[The extended contact tracing: the experience of Prevention Department of the Health Authority of Trapani Province (Sicily Region, Southern Italy)]. / Il contact tracing allargato: l'esperienza del Dipartimento di Prevenzione dell'Azienda Sanitaria Provinciale di Trapani.

During the spread of SARS-CoV-2 virus, contact tracing proved to be a very effective public health tool. Within the local health authority of Trapani (Sicily Region, Southern Italy), contact tracing was managed by physician, prevention technicians, and administrative from the Health Prevention Department who were trained and updated during the evolution of the epidemic. Contact tracing has been extended to migrants who arrived in Trapani with the landings. Extended contact tracing had some critical factors related to language barriers, which reduced the effectiveness of the telephone contacts and psychological counseling during tracing, up to the loss of definition of high or low risk contacts among both migrants and rescuers. The team made up of workers from the Health Prevention Department, the Global Health Center, and Cultural Mediators was important in effectively managing the critical issues. The high number of cases occurred during the outbreak of COVID-19 in January 2022 has shown difficulties to support the contact tracing in this phase.

Transcranial magnetic stimulation effects support an oscillatory model of ERP genesis.

Whether prestimulus oscillatory brain activity contributes to the generation of post-stimulus-evoked neural responses has long been debated, but findings remain inconclusive. We first investigated the hypothesized relationship via EEG recordings during a perceptual task with this correlational evidence causally probed subsequently by means of online rhythmic transcranial magnetic stimulation. Both approaches revealed a close link between prestimulus individual alpha frequency (IAF) and P1 latency, with faster IAF being related to shorter latencies, best explained via phase-reset mechanisms. Moreover, prestimulus alpha amplitude predicted P3 size, best explained via additive (correlational and causal evidence) and baseline shift mechanisms (correlational evidence), each with distinct prestimulus alpha contributors. Finally, in terms of performance, faster prestimulus IAF and shorter P1 latencies were both associated with higher task accuracy, while lower prestimulus alpha amplitudes and higher P3 amplitudes were associated with higher confidence ratings. Our results are in favor of the oscillatory model of ERP genesis and modulation, shedding new light on the mechanistic relationship between prestimulus oscillations and functionally relevant evoked components.

Error-related cardiac deceleration: Functional interplay between error-related brain activity and autonomic nervous system in performance monitoring.

Coordinated interactions between the central and autonomic nervous systems are crucial for survival due to the inherent propensity for human behavior to make errors. In our ever-changing environment, when individuals make mistakes, these errors can have life-threatening consequences. In response to errors, specific reactions occur in both brain activity and heart rate to detect and correct errors. Specifically, there are two brain-related indicators of error detection and awareness known as error-related negativity and error positivity. Conversely, error-related cardiac deceleration denotes a momentary slowing of heart rate following an error, signaling an autonomic response. However, what is the connection between the brain and the heart during error processing? In this review, we discuss the functional and neuroanatomical connections between the brain and heart markers of error processing, exploring the experimental conditions in which they covary. Given the current limitations of available data, future research will continue to investigate the neurobiological factors governing the brain-heart interaction, aiming to utilize them as combined markers for assessing cognitive control in healthy and pathological conditions.

Feasibility interventional study investigating PAIN in neurorehabilitation through wearabLE SensorS (PAINLESS): a study protocol.

