Exploring the Therapeutic Potential of Quadripulse rTMS over the Visual Cortex: A Proof-of-Concept Study in Healthy Volunteers and Chronic Migraine Patients with Medication Overuse Headache.

In chronic migraine with medication overuse (CM-MOH), sensitization of visual cortices is reflected by (i) increased amplitude of stimulus-evoked responses and (ii) habituation deficit during repetitive stimulation. Both abnormalities might be mitigated by inhibitory transcranial neurostimulation. Here, we tested an inhibitory quadripulse repetitive transcranial magnetic stimulation (rTMS-QPI) protocol to decrease durably visual cortex excitability in healthy subjects (HS) and explored its therapeutic potential in CM-MOH patients. Pattern-reversal visual evoked potentials (VEP) were used as biomarkers of effect and recorded before (T1), immediately after (T2), and 3 h after stimulation (T3). In HS, rTMS-QPI durably decreased the VEP 1st block amplitude (p < 0.05) and its habituation (p < 0.05). These changes were more pronounced for the P1N2 component that was modified already at T2 up to T3, while for N1P1 they were significant only at T3. An excitatory stimulation protocol (rTMS-QPE) tended to have an opposite effect, restricted to P1N2. In 12 CM-MOH patients, during a four-week treatment (2 sessions/week), rTMS-QPI significantly reduced monthly headache days (p < 0.01). In patients reversing from CM-MOH to episodic migraine (n = 6), VEP habituation significantly improved after treatment (p = 0.005). rTMS-QPI durably decreases visual cortex responsivity in healthy subjects. In a proof-of-concept study of CM-MOH patients, rTMS-QPI also has beneficial clinical and electrophysiological effects, but sham-controlled trials are needed.

Modelling a multiplex brain network by local transfer entropy.

This paper deals with the information transfer mechanisms underlying causal relations between brain regions under resting condition. fMRI images of a large set of healthy individuals from the 1000 Functional Connectomes Beijing Zang dataset have been considered and the causal information transfer among brain regions studied using Transfer Entropy concepts. Thus, we explored the influence of a set of states in two given regions at time t (At Bt.) over the state of one of them at a following time step (Bt+1) and could observe a series of time-dependent events corresponding to four kinds of interactions, or causal rules, pointing to (de)activation and turn off mechanisms and sharing some features with positive and negative functional connectivity. The functional architecture emerging from such rules was modelled by a directional multilayer network based upon four interaction matrices and a set of indexes describing the effects of the network structure in several dynamical processes. The statistical significance of the models produced by our approach was checked within the used database of homogeneous subjects and predicts a successful extension, in due course, to detect differences among clinical conditions and cognitive states.

Functional connections between and within brain subnetworks under resting-state.

The focus of this paper is on the functional role of brain regions focusing on their modular architecture and individual variability. Our main assumption is that the more variable anti-correlation patterns reflect random connections, while the more conserved ones play a functional role. Within this framework, we expanded on previous results using a different database and a different methodological approach. Aiming to identify the role of specific functional connections within a global network organization which includes subnetworks, we found that the fronto-parietal module acts as the main source of anti-correlations. In addition, the pre-frontal regions (namely: frontal middle, frontal middle orbital, frontal inferior triangular) and the parietal inferior region are highly conserved and, at the same time, act as highly connected nodes, thus confirming their importance in functional modulation.

Neurophysiological Measures of the Perception of Antismoking Public Service Announcements Among Young Population.

Tobacco constitutes a global emergency with totally preventable millions of deaths per year and smoking-related illnesses. Public service announcements (PSAs) are the main tool against smoking and by now their efficacy is still assessed through questionnaires and metrics, only months after their circulation. The present study focused on the young population, because at higher risk of developing tobacco addiction, investigating the reaction to the vision of Effective, Ineffective and Awarded antismoking PSAs through: electroencephalography (EEG), autonomic activity variation (Galvanic skin response-GSR- and Heart Rate-HR-) and Eye-Tracking (ET). The employed indices were: the EEG frontal alpha band asymmetry and the frontal theta; the Emotional Index (EI), deriving from the GSR and HR signals matching; the ET Visual Attention (VA) index, based on the ratio between the total time spent fixating an area of interest (AOI) and its area. Smokers expressed higher frontal alpha asymmetry values in comparison to non-smokers. Concerning frontal theta, Awarded PSAs reported the highest values in comparison to both Effective and Ineffective PSAs. EI results highlighted that lowest values were expressed by Heavy Smokers (HS), and Effective PSAs obtained the highest EI values. Finally, concerning the Effective PSAs, regression analysis highlighted a correlation between the number of cigarettes smoked by participants (independent variable) and frontal alpha asymmetry, frontal theta and EI values. ET results suggested that for the Ineffective PSAs the main focus were texts, while for the Effective and Awarded PSAs were the visual elements. Results support the use of methods aimed at assessing the physiological reaction for the evaluation of PSAs images, in particular when considering the smoking habits of target populations.

