Test anxiety (TA), a recognized form of social anxiety, is the most prominent cause of anxiety among students and, if left unmanaged, can escalate to psychiatric disorders. TA profoundly impacts both central and autonomic nervous systems, presenting as a dual manifestation of cognitive and autonomic components. While limited studies have explored the physiological underpinnings of TA, none have directly investigated the intricate interplay between the CNS and ANS in this context. In this study, we introduce a non-invasive, integrated neurocardiovascular approach to comprehensively characterize the physiological responses of 27 healthy subjects subjected to test anxiety induced via a simulated exam scenario. Our experimental findings highlight that an isolated analysis of electroencephalographic and heart rate variability data fails to capture the intricate information provided by a brain-heart axis assessment, which incorporates an analysis of the dynamic interaction between the brain and heart. With respect to resting state, the simulated examination induced a decrease in the neural control onto heartbeat dynamics at all frequencies, while the studying condition induced a decrease in the ascending heart-to-brain interplay at EEG oscillations up to 12Hz. This underscores the significance of adopting a multisystem perspective in understanding the complex and especially functional directional mechanisms underlying test anxiety.

The ongoing reproducibility crisis in psychology and cognitive neuroscience has sparked increasing calls to re-evaluate and reshape scientific culture and practices. Heeding those calls, we have recently launched the EEGManyPipelines project as a means to assess the robustness of EEG research in naturalistic conditions and experiment with an alternative model of conducting scientific research. One hundred sixty-eight analyst teams, encompassing 396 individual researchers from 37 countries, independently analyzed the same unpublished, representative EEG data set to test the same set of predefined hypotheses and then provided their analysis pipelines and reported outcomes. Here, we lay out how large-scale scientific projects can be set up in a grassroots, community-driven manner without a central organizing laboratory. We explain our recruitment strategy, our guidance for analysts, the eventual outputs of this project, and how it might have a lasting impact on the field.

The recovery of motor functions after stroke is fostered by the functional integration of large-scale brain networks, including the motor network (MN) and high-order cognitive controls networks, such as the default mode (DMN) and executive control (ECN) networks. In this paper, electroencephalography signals are used to investigate interactions among these three resting state networks (RSNs) in subacute stroke patients after motor rehabilitation. A novel metric, the O-information rate (OIR), is used to quantify the balance between redundancy and synergy in the complex high-order interactions among RSNs, as well as its causal decomposition to identify the direction of information flow. The paper also employs conditional spectral Granger causality to assess pairwise directed functional connectivity between RSNs. After rehabilitation, a synergy increase among these RSNs is found, especially driven by MN. From the pairwise description, a reduced directed functional connectivity towards MN is enhanced after treatment. Besides, inter-network connectivity changes are associated with motor recovery, for which the mediation role of ECN seems to play a relevant role, both from pairwise and high-order interactions perspective.

Electrophysiological source imaging (ESI) aims at reconstructing the precise origin of brain activity from measurements of the electric field on the scalp. Across laboratories/research centers/hospitals, ESI is performed with different methods, partly due to the ill-posedness of the underlying mathematical problem. However, it is difficult to find systematic comparisons involving a wide variety of methods. Further, existing comparisons rarely take into account the variability of the results with respect to the input parameters. Finally, comparisons are typically performed using either synthetic data, or in-vivo data where the ground-truth is only roughly known. We use an in-vivo high-density EEG dataset recorded during intracranial single pulse electrical stimulation, in which the true sources are substantially dipolar and their locations are precisely known. We compare ten different ESI methods, using their implementation in the MNE-Python package: MNE, dSPM, LORETA, sLORETA, eLORETA, LCMV beamformers, irMxNE, Gamma Map, SESAME and dipole fitting. We perform comparisons under multiple choices of input parameters, to assess the accuracy of the best reconstruction, as well as the impact of such parameters on the localization performance. Best reconstructions often fall within 1 cm from the true source, with most accurate methods hitting an average localization error of 1.2 cm and outperforming least accurate ones erring by 2.5 cm. As expected, dipolar and sparsity-promoting methods tend to outperform distributed methods. For several distributed methods, the best regularization parameter turned out to be the one in principle associated with low SNR, despite the high SNR of the available dataset. Depth weighting played no role for two out of the six methods implementing it. Sensitivity to input parameters varied widely between methods. While one would expect high variability being associated with low localization error at the best solution, this is not always the case, with some methods producing highly variable results and high localization error, and other methods producing stable results with low localization error. In particular, recent dipolar and sparsity-promoting methods provide significantly better results than older distributed methods. As we repeated the tests with "conventional" (32 channels) and dense (64, 128, 256 channels) EEG recordings, we observed little impact of the number of channels on localization accuracy; however, for distributed methods denser montages provide smaller spatial dispersion. Overall findings confirm that EEG is a reliable technique for localization of point sources and therefore reinforce the importance that ESI may have in the clinical context, especially when applied to identify the surgical target in potential candidates for epilepsy surgery.