INTRODUCTION: Millions of people survive injuries to the central or peripheral nervous system for which neurorehabilitation is required. In addition to the physical and cognitive impairments, many neurorehabilitation patients experience pain, often not widely recognised and inadequately treated. This is particularly true for multiple sclerosis (MS) patients, for whom pain is one of the most common symptoms. In clinical practice, pain assessment is usually conducted based on a subjective estimate. This approach can lead to inaccurate evaluations due to the influence of numerous factors, including emotional or cognitive aspects. To date, no objective and simple to use clinical methods allow objective quantification of pain and the diagnostic differentiation between the two main types of pain (nociceptive vs neuropathic). Wearable technologies and artificial intelligence (AI) have the potential to bridge this gap by continuously monitoring patients' health parameters and extracting meaningful information from them. Therefore, we propose to develop a new automatic AI-powered tool to assess pain and its characteristics during neurorehabilitation treatments using physiological signals collected by wearable sensors. METHODS AND ANALYSIS: We aim to recruit 15 participants suffering from MS undergoing physiotherapy treatment. During the study, participants will wear a wristband for three consecutive days and be monitored before and after their physiotherapy sessions. Measurement of traditionally used pain assessment questionnaires and scales (ie, painDETECT, Doleur Neuropathique 4 Questions, EuroQoL-5-dimension-3-level) and physiological signals (photoplethysmography, electrodermal activity, skin temperature, accelerometer data) will be collected. Relevant parameters from physiological signals will be identified, and AI algorithms will be used to develop automatic classification methods. ETHICS AND DISSEMINATION: The study has been approved by the local Ethical Committee (285-2022-SPER-AUSLBO). Participants are required to provide written informed consent. The results will be disseminated through contributions to international conferences and scientific journals, and they will also be included in a doctoral dissertation. TRIAL REGISTRATION NUMBER: NCT05747040.

Advances in EEG-based functional connectivity approaches to the study of the central nervous system in health and disease.

Functional brain connectivity is closely linked to the complex interactions between brain networks. In the last two decades, measures of functional connectivity based on electroencephalogram (EEG) data have proved to be an important tool for neurologists and clinical and non-clinical neuroscientists. Indeed, EEG-based functional connectivity may reveal the neurophysiological processes and networks underlying human cognition and the pathophysiology of neuropsychiatric disorders. This editorial discusses recent advances and future prospects in the study of EEG-based functional connectivity, with a focus on the main methodological approaches to studying brain networks in health and disease.

Two Oscillatory Correlates of Attention Control in the Alpha-Band with Distinct Consequences on Perceptual Gain and Metacognition.

Behavioral consequences and neural underpinnings of visuospatial attention have long been investigated. Classical studies using the Posner paradigm have found that visual perception systematically benefits from the use of a spatially informative cue pointing to the to-be-attended spatial location, compared with a noninformative cue. Lateralized α amplitude modulation during visuospatial attention shifts has been suggested to account for such perceptual gain. However, recent studies on spontaneous fluctuations of prestimulus α amplitude have challenged this notion. These studies showed that spontaneous fluctuations of prestimulus α amplitude were associated with the subjective appreciation of stimulus occurrence, while objective accuracy was instead best predicted by the frequency of α oscillations, with faster prestimulus α frequency accounting for better perceptual performance. Here, in male and female humans, by using an informative cue in anticipation of lateralized stimulus presentation, we found that the predictive cue not only modulates preparatory α amplitude but also α frequency in a retinotopic manner. Behaviorally, the cue significantly impacted subjective performance measures (metacognitive abilities [meta-d']) and objective performance gain (d'). Importantly, α amplitude directly accounted for confidence levels, with ipsilateral synchronization and contralateral desynchronization coding for high-confidence responses. Crucially, the contralateral α amplitude selectively predicted interindividual differences in metacognitive abilities (meta-d'), thus anticipating decision strategy and not perceptual sensitivity, probably via excitability modulations. Instead, higher perceptual accuracy both within and across participants (d') was associated with faster contralateral α frequency, likely by implementing higher sampling at the attended location. These findings provide critical new insights into the neural mechanisms of attention control and its perceptual consequences.SIGNIFICANCE STATEMENT Prior knowledge serves the anticipation of sensory input to reduce sensory ambiguity. The growing interest in the neural mechanisms governing the integration of sensory input into our internal representations has highlighted a pivotal role of brain oscillations. Here we show that distinct but interacting oscillatory mechanisms are engaged during attentional deployment: one relying on α amplitude modulations and reflecting internal decision processes, associated with subjective perceptual experience and metacognitive abilities; the other relying on α frequency modulations and enabling mechanistic sampling of the sensory input at the attended location to influence objective performance. These insights are crucial for understanding how we reduce sensory ambiguity to maximize the efficiency of our conscious experience, but also in interpreting the mechanisms of atypical perceptual experiences.

