Search

Despite the well-established use of partial directed coherence (PDC) to estimate interactions between brain signals, the assessment of its statistical significance still remains controversial. Commonly used approaches are based on the generation of empirical distributions of the null case, implying a considerable computational time, which may become a serious limitation in practical applications. Recently, rigorous asymptotic distributions for PDC were proposed. The aim of this work is to compare the performances of the asymptotic statistics with those of an empirical approach, in terms of both accuracy and computational time., Methods: Indices of performance were derived for the two approaches by a simulation study implementing different ground-truth networks under different levels of signal-to-noise ratio and amount of data available for the estimate. The two approaches were then applied to the resting-state EEG data acquired in a group of minimally conscious state and vegetative state/unresponsive wakefulness syndrome patients., Results: The performances of the asymptotic statistics in simulations matched those obtained by the empirical approach, with a considerable reduction of the computational time. Results of the application to real data showed that the asymptotic statistics led to the extraction of connectivity-based indices able to discriminate patients in different disorders of consciousness conditions and to correlate significantly with clinical scales. Such results were similar to those obtained by the empirical assessment, but with a considerable time economy., Significance: Asymptotic statistics provide an approach to the assessment of PDC significance with comparable performances with respect to the previously used empirical approaches but with a substantial advantage in terms of computational time.

Cisplatin (Cpt) is known to induce a high level of oxidative stress, resulting in an increase of reactive oxygen species damaging the inner ear and causing hearing loss at high frequencies. Studies on animal models show that antioxidants may lower Cpt-induced ototoxicity. The aim of this study is to evaluate the ototoxic effects of two different protocols of Cpt administration in a Sprague-Dawley rat model, and to test in the same model the synergic protective effects of a solution of coenzyme Q10 terclatrate and Acuval 400®, a multivitamin supplement containing antioxidant agents and minerals (Acu-Qter). The Cpt was administered intraperitoneally in a single dose (14 mg/kg) or in three daily doses (4.6 mg/kg/day) to rats orally treated or untreated with Acu-Qter for 5 days. The auditory function was assessed by measuring auditory brainstem responses from 2 to 32 kHz at day 0 and 5 days after treatment. Similar hearing threshold and body weight alterations were observed in both Cpt administration protocols, but mortality reduced to zero when Cpt was administered in three daily doses. The Acu-Qter treatment was able to prevent and completely neutralize ototoxicity in rats treated with three daily Cpt doses, supporting the synergic protective effects of coenzyme Q terclatrate and Acuval 400® against Cpt-induced oxidative stress. The administration protocol involving three Cpt doses is more similar to common human chemotherapy protocols, therefore it appears more useful for long-term preclinical studies on ototoxicity prevention., Competing Interests: The work was supported by the Scharper Healthcare Srl. (Milan, Italy). This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Investigating the level of similarity between two brain networks, resulting from measures of effective connectivity in the brain, can be of interest from many respects. In this study, we propose and test the idea to borrow measures of association used in machine learning to provide a measure of similarity between the structure of (un-weighted) brain connectivity networks. The measures here explored are the accuracy, Cohen's Kappa (K) and Area Under Curve (AUC). We implemented two simulation studies, reproducing two contexts of application that can be particularly interesting for practical applications, namely: i) in methodological studies, performed on surrogate data, aiming at comparing the estimated network with the corresponding ground-truth network; ii) in applications to real data, when it is necessary to compare the structure of a network obtained in a specific subject with a reference (e.g. a baseline condition or normative data). In the simulations, the level of similarity between two networks was manipulated through different factors. We then investigated the effect of such manipulations on the measures of association. Results showed how the three parameters modulated their values according to the level of similarity between the two networks. In particular, the AUC provided the better performances in terms of its capability to synthetize the similarity between two networks, showing high dynamic and sensitivity.

The coordinated interactions between individuals are fundamental for the success of the activities in some professional categories. We reported on brain-to-brain cooperative interactions between civil pilots during a simulated flight. We demonstrated for the first time how the combination of neuroelectrical hyperscanning and intersubject connectivity could provide indicators sensitive to the humans' degree of synchronization under a highly demanding task performed in an ecological environment. Our results showed how intersubject connectivity was able to i) characterize the degree of cooperation between pilots in different phases of the flight, and ii) to highlight the role of specific brain macro areas in cooperative behavior. During the most cooperative flight phases pilots showed, in fact, dense patterns of interbrain connectivity, mainly linking frontal and parietal brain areas. On the contrary, the amount of interbrain connections went close to zero in the non-cooperative phase. The reliability of the interbrain connectivity patterns was verified by means of a baseline condition represented by formal couples, i.e. pilots paired offline for the connectivity analysis but not simultaneously recorded during the flight. Interbrain density was, in fact, significantly higher in real couples with respect to formal couples in the cooperative flight phases. All the achieved results demonstrated how the description of brain networks at the basis of cooperation could effectively benefit from a hyperscanning approach. Interbrain connectivity was, in fact, more informative in the investigation of cooperative behavior with respect to established EEG signal processing methodologies applied at a single subject level.

Resting state connectivity has been increasingly studied to investigate the effects of aging on the brain. A reduced organization in the communication between brain areas was demonstrated by combining a variety of different imaging technologies (fMRI, EEG, and MEG) and graph theory. In this paper, we propose a methodology to get new insights into resting state connectivity and its variations with age, by combining advanced techniques of effective connectivity estimation, graph theoretical approach, and classification by SVM method. We analyzed high density EEG signals recorded at rest from 71 healthy subjects (age: 20-63 years). Weighted and directed connectivity was computed by means of Partial Directed Coherence based on a General Linear Kalman filter approach. To keep the information collected by the estimator, weighted and directed graph indices were extracted from the resulting networks. A relation between brain network properties and age of the subject was found, indicating a tendency of the network to randomly organize increasing with age. This result is also confirmed dividing the whole population into two subgroups according to the age (young and middle-aged adults): significant differences exist in terms of network organization measures. Classification of the subjects by means of such indices returns an accuracy greater than 80%.