Virtual reality (VR) is a technology that allows users to experience multisensory and interactive environments that simulate real or imaginary scenarios. The effect of different VR immersive technology on mental workload (MWL), i.e., the amount of resources required to perform a task, is still debated; however the potential role of EEG in this context was never exploited. This paper aims to investigate the effects on MWL of performing a cognitive task in a VR environment in two conditions characterized by different degrees of immersion using a multimodal approach which combines well-assessed subjective evaluations of MWL with physiological EEG measures. A cognitive task based on the n-back test was proposed to compare the performance and MWL of participants who used either a head-mounted display (HMD) or a desktop computer to present the stimuli. The task had four different complexity levels (n = 1 or 2 with either visual or visual and audio stimuli). Twenty-seven healthy participants were enrolled in this study and performed the tasks in both conditions. EEG data and NASA Task Load indeX (NASA-TLX) were used to assess changes in objective and subjective MWL, respectively. Error rates (ERs) and reaction times (RTs) were also collected for each condition and task level. Task levels had significant effects on MWL, increasing subjective measures and decreasing performance, in both conditions. EEG MWL index have shown a significant increase especially if compared to rest. Different degrees of immersion did not show significant differences neither in individual's performance nor in MWL as estimated by subjective ratings. However, HMD reduced the EEG-derived MWL in most conditions indicating a lower cognitive load. In conclusion, HMD may reduce the cognitive load of some tasks. The reduced level of MWL, as depicted by the EEG MWL index, may have implications for the design and future evaluation of VR-based applications.

The rapid advancement of biomedical sensor technology has revolutionized the field of functional mapping in medicine, offering novel and powerful tools for diagnosis, clinical assessment, and rehabilitation. The ability to collect and analyze various physiological signals, even in real-time, has provided unprecedented insights into the "hidden" functioning of the human body. Biomedical sensors have not only enhanced our understanding of human physiology but have also significantly impacted clinical decision-making, patient management, and the development of personalized medical interventions. This Special Issue presents a collection of 14 papers that showcase the diverse applications of biomedical sensors in the context of functional mapping. The papers can be grouped into three sections, highlighting their contributions to (i) medical diagnosis, detection and prediction; (ii) neurological and rehabilitation assessment; and (iii) medical applications and monitoring. Together, these papers shed light on the transformative role of biomedical sensors in understanding physiological mechanisms and enhancing healthcare practices.

In clinical scenarios, the use of biomedical sensors, devices and multi-parameter assessments is fundamental to provide a comprehensive portrait of patients' state, in order to adapt and personalize rehabilitation interventions and support clinical decision-making. However, there is a huge gap between the potential of the multidomain techniques available and the limited practical use that is made in the clinical scenario. This paper reviews the current state-of-the-art and provides insights into future directions of multi-domain instrumental approaches in the clinical assessment of patients involved in neuromotor rehabilitation. We also summarize the main achievements and challenges of using multi-domain approaches in the assessment of rehabilitation for various neurological disorders affecting motor functions. Our results showed that multi-domain approaches combine information and measurements from different tools and biological signals, such as kinematics, electromyography (EMG), electroencephalography (EEG), near-infrared spectroscopy (NIRS), and clinical scales, to provide a comprehensive and objective evaluation of patients' state and recovery. This multi-domain approach permits the progress of research in clinical and rehabilitative practice and the understanding of the pathophysiological changes occurring during and after rehabilitation. We discuss the potential benefits and limitations of multi-domain approaches for clinical decision-making, personalized therapy, and prognosis. We conclude by highlighting the need for more standardized methods, validation studies, and the integration of multi-domain approaches in clinical practice and research.