Hierarchical psychophysiological pathways subtend perceptual asymmetries in Neglect.

Stroke patients with left Hemispatial Neglect (LHN) show deficits in perceiving left contralesional stimuli with biased visuospatial perception towards the right hemifield. However, very little is known about the functional organization of the visuospatial perceptual neural network and how this can account for the profound reorganization of space representation in LHN. In the present work, we aimed at (1) identifying EEG measures that discriminate LHN patients against controls and (2) devise a causative neurophysiological model between the discriminative EEG measures. To these aims, EEG was recorded during exposure to lateralized visual stimuli which allowed for pre-and post-stimulus activity investigation across three groups: LHN patients, lesioned controls, and healthy individuals. Moreover, all participants performed a standard behavioral test assessing the perceptual asymmetry index in detecting lateralized stimuli. The between-groups discriminative EEG patterns were entered into a Structural Equation Model for the identification of causative hierarchical associations (i.e., pathways) between EEG measures and the perceptual asymmetry index. The model identified two pathways. A first pathway showed that the combined contribution of pre-stimulus frontoparietal connectivity and individual-alpha-frequency predicts post-stimulus processing, as measured by visual-evoked N100, which, in turn, predicts the perceptual asymmetry index. A second pathway directly links the inter-hemispheric distribution of alpha-amplitude with the perceptual asymmetry index. The two pathways can collectively explain 83.1% of the variance in the perceptual asymmetry index. Using causative modeling, the present study identified how psychophysiological correlates of visuospatial perception are organized and predict the degree of behavioral asymmetry in LHN patients and controls.

The Role of Alpha Oscillations among the Main Neuropsychiatric Disorders in the Adult and Developing Human Brain: Evidence from the Last 10 Years of Research.

Alpha oscillations (7-13 Hz) are the dominant rhythm in both the resting and active brain. Accordingly, translational research has provided evidence for the involvement of aberrant alpha activity in the onset of symptomatological features underlying syndromes such as autism, schizophrenia, major depression, and Attention Deficit and Hyperactivity Disorder (ADHD). However, findings on the matter are difficult to reconcile due to the variety of paradigms, analyses, and clinical phenotypes at play, not to mention recent technical and methodological advances in this domain. Herein, we seek to address this issue by reviewing the literature gathered on this topic over the last ten years. For each neuropsychiatric disorder, a dedicated section will be provided, containing a concise account of the current models proposing characteristic alterations of alpha rhythms as a core mechanism to trigger the associated symptomatology, as well as a summary of the most relevant studies and scientific contributions issued throughout the last decade. We conclude with some advice and recommendations that might improve future inquiries within this field.

Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach.

Accurate outcome detection in neuro-rehabilitative settings is crucial for appropriate long-term rehabilitative decisions in patients with disorders of consciousness (DoC). EEG measures derived from high-density EEG can provide helpful information regarding diagnosis and recovery in DoC patients. However, the accuracy rate of EEG biomarkers to predict the clinical outcome in DoC patients is largely unknown. This study investigated the accuracy of psychophysiological biomarkers based on clinical EEG in predicting clinical outcomes in DoC patients. To this aim, we extracted a set of EEG biomarkers in 33 DoC patients with traumatic and nontraumatic etiologies and estimated their accuracy to discriminate patients' etiologies and predict clinical outcomes 6 months after the injury. Machine learning reached an accuracy of 83.3% (sensitivity = 92.3%, specificity = 60%) with EEG-based functional connectivity predicting clinical outcome in nontraumatic patients. Furthermore, the combination of functional connectivity and dominant frequency in EEG activity best predicted clinical outcomes in traumatic patients with an accuracy of 80% (sensitivity = 85.7%, specificity = 71.4%). These results highlight the importance of functional connectivity in predicting recovery in DoC patients. Moreover, this study shows the high translational value of EEG biomarkers both in terms of feasibility and accuracy for the assessment of DoC.