Communication and control of the external environment can be provided via brain-computer interfaces (BCIs) to replace a lost function in persons with severe diseases and little or no chance of recovery of motor abilities (ie, amyotrophic lateral sclerosis, brainstem stroke). BCIs allow to intentionally modulate brain activity, to train specific brain functions, and to control prosthetic devices, and thus, this technology can also improve the outcome of rehabilitation programs in persons who have suffered from a central nervous system injury (ie, stroke leading to motor or cognitive impairment). Overall, the BCI researcher is challenged to interact with people with severe disabilities and professionals in the field of neurorehabilitation. This implies a deep understanding of the disabled condition on the one hand, and it requires extensive knowledge on the physiology and function of the human brain on the other. For these reasons, a multidisciplinary approach and the continuous involvement of BCI users in the design, development, and testing of new systems are desirable. In this chapter, we will focus on noninvasive EEG-based systems and their clinical applications, highlighting crucial issues to foster BCI translation outside laboratories to eventually become a technology usable in real-life realm., (© 2016 Elsevier B.V. All rights reserved.)

An episode of complete failure to respond during an attentive task accompanied by behavioural signs of sleep is called a behavioural microsleep. We proposed a combination of high-resolution EEG and an advanced method for time-varying effective connectivity estimation for reconstructing the temporal evolution of the causal relations between cortical regions when microsleeps occur during a continuous visuomotor task. We found connectivity patterns involving left-right frontal, left-right parietal, and left-frontal/right-parietal connections commencing in the interval [-500; -250] ms prior to the onset of microsleeps and disappearing at the end of the microsleeps. Our results from global graph indices derived from effective connectivity analysis have revealed EEG-based biomarkers of all stages of microsleeps (preceding, onset, pre-recovery, recovery). In particular, this raises the possibility of being able to predict microsleeps in real-world tasks and initiate a 'wake-up' intervention to avert the microsleeps and, hence, prevent injurious and even multi-fatality accidents., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Visual stimuli quickly activate a broad network of brain areas that often show reciprocal structural connections between them. Activity at short latencies (<100 ms) is thought to represent a feed-forward activation of widespread cortical areas, but fast activation combined with reciprocal connectivity between areas in principle allows for two-way, recurrent interactions to occur at short latencies after stimulus onset. Here we combined EEG source-imaging and Granger-causal modeling with high temporal resolution to investigate whether recurrent and top-down interactions between visual and attentional brain areas can be identified and distinguished at short latencies in humans. We investigated the directed interactions between widespread occipital, parietal and frontal areas that we localized within participants using fMRI. The connectivity results showed two-way interactions between area MT and V1 already at short latencies. In addition, the results suggested a large role for lateral parietal cortex in coordinating visual activity that may be understood as an ongoing top-down allocation of attentional resources. Our results support the notion that indirect pathways allow early, evoked driving from MT to V1 to highlight spatial locations of motion transients, while influence from parietal areas is continuously exerted around stimulus onset, presumably reflecting task-related attentional processes.

Partial Directed Coherence (PDC) is a powerful estimator of effective connectivity. In neuroscience it is used in different applications with the aim to investigate the communication between brain regions during the execution of different motor or cognitive tasks. When multiple trials are available, PDC can be computed over multiple realizations, provided that the assumption of stationarity across trials is verified. This allows to improve the amount of data, which is an important constraint for the estimation accuracy. However, the stationarity of the data across trials is not always guaranteed, especially when dealing with patients. In this study we investigated how the inter-trials variability of an EEG dataset affects the PDC accuracy. Effects of density variations and of changes of connectivity values across trials were first investigated with a simulation study and then tested on real EEG data collected from two post-stroke patients during a motor imagery task and characterized by different inter-trials variability. Results showed the effect of different factors on the PDC accuracy and the robustness of such estimator in a range of conditions met in practical applications.

Hyperscanning consists in the simultaneous recording of hemodynamic or neuroelectrical signals from two or more subjects acting in a social context. Well-established methodologies for connectivity estimation have already been adapted to hyperscanning purposes. The extension of graph theory approach to multi-subjects case is still a challenging issue. In the present work we aim to test the ability of the currently used graph theory global indices in describing the properties of a network given by two interacting subjects. The testing was conducted first on surrogate brain-to-brain networks reproducing typical social scenarios and then on real EEG hyperscanning data recorded during a Joint Action task. The results of the simulation study highlighted the ability of all the investigated indexes in modulating their values according to the level of interaction between subjects. However, only global efficiency and path length indexes demonstrated to be sensitive to an asymmetry in the communication between the two subjects. Such results were, then, confirmed by the application on real EEG data. Global efficiency modulated, in fact, their values according to the inter-brain density, assuming higher values in the social condition with respect to the non-social condition.

Objective: Motor imagery (MI) is assumed to enhance poststroke motor recovery, yet its benefits are debatable. Brain-computer interfaces (BCIs) can provide instantaneous and quantitative measure of cerebral functions modulated by MI. The efficacy of BCI-monitored MI practice as add-on intervention to usual rehabilitation care was evaluated in a randomized controlled pilot study in subacute stroke patients., Methods: Twenty-eight hospitalized subacute stroke patients with severe motor deficits were randomized into 2 intervention groups: 1-month BCI-supported MI training (BCI group, n = 14) and 1-month MI training without BCI support (control group; n = 14). Functional and neurophysiological assessments were performed before and after the interventions, including evaluation of the upper limbs by Fugl-Meyer Assessment (FMA; primary outcome measure) and analysis of oscillatory activity and connectivity at rest, based on high-density electroencephalographic (EEG) recordings., Results: Better functional outcome was observed in the BCI group, including a significantly higher probability of achieving a clinically relevant increase in the FMA score (p < 0.03). Post-BCI training changes in EEG sensorimotor power spectra (ie, stronger desynchronization in the alpha and beta bands) occurred with greater involvement of the ipsilesional hemisphere in response to MI of the paralyzed trained hand. Also, FMA improvements (effectiveness of FMA) correlated with the changes (ie, post-training increase) at rest in ipsilesional intrahemispheric connectivity in the same bands (p < 0.05)., Interpretation: The introduction of BCI technology in assisting MI practice demonstrates the rehabilitative potential of MI, contributing to significantly better motor functional outcomes in subacute stroke patients with severe motor impairments., (© 2015 American Neurological Association.)