Unsupervised versus Supervised Identification of Prognostic Factors in Patients with Localized Retroperitoneal Sarcoma: A Data Clustering and Mahalanobis Distance Approach.

The aim of this report is to unveil specific prognostic factors for retroperitoneal sarcoma (RPS) patients by univariate and multivariate statistical techniques. A phase I-II study on localized RPS treated with high-dose ifosfamide and radiotherapy followed by surgery (ISG-STS 0303 protocol) demonstrated that chemo/radiotherapy was safe and increased the 3-year relapse-free survival (RFS) with respect to historical controls. Of 70 patients, twenty-six developed local, 10 distant, and 5 combined relapse. Median disease-free interval (DFI) was 29.47 months. According to a discriminant function analysis, DFI, histology, relapse pattern, and the first treatment approach at relapse had a statistically significant prognostic impact. Based on scientific literature and clinical expertise, clinicopathological data were analyzed using both a supervised and an unsupervised classification method to predict the prognosis, with similar sample sizes (66 and 65, resp., in casewise approach and 70 in mean-substitution one). This is the first attempt to predict patients' prognosis by means of multivariate statistics, and in this light, it looks noticable that (i) some clinical data have a well-defined prognostic value, (ii) the unsupervised model produced comparable results with respect to the supervised one, and (iii) the appropriate combination of both models appears fruitful and easily extensible to different clinical contexts.

Anticorrelations between Active Brain Regions: An Agent-Based Model Simulation Study.

Anticorrelations among brain areas observed in fMRI acquisitions under resting state are not endowed with a well-defined set of characters. Some evidence points to a possible physiological role for them, and simulation models showed that it is appropriate to explore such an issue. A large-scale brain representation was considered, implementing an agent-based brain-inspired model (ABBM) incorporating the SER (susceptible-excited-refractory) cyclic mechanism of state change. The experimental data used for validation included 30 selected functional images of healthy controls from the 1000 Functional Connectomes Classic collection. To study how different fractions of positive and negative connectivities could modulate the model efficiency, the correlation coefficient was systematically used to check the goodness-of-fit of empirical data by simulations under different combinations of parameters. The results show that a small fraction of positive connectivity is necessary to match at best the empirical data. Similarly, a goodness-of-fit improvement was observed upon addition of negative links to an initial pattern of only-positive connections, indicating a significant information intrinsic to negative links. As a general conclusion, anticorrelations showed that it is crucial to improve the performance of our simulation and, since these cannot be assimilated to noise, should be always considered in order to refine any brain functional model.

Multi-agent Simulations of Population Behavior: A Promising Tool for Systems Biology.

This contribution reports on the simulation of some dynamical events observed in the collective behavior of different kinds of populations, ranging from shape-changing cells in a Petri dish to functionally correlated brain areas in vivo. The unifying methodological approach, based upon a Multi-Agent Simulation (MAS) paradigm as incorporated in the NetLogo™ interpreter, is a direct consequence of the cornerstone that simple, individual actions within a population of interacting agents often give rise to complex, collective behavior.The discussion will mainly focus on the emergence and spreading of synchronous activities within the population, as well as on the modulation of the collective behavior exerted by environmental force-fields. A relevant section of this contribution is dedicated to the extension of the MAS paradigm to Brain Network models. In such a general framework some recent applications taken from the direct experience of the author, and exploring the activation patterns characteristic of specific brain functional states, are described, and their impact on the Systems-Biology universe underlined.

Negative functional brain networks.

The anticorrelations in fMRI measurements are still not well characterized, but some new evidences point to a possible physiological role. We explored the topology of functional brain networks characterized by negative edgess and their possible alterations in schizophrenia, using functional images of 8 healthy subjects and 8 schizophrenic patients in a resting state condition. In order to minimize the insertion of artifactual negative correlations, the preprocessing of images was carried out by the CompCorr procedure, and the results compared with the Global Signal Regression (GSR) procedure. The degree distribution, the centrality, the efficiency and the rich-club behavior were used to characterize the functional brain network with negative links of healthy controls in comparison with schizophrenic patients. The results show that functional brain networks with both positive and negative values have a truncated power-law degree distribution. Moreover, although functional brain networks characterized by negative values have not small-world topology, they show a specific disassortative configuration: the more connected nodes tend to have fewer connections between them. This feature is lost using the GSR procedure. Finally, the comparison with schizophrenic patients showed a decreased (local and global) efficiency associated to a decreased connectivity among central nodes. As a conclusion, functional brain networks characterized by negative values, despite lacking a well defined topology, show specific features, different from random, and indicate an implication in the alterations associated to schizophrenia.

