a Bayesian model selection to determine the effect of interval duration (450 -1,000 ms), serial-order (1-6 produced intervals), task phase (sensory cued or internally driven), and marker modality (auditory or visual) on the duration of the movement-pause and tapping movement. The results showed that the most important parameter used to successfully perform the SCT was the control of the pause duration. We also found that the kinematics of the tapping movements was concordant with a stereotyped ballistic control of the hand pressing the push-button. The present findings support the idea that monkeys used an explicit timing strategy to perform the SCT, where a dedicated timing mechanism controlled the duration of the pauses of movement, while also triggered the execution of fixed movements across each interval of the rhythmic sequence.

Abstract
Automated tracking of axonal neurotransmitter vesicles is a challenging problem in neuroscience. The present vesicle tracking is typically performed manually over confocal microscopy images. NeuronDynamics is a method designed to automate and speed-up the characterization of global vesicle movement in neurons while yielding high accuracy and precision results (similar or better than expert clinicians). For a set of fluorescent-marked vesicles "films", NeuronDynamics performs a two stage approach: 1) Training: the system asks the user to mark a set of vesicles and the position of the cellular body; 2) Detection & tracking: based on the previous training, the system runs a Bayesian classifier over the image sequence to detect and classify vesicles and their movements (speed and direction). The obtained results were compared to another state-of-the-art method (FluoTracker), and were found greatly higher in accuracy, sensitivity, specificity and precision. Although NeuronDynamics is a semi-automated process, it is significantly faster than manual tracking and can be adapted to be used for similar approaches for other biological samples.

Abstract
Movement-related diseases, such as Parkinson's disease (PD), progressively affect the motor function, many times leading to severe motor impairment and dramatic loss of the patients' quality of life. Human motion analysis techniques can be very useful to support clinical assessment of this type of diseases. In this contribution, we present a RGB-D camera (Microsoft Kinect) system and its evaluation for PD assessment. Based on skeleton data extracted from the gait of three PD patients treated with deep brain stimulation and three control subjects, several gait parameters were computed and analyzed, with the aim of discriminating between non-PD and PD subjects, as well as between two PD states (stimulator ON and OFF). We verified that among the several quantitative gait parameters, the variance of the center shoulder velocity presented the highest discriminative power to distinguish between non-PD, PD ON and PD OFF states (p = 0.004). Furthermore, we have shown that our low-cost portable system can be easily mounted in any hospital environment for evaluating patients' gait. These results demonstrate the potential of using a RGB-D camera as a PD assessment tool.

Abstract
The movement of the human body offers neurologists important clues for the diagnosis and follow-up of many neurological diseases. The typical diagnosis approach is accomplished through simple observation of movements of interest (MOI) associated with a specific neurological disease. This approach is highly subjective because it is mainly based on qualitative evaluation of MOIs. Quantitative movement techniques are then obvious diagnosis-aid systems to approach these cases. Nevertheless, the use of motion quantification techniques in these pathologies is still relatively rare. In this paper, we intend to review this area and provide a clear picture of the current state of the art, both in the methods used and their applications to the main movement-related neurological diseases. We approach some historic aspects and the current state of the motion capture techniques and present the results of a survey to the literature that includes 82 papers, since 2006, covering the usage of these techniques in neurological diseases. Furthermore, we discuss the pros and cons of using quantitative approaches in these clinical scenarios. Finally, we present some conclusions and discuss the trends we foresee for the future. © 2008-2011 IEEE.