Background: Maximal inspiratory pressure (MIP) can help to evaluate inspiratory muscle strength. However its determination in ventilated patients is cumbersome and needs special equipment. We hypothesized that MIP could be obtained by using the expiratory hold knob of the ventilator. The aim of this study was to verify whether: 1) the end expiratory occlusion technique can be used for MIP determination; and 2) if this technique provides different results compared to those obtained by the traditional method of MIP calculation., Methods: We studied 23 consecutive patients undergoing mechanical ventilation for acute respiratory failure. The MIP was determined by two different methods, both based on occluding the airway for 20 seconds. This occlusion was obtained either by pressing the expiratory hold knob of the ventilator; or by detaching the patient from the ventilator circuit and using a noiseless pneumatic shutter placed on the inspiratory line of a two-way valve that allows expiration but prevents inspiration., Results: The average values of MIP obtained by using either the hold knob of the ventilator or the noiseless pneumatic shutter were -46±14 cmH2O and -56±13 cmH2O, respectively. The linear regression analysis showed a significant correlation between MIPVent and MIPOcc (r2=0.95), although the Bland- Altman analysis revealed that they are not clinically comparable., Conclusion: MIP can be easily determined at the bedside by pressing the expiratory hold knob of ventilator. However, MIPVent and MIPOcc are different in terms of absolute value probably because they were determined at diverse lung volume.

The recently developed technique of hyperscanning consists of the simultaneous recording of brain activity from multiple subjects involved in social interaction. The multivariate analysis of data coming from different subjects allows to model a system made of multiple brains interacting, and to characterize it in relation with different processes at the basis of social cognition. In this study, we investigate the empathy established between two subjects during a Third Party Punishment paradigm, in terms of the properties of the multiple-brain network obtained from EEG hyperscanning. Preliminary results show that significantly different multiple-brain network structures characterize a social situation operated by a human agent with respect to a computer based condition, and that the different levels of empathy induced by a fair or unfair treatment received by one of the subjects are characterized by denser inter-subjects connectivity and lower divisibility in the two single brain networks.

In this paper we motivate and describe spectral weighting in methods based on the Granger-causal modeling framework. We show how these methods were validated in recordings from an animal model (rats) with relatively well-understood dynamic connectivity, and provide a comparison of their performances in terms of physiological interpretability and time resolution. Having shown that spectrally weighted Partial Directed Coherence (wPDC) shows good performances in real animal data, we provide an example of the application of this method to EEG data recorded from patients with left or right temporal lobe epilepsy. The result showed that wPDC correctly identified the major drivers of interictal epileptic spiking activity, in line with invasive validation and surgical outcome, and furthermore that right temporal lobe epilepsy is characterized by more inter-hemispheric influence than left temporal lobe epilepsy.

Cortical processing of sensory stimuli typically recruits multiple areas, but how each area dynamically incorporates activity from other areas is not well understood. We investigated interactions between cortical columns of bilateral primary sensory regions (S1s) in rats by recording local field potentials and multi-unit activity simultaneously in both S1s with electrodes positioned at each cortical layer. Using dynamic connectivity analysis based on Granger-causal modeling, we found that, shortly after whisker stimulation (< 10 ms), contralateral S1 (cS1) already relays activity to granular and infragranular layers of S1 in the other hemisphere, after which cS1 shows a pattern of within-column interactions that directs activity upwards toward superficial layers. This pattern of predominant upward driving was also observed in S1 ipsilateral to stimulation, but at longer latencies. In addition, we found that interactions between the two S1s most strongly target granular and infragranular layers. Taken together, the results suggest a possible mechanism for how cortical columns integrate local and large-scale neocortical computation by relaying information from deeper layers to local processing in superficial layers., (© 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

Objective: A "soft surgery" technique was applied, using various types of specifically designed dummy electrodes, to mimic cochlear implantation in a guinea pig model, and the degree of hearing-preservation/cochlear damage was assessed., Methods: Tricolor guinea pigs were divided into 3 groups: group A were implanted with electrodes without any contacts or wires (soft electrode), group B were implanted with electrodes having a metallic wire inside (stiff electrode), and group C underwent a cochleostomy procedure without implantation. Compound action potentials, in the range of 4 to 32 kHz, were used to assess electrophysiologic changes in the hearing function presurgery and postsurgery. Data were collected before surgery, at times t = 0 (immediately after surgery) and at 3, 7, 14, and 30 days., Results: At low frequencies (4-8 kHz), an immediate elevation of hearing threshold was observed in all 3 groups. Higher threshold shifts were more consistent for group B implanted with a stiff electrode, in comparison to the other 2 groups. Animals from group C presented a recovery from hearing loss, starting 3 days after surgery. At high frequencies (16-32 kHz), the elevation of hearing threshold was higher, as compared with the data from the low frequencies. Group C animals presented oscillatory threshold shifts twice, and the recovery to normal threshold values occurred approximately at t = 14 days., Conclusion: The data suggest that cochleostomy is minimally harmful to the inner ear and that a soft electrode might better preserve the inner ear integrity than a rigid electrode.

Time-varying connectivity methods are increasingly used to study directed interactions between brain regions from electrophysiological signals. These methods often show good results in simulated data but it is unclear to what extent connectivity results obtained from real data are physiologically plausible. Here we introduce a benchmark approach using multichannel somatosensory evoked potentials (SEPs) measured across rat cortex, where the structural and functional connectivity is relatively simple and well-understood. Rat SEPs to whisker stimulation are exclusively initiated by contralateral primary sensory cortex (S1), at known latencies, and with activity spread from S1 to specific cortical regions. This allows for a comparison of time-varying connectivity measures according to fixed criteria. We thus evaluated the performance of time-varying Partial Directed Coherence (PDC) and the Directed Transfer Function (DTF), comparing row- and column-wise normalization and the effect of weighting by the power spectral density (PSD). The benchmark approach revealed clear differences between methods in terms of physiological plausibility, effect size and temporal resolution. The results provide a validation of time-varying directed connectivity methods in an animal model and suggest a driving role for ipsilateral S1 in the later part of the SEP. The benchmark SEP dataset is made freely available., (Copyright © 2014 Elsevier Inc. All rights reserved.)

