Frontal brain asymmetries as effective parameters to assess the quality of audiovisual stimuli perception in adult and young cochlear implant users

How is music perceived by cochlear implant (CI) users? This question arises as "the next step" given the impressive performance obtained by these patients in language perception. Furthermore, how can music perception be evaluated beyond self-report rating, in order to obtain measurable data? To address this question, estimation of the frontal electroencephalographic (EEG) alpha activity imbalance, acquired through a 19-channel EEG cap, appears to be a suitable instrument to measure the approach/withdrawal (AW index) reaction to external stimuli. Specifically, a greater value of AW indicates an increased propensity to stimulus approach, and vice versa a lower one a tendency to withdraw from the stimulus. Additionally, due to prelingually and postlingually deafened pathology acquisition, children and adults, respectively, would probably differ in music perception. The aim of the present study was to investigate children and adult CI users, in unilateral (UCI) and bilateral (BCI) implantation conditions, during three experimental situations of music exposure (normal, distorted and mute). Additionally, a study of functional connectivity patterns within cerebral networks was performed to investigate functioning patterns in different experimental populations. As a general result, congruency among patterns between BCI patients and control (CTRL) subjects was seen, characterised by lowest values for the distorted condition (vs. normal and mute conditions) in the AW index and in the connectivity analysis. Additionally, the normal and distorted conditions were significantly different in CI and CTRL adults, and in CTRL children, but not in CI children. These results suggest a higher capacity of discrimination and approach motivation towards normal music in CTRL and BCI subjects, but not for UCI patients. Therefore, for perception of music CTRL and BCI participants appear more similar than UCI subjects, as estimated by measurable and not self-reported parameters

Neuroelectrical Correlates of Trustworthiness and Dominance Judgments Related to the Observation of Political Candidates

The present research investigates the neurophysiological activity elicited by fast observations of faces of real candidates during simulated political elections. We used simultaneous recording of electroencephalographic (EEG) signals as well as galvanic skin response (GSR) and heart rate (HR) as measurements of central and autonomic nervous systems. Twenty healthy subjects were asked to give judgments on dominance, trustworthiness, and a preference of vote related to the politicians’ faces. We used high-resolution EEG techniques to map statistical differences of power spectral density (PSD) cortical activity onto a realistic head model as well as partial directed coherence (PDC) and graph theory metrics to estimate the functional connectivity networks and investigate the role of cortical regions of interest (ROIs). Behavioral results revealed that judgment of dominance trait is the most predictive of the outcome of the simulated elections. Statistical comparisons related to PSD and PDC values highlighted an asymmetry in the activation of frontal cortical areas associated with the valence of the judged trait as well as to the probability to cast the vote. Overall, our results highlight the existence of cortical EEG features which are correlated with the prediction of vote and with the judgment of trustworthy and dominant faces

Graph analysis of functional brain networks: practical issues in translational neuroscience

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires a know-how of all the methodological steps of the processing pipeline that manipulates the input brain signals and extract the functional network properties. On the other hand, a knowledge of the neural phenomenon under study is required to perform physiological-relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes

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

The present research investigates the neurophysiological activity elicited by fast observations of faces of real candidates during simulated political elections. We used simultaneous recording of electroencephalographic (EEG) signals as well as galvanic skin response (GSR) and heart rate (HR) as measurements of central and autonomic nervous systems. Twenty healthy subjects were asked to give judgments on dominance, trustworthiness, and a preference of vote related to the politicians' faces. We used high-resolution EEG techniques to map statistical differences of power spectral density (PSD) cortical activity onto a realistic head model as well as partial directed coherence (PDC) and graph theory metrics to estimate the functional connectivity networks and investigate the role of cortical regions of interest (ROIs). Behavioral results revealed that judgment of dominance trait is the most predictive of the outcome of the simulated elections. Statistical comparisons related to

Default Mode Network alterations in alexithymia: An EEG power spectra and connectivity study

Recent neuroimaging studies have shown that alexithymia is characterized by functional alterations in different brain areas [e.g., posterior cingulate cortex (PCC)], during emotional/social tasks. However, only few data are available about alexithymic cortical networking features during resting state (RS). We have investigated the modifications of electroencephalographic (EEG) power spectra and EEG functional connectivity in the default mode network (DMN) in subjects with alexithymia. Eighteen subjects with alexithymia and eighteen subjects without alexithymia matched for age and gender were enrolled. EEG was recorded during 5 min of RS. EEG analyses were conducted by means of the exact Low Resolution Electric Tomography software (eLORETA). Compared to controls, alexithymic subjects showed a decrease of alpha power in the right PCC. In the connectivity analysis, compared to controls, alexithymic subjects showed a decrease of alpha connectivity between: (i) right anterior cingulate cortex and right PCC, (ii) right frontal lobe and right PCC, and (iii) right parietal lobe and right temporal lobe. Finally, mediation models showed that the association between alexithymia and EEG connectivity values was directed and was not mediated by psychopathology severity. Taken together, our results could reflect the neurophysiological substrate of some core features of alexithymia, such as the impairment in emotional awareness