Background: Major Depressive Disorder (MDD) is a psychiatric illness that is often associated with potentially life -threatening physiological changes and increased risk for suicidal behavior. Electroencephalography (EEG) research suggests an association between depression and specific frequency imbalances in the frontal brain re-gion. Further, while recently developed technology has been proposed to simplify EEG data acquisition, more research is still needed to support its use in patients with MDD.Methods: Using the 14-channel EMOTIV EPOC cap, we recorded resting state EEG from 15 MDD patients with suicidal ideation (SI) vs. 12 healthy controls (HC) to investigate putative power spectral density (PSD) between -group differences at the F3 and F4 electrode sites. Specifically, we explored 1) between-group alpha power asymmetries (AA), 2) between-group differences in delta, theta, alpha and beta power, 3) correlations between PSD data and scores in the Beck's Depression Inventory-II (BDI-II), Beck's Anxiety Inventory (BAI), Reasons for Living Inventory (RFL), and Self-Disgust Questionnaire (SDS).Results: When compared to HC, patients had higher scores on the BAI (p = 0.0018), BDI-II (p = 0.0001) or SDS (p = 0.0142) scale and lower scores in the RFL (p = 0.0006) scale. The PSD analysis revealed no between-group difference or correlation with questionnaire scores for any of the measures considered.Conclusions: The present study could not confirm previous research suggesting frequency-specific anomalies in depressed persons with SI but might suggest that frontal EEG imbalances reflect greater anxiety and negative self -referencing. Future studies should confirm these findings in a larger population sample.

In aviation, any detail can have massive consequences. Among the potential sources of failure, human error is still the most troublesome to handle. Therefore, research concerning the management of mental workload, attention, and stress is of special interest in aviation. Recognizing conditions in which a pilot is over-challenged or cannot act lucidly could avoid serious outcomes. Furthermore, knowing in depth a pilot's neurophysiological and cognitive-behavioral responses could allow for the optimization of equipment and procedures to minimize risk and increase safety. Furthermore, it could translate into a general enhancement of both the physical and mental well-being of pilots, producing a healthier and more ergonomic work environment. This review brings together literature on the study of stress and workload in the specific case of pilots of both civil and military aircraft. The most common approaches for studying these phenomena in the avionic context are explored in this review, with a focus on objective methodologies (e.g., the collection and analysis of neurophysiological signals). This review aims to identify the pros, cons, and applicability of the various approaches, to enable the design of an optimal protocol for a comprehensive study of these issues.

Background and Objective: Mental workload (MWL) is a relevant construct involved in all cognitively demanding activities, and its assessment is an important goal in many research fields. This paper aims at evaluating the reproducibility and sensitivity of MWL assessment from EEG signals considering the effects of different electrode configurations and pre-processing pipelines (PPPs). Methods: Thirteen young healthy adults were enrolled and were asked to perform 45 min of Simon's task to elicit a cognitive demand. EEG data were collected using a 32-channel system with different electrode configurations (fronto-parietal; Fz and Pz; Cz) and analyzed using different PPPs, from the simplest bandpass filtering to the combination of filtering, Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). The reproducibility of MWL indexes estimation and the sensitivity of their changes were assessed using Intraclass Correlation Coefficient and statistical analysis. Results: MWL assessed with different PPPs showed reliability ranging from good to very good in most of the electrode configurations (average consistency > 0.87 and average absolute agreement > 0.92). Larger fronto-parietal electrode configurations, albeit being more affected by the choice of PPPs, provide better sensitivity in the detection of MWL changes if compared to a single-electrode configuration (18 vs. 10 statistically significant differences detected, respectively). Conclusions: The most complex PPPs have been proven to ensure good reliability (>0.90) and sensitivity in all experimental conditions. In conclusion, we propose to use at least a two-electrode configuration (Fz and Pz) and complex PPPs including at least the ICA algorithm (even better including ASR) to mitigate artifacts and obtain reliable and sensitive MWL assessment during cognitive tasks.