This paper reviews published papers related to neurophysiological measurements (electroencephalography: EEG, electrooculography EOG; heart rate: HR) in pilots/drivers during their driving tasks. The aim is to summarise the main neurophysiological findings related to the measurements of pilot/driver's brain activity during drive performance and how particular aspects of this brain activity could be connected with the important concepts of "mental workload", "mental fatigue" or "situational awareness". Review of the literature suggests that exists a coherent sequence of changes for EEG, EOG and HR variables during the transition from normal drive, high mental workload and eventually mental fatigue and drowsiness. In particular, increased EEG power in theta band and a decrease in alpha band occurred in high mental workload. Successively, increased EEG power in theta as well as delta and alpha bands characterise the transition between mental workload and mental fatigue. Drowsiness is also characterised by increased blink rate and decreased HR values. The detection of such mental states is actually performed "offline" with accuracy around 90% but not online. A discussion on the possible future applications of findings provided by these neurophysiological measurements in order to improve the safety of the vehicles will be also presented., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

This paper reviews the published literature on the hyperscanning methodologies using hemodynamic or neuro-electric modalities. In particular, we describe how different brain recording devices have been employed in different experimental paradigms to gain information about the subtle nature of human interactions. This review also included papers based on single-subject recordings in which a correlation was found between the activities of different (non-simultaneously recorded) participants in the experiment. The descriptions begin with the methodological issues related to the simultaneous measurements and the descriptions of the results generated by such approaches will follow. Finally, a discussion of the possible future uses of such new approaches to explore human social interactions will be presented., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Anti-inflammatory drugs can minimize the trauma and inflammation in the inner ear caused by cochlear implantation surgery. For this reason, much effort has recently been devoted to finding the best way to administer these anti-inflammatory drugs for a prolonged time and in a personalized dosage. One solution is constructing an electrode with a dispenser filled with anti-inflammatory drugs with a dosage adapted to suit each new cochlear implant user. The purpose of this study was to measure in vitro, by high-performance liquid chromatography-mass spectrometry assay, the amount of dexamethasone released in 78 days in a physiological solution by a filled dispenser. The drug release continued for more than 2 months in three different phases: (1) during the first 1-5 days, (2) within about 2 weeks, and (3) from about 3 weeks until the end of experiment. This release trend is in accordance with the 3 main phases of damage caused by the cochlear implantation: (1) insertion trauma within the first 2 days, (2) inflammation within 2 weeks, and finally (3) an intracochlear chronic fibrosis reaction. Future animal model studies should consider using this dispenser in order to establish its effectiveness in preventing damage caused by cochlear implantation., (Copyright © 2013 Wiley Periodicals, Inc.)

The aim of the present study is to investigate the neurophysiological basis of the cognitive functions underlying the execution of joint actions, by means of the recent technique called hyperscanning. Neuroelectrical hyperscanning is based on the simultaneous recording of brain activity from multiple subjects and includes the analysis of the functional relation between the brain activity of all the interacting individuals. We recorded simultaneous high density electroencephalography (hdEEG) from 16 pairs of subjects involved in a computerized joint action paradigm, with controlled levels of cooperation. Results of cortical connectivity analysis returned significant differences, in terms of inter-brain functional causal links, between the condition of cooperative joint action and a condition in which the subjects were told they were interacting with a PC, while actually interacting with another human subject. Such differences, described by selected brain connectivity indices, point toward an integration between the two subjects' brain activity in the cooperative condition, with respect to control conditions.

The present research investigates the neurophysiological activity elicited by fast observations of faces of real candidates during simulated political elections. We used simultaneous recording of electroencephalographic (EEG) signals as well as galvanic skin response (GSR) and heart rate (HR) as measurements of central and autonomic nervous systems. Twenty healthy subjects were asked to give judgments on dominance, trustworthiness, and a preference of vote related to the politicians' faces. We used high-resolution EEG techniques to map statistical differences of power spectral density (PSD) cortical activity onto a realistic head model as well as partial directed coherence (PDC) and graph theory metrics to estimate the functional connectivity networks and investigate the role of cortical regions of interest (ROIs). Behavioral results revealed that judgment of dominance trait is the most predictive of the outcome of the simulated elections. Statistical comparisons related to PSD and PDC values highlighted an asymmetry in the activation of frontal cortical areas associated with the valence of the judged trait as well as to the probability to cast the vote. Overall, our results highlight the existence of cortical EEG features which are correlated with the prediction of vote and with the judgment of trustworthy and dominant faces.

Methods based on the multivariate autoregressive (MVAR) approach are commonly used for effective connectivity estimation as they allow to include all available sources into a unique model. To ensure high levels of accuracy for high model dimensions, all the observations are used to provide a unique estimation of the model, and thus of the network and its properties. The unavailability of a distribution of connectivity values for a single experimental condition prevents to perform statistical comparisons between different conditions at a single subject level. This is a major limitation, especially when dealing with the heterogeneity of clinical conditions presented by patients. In the present paper we proposed a novel approach to the construction of a distribution of connectivity in a single subject case. The proposed approach is based on small perturbations of the networks properties and allows to assess significant changes in brain connectivity indexes derived from graph theory. Its feasibility and applicability were investigated by means of a simulation study and an application to real EEG data.

One of the main limitations commonly encountered when dealing with the estimation of brain connectivity is the difficulty to perform a statistical assessment of significant changes in brain networks at a single-subject level. This is mainly due to the lack of information about the distribution of the connectivity estimators at different conditions. While group analysis is commonly adopted to perform a statistical comparison between conditions, it may impose major limitations when dealing with the heterogeneity expressed by a given clinical condition in patients. This holds true particularly for stroke when seeking for quantitative measurements of the efficacy of any rehabilitative intervention promoting recovery of function. The need is then evident of an assessment which may account for individual pathological network configuration associated with different level of patients' response to treatment; such network configuration is highly related to the effect that a given brain lesion has on neural networks. In this study we propose a resampling-based approach to the assessment of statistically significant changes in cortical connectivity networks at a single subject level. First, we provide the results of a simulation study testing the performances of the proposed approach under different conditions. Then, to show the sensitivity of the method, we describe its application to electroencephalographic (EEG) data recorded from two post-stroke patients who showed different clinical recovery after a rehabilitative intervention.

In clinical practice, cognitive impairment is often observed after stroke. The efficacy of rehabilitative interventions is routinely assessed by means of a neuropsychological test battery. Nowadays, more evidences indicate that the neuroplasticity which occurs after stroke can be better understood by investigating changes in brain networks. In this study we applied advanced methodologies for effective connectivity estimation in combination with graph theory approach, to define EEG derived descriptors of brain networks underlying memory tasks. In particular, we proposed such descriptors to identify substrates of efficacy of a Brain-Computer Interface (BCI) controlled neurofeedback intervention to improve cognitive function after stroke. Electroencephalographic (EEG) data were collected from two stroke patients before and after a neurofeedback-based training for memory deficits. We show that the estimated brain connectivity indices were sensitive to different training intervention outcomes, thus suggesting an effective support to the neuropsychological assessment in the evaluation of the changes induced by the BCI-based cognitive rehabilitative intervention.

