Spatio-temporal evolution of interictal epileptic activity : a study with unaveraged multichannel MEG data in association with MRIs.

This thesis addresses issues relating to MEG modelling, analysis and interpretation of results. A source model employing current density distributions, namely Magnetic Field Tomography (MET), is used to obtain the MEG results. The first issue of concern refers to the registration of MEG data with structural MR images in an attempt to improve the localisation capability of MEG/MET. Simulations testing some spatial and tem poral aspects of the reconstruction capability of MET are also provided. A novel way of conducting MET studies in depth is suggested and implemented: the iterative use of a source space designed to cover deep situated structures on either side of the brain. The main bulk of this thesis is concerned with research into interictal epileptic activity as recorded by means of multichannel MEG system s and analysed using MET. The major aim is to investigate whether or not MET analysis of unaveraged MEG data (single epochs) is feasible in cases of pathophysiological signals and more specifically interictal signals from patients with epilepsy of a complex partial type. The investigation is undertaken against the "traditional" view of the impropriety and absurdity of using single epoch records in the MEG analysis due to noise dominance; we provide evidence that analysis of single, unaveraged epileptic spikes is actually feasible: we demonstrate spatio-temporal coherence in the MET results of the various single interictal events and show that activity extracted from the "averaged event" is made up of activity contributions which occur intermittently and at variable latencies. Our statements are drawn from the study of both superficial and deep activity

A review on brain computer interfaces: contemporary achievements and future goals towards movement restoration

Restoration of motor functions of patients with loss of mobility constitutes a yet unsolved medical problem, but also one of the most prominent research areas of neurosciences. Among suggested solutions, Brain Computer Interfaces have received much attention. BCI systems use electric, magnetic or metabolic brain signals to allow for control of external devices, such as wheelchairs, computers or neuroprosthetics, by disabled patients. Clinical applications includespinal cord injury, cerebrovascular accident rehabilitation, Amyotrophic Lateral Sclerosis patients. Various BCI systems are under re­search, facilitated by numerous measurement techniques including EEG, fMRI, MEG, nIRS and ECoG, each with its own advantages and disadvantages.Current research effort focuses on brain signal identification and extraction. Virtual Reality environments are also deployed for patient training. Wheelchair or robotic arm control has showed up as the first step towards actual mobility restoration. The next era of BCI research is envisaged to lie along the transmission of brain signals to systems that will control and restore movement of disabled patients via mechanical appendixes or directly to the muscle system by neurosurgical means

A proposed framework of an interactive semi-virtual environment for enhanced education of children with autism spectrum disorders

Education of people with special needs has recently been considered as a key element in the field of medical education. Recent development in the area of information and communication technologies may enable development of collaborative interactive environments which facilitate early stage education and provide specialists with robust tools indicating the person's autism spectrum disorder level. Towards the goal of establishing an enhanced learning environment for children with autism this paper attempts to provide a framework of a semi-controlled real-world environment used for the daily education of an autistic person according to the scenarios selected by the specialists. The proposed framework employs both real-world objects and virtual environments equipped with humanoids able to provide emotional feedback and to demonstrate empathy. Potential examples and usage scenarios for such environments are also described

Using affective avatars and rich multimedia content for education of children with autism

Autism is a communication disorder that mandates early and continuous educational interventions on various levels like the everyday social, communication and reasoning skills. Computer-aided education has recently been considered as a likely intervention method for such cases, and therefore different systems have been proposed and developed worldwide. In more recent years, affective computing applications for the aforementioned interventions have also been proposed to shed light on this problem. In this paper, we examine the technological and educational needs of affective interventions for autistic persons. Enabling affective technologies are visited and a number of possible exploitation scenarios are illustrated. Emphasis is placed in covering the continuous and long term needs of autistic persons by unobtrusive and ubiquitous technologies with the engagement of an affective speaking avatar. A personalised prototype system facilitating these scenarios is described. In addition the feedback from educators for autistic persons is provided for the system in terms of its usefulness, efficiency and the envisaged reaction of the autistic persons, collected by means of an anonymous questionnaire. Results illustrate the clear potential of this effort in facilitating a very promising autism intervention

Functional disorganization of small-world brain networks in mild Alzheimer's disease and amnestic Mild cognitive impairment:An EEG study using Relative Wavelet Entropy (RWE)

Previous neuroscientific findings have linked Alzheimer's disease (AD) with less efficient information processing and brain network disorganization. However, pathological alterations of the brain networks during the preclinical phase of amnestic Mild Cognitive Impairment (aMCI) remain largely unknown. The present study aimed at comparing patterns of the detection of functional disorganization in MCI relative to Mild Dementia (MD). Participants consisted of 23 cognitively healthy adults, 17 aMCI and 24 mild AD patients who underwent electroencephalographic (EEG) data acquisition during a resting-state condition. Synchronization analysis through the Orthogonal Discrete Wavelet Transform (ODWT), and directional brain network analysis were applied on the EEG data. This computational model was performed for networks that have the same number of edges (N=500, 600, 700, 800 edges) across all participants and groups (fixed density values). All groups exhibited a small-world (SW) brain architecture. However, we found a significant reduction in the SW brain architecture in both aMCI and MD patients relative to the group of Healthy controls. This functional disorganization was also correlated with the participant's generic cognitive status. The deterioration of the network's organization was caused mainly by deficient local information processing as quantified by the mean cluster coefficient value. Functional hubs were identified through the normalized betweenness centrality metric. Analysis of the local characteristics showed relative hub preservation even with statistically significant reduced strength. Compensatory phenomena were also evident through the formation of additional hubs on left frontal and parietal regions. Our results indicate a declined functional network organization even during the prodromal phase. Degeneration is evident even in the preclinical phase and coexists with transient network reorganization due to compensation