Evaluation of different cochlear implants in unilateral hearing patients during word listening tasks: A brain connectivity study.

Advanced methodologies used for the biomedical signal interpretation allow using cerebral signals to assess important cognitive functions in humans. In the present study, as parameter of cerebral effort, has been employed the isolated effective coherence, in order to estimate the effective connectivity and network organization. The hypothesis was that the lower the number of inter-connections engaged, the lower the cerebral effort induced by the experimental condition. In the present research this index has been applied to test the reaction to the use of different cochlear implant processors (Freedom, CP810 and CP910 - Cochlear Ltd), with the aim to identify the most performing device during a word in noise recognition task. Results support the capability of identifying the device eliciting less brain area connections. In particular, the CP910 was the processor inducing the lower number of inter-connections among the tested ones. This investigation appeared to be worthy, since representing a tool to identify devices that would make available user's cognitive resources for additional tasks, a matter susceptible of generalization to various fields of application. The employment of the cerebral signals therefore open the way to the evaluation of the impact of different sensors and prosthetic devices, also using connectivity measures.

EEG-based Approach-Withdrawal index for the pleasantness evaluation during taste experience in realistic settings.

The taste is a vital sense in humans, because of its active role in regulating nutrition or avoiding harmful substances. Several studies showed the important role of the brain Pre-Frontal Cortex in decoding information coming from the gustatory system. It is also widely known, in neuroscientific literature, that the asymmetry of Pre-Frontal Cortex Activity is closely linked to the feeling of pleasantness experienced by the subject during sensorial stimulation. In this regard, from the electroencephalographic (EEG) signal it is possible to estimate the Approach/Withdrawal (AW) index, which has been largely investigated and validated in scientific literature, regarding visual, acoustic and olfactory stimuli.

How the workload impacts on cognitive cooperation: A pilot study.

Cooperation degradation can be seen as one of the main causes of human errors. Poor cooperation could arise from aberrant mental processes, such as mental overload, that negatively affect the user's performance. Using different levels of difficulty in a cooperative task, we combined behavioural, subjective and neurophysiological data with the aim to i) quantify the mental workload under which the crew was operating, ii) evaluate the degree of their cooperation, and iii) assess the impact of the workload demands on the cooperation levels. The combination of such data showed that high workload demand impacted significantly on the performance, workload perception, and degree of cooperation.

An eye tracking index for the salience estimation in visual stimuli.

Every day we face visual stimuli able to catch our attention, but this aspect becomes crucial if the visual material has the purpose to spread a message aimed at engaging the observer. In this framework, a worthy aspect is how to measure the "visual engagement" produced by visual stimuli exposure. To this purpose, in the present study, employing the eye tracking technique, an index of visual attention (VA) has been proposed, and applied to pictures belonging to antismoking public service announcements, so to investigate the saliency of health-promoting messages in a young sample. The VA index is a non-dimensional index, defined as the ratio between the percentage of the total time spent fixating an area of interest (AOI) weighted on the total time the picture is showed on the screen, and the percentage of the area occupied by the AOI weighted on the total dimension of the picture. It could be predicted that AOI reporting higher VA values will be the ones having more saliency. Three antismoking Public Service Announcements (PSAs) images have been selected for the study and for each of them were identified: i) "picture" (such as a young man with a sarcastic expression depicted while smoking a cigarette, or the image of a lady who underwent a tracheotomy) and ii) "writing" (text of the antismoking message) AOIs. Main results of the analysis revealed that writing AOIs obtained statistically significant higher VA values than visual AOIs (p=0.03), but these held true only for an ineffective PSA, probably because the text was not perceived as pertinent with the surrounding image. On the other hand, an effective PSA obtained higher VA values in response to visual than writing AOIs observation (p=0.02). The VA index appears therefore to represent a useful tool to measure the saliency of visual stimuli elements.

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.

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.

Passive BCI in Operational Environments: Insights, Recent Advances, and Future Trends.