In BCI applications for stroke rehabilitation, BCI systems are used with the aim of providing patients with an instrument that is capable of monitoring and reinforcing EEG patterns generated by motor imagery (MI). In this study we proposed an offline analysis on data acquired from stroke patients subjected to a BCI-assisted MI training in order to define an index for the evaluation of MI-BCI training session which is independent from the settings adopted for the online control and which is able to describe the properties of neuroelectrical activations across sessions. Results suggest that such index can be adopted to sort the trails within a session according to the adherence to the task.

Disorders of Consciousness (DOC) like Vegetative State (VS), and Minimally Conscious State (MCS) are clinical conditions characterized by the absence or intermittent behavioral responsiveness. A neurophysiological monitoring of parameters like Event-Related Potentials (ERPs) could be a first step to follow-up the clinical evolution of these patients during their rehabilitation phase. Eleven patients diagnosed as VS (n = 8) and MCS (n = 3) by means of the JFK Coma Recovery Scale Revised (CRS-R) underwent scalp EEG recordings during the delivery of a 3-stimuli auditory oddball paradigm, which included standard, deviant tones and the subject own name (SON) presented as a novel stimulus, administered under passive and active conditions. Four patients who showed a change in their clinical status as detected by means of the CRS-R (i.e., moved from VS to MCS), were subjected to a second EEG recording session. All patients, but one (anoxic etiology), showed ERP components such as mismatch negativity (MMN) and novelty P300 (nP3) under passive condition. When patients were asked to count the novel stimuli (active condition), the nP3 component displayed a significant increase in amplitude (p = 0.009) and a wider topographical distribution with respect to the passive listening, only in MCS. In 2 out of the 4 patients who underwent a second recording session consistently with their transition from VS to MCS, the nP3 component elicited by passive listening of SON stimuli revealed a significant amplitude increment (p < 0.05). Most relevant, the amplitude of the nP3 component in the active condition, acquired in each patient and in all recording sessions, displayed a significant positive correlation with the total scores (p = 0.004) and with the auditory sub-scores (p < 0.00001) of the CRS-R administered before each EEG recording. As such, the present findings corroborate the value of ERPs monitoring in DOC patients to investigate residual unconscious and conscious cognitive function.

Mutations in mitochondrial DNA (mtDNA) have been shown to be an important cause of sensorineural hearing loss (SNHL). In this study, we performed a clinical and genetic analysis of 169 hearing-impaired patients and some of their relatives suffering from idiopathic SNHL, both familial and sporadic. The analysis of four fragments of their mtDNA identified several polymorphisms, the well known pathogenic mutation, A1555G, and some novel mutations in different genes, implying changes in the aminoacidic sequence. A novel sporadic mutation in 12S rRNA (MT-RNR1), not previously reported in the literature, was found in a case of possible aminoglycoside-induced progressive deafness.

The GJB2 gene is located on chromosome 13q12 and it encodes the connexin 26, a transmembrane protein involved in cell-cell attachment of almost all tissues. GJB2 mutations cause autosomal recessive (DFNB1) and sometimes dominant (DFNA3) non-syndromic sensorineural hearing loss. Moreover, it has been demonstrated that connexins are involved in regulation of growth and differentiation of epidermal tissues. Hence, mutations in GJB2 gene, which is responsible for non-syndromic deafness, may be associated with an abnormal skin and hair phenotype. We analyzed hair samples from 96 subjects: a study group of 42 patients with hearing impairments of genetic origin (38 with a non-syndromic form, 4 with a syndromic form), and a control group including 54 people, i.e. 43 patients with other, non-genetic hearing impairments and 11 healthy volunteers aged up to 10 years old. The surface structure of 49 hair samples was normal, whereas in 45 cases it was altered, with a damaged appearance. Two hair samples were considered unclassifiable: one from the patient heterozygotic for the pendrin mutation (Fig. 2C), the other from a patient from Ghana with a R134W mutation (Fig. 2D). Among the 43 altered hair samples, 31 belonged to patients with connexin mutations and the other 12 came from patients without connexin mutations., (Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.)

For the past decade, the detection and quantification of interactions within and between physiological networks has become a priority-in-common between the fields of biomedicine and computer science. Prominent examples are the interaction analysis of brain networks and of the cardiovascular-respiratory system. The aim of the study is to show how and to what extent results from time-variant partial directed coherence analysis are influenced by some basic estimator and data parameters. The impacts of the Kalman filter settings, the order of the autoregressive (AR) model, signal-to-noise ratios, filter procedures and volume conduction were investigated. These systematic investigations are based on data derived from simulated connectivity networks and were performed using a Kalman filter approach for the estimation of the time-variant multivariate AR model. Additionally, the influence of electrooculogram artefact rejection on the significance and dynamics of interactions in 29 channel electroencephalography recordings, derived from a photic driving experiment, is demonstrated. For artefact rejection, independent component analysis was used. The study provides rules to correctly apply particular methods that will aid users to achieve more reliable interpretations of the results.

Cisplatin is the most common antineoplastic drug used for the therapy of solid tumours. To date, researchers have focused on the dosage to be administered for each specific tumour, mainly considering the local adverse effects. The aim of this study was to correlate the severity of the adverse effects with: i) the dosage of cisplatin; ii) the specific site of the tumour; iii) the association with other drugs; and iv) the symptoms. We analysed data from 123 patients with 11 different tumour classes undergoing therapy from 2007 to 2008 at St. Anna Hospital (Ferrara, Italy), using the Spearman non-parametric correlation index. Even though significant correlations were found among the variables, the overall results showed that the main factor influencing the severity of the adverse effects was the dosage of cisplatin administered.

We report the discovery of a new putatively active Tc1-like transposable element (Tana1) in the genome of sturgeons, an ancient group of fish considered as living fossils. The complete sequence of Tana1 was first characterized in the 454-sequenced transcriptome of the Adriatic sturgeon (Acipenser naccarii) and then isolated from the genome of the same species and from 12 additional sturgeons including three genera of the Acipenseridae (Acipenser, Huso, Scaphirhynchus). The element has a total length of 1588bp and presents inverted repeats of 210bp, one of which partially overlapping the 3' region of the transposase gene. The spacing of the DDE motif within the catalytic domain in Tana1 is unique (DD38E) and indicates that Tana1 can be considered as the first representative of a new Tc1 subfamily. The integrity of the native form (with no premature termination codons within the transposase), the presence of all expected functional domains and its occurrence in the sturgeon transcriptome suggest a current or recent activity of Tana1. The presence of Tana1 in the genome of the 13 sturgeon species in our study points to an ancient origin of the element that existed before the split of the group 170 million years ago. The dissemination of Tana1 across sturgeon genomes could be interpreted by postulating vertical transmission from an ancestral Tana1 with a particularly slow evolutionary rate Horizontal transmission might have also played a role in the dissemination of Tana1 as evidenced by the presence of a complete copy in the genome of Atlantic salmon. Vertical and horizontal transmission are not mutually exclusive and may have concurred in shaping the evolution of Tana1., (Copyright © 2012 Elsevier Inc. All rights reserved.)