A TMS/EEG protocol for the causal assessment of the functions of the oscillatory brain rhythms in perceptual and cognitive processes.

The combined use of transcranial magnetic stimulation (TMS), electroencephalogram (EEG), and behavioral performance allows investigation of causal relationships between neural markers and their functional relevance across a number of perceptual and cognitive processes. Here, we present a protocol for combining and applying these techniques on human subjects. We describe correlation approach and causal approach to disentangle the role of different oscillatory parameters, namely alpha frequency and amplitude that control for accuracy and metacognitive abilities, respectively, in a visual detection task. For complete details on the use and execution of this protocol, please refer to Di Gregorio et al. (2022).

Early correlates of error-related brain activity predict subjective timing of error awareness.

Humans are remarkably reliable in detecting errors in their behavior. Whereas error awareness has been assumed to emerge not until 200-400 ms after an error, the so-called early error sensations refer to the subjective feeling of having detected an error even before the erroneous response was executed. Here, we collected electroencephalogram (EEG) to track how early error sensations are reflected in neural correlates of performance monitoring. Participants first had to perform a task, and then had to indicate whether an error has occurred and whether this error was detected before or after response execution. EEG results showed that early error sensations were associated with an earlier peak of the error-related negativity (Ne/ERN), a component of error-related brain activity that occurs briefly after the error response. This demonstrates that early error-related activity influences metacognitive judgments on the time course of error awareness, and thus contributes to error awareness.

Tuning alpha rhythms to shape conscious visual perception.

It is commonly held that what we see and what we believe we see are overlapping phenomena. However, dissociations between sensory events and their subjective interpretation occur in the general population and in clinical disorders, raising the question as to whether perceptual accuracy and its subjective interpretation represent mechanistically dissociable events. Here, we uncover the role that alpha oscillations play in shaping these two indices of human conscious experience. We used electroencephalography (EEG) to measure occipital alpha oscillations during a visual detection task, which were then entrained using rhythmic-TMS. We found that controlling prestimulus alpha frequency by rhythmic-TMS modulated perceptual accuracy, but not subjective confidence in it, whereas controlling poststimulus (but not prestimulus) alpha amplitude modulated how well subjective confidence judgments can distinguish between correct and incorrect decision, but not accuracy. These findings provide the first causal evidence of a double dissociation between alpha speed and alpha amplitude, linking alpha frequency to spatiotemporal sampling resources and alpha amplitude to the internal, subjective representation and interpretation of sensory events.

Efficacy of Repetitive Transcranial Magnetic Stimulation Combined With Visual Scanning Treatment on Cognitive-Behavioral Symptoms of Unilateral Spatial Neglect in Patients With Traumatic Brain Injury: Study Protocol for a Randomized Controlled Trial.

Left hemispatial neglect (LHSN) is a frequent and disabling condition affecting patients who suffered from traumatic brain injury (TBI). LHSN is a neuropsychological syndrome characterized clinically by difficulties in attending, responding, and consciously representing the right side of space. Despite its frequency, scientific evidence on effective treatments for this condition in TBI patients is still low. According to existing literature, we hypothesize that in TBI, LHSN is caused by an imbalance in inter-hemispheric activity due to hyperactivity of the left hemisphere, as observed in LHSN after right strokes. Thus, by inhibiting this left hyperactivity, repetitive Transcranial Magnetic Stimulation (rTMS) would have a rebalancing effect, reducing LHSN symptoms in TBI patients. We plan to test this hypothesis within a single-blind, randomized SHAM controlled trial in which TBI patients will receive inhibitory i-rTMS followed by cognitive treatment for 15 days. Neurophysiological and clinical measures will be collected before, afterward, and in the follow-up. This study will give the first empirical evidence about the efficacy of a novel approach to treating LHSN in TBI patients. Clinical Trial Registration: https://www.clinicaltrials.gov/ct2/show/NCT04573413?cond=Neglect%2C+Hemispatial&cntry=IT&city=Bologna&draw=2&rank=2, identifier: NCT04573413.

Resting state alpha oscillatory activity is a valid and reliable marker of schizotypy.

