Brain interaction during cooperation: Evaluating local properties of multiple-brain network

Subjects’ interaction is the core of most human activities. This is the reason why a lack of coordination is often the cause of missing goals, more than individual failure. While there are different subjective and objective measures to assess the level of mental effort required by subjects while facing a situation that is getting harder, that is, mental workload, to define an objective measure based on how and if team members are interacting is not so straightforward. In this study, behavioral, subjective and synchronized electroencephalographic data were collected from couples involved in a cooperative task to describe the relationship between task difficulty and team coordination, in the sense of interaction aimed at cooperatively performing the assignment. Multiple-brain connectivity analysis provided information about the whole interacting system. The results showed that averaged local properties of a brain network were affected by task difficulty. In particular, strength changed significantly with task difficulty and clustering coefficients strongly correlated with the workload itself. In particular, a higher workload corresponded to lower clustering values over the central and parietal brain areas. Such results has been interpreted as less efficient organization of the network when the subjects’ activities, due to high workload tendencies, were less coordinated

Abnormal salivary total and oligomeric alpha-synuclein in Parkinson's disease

In Parkinson’s disease (PD), alpha-synuclein (a-syn) can be detected in biological fluids including saliva. Although previous studies found reduced a-syn total (a-syntotal) concentration in saliva of PD patients, no studies have previously examined salivary a-syn oligomers (a-synolig) concentrations or assessed the correlation between salivary a-syntotal, a-synolig and clinical features in a large cohort of PD patients. Is well known that a-synolig exerts a crucial neurotoxic effect in PD. We collected salivary samples from 60 PD patients and 40 age- and sex-comparable healthy subjects. PD was diagnosed according to the United Kingdom Brain Bank Criteria. Samples of saliva were analyzed by specific anti-a-syn and anti-oligomeric a-syn ELISA kits. A complete clinical evaluation of each patient was performed using MDS-Unified Parkinson's Disease Rating Scale, Beck Depression Inventory, Montreal Cognitive Assessment and Frontal Assessment Battery. Salivary a-syntotal was lower, whereas a-synolig was higher in PD patients than healthy subjects. The a-synolig/a-syntotal ratio was also higher in patients than in healthy subjects. Salivary a-syntotal concentration negatively correlated with that of a-synolig and correlated with several patients’ clinical features. In PD, decreased salivary concentration of a-syntotal may reflect the reduction of a-syn monomers (a-synmon), as well as the formation of insoluble intracellular inclusions and soluble oligomers. The combined detection of a-syntotal and a-synolig in the saliva might help the early diagnosis of P

Corticobasal syndrome: neuroimaging and neurophysiological advances

Corticobasal degeneration (CBD) is a neurodegenerative condition characterized by 4R-tau protein deposition in several brain regions that clinically manifests itself as a heterogeneous atypical parkinsonism typically expressing in the adulthood. The prototypical clinical phenotype of CBD is corticobasal syndrome (CBS). Important insights into the pathophysiological mechanisms underlying motor and higher cortical symptoms in CBS have been gained by using advanced neuroimaging and neurophysiological techniques. Structural and functional neuroimaging studies often showed asymmetric cortical and subcortical abnormalities, mainly involving perirolandic and parietal regions and basal ganglia structures. Neurophysiological investigations including electroencephalography and somatosensory evoked potentials provided useful information on the origin of myoclonus and on cortical sensory loss. Transcranial magnetic stimulation demonstrated heterogeneous and asymmetric changes in the excitability and plasticity of primary motor cortex and abnormal hemispheric connectivity. Neuroimaging and neurophysiological abnormalities in multiple brain areas reflect the asymmetric neurodegeneration, leading to the asymmetric motor and higher cortical symptoms in CBS. This article is protected by copyright. All rights reserved

A new perspective for the training assessment: Machine learning-based neurometric for augmented user's evaluation

