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Objective: Essential tremor (ET) represents the most common movement disorder. Drug‐resistant ET can benefit from standard stereotactic procedures (deep brain stimulation or radiofrequency thalamotomy) or alternatively minimally invasive high‐focused ultrasound or radiosurgery. All aim at same target, thalamic ventro‐intermediate nucleus (Vim). Methods: The study included a cohort of 17 consecutive patients, with ET, treated only with left unilateral stereotactic radiosurgical thalamotomy (SRS‐T) between September 2014 and August 2015. The mean time to tremor improvement was 3.32 months (SD 2.7, 0.5‐10). Neuroimaging data were collected at baseline (n = 17). Standard tremor scores, including activities of daily living (ADL) and tremor score on treated hand (TSTH), were completed pretherapeutically and 1 year later. We further correlate these scores with baseline inter‐connectivity in twenty major large‐scale brain networks. Results: We report as predictive three networks, with the interconnected statistically significant clusters: primary motor cortex interconnected with inferior olivary nucleus , bilateral thalamus interconnected with motor cerebellum lobule V2 (ADL), and anterior default‐mode network interconnected with Brodmann area 103 (TSTH). For all, more positive pretherapeutic interconnectivity correlated with higher drop in points on the respective scores. Age, disease duration, or time‐to‐response after SRS‐T were not statistically correlated with pretherapeutic brain connectivity measures (P > .05). The same applied to pretherapeutic tremor scores, after using the same methodology described above. Conclusions: Our findings have clinical implications for predicting clinical response after SRS‐T. Here, using pretherapeutic magnetic resonance imaging and data processing without prior hypothesis, we show that pretherapeutic network(s) interconnectivity strength predicts tremor arrest in drug‐naïve ET, following stereotactic radiosurgical thalamotomy. [ABSTRACT FROM AUTHOR]

Background: Patients who experience severe brain injuries are at risk of secondary brain damage, because of delayed vasospasm and edema. Traditionally, many of these patients are kept on prolonged bed rest in order to maintain adequate cerebral blood flow, especially in the case of subarachnoid hemorrhage. On the other hand, prolonged bed rest carries important morbidity. There may be a clinical benefit in early mobilization and our hypothesis is that early gradual mobilization is safe in these patients. The aim of this study was to observe and quantify the changes in sympathetic activity, mainly related to stress, and blood pressure in gradual postural changes by the verticalization robot (Erigo®) and after training by a lower body ergometer (MOTOmed-letto®), after prolonged bed rest of minimum 7 days.Methods: Thirty patients with severe neurological injuries were randomized into 3 groups with different protocols of mobilization: Standard, MOTOmed-letto® or Erigo® protocol. We measured plasma catecholamines, metanephrines and blood pressure before, during and after mobilization.Results: Blood pressure does not show any significant difference between the 3 groups. The analysis of the catecholamines suggests a significant increase in catecholamine production during Standard mobilization with physiotherapists and with MOTOmed-letto® and no changes with Erigo®.Conclusions: This preliminary prospective randomized study shows that the mobilization of patients with severe brain injuries by means of Erigo® does not increase the production of catecholamines. It means that Erigo® is a well-tolerated method of mobilization and can be considered a safe system of early mobilization of these patients. Further studies are required to validate our conclusions.Trial Registration: The study was registered in the ISRCTN registry with the trial registration number ISRCTN56402432 . Date of registration: 08.03.2016. Retrospectively registered. [ABSTRACT FROM AUTHOR]

The increasing availability of network data is leading to a growing interest in processing of signals on graphs. One notable tool for extending conventional signal-processing operations to networks is the graph Fourier transform that can be obtained as the eigendecomposition of the graph Laplacian. In this letter, we used the graph Fourier transform to define a new method for generating surrogate graph signals. The approach is based on sign-randomization of the graph Fourier coefficients and, therefore, the correlation structure of the surrogate graph signals (i.e., smoothness on the graph topology) is imposed by the measured data. The proposed method of surrogate data generation can be widely applied for nonparametric statistical hypothesis testing. Here, we showed a proof-of-concept with a high-density electroencephalography dataset. [ABSTRACT FROM PUBLISHER]

The 16 papers in this special section focused on advanced signal processing techniques for brain networks. Network models of the brain have become an important tool of modern neurosciences to study fundamental organizational principles of brain structure & function. Their connectivity is captured by the so-called connectome, the complete set of structural and functional links of the network. Advancing current methodology remains an important need in the field; e.g., increasing large-scale models; incorporating multimodal information in multiplex graph models; dealing with dynamical aspects of network models; and matching data-driven and theoretical models. These challenges form multiple opportunities to develop and adapt emerging signal processing theories and methods at the interface of graph theory, machine learning, applied statistics, simulation, and so on, to play a key role in analysis and modeling and to bring our understanding of brain networks to the next level for key applications in cognitive and clinical neurosciences, including brain-computer interfaces. [ABSTRACT FROM PUBLISHER]

We consider the problem of locating point sources in the planar domain from overdetermined boundary measurements of solutions of Poisson's equation. In this paper, we propose a novel technique, termed "analytic sensing," which combines the application of Green's theorem to functions with vanishing Laplacian-known as the "reciprocity gap" principle-with the careful selection of analytic functions that "sense" the manifestation of the sources in order to determine their positions and intensities. Using this formalism we express the problem at hand as a generalized sampling problem, where the signal to be reconstructed is the source distribution. To determine the positions of the sources, which is a nonlinear problem, we extend the annihilating-filter method, which reduces the problem to solving a linear system of equations for a polynomial whose roots are the positions of the point sources. Once these positions are found, resolving the according intensities boils down to solving a linear system of equations. We demonstrate the performance of our technique in the presence of noise by comparing the achieved accuracy with the theoretical lower bound provided by Cramér-Rao theory. [ABSTRACT FROM AUTHOR]

Model-based statistical analysis of functional magnetic resonance imaging (fMRI) data relies on the general linear model and statistical hypothesis testing. Due to the large number of intracranial voxels, it is important to deal with the multiple comparisons problem. Many fMRI analysis tools utilize Gaussian random field theory to obtain a more sensitive thresholding; this typically involves Gaussian smoothing as a preprocessing step. Wavelet-based statistical parametric mapping (WSPM) is an alternative method to obtain parametric maps from non-smoothed data. It relies on adaptive thresholding of the parametric maps in the wavelet domain, followed by voxel-wise statistical testing. The procedure is conservative; it uses Bonferroni correction for strong type I error control. Yet, its sensitivity is close to SPM's due to the excellent denoising properties of the wavelet transform. Here, we adapt the false discovery rate (FDR) principle to the WSPM framework. Although explicit p-values cannot be obtained, we show that it is possible to retrieve the FDR threshold by a simple iterative scheme. We then validate the approach with an event-related visual stimulation task. Our results show better sensitivity with preservation of spatial resolution; i.e., activation clusters align well with the gray matter structures in the visual cortex. The spatial resolution of the activation maps is even high enough to easily identify a voxel that is very likely to be caused by the draining-vein effect. [ABSTRACT FROM PUBLISHER]