A novel approach to measure brain-to-brain spatial and temporal alignment during positive empathy.

Empathy is defined as the ability to vicariously experience others' suffering (vicarious pain) or feeling their joy (vicarious reward). While most neuroimaging studies have focused on vicarious pain and describe similar neural responses during the observed and the personal negative affective involvement, only initial evidence has been reported for the neural responses to others' rewards and positive empathy. Here, we propose a novel approach, based on the simultaneous recording of multi-subject EEG signals and exploiting the wavelet coherence decomposition to measure the temporal alignment between ERPs in a dyad of interacting subjects. We used the Third-Party Punishment (TPP) paradigm to elicit the personal and vicarious experiences. During a positive experience, we observed the simultaneous presence in both agents of the Late Positive Potential (LPP), an ERP component related to emotion processing, as well as the existence of an inter-subject ERPs synchronization in the related time window. Moreover, the amplitude of the LPP synchronization was modulated by the presence of a human-agent. Finally, the localized brain circuits subtending the ERP-synchronization correspond to key-regions of personal and vicarious reward. Our findings suggest that the temporal and spatial ERPs alignment might be a novel and direct proxy measure of empathy.

Decellularized normal and cancer tissues as tools for cancer research.

Today it is widely accepted that molecular mechanisms triggering cancer initiate with a genetic modification. However, a genetic alteration providing the aberrant clone with a growing advantage over neighboring cells is not sufficient to develop cancer. Currently, tumors are considered a heterogeneous population of cells and an extracellular matrix (ECM) that make up a characteristic microenvironment. Interactions between tumor cells and cancer microenvironment define cancer progression and therapeutic response. To investigate and clarify the role of ECM in the regulation of cancer cell behavior and response to therapy, the decellularization of ECM, a widely used technique in tissue engineering, has been recently employed to develop 3D culture model of disease. In this review, we briefly explore the different components of healthy and pathological ECM and the methods to obtain and characterize the ECM from native bioptic tissue. Finally, we highlight the most relevant applications of ECM in translational cancer research strategies: decellularized ECM, ECM-hydrogel and 3D bioprinting.

Patient-Clinician Brain Response During Clinical Encounter and Pain Treatment.

The patient-clinician relationship is known to significantly affect the pain experience, as empathy, mutual trust and therapeutic alliance can significantly modulate pain perception and influence clinical therapy outcomes. The aim of the present study was to use an EEG hyperscanning setup to identify brain and behavioral mechanisms supporting the patient-clinician relationship while this clinical dyad is engaged in a therapeutic interaction. Our previous study applied fMRI hyperscanning to investigate whether brain concordance is linked with analgesia experienced by a patient while undergoing treatment by the clinician. In this current hyperscanning project we investigated similar outcomes for the patient-clinician dyad exploiting the high temporal resolution of EEG and the possibility to acquire the signals while patients and clinicians were present in the same room and engaged in a face-to-face interaction under an experimentally-controlled therapeutic context. Advanced source localization methods allowed for integration of spatial and spectral information in order to assess brain correlates of therapeutic alliance and pain perception in different clinical interaction contexts. Preliminary results showed that both behavioral and brain responses across the patient-clinician dyad were significantly affected by the interaction style.Clinical Relevance- The context of a clinical intervention can significantly impact the treatment of chronic pain. Effective therapeutic alliance, based on empathy, mutual trust, and warmth can improve treatment adherence and clinical outcomes. A deeper scientific understanding of the brain and behavioral mechanisms underlying an optimal patient-clinician interaction may lead to improved quality of clinical care and physician training, as well as better understanding of the social aspects of the biopsychosocial model mediating analgesia in chronic pain patients.

Impact of COVID-19 pandemic in the activity of a Therapeutic Apheresis unit in Italy.

INTRODUCTION: The recent Coronavirus Disease 2019 (COVID-19) outbreak has led to profound and rapid changes in the Italian and Veneto Region Healthcare System. This context also includes the quick reorganization which the Apheresis Unit (AU) of the Padova University Hospital, i.e. the Regional Reference Center for Therapeutic Apheresis (TA), had to face. MATERIAL AND METHODS: The study retrospectively evaluated the TA activity (procedures performed, patients treated and consultations) during the COVID-19 pandemic, from March to April 2020, comparing the activity in the same time period in 2018 and 2019. RESULTS: In the period analyzed, a significant reduction in both the total number of procedures performed and of patients treated, respectively by 17 % and 16 % for the procedures and by 19 % and 20 % for patients treated compared to the same period of 2018 and 2019, respectively, was observed. A concomitant reduction in requests for TA consultation for new patients (both outpatients and inpatients) was observed, equal to 32 % and 21 % compared to 2018 and 2019, respectively. CONCLUSION: Many reasons determined the observed reduction in the TA activity during the recent COVID-19 outbreak. The AU itself was quickly reorganized in terms of location and supplies to allow for the appropriate COVID-19 patients care. Many non urgent cases, after multidisciplinary discussion between Clinicians and Apheresis Specialists, were deferred, maintaining close phone and e-mail contact with patients.

