1,721,158 research outputs found
A novel approach to measure brain-to-brain spatial and temporal alignment during positive empathy
Full text linkToppi J, Siniatchkin M, Vogel P, Freitag CM, Astolfi L, Ciaramidaro A. A novel approach to measure brain-to-brain spatial and temporal alignment during positive empathy. Scientific reports. 2022;12(1): 17282.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. © 2022. The Author(s)
Solving the Brain Connectivity Puzzle: Methodological Advancements and Future Challenges
No full textStem Cells and Nanotechnology
No full textThe chapter is divided into two parts, respectively, describing new findings in the fields of regenerative medicine and nanotechnologies applied to hearing therapies. The first part will begin with an introduction on stem cell classification, genesis, and presence in ear tissues. Applications of stem cells in regenerating the three ear regions (outer, middle, and inner ear) will then be examined, analyzing advantages, and disadvantages of each procedure. The discussion shall include recent advances in the development of medical devices. The second part will introduce nanotechnologies applied to hearing therapies, discussing the different uses of nanomaterials and nanoparticles. The chemical and physical characteristics of nano compounds will be reported according to their applications in hearing therapies. The conclusion of the chapter will extensively analyze the concept of biocompatibility, the most relevant issue in application of exogenous compounds
Cisplatin ototoxicity and role of antioxidant on its prevention
No full textObjective: Highlight the novelties of cisplatin ototoxicity and its prevention, focussing on the role of oxidative stress. Methods: We screened various electronic databases and selected the reports concerning clinical data and animal and in vitro models investigations related to cisplatin-induced ototoxicity and or its prevention by antioxidants. Results: Clinical evidence showed that great advances have been made in the chemotherapy outcomes; nevertheless, the ototoxicity, mainly in paediatric patients, is a crucial issue that must be solved. In vitro and in vivo models were pivotal to deeply describe the toxic effect of the cisplatin and which are the mechanisms of action. These knowledges will help the researcher to improve the chemotherapy protocols in order to avoid the adverse effects without compromise the antitumoral action. Conclusion: Antioxidants seem to be the most promising drugs among the large number of substances tested against cisplatin ototoxicity, however, the inflammatory pathway must be taken into consideration
Connectivity analysis based on multielectrode EEG inversion methods with and without fMRI a priori information
No full text[No abstract available
Editorial: Pathobiological defects in sensorineural hearing loss: from identification to rescue
No full textTesting different methodologies for Granger causality estimation: A simulation study
No full textGranger causality (GC) is a method for determining whether and how two time series exert causal influences one over the other. As it is easy to implement through vector autoregressive (VAR) models and can be generalized to the multivariate case, GC has spread in many different areas of research such as neuroscience and network physiology. In its basic formulation, the computation of GC involves two different regressions, taking respectively into account the whole past history of the investigated multivariate time series (full model) and the past of all time series except the putatively causal time series (restricted model). However, the restricted model cannot be represented through a finite order VAR process and, when few data samples are available or the number of time series is very high, the estimation of GC exhibits a strong reduction in accuracy. To mitigate these problems, improved estimation strategies have been recently implemented, including state space (SS) models and partial conditioning (PC) approaches. In this work, we propose a new method to compute GC which combines SS and PC and tests it together with other four commonly used estimation approaches. In simulated networks of linearly interacting time series, we show the possibility to reconstruct the network structure even in challenging conditions of data samples available
A Comprehensive Analysis of Multilayer Community Detection Algorithms for Application to EEG-Based Brain Networks
Full text linkModular organization is an emergent property of brain networks, responsible for shaping communication processes and underpinning brain functioning. Moreover, brain networks are intrinsically multilayer since their attributes can vary across time, subjects, frequency, or other domains. Identifying the modular structure in multilayer brain networks represents a gateway toward a deeper understanding of neural processes underlying cognition. Electroencephalographic (EEG) signals, thanks to their high temporal resolution, can give rise to multilayer networks able to follow the dynamics of brain activity. Despite this potential, the community organization has not yet been thoroughly investigated in brain networks estimated from EEG. Furthermore, at the state of the art, there is still no agreement about which algorithm is the most suitable to detect communities in multilayer brain networks, and a way to test and compare them all under a variety of conditions is lacking. In this work, we perform a comprehensive analysis of three algorithms at the state of the art for multilayer community detection (namely, genLouvain, DynMoga, and FacetNet) as compared with an approach based on the application of a single-layer clustering algorithm to each slice of the multilayer network. We test their ability to identify both steady and dynamic modular structures. We statistically evaluate their performances by means of ad hoc benchmark graphs characterized by properties covering a broad range of conditions in terms of graph density, number of clusters, noise level, and number of layers. The results of this simulation study aim to provide guidelines about the choice of the more appropriate algorithm according to the different properties of the brain network under examination. Finally, as a proof of concept, we show an application of the algorithms to real functional brain networks derived from EEG signals collected at rest with closed and open eyes. The test on real data provided results in agreement with the conclusions of the simulation study and confirmed the feasibility of multilayer analysis of EEG-based brain networks in both steady and dynamic conditions
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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