Background Dravet Syndrome is a severe childhood pharmaco-resistant epileptic disorder mainly caused by mutations in the SCN1A gene, which encodes for the alpha 1 subunit of the type I voltage-gated sodium channel (Na(V)1.1), that causes imbalance between excitation and inhibition in the brain. We recently found that eEF2K knock out mice displayed enhanced GABAergic transmission and tonic inhibition and were less susceptible to epileptic seizures. Thus, we investigated the effect of inhibition of eEF2K on the epileptic and behavioral phenotype of Scn1a +/- mice, a murine model of Dravet Syndrome. Methods To elucidate the role of eEF2K pathway in the etiopathology of Dravet syndrome we generated a new mouse model deleting the eEF2K gene in Scn1a +/- mice. By crossing Scn1a +/- mice with eEF2K-/- mice we obtained the three main genotypes needed for our studies, Scn1a+/+ eEF2K+/+ (WT mice), Scn1a +/- eEF2K+/+ mice (Scn1a +/- mice) and Scn1a +/- eEF2K-/- mice, that were fully characterized for EEG and behavioral phenotype. Furthermore, we tested the ability of a pharmacological inhibitor of eEF2K in rescuing EEG alterations of the Scn1a +/- mice. Results We showed that the activity of eEF2K/eEF2 pathway was enhanced in Scn1a +/- mice. Then, we demonstrated that both genetic deletion and pharmacological inhibition of eEF2K were sufficient to ameliorate the epileptic phenotype of Scn1a +/- mice. Interestingly we also found that motor coordination defect, memory impairments, and stereotyped behavior of the Scn1a +/- mice were reverted by eEF2K deletion. The analysis of spontaneous inhibitory postsynaptic currents (sIPSCs) suggested that the rescue of the pathological phenotype was driven by the potentiation of GABAergic synapses. Limitations Even if we found that eEF2K deletion was able to increase inhibitory synapses function, the molecular mechanism underlining the inhibition of eEF2K/eEF2 pathway in rescuing epileptic and behavioral alterations in the Scn1a +/- needs further investigations. Conclusions Our data indicate that pharmacological inhibition of eEF2K could represent a novel therapeutic intervention for treating epilepsy and related comorbidities in the Dravet syndrome.

Internet Addiction (IA) is representing one of the main concerns in the domain of neuropsychiatry in last years, with more and more subjects being affected by this clinical condition as time flows, especially in cohorts of younger individuals, particularly adolescents. Until some time ago, IA was mainly studied using standardized questionnaires, employed for their easy administration, but carrying out several drawbacks, including bias and lack of objectivity. More recently, technological tools have been employed in this domain with some success, enabling a more accurate, unbiased analysis of the phenomenon at both an individual and cohort level. This short paper summarizes the main literature evidences related to the use of consumer technologies in studies concerning IA, raising questions and looking forward to future perspectives for this increasingly prevalent condition.

This proof-of-concept (PoC) study presents a pipeline made by two blocks: 1. the identification of the network that generates interictal epileptic activity; and 2. the study of the time course of the electrical activity that it generates, called neurodynamics, and the study of its functional connectivity to the other parts of the brain. Network identification is achieved with the Functional Source Separation (FSS) algorithm applied to electroencephalographic (EEG) recordings, the neurodynamics quantified through signal complexity with the Higuchi Fractal Dimension (HFD), and functional connectivity with the Directed Transfer Function (DTF). This PoC is enhanced by the data collected before and after neuromodulation via transcranial Direct Current Stimulation (tDCS, both Real and Sham) in a single drug-resistant epileptic person. We observed that the signal complexity of the epileptogenic network, reduced in the pre-Real, pre-Sham, and post-Sham, reached the level of the rest of the brain post-Real tDCS. DTF changes post-Real tDCS were maintained after one month. The proposed approach can represent a valuable tool to enhance understanding of the relationship between brain neurodynamics characteristics, the effects of non-invasive brain stimulation, and epileptic symptoms.

Connectivity among different areas within the brain is a topic that has been notably studied in the last decade. In particular, EEG-derived measures of effective connectivity examine the directionalities and the exerted influences raised from the interactions among neural sources that are masked out on EEG signals. This is usually performed by fitting multivariate autoregressive models that rely on the stationarity that is assumed to be maintained over shorter bits of the signals. However, despite being a central condition, the selection process of a segment length that guarantees stationary conditions has not been systematically addressed within the effective connectivity framework, and thus, plenty of works consider different window sizes and provide a diversity of connectivity results. In this study, a segment-size-selection procedure based on fourth-order statistics is proposed to make an informed decision on the appropriate window size that guarantees stationarity both in temporal and spatial terms. Specifically, kurtosis is estimated as a function of the window size and used to measure stationarity. A search algorithm is implemented to find the segments with similar stationary properties while maximizing the number of channels that exhibit the same properties and grouping them accordingly. This approach is tested on EEG signals recorded from six healthy subjects during resting-state conditions, and the results obtained from the proposed method are compared to those obtained using the classical approach for mapping effective connectivity. The results show that the proposed method highlights the influence that arises in the Default Mode Network circuit by selecting a window of 4 s, which provides, overall, the most uniform stationary properties across channels. ? 2022 by the authors. Licensee MDPI, Basel, Switzerland.