Statistical causality in the EEG for the study of cognitive functions in healthy and pathological brains

Understanding brain functions requires not only information about the spatial localization of neural activity, but also about the dynamic functional links between the involved groups of neurons, which do not work in an isolated way, but rather interact together through ingoing and outgoing connections. The work carried on during the three years of PhD course returns a methodological framework for the estimation of the causal brain connectivity and its validation on simulated and real datasets (EEG and pseudo-EEG) at scalp and source level. Important open issues like the selection of the best algorithms for the source reconstruction and for time-varying estimates were addressed. Moreover, after the application of such approaches on real datasets recorded from healthy subjects and post-stroke patients, we extracted neurophysiological indices describing in a stable and reliable way the properties of the brain circuits underlying different cognitive states in humans (attention, memory). More in detail: I defined and implemented a toolbox (SEED-G toolbox) able to provide a useful validation instrument addressed to researchers who conduct their activity in the field of brain connectivity estimation. It may have strong implication, especially in methodological advancements. It allows to test the ability of different estimators in increasingly less ideal conditions: low number of available samples and trials, high inter-trial variability (very realistic situations when patients are involved in protocols) or, again, time varying connectivity patterns to be estimate (where stationary hypothesis in wide sense failed). A first simulation study demonstrated the robustness and the accuracy of the PDC with respect to the inter-trials variability under a large range of conditions usually encountered in practice. The simulations carried on the time-varying algorithms allowed to highlight the performance of the existing methodologies in different conditions of signals amount and number of available trials. Moreover, the adaptation of the Kalman based algorithm (GLKF) I implemented, with the introduction of the preliminary estimation of the initial conditions for the algorithm, lead to significantly better performance. Another simulation study allowed to identify a tool combining source localization approaches and brain connectivity estimation able to provide accurate and reliable estimates as less as possible affected to the presence of spurious links due to the head volume conduction. The developed and tested methodologies were successfully applied on three real datasets. The first one was recorded from a group of healthy subjects performing an attention task that allowed to describe the brain circuit at scalp and source level related with three important attention functions: alerting, orienting and executive control. The second EEG dataset come from a group of healthy subjects performing a memory task. Also in this case, the approaches under investigation allowed to identify synthetic connectivity-based descriptors able to characterize the three main memory phases (encoding, storage and retrieval). For the last analysis I recorded EEG data from a group of stroke patients performing the same memory task before and after one month of cognitive rehabilitation. The promising results of this preliminary study showed the possibility to follow the changes observed at behavioural level by means of the introduced neurophysiological indices

Measuring Directed Functional Connectivity Using Non-Parametric Directionality Analysis : Validation and Comparison with Non-Parametric Granger Causality

BACKGROUND: 'Non-parametric directionality' (NPD) is a novel method for estimation of directed functional connectivity (dFC) in neural data. The method has previously been verified in its ability to recover causal interactions in simulated spiking networks in Halliday et al. (2015). METHODS: This work presents a validation of NPD in continuous neural recordings (e.g. local field potentials). Specifically, we use autoregressive models to simulate time delayed correlations between neural signals. We then test for the accurate recovery of networks in the face of several confounds typically encountered in empirical data. We examine the effects of NPD under varying: a) signal-to-noise ratios, b) asymmetries in signal strength, c) instantaneous mixing, d) common drive, e) data length, and f) parallel/convergent signal routing. We also apply NPD to data from a patient who underwent simultaneous magnetoencephalography and deep brain recording. RESULTS: We demonstrate that NPD can accurately recover directed functional connectivity from simulations with known patterns of connectivity. The performance of the NPD measure is compared with non-parametric estimators of Granger causality (NPG), a well-established methodology for model-free estimation of dFC. A series of simulations investigating synthetically imposed confounds demonstrate that NPD provides estimates of connectivity that are equivalent to NPG, albeit with an increased sensitivity to data length. However, we provide evidence that: i) NPD is less sensitive than NPG to degradation by noise; ii) NPD is more robust to the generation of false positive identification of connectivity resulting from SNR asymmetries; iii) NPD is more robust to corruption via moderate amounts of instantaneous signal mixing. CONCLUSIONS: The results in this paper highlight that to be practically applied to neural data, connectivity metrics should not only be accurate in their recovery of causal networks but also resistant to the confounding effects often encountered in experimental recordings of multimodal data. Taken together, these findings position NPD at the state-of-the-art with respect to the estimation of directed functional connectivity in neuroimaging