The first child (proband) of nonconsanguineous Caucasian parents underwent genetic investigation because she was affected with congenital choanal atresia, heart defects and kidney hyposplasia with mild transient renal insufficiency. The direct DNA sequencing after PCR of the CHD7 gene, which is thought to be responsible for approximately 60-70% of the cases of CHARGE syndrome/association, found no mutations. The cytogenetic analysis (standard GTG banding karyotype) revealed the presence of extrachromosomal material on 10q. The chromosome analysis was completed with array CGH (30 kb resolution), MLPA and FISH, which allowed the identification of three 6p regions (6p.25.3p23 × 3): 2 of these regions are normally located on chromosome 6, and the third region is translocated to the long arm of chromosome 10. The same chromosomal rearrangement was subsequently found in the father, who was affected with congenital ptosis and progressive hearing loss, and in the proband's sister, the second child, who presented at birth with choanal atresia and congenital heart defects. The mutated karyotypes, which were directly inherited, are thought to be responsible for a variable phenotype, including craniofacial dysmorphisms, choanal atresia, congenital ptosis, sensorineural hearing loss, heart defects, developmental delay, and renal dysfunction. Nevertheless, to achieve a complete audiological assessment of the father, he underwent further investigation that revealed an increased level of the coagulation factor XIII (300% increased activity), fluctuating levels of fibrin D-dimer degradation products (from 296 to 1,587 ng/ml) and a homoplasmic mitochondrial DNA mutation: T961G in the MTRNR1 (12S rRNA) gene. He was made a candidate for cochlear implantation. Preoperative high-resolution computed tomography and magnetic resonance imaging of the temporal bone revealed the presence of an Arnold-Chiari malformation type I. To the best of our knowledge, this study is the second report on partial 6p trisomy that involves the 10q terminal region. Furthermore, we report the first case of documented Arnold-Chiari malformation type I and increased factor XIII activity associated with 6p trisomy. We present a comprehensive report of the familial cases and an exhaustive literature review.

Partial Directed Coherence (PDC) is a spectral multivariate estimator for effective connectivity, relying on the concept of Granger causality. Even if its original definition derived directly from information theory, two modifies were introduced in order to provide better physiological interpretations of the estimated networks: i) normalization of the estimator according to rows, ii) squared transformation. In the present paper we investigated the effect of PDC normalization on the performances achieved by applying the statistical validation process on investigated connectivity patterns under different conditions of Signal to Noise ratio (SNR) and amount of data available for the analysis. Results of the statistical analysis revealed an effect of PDC normalization only on the percentages of type I and type II errors occurred by using Shuffling procedure for the assessment of connectivity patterns. No effects of the PDC formulation resulted on the performances achieved during the validation process executed instead by means of Asymptotic Statistic approach. Moreover, the percentages of both false positives and false negatives committed by Asymptotic Statistic are always lower than those achieved by Shuffling procedure for each type of normalization.

Graph theory is a powerful mathematical tool recently introduced in neuroscience field for quantitatively describing the main properties of investigated connectivity networks. Despite the technical advancements provided in the last few years, further investigations are needed for overcoming actual limitations in the field. In fact, the absence of a common procedure currently applied for the extraction of the adjacency matrix from a connectivity pattern has been leading to low consistency and reliability of ghaph indexes among the investigated population. In this paper we proposed a new approach for adjacency matrix extraction based on a statistical threshold as valid alternative to empirical approaches, extensively used in Neuroscience field (i.e. fixing the edge density). In particular we performed a simulation study for investigating the effects of the two different extraction approaches on the topological properties of the investigated networks. In particular, the comparison was performed on two different datasets, one composed by uncorrelated random signals (null-model) and the other one by signals acquired on a mannequin head used as a phantom (EEG null-model). The results highlighted the importance to use a statistical threshold for the adjacency matrix extraction in order to describe the real existing topological properties of the investigated networks. The use of an empirical threshold led to an erroneous definition of small-world properties for the considered connectivity patterns.

The perception of the music in cochlear implanted (CI) patients is an important aspect of their quality of life. In fact, the pleasantness of the music perception by such CI patients can be analyzed through a particular analysis of EEG rhythms. Studies on healthy subjects show that exists a particular frontal asymmetry of the EEG alpha rhythm which can be correlated with pleasantness of the perceived stimuli (approach-withdrawal theory). In particular, here we describe differences between EEG activities estimated in the alpha frequency band for a monolateral CI group of children and a normal hearing one during the fruition of a musical cartoon. The results of the present analysis showed that the alpha EEG asymmetry patterns related to the normal hearing group refers to a higher pleasantness perception when compared to the cerebral activity of the monolateral CI patients. In fact, the present results support the statement that a monolateral CI group could perceive the music in a less pleasant way when compared to normal hearing children.

The aim of the study is to analyze the variation of the EEG power spectra in theta band when a novice starts to learn a new task. In particular, the goal is to find out the differences from the beginning of the training to the session in which the performance level is good enough for considering him/her able to complete the task without any problems. While the novices were engaged in the flight simulation tasks we recorded the brain activity by using high resolution EEG techniques as well as neurophysiologic variables such as heart rate (HR) and eye blinks rate (EBR). Results show clear changes in the EEG power spectra in theta band over the frontal brain areas, either over the left, the midline and the right side, during the learning process of the task. These results are also supported by the autonomic signals of HR and EBR, by the performances' trends and by the questionnaires for the evaluation of the perceived workload level.

Recent studies have investigated changes in the human brain network organization during the normal aging. A reduction of the connectivity between brain areas was demonstrated by combining neuroimaging technologies and graph theory. Clustering, characteristic path length and small-worldness are key topological measures and they are widely used in literature. In this paper we propose a new methodology that combine advanced techniques of effective connectivity estimation, graph theoretical approach and classification by SVM method. EEG signals recording during rest condition from 20 young subjects and 20 mid-aged adults were studied. Partial Directed Coherence was computed by means of General Linear Kalman Filter and graph indexes were extracted from estimated patterns. At last small-worldness was used as feature for the SVM classifier. Results show that topological differences of brain networks exist between young and mid-aged adults: small-worldness is significantly different between the two populations and it can be used to classify the subjects with respect to age with an accuracy of 69%.