Schizophrenia is among the most debilitating neuropsychiatric disorders. However, clear neurophysiological markers that would identify at-risk individuals represent still an unknown. The aim of this study was to investigate possible alterations in the resting alpha oscillatory activity in normal population high on schizotypy trait, a physiological condition known to be severely altered in patients with schizophrenia. Direct comparison of resting-state EEG oscillatory activity between Low and High Schizotypy Group (LSG and HSG) has revealed a clear right hemisphere alteration in alpha activity of the HSG. Specifically, HSG shows a significant slowing down of right hemisphere posterior alpha frequency and an altered distribution of its amplitude, with a tendency towards a reduction in the right hemisphere in comparison to LSG. Furthermore, altered and reduced connectivity in the right fronto-parietal network within the alpha range was found in the HSG. Crucially, a trained pattern classifier based on these indices of alpha activity was able to successfully differentiate HSG from LSG on tested participants further confirming the specific importance of right hemispheric alpha activity and intrahemispheric functional connectivity. By combining alpha activity and connectivity measures with a machine learning predictive model optimized in a nested stratified cross-validation loop, current research offers a promising clinical tool able to identify individuals at-risk of developing psychosis (i.e., high schizotypy individuals).

Efficacy of repetitive transcranial magnetic stimulation combined with visual scanning treatment on cognitive and behavioral symptoms of left hemispatial neglect in right hemispheric stroke patients: study protocol for a randomized controlled trial.

BACKGROUND: Left hemispatial neglect (LHN) is a neuropsychological syndrome often associated with right hemispheric stroke. Patients with LHN have difficulties in attending, responding, and consciously representing the right side of space. Various rehabilitation protocols have been proposed to reduce clinical symptoms related to LHN, using cognitive treatments, or on non-invasive brain stimulation. However, evidence of their benefit is still lacking; in particular, only a few studies focused on the efficacy of combining different approaches in the same patient. METHODS: In the present study, we present the SMART ATLAS trial (Stimolazione MAgnetica Ripetitiva Transcranica nell'ATtenzione LAteralizzata dopo Stroke), a multicenter, randomized, controlled trial with pre-test (baseline), post-test, and 12 weeks follow-up assessments based on a novel rehabilitation protocol based on the combination of brain stimulation and standard cognitive treatment. In particular, we will compare the efficacy of inhibitory repetitive-transcranial magnetic stimulation (r-TMS), applied over the left intact parietal cortex of LHN patients, followed by visual scanning treatment, in comparison with a placebo stimulation (SHAM control) followed by the same visual scanning treatment, on visuospatial symptoms and neurophysiological parameters of LHN in a population of stroke patients. DISCUSSION: Our trial results may provide scientific evidence of a new, relatively low-cost rehabilitation protocol for the treatment of LHN. TRIAL REGISTRATION: ClinicalTrials.gov NCT04080999 . Registered on September 2019.

Alteration of Smell and Taste in Asymptomatic and Symptomatic COVID-19 Patients in Sicily, Italy.

OBJECTIVES: Alteration of smell and taste has been reported in patients with coronavirus disease 2019 (COVID-19). The incidence and clinical-symptomatic manifestation of COVID-19 is different between northern and southern Italy. This study aims to evaluate the onset of alteration of smell and taste in asymptomatic and symptomatic patients in Sicily (extreme south of Italy). METHODS: This prospective cross-sectional study was performed on asymptomatic and symptomatic COVID-19 patients tested for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) from May 1 to May 15, 2020. A questionnaire was used for evaluating the prevalence of smell and taste disorders in COVID-19 patients before performing nasopharyngeal swab. RESULTS: Of the total 292 patients, 242 (83.2%) were negative for SARS-CoV-2 and 50 were positive (16.8%). Twenty-six of the 50 (52%) SARS-CoV-2 positive patients reported smell/taste disorders. Twenty-eight of the 50 (57.1%) SARS-CoV-2 positive patients were hospitalized (group A), and 22 (42.9%) were nonhospitalized (group B). The mean age in group A and group B was 45.4 ± 13.7 years and 57.0 ± 15.0, respectively (P = .007). The symptoms reported by hospitalized patients were fever (71.4%), cough (64.2%), fatigue (82.1%), and dyspnea (100%), while in nonhospitalized patients, the most reported symptoms were sore throat (72.7%), rhinorrhea (77.2%), and altered smell (81.8%). Anosmia/hyposmia reported in group A and group B was 28.5% and 81.8%, respectively (P = .001). CONCLUSION: These preliminary results indicate that the majority of SARS-Cov-2 positive patients in southern Italy did not require hospitalization and presented with milder symptoms or no symptoms and the alterations in smell and taste occurred.