The Optimal Setting for Multilayer Modularity Optimization in Multilayer Brain Networks.

Community detection plays a key role in the study of brain networks, as mechanisms of modular integration and segregation are known to characterize the brain functioning. Moreover, brain networks are intrinsically multilayer: they can vary across time, frequency, subjects, conditions, and meaning, according to different definitions of connectivity. Several algorithms for the multilayer community detection were defined to identify communities in time-varying networks. The most used one is based on the optimization of a multilayer formulation of the modularity, in which two parameters linked to the spatial (γ) and temporal (ω) resolution of the uncovered communities can be set. While the effect of different γ-values has been largely explored, which ω-values are most suitable to different purposes and conditions is still an open issue. In this work, we test the algorithm performances under different values of ω by means of ad hoc implemented benchmark graphs that cover a wide range of conditions. Results provide a guide to the choice of the ω-values according to the network features. Finally, we show an application of the algorithm to real functional brain networks estimated from electro-encephalographic (EEG) signals collected at rest with closed and open eyes. The application to real data agrees with the results of the simulation study and confirms the conclusion drawn from it.

Multiple-Brain Connectivity During Third Party Punishment: an EEG Hyperscanning Study.

Compassion is a particular form of empathic reaction to harm that befalls others and is accompanied by a desire to alleviate their suffering. This altruistic behavior is often manifested through altruistic punishment, wherein individuals penalize a deprecated human's actions, even if they are directed toward strangers. By adopting a dual approach, we provide empirical evidence that compassion is a multifaceted prosocial behavior and can predict altruistic punishment. In particular, in this multiple-brain connectivity study in an EEG hyperscanning setting, compassion was examined during real-time social interactions in a third-party punishment (TPP) experiment. We observed that specific connectivity patterns were linked to behavioral and psychological intra- and interpersonal factors. Thus, our results suggest that an ecological approach based on simultaneous dual-scanning and multiple-brain connectivity is suitable for analyzing complex social phenomena.

Speech perception in noise by young sequential bilingual children.

The objective of this study was to ascertain the effects of competitive noise on second language perception skills of sequentially bilingual children and to compare the results with those relating to matched monolingual peers. Fifteen bilingual immigrant children (aged 6-10 years) (BL) learning through their second language (L2), which was Italian, were matched with 15 peers who only spoke Italian (IO). All immigrant children had arrived in Italy and were exposed to L2 after their 4th year of life. The speech-to-noise ratio (SNR) needed to obtain 50% intelligibility - the speech reception threshold (SRT) - for Italian words was measured against the Italian version of ICRA noise, using an adaptive method. Moreover, presentation of phrases against a contralateral continuous discourse (informational masking) was carried out to exclude possible biases due to differences in memory, attention, or other central auditory processing disorders between groups. The SNR was -2.7 dB (SD 1.7; range: -5.5 to + 0.9) for the BL group and -5.3 dB (SD 2.3; range: -8.8 to -0.9) for the IO group (p < 0.01). With contralateral continuous discourse presentation the SNR were -32.8 dB (SD 2.4; range: -36.1 to -28.2) for the BL group and -27.8 dB (SD 2.1; range: -31.7 to -24.1) for the OI group (p < 0.01). Even sequential bilingual individuals exposed to L2 at 4 years old had worse speech perception in noise than their matched IO peers. On the other hand, the BL group demonstrated superior divided attention skills in tests with competitive contralateral discourse (p < 0.01).

EEG source estimation accuracy in presence of simulated cortical lesions.

Methods to reconstruct the neuroelectrical activity in the brain source space can be used to improve the spatial resolution of scalp-recorded EEG and to estimate the locations of electrical sources in the brain. This procedure can improve the investigation of the functional organization of the human brain, exploiting the high temporal resolution of EEG to follow the temporal dynamics of information processing. As for today, the uncertainties about the effects of inhomogeneities due to brain lesions preclude the adoption of EEG functional mapping on patients with lesioned brain. The aim of this work is to quantify the accuracy of a distributed source localization method in recovering extended sources of activated cortex when cortical lesions of different dimensions are introduced in simulated data. For this purpose, EEG source-distributed activity estimated from real data was modified including silent lesion areas. Then, for each simulated lesion, forward and inverse calculations were carried out to localize the produced scalp activity and the reconstructed cortical activity. Finally, the error induced in the reconstruction by the presence of the lesion was computed and analyzed in relation to the number of electrodes and to the size of the simulated lesion. Results returned values of global error in the whole cortex and of error in the non-lesioned area which are strongly dependent on the number of recorded scalp sensors, as they increase when a lower spatial sampling is performed on the scalp (64 versus 32 EEG channels). For increasing spatial sampling frequencies, the accuracy of the source reconstruction improves and even the presence of small lesions induces significantly higher error levels with respect to the lesion-free condition.