Memory processes are based on large cortical networks characterized by non-stationary properties and time scales which represent a limitation to the traditional connectivity estimation methods. The recent development of connectivity approaches able to consistently describe the temporal evolution of large dimension connectivity networks, in a fully multivariate way, represents a tool that can be used to extract novel information about the processes at the basis of memory functions. In this paper, we applied such advanced approach in combination with the use of state-of-the-art graph theory indexes, computed on the connectivity networks estimated from high density electroencephalographic (EEG) data recorded in a group of healthy adults during the Sternberg Task. The results show how this approach is able to return a characterization of the main phases of the investigated memory task which is also sensitive to the increased length of the numerical string to be memorized.

Background: General anesthesia could imply that the closing capacity exceed the functional residual capacity. This phenomenon, associated with a reduction of maximal expiratory flow, could lead to expiratory flow limitation (EFL). The aim of our study was to verify 1) a new method of determining EFL during anesthesia (PEEP test); 2) if anesthesia could be associated with the development of EFL; 3) if the use a small amount of PEEP is able to reverse the possible negative effects of low lung volume ventilation., Methods: Fifty two patients scheduled for abdominal surgery were prospectively randomized in: 1) group ZEEP, ventilated at PEEP 0 H(2)O and 2) group PEEP ventilated at PEEP 5 cm H2O. The presence of EFL was determined by the negative expiratory pressure (NEP) test the day before surgery and by the PEEP test during surgery. Data of respiratory mechanics were calculated at the beginning and at the end of anesthesia., Results: 1) The PEEP test allows the detection of EFL; 2) anesthesia was associated with EFL: 8 patients developed EFL after induction. At the end of surgery, 7 more patients became flow limited in the group ZEEP, while only 1 in the group PEEP. The group ZEEP exhibited a marked decrease of expiratory flow and a worsening of respiratory mechanics at the end of surgery., Conclusion: The PEEP test allowed to verify that EFL during anesthesia is a valuable phenomenon. The use of 5 cmH(2)O of PEEP was helpful to prevent the deterioration of lung mechanics that occurs during surgery.

Monodisperse mesoporous silica (mSiO(2) ) coated superparamagnetic iron oxide (Fe(3) O(4) @mSiO(2) ) nanoparticles (NPs) have been developed as a potential magnetic resonance imaging (MRI) T(2) contrast agent. To evaluate the effect of surface coating on MRI contrast efficiency, we examined the proton relaxivities of Fe(3) O(4) @mSiO(2) NPs with different coating thicknesses. It was found that the mSiO(2) coating has a significant impact on the efficiency of Fe(3) O(4) NPs for MRI contrast enhancement. The efficiency increases with the thickness of mSiO(2) coating and is much higher than that of the commercial contrast agents. Nuclear magnetic resonance (NMR) relaxometry of Fe(3) O(4) @mSiO(2) further revealed that mSiO(2) coating is partially permeable to water molecules and therefore induces the decrease of longitudinal relaxivity, r(1) . Biocompatibility evaluation of various sized (ca. 35-95 nm) Fe(3) O(4) @mSiO(2) NPs was tested on OC-k3 cells and the result showed that these particles have no negative impact on cell viability. The enhanced MRI efficiency of Fe(3) O(4) @mSiO(2) highlights these core-shell particles as highly efficient T(2) contrast agents with high biocompatibility., (Copyright © 2012 John Wiley & Sons, Ltd.)

Objectives: Mutations in the SLC26A4 gene (7q22.3-7q31.1) are considered one of the most common causes of genetic hearing loss. There are two clinical forms related to these mutations: syndromic and non-syndromic deafness. The first one is named Pendred Syndrome (PS) when deafness is associated with thyroid goiter; the second is called DFNB4, when no other symptoms are present. Both are transmitted as an autosomal recessive trait, but simple heterozygotes can develop both forms of deafness. Actually it is thought that Pendred Syndrome occurs when both alleles of SLC26A4 gene are mutated; DFNB4 seems due to monoallelic mutations. PS and DFNB4 can be associated with inner ear malformations. In most of the cases (around 80%), these consist in Enlarged Vestibular Aqueduct (EVA). EVA can also be present without SLC26A4 mutations. Understanding the role of new SLC26A4 variants should facilitate clinical assessment, as well as diagnostic and therapeutic approaches. This investigation aims to detect and report genetic causes of two unrelated Italian boys with hearing loss., Methods: Patients and family members underwent clinical, audiological and genetic evaluations. To identify genetic mutations, DNA sequencing of SLC26A4 gene (including all 21 exons, exon-intron boundaries and promoter region) was carried out., Results: Both probands were affected by congenital, progressive and fluctuating mixed hearing loss. Temporal bone imaging revealed a bilateral EVA with no other abnormalities in both cases. Probands were heterozygotes for previously undescribed mutations in the SLC26A4 gene: R409H/IVS2+1delG (proband 1) and L236P/K590X (proband 2). No other mutations were detected in GJB2, GJB6 genes or mitochondrial DNA (mit-DNA)., Conclusions: The IVS2+1delG and K590X mutations have not yet been described in literature but there is some evidence to suggest that they have a pathological role. The results underlined the importance of considering the complete DNA sequencing of the SLC26A4 gene for differential molecular diagnosis of deafness, especially in those patients affected by congenital, progressive and fluctuating mixed hearing loss with bilateral EVA., (Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.)

Zebrafish, Danio rerio, has been a prominent model vertebrate for the study of chemical toxicity and human disease. Zebrafish hair cells (HCs) show significant structural, functional and molecular similarities to the mammalian inner ear HCs. We examined the effects of cisplatin, an anti-cancer drug, on HCs of the inner ear and on HCs and support cells (SCs) of neuromasts in zebrafish using transmission and scanning electron microscopy. Forty-five zebrafish larvae, 12 days post-fertilization, were assessed: 15 unexposed controls, 15 exposed to 10 µM cisplatin solution, and 15 exposed to 50 µM cisplatin solution. Hair cells in the cristae and maculae of the inner ear and of neuromasts were extremely sensitive to cisplatin. The drug was associated with vacuolization and the presence of myelinoid bodies in HC cytoplasm and with a condensation of the nuclear chromatin. The predominant pattern of injury was widespread degeneration of mitochondria, which appeared swollen and less electron-dense with disorganized or reduced cristae. Severity of damage seemed to be concentration-dependent, and the inner ear suffered more damage than the lateral line. Alterations similar to those in HCs were also observed in SCs of the neuromasts. Scanning electron microscopy showed loss of kinocilia in neuromasts of fish exposed to the higher concentration of cisplatin., (Copyright © 2011 John Wiley & Sons, Ltd.)

