OBJECTIVE Deep brain stimulation of the thalamus was introduced more than 40 years ago with the objective of improving the performance and attention of patients in a vegetative or minimally conscious state. Here, the authors report the results of the Cortical Activation by Thalamic Stimulation (CATS) study, a prospective multiinstitutional study on the effects of bilateral chronic stimulation of the anterior intralaminar thalamic nuclei and adjacent paralaminar regions in patients affected by a disorder of consciousness. METHODS The authors evaluated the clinical and radiological data of 29 patients in a vegetative state (unresponsive wakefulness syndrome) and 11 in a minimally conscious state that lasted for more than 6 months. Of these patients, 5 were selected for bilateral stereotactic implantation of deep brain stimulating electrodes into their thalamus. A definitive consensus for surgery was obtained for 3 of the selected patients. All 3 patients (2 in a vegetative state and 1 in a minimally conscious state) underwent implantation of bilateral thalamic electrodes and submitted to chronic stimulation for a minimum of 18 months and a maximum of 48 months. RESULTS In each case, there was an increase in desynchronization and the power spectrum of electroencephalograms, and improvement in the Coma Recovery Scale-Revised scores was found. Furthermore, the severity of limb spasticity and the number and severity of pathological movements were reduced. However, none of these patients returned to a fully conscious state. CONCLUSIONS Despite the limited number of patients studied, the authors confirmed that bilateral thalamic stimulation can improve the clinical status of patients affected by a disorder of consciousness, even though this stimulation did not induce persistent, clinically evident conscious behavior in the patients. Clinical trial registration no.: NCT01027572 ( ClinicalTrials.gov ).

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Active vibration control of a free-edge rectangular sandwich plate is proposed and tested. The experimental setup consists of a honeycomb panel having a carbon-fiber reinforced polymer (CFRP) outer skins and a polymer-paper core, subjected to an orthogonal disturbance, due to an electrodynamics exciter and controlled by Macro Fibre Composite (MFC) actuators and sensors. MFC parches consist of rectangular piezoceramic rods sandwiched between layers of adhesive, electrodes and polyamide film. The MFC actuators and sensors are controlled by a programmable digital dSPACE (R) controller board. The control algorithm proposed in this paper is based on the Positive Position Feedback (PPF) technique and is successfully applied with different combinations of inputs/outputs (Single Input Single Output, MultiSISO, Multi Input Multi Output) in order to control the first four normal modes. The control appears to be robust and efficient in reducing vibration in linear (small amplitude) and nonlinear (large amplitude) vibrations regimes, although the structure under investigation exhibits a relativity high modal density, i.e., four resonances in a range of about 100 Hz. The control strategy allows to effectively control each resonance both individually or simultaneously

In this paper, the novel wavelet spectral kurtosis (WSK) technique is applied for the early diagnosis of gear tooth faults. Two variants of the wavelet spectral kurtosis technique, called variable resolution WSK and constant resolution WSK, are considered for the diagnosis of pitting gear faults. The gear residual signal, obtained by filtering the gear mesh frequencies, is used as the input to the SK algorithm. The advantages of using the wavelet-based SK techniques when compared to classical Fourier transform (FT)-based SK is confirmed by estimating the toothwise Fisher's criterion of diagnostic features. The final diagnosis decision is made by a three-stage decision-making technique based on the weighted majority rule. The probability of the correct diagnosis is estimated for each SK technique for comparison. An experimental study is presented in detail to test the performance of the wavelet spectral kurtosis techniques and the decision-making technique

The mystery behind the origin of the pain and the difficulty to propose methodologies for its quantitative characterization fascinated philosophers (and then scientists) from the dawn of our modern society. Nowadays, studying patterns of information flow in mesoscale activity of brain networks is a valuable strategy to offer answers in computational neuroscience. In this paper, complex network analysis was performed on the time-varying brain functional connectomes of a rat model of persistent peripheral neuropathic pain, obtained by means of local field potential and spike train analysis. A wide range of topological network measures (14 in total, the code is publicly released at: https://github.com/biomedical-cybernetics/topological_measures_wide_analysis) was employed to quantitatively investigate the rewiring mechanisms of the brain regions responsible for development and upkeep of pain along time, from three hours to 16 days after nerve injury. The time trend (across the days) of each network measure was correlated with a behavioural test for rat pain, and surprisingly we found that the rewiring mechanisms associated with two local topological measure, the local-community-paradigm and the power-lawness, showed very high statistical correlations (higher than 0.9, being the maximum value 1) with the behavioural test. We also disclosed clear functional connectivity patterns that emerged in association with chronic pain in the primary somatosensory cortex (S1) and ventral posterolateral (VPL) nuclei of thalamus. This study represents a pioneering attempt to exploit network science models in order to elucidate the mechanisms of brain region re-wiring and engram formations that are associated with chronic pain in mammalians. We conclude that the local-community-paradigm is a model of complex network organization that triggers a local learning rule, which seems associated to processing, learning and memorization of chronic pain in the brain functional connectivity. This rule is based exclusively on the network topology, hence was named epitopological learning.Electronic supplementary materialThe online version of this article (doi:10.1007/s41109-017-0048-x) contains supplementary material, which is available to authorized users.

Chronic pain (CP) is a condition with a large repertory of clinical signs and symptoms with diverse expressions. Though widely analyzed, an appraisal at the level of single neuron and neuronal networks in CP is however missing. The present research proposes an empirical and theoretic framework which identifies a complex network correlate nested in the somatosensory thalamocortical (TC) circuit in diverse CP models. In vivo simultaneous extracellular neuronal electrophysiological high-density recordings have been performed from the TC circuit in rats. Wide functional network statistics neatly discriminated CP from control animals identifying collective dynamical traits. In particular, a collapsed functional connectivity and an altered modular architecture of the thalamocortical circuit have been evidenced. These results envisage CP as a functional connectivity disorder and give the clue for unveiling innovative therapeutic strategies.

Alzheimer's Disease (AD) is an invalidating neurodegenerative disorders frequently affecting the aging population. In view of the increase of elderlies, not only in western countries, the related growing societal problems urge for identifying clinical biomarkers in view of potential treatments interfering or blocking the disease course. Among the plenty of anatomo-functional in vivo imaging techniques to inspect brain circuits and physiology, the Magnetic Resonance Imaging (MRI), the functional MRI (fMRI), the Electroencephalography (EEG) and Magnetoencephalography (MEG), have been extensively used for the study of AD, with different achievements and limitations. Eventually, the methodologies summoned by brain connectomics further strengthen the expectations in this field, as shown by recent results obtained with [18F]2-fluoro-2-deoxyglucose 18FDG-PET and fMRI in the prediction of the AD in early stages. However, the inherent complexity of the pathophysiology of the AD suggests that only integrative approaches combining different techniques and methodologies of brain scanning could produce significant breakthroughs in the study of AD. This review proposes a formal framework able to combine brain connectomic data from multimodal acquisitions by means of different in vivo neuroimaging techniques, briefly reporting their different advantages and drawbacks. Indeed, a specialized complex multiplex network, where nodes interact in layers linking the same pair of nodes and each layer reflects a distinct type of brain acquisition, can model the plurality of connectomes recommended in this framework.

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