Definition of Neurophysiological Indices to Describe and Quantify the Cortical Plasticity Induced by Neuro-Rehabilitation

The general objective of the PhD project was to develop a methodology for the definition and analysis of neurophysiological indices able to provide a stable and reliable measure of changes induced by a rehabilitative intervention in the brain activity and organization, with the aim to: i) provide a neurophysiological description of the modifications subtending a functional recovery; ii) allow the evaluation of the effects of rehabilitation treatments in terms of brain reorganization; iii) describe specific properties in the brain general organization to be correlated with the outcome of the intervention, with possible prognostic/decision support value. For this purpose, the research activity was focused on the development of an approach for the extraction of neurophysiological indices from non-invasive estimation of the cerebral activity and connectivity based on electroencephalographic recordings. Brain activity and its changes in time were investigated at three different interconnected levels: spectral analysis, connectivity estimation and graph theory. For each of these, the state of the art methods were evaluated and methodological advancements were proposed on the basis of open problems presented by the nature of the data and by the clinical problem. Experimental data were acquired from 56 stroke patients subjected to a rehabilitative intervention based on Motor Imagery (MI). A subgroup of randomly selected patients were trained in the MI task with the support of Brain Computer Interface. New spectral and functional indices were defined and computed from EEG recorded during the execution of specific tasks (e.g. motor execution), but also from resting state brain activity, to capture both specific and general brain functional modifications. The application of the developed methods allowed to return a proof of the nature, quality and properties of the brain description and quantitative indices that can be derived from data easily recordable from a wide range of patients

Spatial Characterization and Estimation of Intracardiac Propagation Patterns During Atrial Fibrillation

This doctoral thesis is in the field of biomedical signal processing with focus on methods for the analysis of atrial fibrillation (AF). Paper I of the present thesis addresses the challenge of extracting spatial properties of AF from body surface signals. Different parameters are extracted to estimate the preferred direction of atrial activation and the complexity of the atrial activation pattern. In addition, the relation of the spatial properties to AF organization, which is quantified by AF frequency, is evaluated. While no significant correlation between the preferred direction of atrial activation and AF frequency could be observed, the complexity of the atrial activation pattern was found to increase with AF frequency. The remaining three papers deal with the analysis of the propagation of the electrical activity in the atria during AF based on intracardiac signals. In Paper II, a time-domain method to quantify propagation patterns along a linear catheter based on the detected atrial activation times is developed. Taking aspects on intra-atrial signal organization into account, the detected activation times are combined into wavefronts, and parameters related to the consistency of the wavefronts over time and the activation order along the catheter are extracted. Furthermore, the potential relationship of the extracted parameters to established measures from body surface signals is investigated. While the degree of wavefront consistency was not reflected by the applied body surface measures, AF frequency could distinguish between recordings with different degrees of intra-atrial signal organization. This supports the role of AF frequency as an organization measure of AF. In Paper III, a novel method to analyze intracardiac propagation patterns based on causality analysis in the frequency domain is introduced. In particular, the approach is based on the partial directed coherence (PDC), which evaluates directional coupling between multiple signals in the frequency domain. The potential of the method is illustrated with simulation scenarios based on a detailed ionic model of the human atrial cell as well as with real data recordings, selected to present typical propagation mechanisms and recording situations in atrial tachyarrhythmias. For simulated data, the PDC is correctly reflecting the direction of coupling and thus the propagation between all recording sites. For real data, clear propagation patterns are identified which agree with previous clinical observations. Thus, the results illustrate the ability of the novel approach to identify propagation patterns from intracardiac signals during AF which can provide important information about the underlying AF mechanisms, potentially improving the planning and outcome of ablation. However, spurious couplings over long distances can be observed when analyzing real data comprised by a large number of simultaneously recorded signals, which gives room for further improvement of the method. The derivation of the PDC is entirely based on the fit of a multivariate autoregressive (MVAR) model, commonly estimated by the least-squares (LS) method. In Paper IV, the adaptive group least absolute selection and shrinkage operator (LASSO) is introduced in order to avoid overfitting of the MVAR model and to incorporate prior information such as sparsity of the solution. The sparsity can be motivated by the observation that direct couplings over longer distances are likely to be zero during AF; an information which has been further incorporated by proposing distance-adaptive group LASSO. In simulations, adaptive and distance-adaptive group LASSO are found to be superior to LS estimation in terms of both detection and estimation accuracy. In addition, the results of both simulations and real data analysis indicate that further improvements can be achieved when the distance between the recording sites is known or can be estimated. This further promotes the PDC as a method for analysis of AF propagation patterns, which may contribute to a better understanding of AF mechanisms as well as improved AF treatment