Episodes of complete failure to respond during attentive tasks--lapses of responsiveness ('lapses')--accompanied by behavioral signs of sleep such as slow-eye-closure are known as behavioral microsleeps (BMs). The occurrence of BMs can have serious/fatal consequences, particularly in the transport sectors, and therefore further investigations on neurophysiological correlates of BMs are highly desirable. In this paper we propose a combination of High Resolution EEG techniques and an advanced method for time-varying functional connectivity estimation for reconstructing the temporal evolution of causal relations between cortical regions of BMs occurring during a visuomotor tracking task. The preliminary results highlight connectivity patterns involving parietal and fronto-parietal areas both preceding and following the onset of a BM.

Driving tasks are vulnerable to the effects of sleep deprivation and mental fatigue, diminishing driver's ability to respond effectively to unusual or emergent situations. Physiological and brain activity analysis could help to understand how to provide useful feedback and alert signals to the drivers for avoiding car accidents. In this study we analyze the insurgence of mental fatigue or drowsiness during car driving in a simulated environment by using high resolution EEG techniques as well as neurophysiologic variables such as heart rate (HR) and eye blinks rate (EBR). Results suggest that it is possible to introduce a EEG-based cerebral workload index that it is sensitive to the mental efforts of the driver during drive tasks of different levels of difficulty. Workload index was based on the estimation of increase of EEG power spectra in the theta band over prefrontal areas and the simultaneous decrease of EEG power spectra over parietal areas in alpha band during difficult drive conditions. Such index could be used in a future to assess on-line the mental state of the driver during the drive task.

The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neuroelectrical signals has provided an important step to the interpretation and statistical analysis of such functional networks. The properties of a network are derived from the adjacency matrix describing a connectivity pattern obtained by one of the available functional connectivity methods. However, no common procedure is currently applied for extracting the adjacency matrix from a connectivity pattern. To understand how the topographical properties of a network inferred by means of graph indices can be affected by this procedure, we compared one of the methods extensively used in Neuroscience applications (i.e. fixing the edge density) with an approach based on the statistical validation of achieved connectivity patterns. The comparison was performed on the basis of simulated data and of signals acquired on a polystyrene head used as a phantom. The results showed (i) the importance of the assessing process in discarding the occurrence of spurious links and in the definition of the real topographical properties of the network, and (ii) a dependence of the small world properties obtained for the phantom networks from the spatial correlation of the neighboring electrodes.

Controlling an aircraft during a flight is a compelling condition, which requires a strict and well coded interaction between the crew. The interaction level between the Captain and the First Officer changes during the flight, ranging from a maximum (during takeoff and landing, as well as in case of a failure of the instrumentation or other emergency situations) to a minimum during quiet mid-flight. In this study, our aim is to investigate the neural correlates of different kinds and levels of interaction between couples of professional crew members by means of the innovative technique called brain hyperscanning, i.e. the simultaneous recording of the hemodynamic or neuroelectrical activity of different human subjects involved in interaction tasks. This approach allows the observation and modeling of the neural signature specifically dependent on the interaction between subjects, and, even more interestingly, of the functional links existing between the brain activities of the subjects interacting together. In this EEG hyperscanning study, different phases of a flight were reproduced in a professional flight simulator, which allowed, on one side, to reproduce the ecological setting of a real flight, and, on the other, to keep under control the different levels of interaction induced in the crew by means of systematic and simulated failures of the aircraft instrumentation. Results of the procedure of linear inverse estimation, together with functional hyperconnectivity estimated by means of Partial Directed Coherence, showed a dense network of connections between the activity in the two brains in the takeoff and landing phases, when the cooperation between the crew is maximal, while conversely no significant links were shown during the phases in which the activity of the two pilots was independent.

Background: Data from animal studies show that antioxidants can compensate against noise-induced stress and sensory hair cell death. The aim of this study was to evaluate the otoprotection efficacy of various versions of orally administered Acuval 400 against noise damage in a rat animal model., Material/methods: Fifty-five Sprague Dawley rats were divided into 4 groups: A) noise-exposed animals; B) animals exposed to noise and treated with the Acuval; C) animals exposed to noise and treated with a combination of Coenzyme Q10 and Acuval; D) animals treated only with Acuval and Coenzyme Q10 and with no exposure to noise. All solutions were administered orally 5 times: 24 and 2 hrs prior to noise exposure, and then daily for 3 days. The auditory function was assessed by measuring auditory brainstem responses (ABR) in the range from 2 to 32 kHz at times =1, 7, 14 and 21 days after noise exposure., Results: At low frequencies (click and 4 kHz) animals from both A and B groups showed significant threshold shifts in the majority of the tested frequencies and tested times. For the same frequencies, animals from group C presented threshold levels similar to those from group D. At frequencies ≥ 8 kHz the protective performance of the 2 Acuval groups is more clearly distinguished from the noise group A. At 32 kHz the 2 Acuval groups perform equally well in terms of otoprotection. Animals in Group D did not show any significant differences in the hearing threshold during the experiment., Conclusions: The data of this study suggest that a solution containing Coenzyme Q10 and Acuval 400, administered orally, protects from noise-induced hearing loss.

One of the main limitations of the brain functional connectivity estimation methods based on Autoregressive Modeling, like the Granger Causality family of estimators, is the hypothesis that only stationary signals can be included in the estimation process. This hypothesis precludes the analysis of transients which often contain important information about the neural processes of interest. On the other hand, previous techniques developed for overcoming this limitation are affected by problems linked to the dimension of the multivariate autoregressive model (MVAR), which prevents from analysing complex networks like those at the basis of most cognitive functions in the brain. The General Linear Kalman Filter (GLKF) approach to the estimation of adaptive MVARs was recently introduced to deal with a high number of time series (up to 60) in a full multivariate analysis. In this work we evaluated the performances of this new method in terms of estimation quality and adaptation speed, by means of a simulation study in which specific factors of interest were systematically varied in the signal generation to investigate their effect on the method performances. The method was then applied to high density EEG data related to an imaginative task. The results confirmed the possibility to use this approach to study complex connectivity networks in a full multivariate and adaptive fashion, thus opening the way to an effective estimation of complex brain connectivity networks.