Inappropriate training assessment might have either high social costs and economic impacts, especially in high risks categories, such as Pilots, Air Traffic Controllers, or Surgeons. One of the current limitations of the standard training assessment procedures is the lack of information about the amount of cognitive resources requested by the user for the correct execution of the proposed task. In fact, even if the task is accomplished achieving the maximum performance, by the standard training assessment methods, it would not be possible to gather and evaluate information about cognitive resources available for dealing with unexpected events or emergency conditions. Therefore, a metric based on the brain activity (neurometric) able to provide the Instructor such a kind of information should be very important. As a first step in this direction, the Electroencephalogram (EEG) and the performance of 10 participants were collected along a training period of 3 weeks, while learning the execution of a new task. Specific indexes have been estimated from the behavioral and EEG signal to objectively assess the users' training progress. Furthermore, we proposed a neurometric based on a machine learning algorithm to quantify the user's training level within each session by considering the level of task execution, and both the behavioral and cognitive stabilities between consecutive sessions. The results demonstrated that the proposed methodology and neurometric could quantify and track the users' progresses, and provide the Instructor information for a more objective evaluation and better tailoring of training programs. © 2017 Borghini, Aricò, Di Flumeri, Sciaraffa, Colosimo, Herrero, Bezerianos, Thakor and Babiloni

Passive BCI in operational environments: insights, recent advances and future trends

this mini-review aims to highlight recent important aspects to consider and evaluate when passive Brain-Computer Interface (pBCI) systems would be developed and used in operational environments, and remarks future directions of their applications

EEG-based cognitive control behaviour assessment: an ecological study with professional air traffic controllers

Several models defining different types of cognitive human behaviour are available. For this work, we have selected the Skill, Rule and Knowledge (SRK) model proposed by Rasmussen in 1983. This model is currently broadly used in safety critical domains, such as the aviation. Nowadays, there are no tools able to assess at which level of cognitive control the operator is dealing with the considered task, that is if he/she is performing the task as an automated routine (skill level), as procedures-based activity (rule level), or as a problem-solving process (knowledge level). Several studies tried to model the SRK behaviours from a Human Factor perspective. Despite such studies, there are no evidences in which such behaviours have been evaluated from a neurophysiological point of view, for example, by considering brain activity variations across the different SRK levels. Therefore, the proposed study aimed to investigate the use of neurophysiological signals to assess the cognitive control behaviours accordingly to the SRK taxonomy. The results of the study, performed on 37 professional Air Traffic Controllers, demonstrated that specific brain features could characterize and discriminate the different SRK levels, therefore enabling an objective assessment of the degree of cognitive control behaviours in realistic setting

Review on Occupational Personal Solar UV Exposure Measurements

During leisure time, people can decide if they want to expose themselves to solar ultraviolet (UV) radiation and to which extent. During occupation, people do not have this choice. Outdoor workers are exposed to solar UV radiation (UVR) on a daily basis. This may hold a certain health risk, which can be estimated when the personal solar UVR exposure (PE) is known. During the past decades, a variety of studies was conducted to measure PE of outdoor workers and our knowledge on the PE of outdoor workers has remarkably increased. As shown by this review, studies clearly indicate that PE of most of outdoor workers exceeds the internationally proposed threshold limit value, which is comparable to 1.0 to 1.3 SED, respectively to 1.1 to 1.5 UV Index received over one hour. Besides working in a high UVR environment, monotonic workflow (limited movement, nearly static posture) is a risk factor. In such cases, PE can be higher than ambient UVR. In this review, we provide also a list of milestone, depicting the progress and the most important findings in this field during the past 45 years. However, in many respects our knowledge is still rudimentary, because of several reasons. Different measuring positions have been used so that measured PE is not comparable. Few studies were designed to enable extension of measured PE to other locations or dates. Although the importance of a proper calibration of the measuring devices in respect to the changing solar spectrum was pointed out from the beginning, this is often not performed, which leads to high uncertainties in the presented PE levels. At the end of our review, we provide some key points, which can be used to evaluate the quality of a study respectively to support the design of future studies

Neuroelectrical correlates of trustworthiness and dominance judgments related to the observation of political candidates

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