Are errors detected before they occur? Early error sensations revealed by metacognitive judgments on the timing of error awareness.

Errors in choice tasks are not only detected fast and reliably, participants often report that they knew that an error occurred already before a response was produced. These early error sensations stand in contrast with evidence suggesting that the earliest neural correlates of error awareness emerge around 300 ms after erroneous responses. The present study aimed to investigate whether anecdotal evidence for early error sensations can be corroborated in a controlled study in which participants provide metacognitive judgments on the subjective timing of error awareness. In Experiment 1, participants had to report whether they became aware of their errors before or after the response. In Experiment 2, wemeasured confidence in these metacognitive judgments. Our data show that participants report early error sensations with high confidence in the majority of error trials across paradigms and experiments. These results provide first evidence for early error sensations, informing theories of error awareness.

Differential effects of instructed and objective feedback reliability on feedback-related brain activity.

Feedback reliability refers to the probability that the same decision leads to the same positive or negative feedback in the future. Previous research has shown that unreliable feedback is associated with attenuated feedback-related brain activity in ERPs, represented by a reduced fronto-central valence effect (feedback-related negativity or reward positivity) and a reduced feedback-related P3. Here, we asked whether these effects reflect top-down mechanisms or whether they can be explained by implicit feedback-outcome contingency learning. In two experiments, participants performed a trial-and-error learning task while subjective or objective feedback reliability was varied across blocks. In Experiment 1, we manipulated instructed feedback reliability while holding objective feedback reliability constant. Low instructed feedback reliability led to an attenuation of the fronto-central valence effect and the P3. In Experiment 2, we manipulated objective feedback reliability while holding instructed feedback reliability constant. Here, no modulation of feedback-related brain activity was observed. These results suggest that effects of feedback reliability are driven by top-down mechanisms based on explicit knowledge. Specifically, effects on the fronto-central valence effect could indicate a devaluation of unreliable feedback or a bias on the generation or utilization of reward prediction errors.

Errors can elicit an error positivity in the absence of an error negativity: Evidence for independent systems of human error monitoring.

Errors in human behavior elicit a cascade of brain activity related to performance monitoring and error detection. Whereas the early error-related negativity (Ne/ERN) has been assumed to reflect a fast mismatch or prediction error signal in the medial frontal cortex, the later error positivity (Pe) is viewed as a correlate of conscious error processing. A still open question is whether these components represent two independent systems of error monitoring that rely on different types of information to detect an error. Here, we investigated the prediction that the Ne/ERN but not the Pe requires a representation of the correct response to emerge. To this end, we created a condition in which no information about the correct response was available while error detection was still possible. We hypothesized that a Pe, but no Ne/ERN should be obtained in this case. Participants had to classify targets but ignore flankers that were always associated with an incorrect response. Targets but not flankers were masked with varying target-masking intervals. Crucially, on some trials no target at all was presented, thus preventing the representation of a correct response and the emergence of an Ne/ERN. However, because flankers were easily visible and responses to the flankers were always incorrect, detection of these flanker errors was still possible. In line with predictions of a multiple-systems account, we observed a robust Pe in the absence of an Ne/ERN for these errors. Moreover, this Pe relied on the same neural activity as that on trials with a visible target, as revealed by multivariate pattern analysis. These findings demonstrate that the mechanisms reflected by the two components use different types of information to detect errors, providing evidence for independent systems of human error monitoring.