GOAL: This minireview 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. METHODS: Electroencephalography (EEG) based pBCI has become an important tool for real-time analysis of brain activity since it could potentially provide covertly-without distracting the user from the main task-and objectively-not affected by the subjective judgment of an observer or the user itself-information about the operator cognitive state. RESULTS: Different examples of pBCI applications in operational environments and new adaptive interface solutions have been presented and described. In addition, a general overview regarding the correct use of machine learning techniques (e.g., which algorithm to use, common pitfalls to avoid, etc.) in the pBCI field has been provided. CONCLUSION: Despite recent innovations on algorithms and neurotechnology, pBCI systems are not completely ready to enter the market yet, mainly due to limitations of the EEG electrodes technology, and algorithms reliability and capability in real settings. SIGNIFICANCE: High complexity and safety critical systems (e.g., airplanes, ATM interfaces) should adapt their behaviors and functionality accordingly to the user' actual mental state. Thus, technologies (i.e., pBCIs) able to measure in real time the user's mental states would result very useful in such "high risk" environments to enhance human machine interaction, and so increase the overall safety.

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 settings.

Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment.

Adaptive Automation (AA) is a promising approach to keep the task workload demand within appropriate levels in order to avoid both the under- and over-load conditions, hence enhancing the overall performance and safety of the human-machine system. The main issue on the use of AA is how to trigger the AA solutions without affecting the operative task. In this regard, passive Brain-Computer Interface (pBCI) systems are a good candidate to activate automation, since they are able to gather information about the covert behavior (e.g., mental workload) of a subject by analyzing its neurophysiological signals (i.e., brain activity), and without interfering with the ongoing operational activity. We proposed a pBCI system able to trigger AA solutions integrated in a realistic Air Traffic Management (ATM) research simulator developed and hosted at ENAC (École Nationale de l'Aviation Civile of Toulouse, France). Twelve Air Traffic Controller (ATCO) students have been involved in the experiment and they have been asked to perform ATM scenarios with and without the support of the AA solutions. Results demonstrated the effectiveness of the proposed pBCI system, since it enabled the AA mostly during the high-demanding conditions (i.e., overload situations) inducing a reduction of the mental workload under which the ATCOs were operating. On the contrary, as desired, the AA was not activated when workload level was under the threshold, to prevent too low demanding conditions that could bring the operator's workload level toward potentially dangerous conditions of underload.

Functional heterogeneity as reflected by topological parameters in a classical protein molecular model: t4 phage lysozyme.

A systematic comparison with the Wild-Type (WT) of one-point mutants of bacteriophage T4 lysozyme was carried out using as difference markers the topological parameters of the protein contact networks corresponding to each crystallographic structure. The investigation concerned changes at the resolution level of single residue along the protein sequence. The results were correlated with (reported) changes in functional properties and (observed) changes in the information provided by the energy dissipation algorithm of the "Turbine" software simulation tool. The critical factor leading to significant difference among mutants and WT is in most cases associated to the sensitivity towards mutation of relatively short windows in the amino acidic sequence not necessarily contiguous to the active site.

Neurophysiological measures for users' training objective assessment during simulated robot-assisted laparoscopic surgery.

Minimally invasive surgery can be performed with robotic assistance, as evolution of laparoscopic surgery. Robots for assisted surgery are far from being user friendly and require extensive training. To this end, ad-hoc devices and experimental set-ups are needed. The da Vinci system is one of the most diffused surgical robotics technology. The aim of the study was two-fold: i) to propose a neurophysiological measure by which objectively assess the learning progress of the users by means of a simulator of the da Vinci system, and ii) to demonstrate the advantages of cognitive assessment with respect to the standard methodologies for the evaluation of training efficiency.

A new regression-based method for the eye blinks artifacts correction in the EEG signal, without using any EOG channel.

Eye blinks artifacts correction in the EEG signal is a best practice in many applications. Nowadays, different approaches can be used to overcome such an issue: the most used methods are based on regression techniques and Independent Component Analysis. It is not clear which is the best performing method, thus the choice of which method to adopt depends on the specific application, on the basis of the method limitations. In fact, on one hand the regression-based methods require at least one EOG channel, and are affected by the mutual contamination between EEG and EOG signals. On the other hand, the ICA-based methods need a higher number of electrodes and a greater computational effort than the regression-based ones. In this study, a new regression-based method has been proposed and compared with three of the most used algorithms (Gratton, extended InfoMax, SOBI) for eye blinks correction. The results showed that the proposed algorithm was able (i) to achieve similar efficiency of the other methods in correcting the blinks, but without requiring neither EOG channels, nor a great electrodes number, nor a high computational effort, and (ii) to preserve EEG information in blink-free signal segments.