In questo deliverable si riportano i protocolli di alcuni studi già condotti o da finalizzare nel corso del progetto Rip@rto. Il deliverable sarà poi aggiornato più avanti quando sarà possibile stilare il protocollo completo per la validazione del prototipo di Rip@rto. A causa dei ritardi nell'approvvigionamento della piattaforma, infatti, il gruppo di lavoro ha deciso di pianificare e svolgere una serie di esperimenti preliminari volti allo studio di singole componenti del sistema. Questi studi, oltre ad offrire uno spunto per la scelta di alcune tecnologie e sensori, consentono anche di valutare l'influenza del setup sui segnali fisiologici di interesse e di fare alcune valutazioni preliminari rispetto all'interazione dell'utente con la tecnologia, sia dal punto di vista oggettivo, sia dal punto di vista soggettivo.

Brain plasticity and functional reorganization are mechanisms behind functional motor recovery of patients after an ischemic stroke. The study of resting-state motor network functional connectivity by means of EEG proved to be useful in investigating changes occurring in the information flow and find correlation with motor function recovery. In the literature, most studies applying EEG to post-stroke patients investigated the undirected functional connectivity of interacting brain regions. Quite recently, works started to investigate the directionality of the connections and many approaches or features have been proposed, each of them being more suitable to describe different aspects, e.g., direct or indirect information flow between network nodes, the coupling strength or its characteristic oscillation frequency. Each work chose one specific measure, despite in literature there is not an agreed consensus, and the selection of the most appropriate measure is still an open issue. In an attempt to shed light on this methodological aspect, we propose here to combine the information of direct and indirect coupling provided by two frequency-domain measures based on Granger's causality, i.e., the directed coherence (DC) and the generalized partial directed coherence (gPDC), to investigate the longitudinal changes of resting-state directed connectivity associated with sensorimotor rhythms ? and ?, occurring in 18 sub-acute ischemic stroke patients who followed a rehabilitation treatment. Our results showed a relevant role of the information flow through the pre-motor regions in the reorganization of the motor network after the rehabilitation in the sub-acute stage. In particular, DC highlighted an increase in intra-hemispheric coupling strength between pre-motor and primary motor areas, especially in ipsi-lesional hemisphere in both ? and ? frequency bands, whereas gPDC was more sensitive in the detection of those connection whose variation was mostly represented within the population. A decreased causal flow from contra-lesional premotor cortex towards supplementary motor area was detected in both ? and ? frequency bands and a significant reinforced inter-hemispheric connection from ipsi to contra-lesional pre-motor cortex was observed in ? frequency. Interestingly, the connection from contra towards ipsilesional pre-motor area correlated with upper limb motor recovery in ? band. The usage of two different measures of directed connectivity allowed a better comprehension of those coupling changes between brain motor regions, either direct or mediated, which mostly were influenced by the rehabilitation, revealing a particular involvement of the pre-motor areas in the cerebral functional reorganization.

Rapporto delle attività svolte nel corso del primo anno del progetto Rip@rto.

Objective Paroxysmal epileptiform abnormalities on electroencephalography (EEG) are the hallmark of epilepsies, but it is uncertain to what extent epilepsy and background EEG oscillations share neurobiological underpinnings. Here, we aimed to assess the genetic correlation between epilepsy and background EEG oscillations.

Gait training in a virtual reality (VR) environment is promising for children affected by different disorders. However, the efficacy of VR therapy is still under debate, and more research is needed to clarify its effects on clinical conditions. The combination of VR with neuroimaging methods, such as the electroencephalography (EEG), might help in answering this need. The aim of the present work was to set up and test a system for the multimodal analysis of the gait pattern during VR gait training of pediatric populations by analyzing the EEG correlates as well as the kinematic and kinetic parameters of the gait. An EEG system was integrated with the Gait Real-time Analysis Interactive Lab (GRAIL). We developed and validated, with healthy adults (n = 5) and children (n = 4, healthy or affected by cerebral palsy (CP)), the hardware and software integration of the two systems, which allowed the synchronization of the acquired signals and a reliable identification of the initial contact (IC) of each gait cycle, showing good sensitivity and critical success index values. Moreover, we tested the multimodal acquisition by successfully analyzing EEG data and kinematic and kinetic parameters of one healthy child and one child with CP. This system gives the possibility of monitoring the effect of the VR therapy and studying the neural correlates of gait.