Delta power during sleep is modulated by EEG-gated auricular vagal afferent nerve stimulation (EAVANS).

Vagus nerve stimulation (VNS) has many clinical applications under development. In particular, there is a large interest in transcutaneous auricular VNS (taVNS) because it is non-invasive and provides easy access to neuromodulation. The present study proposes a novel approach for electroencephalography (EEG)-gated taVNS, with the ultimate goal of enhancing therapeutic outcomes, including for the treatment of delirium. Delirium arises from an altered state of consciousness and is the most common neuropsychiatric disorder observed in hospitalized patients, especially the elderly. Delirium has been linked to specific disturbances in EEG rhythms. Here, we propose an EEG-gated auricular vagal afferent nerve stimulation (EAVANS) approach to deliver stimulation targeting a specific instantaneous phase of the EEG Delta rhythm to modulate arousal and downstream reduction of neuroinflammation, two of the contributing factors to delirium. We hypothesize that treatment with EAVANS will modulate Delta power, which has been linked with delirium. As dominant Delta power is also a typical feature of non-rapid eye movement (NREM) sleep, we applied a prototype of an EAVANS device on healthy volunteers during sleep to establish preliminary validation. We successfully employed our closed-loop approach to target vagal afference during the rising Delta phase in the range [-π/2 0] radians. We found a significant reduction in Delta wave power for stimulation during the rising Delta phase compared to 1) absence of stimulation, 2) active stimulation during the descending Delta phase, and 3) active stimulation targeting non-vagal territory (i.e. greater auricular nerve) during the rising Delta phase. Further validation of our EEG-gated taVNS approach in the peri-operative period will be needed. As there is presently a lack of effective treatments for delirium, our non-pharmacological and non-invasive approach, if validated, could be easily deployed in clinical settings.Clinical Relevance- Given the serious health consequences and costs associated with delirium, and the absence of effective non-pharmacological treatments, the proposed neuromodulatory approach may be a promising option for reducing delirium and other disorders of consciousness. Our EAVANS prototype system has been tested on healthy volunteers during a NREM sleep state and will require further validation in different patient populations to optimize the proposed technology and gather more evidence to support its clinical utility. This novel non-pharmacological and non-invasive closed-loop neuromodulatory device could be used peri-operatively and in inpatient hospital settings to treat patients at risk of developing delirium. For instance, in a pre-operative setting, this technology may provide an effective preventative "pre-habilitation" approach for patients at high risk of developing delirium. Post-operatively, our technology may help manage patients with delirium more effectively.

Patient-Clinician Brain Response During Clinical Encounter and Pain Treatment.

The patient-clinician relationship is known to significantly affect the pain experience, as empathy, mutual trust and therapeutic alliance can significantly modulate pain perception and influence clinical therapy outcomes. The aim of the present study was to use an EEG hyperscanning setup to identify brain and behavioral mechanisms supporting the patient-clinician relationship while this clinical dyad is engaged in a therapeutic interaction. Our previous study applied fMRI hyperscanning to investigate whether brain concordance is linked with analgesia experienced by a patient while undergoing treatment by the clinician. In this current hyperscanning project we investigated similar outcomes for the patient-clinician dyad exploiting the high temporal resolution of EEG and the possibility to acquire the signals while patients and clinicians were present in the same room and engaged in a face-to-face interaction under an experimentally-controlled therapeutic context. Advanced source localization methods allowed for integration of spatial and spectral information in order to assess brain correlates of therapeutic alliance and pain perception in different clinical interaction contexts. Preliminary results showed that both behavioral and brain responses across the patient-clinician dyad were significantly affected by the interaction style.Clinical Relevance- The context of a clinical intervention can significantly impact the treatment of chronic pain. Effective therapeutic alliance, based on empathy, mutual trust, and warmth can improve treatment adherence and clinical outcomes. A deeper scientific understanding of the brain and behavioral mechanisms underlying an optimal patient-clinician interaction may lead to improved quality of clinical care and physician training, as well as better understanding of the social aspects of the biopsychosocial model mediating analgesia in chronic pain patients.

Brain connectivity networks at the basis of human attention components: An EEG study.