Brain connectivity networks at the basis of human attention components: An EEG study.

The Attention Network Task (ANT) was developed to disentangle the three components of attention identified in the Posner's theoretical model (alerting, orienting and executive control) and to measure the corresponding behavioral efficiency. Several fMRI studies have already provided evidences on the anatomical separability and interdependency of these three networks, and EEG studies have also unveiled the associated brain rhythms. What is still missing is a characterization of the brain circuits subtending the attentional components in terms of directed relationships between the brain areas and their frequency content. Here, we want to exploit the high temporal resolution of the EEG, improving its spatial resolution by means of advanced source localization methods, and to integrate the resulting information by a directed connectivity analysis. The results showed in the present study demonstrate the possibility to associate a specific directed brain circuit to each attention component and to identify synthetic indices able to selectively describe their neurophysiological, spatial and spectral properties.

Community detection: Comparison among clustering algorithms and application to EEG-based brain networks.

Community structure is a feature of complex networks that can be crucial for the understanding of their internal organization. This is particularly true for brain networks, as the brain functioning is thought to be based on a modular organization. In the last decades, many clustering algorithms were developed with the aim to identify communities in networks of different nature. However, there is still no agreement about which one is the most reliable, and to test and compare these algorithms under a variety of conditions would be beneficial to potential users. In this study, we performed a comparative analysis between six different clustering algorithms, analyzing their performances on a ground-truth consisting of simulated networks with properties spanning a wide range of conditions. Results show the effect of factors like the noise level, the number of clusters, the network dimension and density on the performances of the algorithms and provide some guidelines about the use of the more appropriate algorithm according to the different conditions. The best performances under a wide range of conditions were obtained by Louvain and Leicht & Newman algorithms, while Ronhovde and Infomap proved to be more appropriate in very noisy conditions. Finally, as a proof of concept, we applied the algorithms under exam to brain functional connectivity networks obtained from EEG signals recorded during a sustained movement of the right hand, obtaining a clustering of scalp electrodes which agrees with the results of the simulation study conducted.

Transcranial cerebellar direct current stimulation: Effects on brain resting state oscillatory and network activity.

Transcranial cerebellar direct current stimulation (tcDCS) can offer new insights into the cerebellar function and disorders, by modulating noninvasively the activity of cerebellar networks. Taking into account the functional interplay between the cerebellum and the cerebral cortex, we addressed the effects of unilateral tcDCS (active electrode positioned over the right cerebellar hemisphere) on the electroencephalographic (EEG) oscillatory activity and on the cortical network organization at resting state. Effects on spectral (de)synchronizations and functional connectivity after anodal and cathodal stimulation were assessed with respect to a sham condition. A lateralized synchronization over the sensorimotor area in gamma band, as well as an increase of the network segregation in sensory-motor rhythms and a higher communication between hemispheres in gamma band, were detected after anodal stimulation. The same measures after cathodal tcDCS returned responses similar to the sham condition.

Interfacing brain with computer to improve communication and rehabilitation after brain damage.

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.

Cochlear implant and inflammation reaction: Safety study of a new steroid-eluting electrode.

Dexamethasone is a common anti-inflammatory agent added to cochlear implants to reduce hearing loss due to electrode insertion trauma. We evaluated the safety of eluting silicone rods containing 10% dexamethasone in a Guinea pig model. Animals were implanted with a dexamethasone eluting silicone electrode (DER) or with a non-eluting electrode (NER). The control group only underwent a cochleostomy (CS). Prior to implantation and during the two weeks following implantation, the hearing status of the animals was assessed by means of Compound Action Potentials (CAPs) with an electrode placed near the round window. Two weeks after implantation, the mean click threshold shifts were 1 dB ± 10 dB in the DER group, 10 dB ± 10 dB in the NER group and -4 dB ± 10 dB in the control group. After two weeks the bullae of each animal were extracted to verify the presence of macrophages, the percent of tissue growth in the scala tympani and the tissue sealing around cochleostomy. Silicone electrodes samples were also explanted and examined for bacterial infection. Neither bacterial infection nor enhanced number of macrophages were observed. A limited, but not significant, tissue growth was found in the scala tympani between the experimental and the control group. The data suggest that, in the Guinea pig model, the use of DER is apparently safe as an anti-inflammatory slow-release additive to the cochlear implant.