Math anxiety:brain cortical network changes in anticipation of doing mathematics

Following our previous work regarding the involvement of math anxiety (MA) in math-oriented tasks, this study tries to explore the differences in the cerebral networks' topology between self-reported low math-anxious (LMA) and high math-anxious (HMA) individuals, during the anticipation phase prior to a mathematical related experiment. For this reason, multichannel EEG recordings were adopted, while the solution of the inverse problem was applied in a generic head model, in order to obtain the cortical signals. The cortical networks have been computed for each band separately, using the magnitude square coherence metric. The main graph theoretical parameters, showed differences in segregation and integration in almost all EEG bands of the HMAs in comparison to LMAs, indicative of a great influence of the anticipatory anxiety prior to mathematical performance

Source Detection and Functional Connectivity of the Sensorimotor Cortex during Actual and Imaginary Limb Movement:A Preliminary Study on the Implementation of eConnectome in Motor Imagery Protocols

Introduction . Sensorimotor cortex is activated similarly during motor execution and motor imagery. The study of functional connectivity networks (FCNs) aims at successfully modeling the dynamics of information flow between cortical areas. Materials and Methods . Seven healthy subjects performed 4 motor tasks (real foot, imaginary foot, real hand, and imaginary hand movements), while electroencephalography was recorded over the sensorimotor cortex. Event-Related Desynchronization/Synchronization (ERD/ERS) of the mu-rhythm was used to evaluate MI performance. Source detection and FCNs were studied with eConnectome. Results and Discussion . Four subjects produced similar ERD/ERS patterns between motor execution and imagery during both hand and foot tasks, 2 subjects only during hand tasks, and 1 subject only during foot tasks. All subjects showed the expected brain activation in well-performed MI tasks, facilitating cortical source estimation. Preliminary functional connectivity analysis shows formation of networks on the sensorimotor cortex during motor imagery and execution. Conclusions . Cortex activation maps depict sensorimotor cortex activation, while similar functional connectivity networks are formed in the sensorimotor cortex both during actual and imaginary movements. eConnectome is demonstrated as an effective tool for the study of cortex activation and FCN. The implementation of FCN in motor imagery could induce promising advancements in Brain Computer Interfaces

Neuroplastic Effects of Combined Computerized Physical and Cognitive Training in Elderly Individuals at Risk for Dementia: An eLORETA Controlled Study on Resting States

The present study investigates whether a combined cognitive and physical training may induce changes in the cortical activity as measured via electroencephalogram (EEG) and whether this change may index a deceleration of pathological processes of brain aging. Seventy seniors meeting the clinical criteria of mild cognitive impairment (MCI) were equally divided into 5 groups: 3 experimental groups engaged in eight-week cognitive and/or physical training and 2 control groups: active and passive. A 5-minute long resting state EEG was measured before and after the intervention. Cortical EEG sources were modelled by exact low resolution brain electromagnetic tomography (eLORETA). Cognitive function was assessed before and after intervention using a battery of neuropsychological tests including the minimental state examination (MMSE). A significant training effect was identified only after the combined training scheme: a decrease in the post-compared to pre-training activity of precuneus/posterior cingulate cortex in delta, theta, and beta bands. This effect was correlated to improvements in cognitive capacity as evaluated by MMSE scores. Our results indicate that combined physical and cognitive training shows indices of a positive neuroplastic effect in MCI patients and that EEG may serve as a potential index of gains versus cognitive declines and neurodegeneration. This trial is registered with ClinicalTrials.gov Identifier NCT02313935

Mapping the Spatiotemporal Evolution of Emotional Processing: An MEG Study Across Arousal and Valence Dimensions

Electrophysiological and functional neuroimaging findings indicate that the neural mechanisms underlying the processing of emotional dimensions (i.e., valence, arousal) constitute a spatially and temporally distributed emotional network, modulated by the arousal and/or valence of the emotional stimuli. We examined the time course and source distribution of gamma time-locked magnetoencephalographic activity in response to a series of emotional stimuli viewed by healthy adults. We used a beamformer and a sliding window analysis to generate a succession of spatial maps of event-related brain responses across distinct levels of valence (pleasant/unpleasant) and arousal (high/low) in 30–100 Hz. Our results show parallel emotion-related responses along specific temporal windows involving mainly dissociable neural pathways for valence and arousal during emotional picture processing. Pleasant valence was localized in the left inferior frontal gyrus, while unpleasant valence in the right occipital gyrus, the precuneus, and the left caudate nucleus. High arousal was processed by the left orbitofrontal cortex, amygdala, and inferior frontal gyrus, as well as the right middle temporal gyrus, inferior parietal lobule, and occipital gyrus. Pleasant by high arousal interaction was localized in the left inferior and superior frontal gyrus, as well as the right caudate nucleus, putamen, and gyrus rectus. Unpleasant by high arousal interaction was processed by the right superior parietal gyrus. Valence was prioritized (onset at ∼60 ms) to all other effects, while pleasant valence was short lived in comparison to unpleasant valence (offsets at ∼110 and ∼320 ms, respectively). Both arousal and valence × arousal interactions emerged relatively early (onset at ∼150 ms, and ∼170 ms, respectively). Our findings support the notion that brain regions differentiate between valence and arousal, and demonstrate, for the first time, that these brain regions may also respond to distinct combinations of these two dimensions within specific time windows