The Attention Network Task (ANT) was developed to disentangle the three components of attention identified in the Posner's theoretical model (alerting, orienting and executive control) and to measure the corresponding behavioral efficiency. Several fMRI studies have already provided evidences on the anatomical separability and interdependency of these three networks, and EEG studies have also unveiled the associated brain rhythms. What is still missing is a characterization of the brain circuits subtending the attentional components in terms of directed relationships between the brain areas and their frequency content. Here, we want to exploit the high temporal resolution of the EEG, improving its spatial resolution by means of advanced source localization methods, and to integrate the resulting information by a directed connectivity analysis. The results showed in the present study demonstrate the possibility to associate a specific directed brain circuit to each attention component and to identify synthetic indices able to selectively describe their neurophysiological, spatial and spectral properties.

Measuring the agreement between brain connectivity networks.

Investigating the level of similarity between two brain networks, resulting from measures of effective connectivity in the brain, can be of interest from many respects. In this study, we propose and test the idea to borrow measures of association used in machine learning to provide a measure of similarity between the structure of (un-weighted) brain connectivity networks. The measures here explored are the accuracy, Cohen's Kappa (K) and Area Under Curve (AUC). We implemented two simulation studies, reproducing two contexts of application that can be particularly interesting for practical applications, namely: i) in methodological studies, performed on surrogate data, aiming at comparing the estimated network with the corresponding ground-truth network; ii) in applications to real data, when it is necessary to compare the structure of a network obtained in a specific subject with a reference (e.g. a baseline condition or normative data). In the simulations, the level of similarity between two networks was manipulated through different factors. We then investigated the effect of such manipulations on the measures of association. Results showed how the three parameters modulated their values according to the level of similarity between the two networks. In particular, the AUC provided the better performances in terms of its capability to synthetize the similarity between two networks, showing high dynamic and sensitivity.

Effect of inter-trials variability on the estimation of cortical connectivity by Partial Directed Coherence.

Partial Directed Coherence (PDC) is a powerful estimator of effective connectivity. In neuroscience it is used in different applications with the aim to investigate the communication between brain regions during the execution of different motor or cognitive tasks. When multiple trials are available, PDC can be computed over multiple realizations, provided that the assumption of stationarity across trials is verified. This allows to improve the amount of data, which is an important constraint for the estimation accuracy. However, the stationarity of the data across trials is not always guaranteed, especially when dealing with patients. In this study we investigated how the inter-trials variability of an EEG dataset affects the PDC accuracy. Effects of density variations and of changes of connectivity values across trials were first investigated with a simulation study and then tested on real EEG data collected from two post-stroke patients during a motor imagery task and characterized by different inter-trials variability. Results showed the effect of different factors on the PDC accuracy and the robustness of such estimator in a range of conditions met in practical applications.

Time varying effective connectivity for describing brain network changes induced by a memory rehabilitation treatment.

In clinical practice, cognitive impairment is often observed after stroke. The efficacy of rehabilitative interventions is routinely assessed by means of a neuropsychological test battery. Nowadays, more evidences indicate that the neuroplasticity which occurs after stroke can be better understood by investigating changes in brain networks. In this study we applied advanced methodologies for effective connectivity estimation in combination with graph theory approach, to define EEG derived descriptors of brain networks underlying memory tasks. In particular, we proposed such descriptors to identify substrates of efficacy of a Brain-Computer Interface (BCI) controlled neurofeedback intervention to improve cognitive function after stroke. Electroencephalographic (EEG) data were collected from two stroke patients before and after a neurofeedback-based training for memory deficits. We show that the estimated brain connectivity indices were sensitive to different training intervention outcomes, thus suggesting an effective support to the neuropsychological assessment in the evaluation of the changes induced by the BCI-based cognitive rehabilitative intervention.

Investigating statistical differences in connectivity patterns properties at single subject level: a new resampling approach.

Methods based on the multivariate autoregressive (MVAR) approach are commonly used for effective connectivity estimation as they allow to include all available sources into a unique model. To ensure high levels of accuracy for high model dimensions, all the observations are used to provide a unique estimation of the model, and thus of the network and its properties. The unavailability of a distribution of connectivity values for a single experimental condition prevents to perform statistical comparisons between different conditions at a single subject level. This is a major limitation, especially when dealing with the heterogeneity of clinical conditions presented by patients. In the present paper we proposed a novel approach to the construction of a distribution of connectivity in a single subject case. The proposed approach is based on small perturbations of the networks properties and allows to assess significant changes in brain connectivity indexes derived from graph theory. Its feasibility and applicability were investigated by means of a simulation study and an application to real EEG data.