Cochlear implantation in post-lingually deafened adults and elderly patients: analysis of audiometric and speech perception outcomes during the first year of use.

The aim of this study was to analyse audiometric and speech perception outcomes after cochlear implantation (CI) in adult and elderly patients in the first year post-CI activation. We evaluated 42 subjects who underwent CI at the Otorhinolaryngological Clinic of Padua Hospital. The subjects enrolled were post-lingually deafened patients who were unilaterally implanted for bilateral, severe-to-profound hearing loss. The overall sample was divided into three groups according to the age at the time of implantation: group A (35-49 years), group B (50-64 years) and group C (≥ 65 years). The subjects were assessed, both before and after surgery (at months 1, 3, 6 and 12), using pure tone audiometry, speech audiometry and speech perception tests and the CAP questionnaire. Statistical analysis of outcomes was using a Student's t-test for paired data. In all study groups a significant improvement was demonstrated in auditory performance examinations post-CI compared to the pre-operative scores. All subjects in all age groups obtained significant improvements in PTA scores before surgery and post-CI activation. Comparison of PTA values among the three age groups did not reveal any significant difference. Considerable improvement was obtained even in the speech audiometry thresholds in all groups at follow-up, with no significant differences between groups. The speech perception examination and CAP questionnaire showed good progress in all study groups, although younger patients tended to achieve more complex categories than older ones. In conclusion, CI is an effective treatment for severe-to-profound hearing loss with no significant differences in auditory performances between older and younger CI recipients. Even if somewhat slower, subjects older than 65 reached good performance and therefore are good candidates for a cochlear implant.

Measuring the agreement between brain connectivity networks.

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.

Sensorineural hearing loss and ischemic injury: Development of animal models to assess vascular and oxidative effects.

Hearing loss may be genetic, associated with aging or exposure to noise or ototoxic substances. Its aetiology can be attributed to vascular injury, trauma, tumours, infections or autoimmune response. All these factors could be related to alterations in cochlear microcirculation resulting in hypoxia, which in turn may damage cochlear hair cells and neurons, leading to deafness. Hypoxia could underlie the aetiology of deafness, but very few data about it are presently available. The aim of this work is to develop animal models of hypoxia and ischemia suitable for study of cochlear vascular damage, characterizing them by electrophysiology and gene/protein expression analyses. The effects of hypoxia in infarction were mimicked in rat by partial permanent occlusion of the left coronary artery, and those of ischemia in thrombosis by complete temporary carotid occlusion. In our models both hypoxia and ischemia caused a small but significant hearing loss, localized at the cochlear apex. A slight induction of the coagulation cascade and of oxidative stress pathways was detected as cell survival mechanism, and cell damages were found on the cuticular plate of outer hair cells only after carotid ischemia. Based on these data, the two developed models appear suitable for in vivo studies of cochlear vascular damage.

Graph theory in brain-to-brain connectivity: A simulation study and an application to an EEG hyperscanning experiment.

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.

Effect of inter-trials variability on the estimation of cortical connectivity by Partial Directed Coherence.

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.

A methodological approach for determination of maximal inspiratory pressure in patients undergoing invasive mechanical ventilation.

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.

Investigating the effects of a sensorimotor rhythm-based BCI training on the cortical activity elicited by mental imagery.

OBJECTIVE: It is well known that to acquire sensorimotor (SMR)-based brain-computer interface (BCI) control requires a training period before users can achieve their best possible performances. Nevertheless, the effect of this training procedure on the cortical activity related to the mental imagery ability still requires investigation to be fully elucidated. The aim of this study was to gain insights into the effects of SMR-based BCI training on the cortical spectral activity associated with the performance of different mental imagery tasks. APPROACH: Linear cortical estimation and statistical brain mapping techniques were applied on high-density EEG data acquired from 18 healthy participants performing three different mental imagery tasks. Subjects were divided in two groups, one of BCI trained subjects, according to their previous exposure (at least six months before this study) to motor imagery-based BCI training, and one of subjects who were naive to any BCI paradigms. MAIN RESULTS: Cortical activation maps obtained for trained and naive subjects indicated different spectral and spatial activity patterns in response to the mental imagery tasks. Long-term effects of the previous SMR-based BCI training were observed on the motor cortical spectral activity specific to the BCI trained motor imagery task (simple hand movements) and partially generalized to more complex motor imagery task (playing tennis). Differently, mental imagery with spatial attention and memory content could elicit recognizable cortical spectral activity even in subjects completely naive to (BCI) training. SIGNIFICANCE: The present findings contribute to our understanding of BCI technology usage and might be of relevance in those clinical conditions when training to master a BCI application is challenging or even not possible.