Parallel modeling of the electric field distribution in the brain

The term "Brain Imaging" identi�es a set of techniques to analyze the structure and/or functional behavior of the brain in normal and/or pathological situations. These techniques are largely used in the study of brain activity. In addition to clinical usage, analysis of brain activity is gaining popularity in others recent �fields, i.e. Brain Computer Interfaces (BCI) and the study of cognitive processes. In this context, usage of classical solutions (e.g. f MRI, PET-CT) could be unfeasible, due to their low temporal resolution, high cost and limited portability. For these reasons alternative low cost techniques are object of research, typically based on simple recording hardware and on intensive data elaboration process. Typical examples are ElectroEncephaloGraphy (EEG) and Electrical Impedance Tomography (EIT), where electric potential at the patient's scalp is recorded by high impedance electrodes. In EEG potentials are directly generated from neuronal activity, while in EIT by the injection of small currents at the scalp. To retrieve meaningful insights on brain activity from measurements, EIT and EEG relies on detailed knowledge of the underlying electrical properties of the body. This is obtained from numerical models of the electric �field distribution therein. The inhomogeneous and anisotropic electric properties of human tissues make accurate modeling and simulation very challenging, leading to a tradeo�ff between physical accuracy and technical feasibility, which currently severely limits the capabilities of these techniques. Moreover elaboration of data recorded requires usage of regularization techniques computationally intensive, which influences the application with heavy temporal constraints (such as BCI). This work focuses on the parallel implementation of a work-flow for EEG and EIT data processing. The resulting software is accelerated using multi-core GPUs, in order to provide solution in reasonable times and address requirements of real-time BCI systems, without over-simplifying the complexity and accuracy of the head models.

Methods for the estimation of the cortical activity and connectivity during cognitive tasks in humans

The research activity characterizing the present thesis was mainly centered on the design, development and validation of methodologies for the estimation of stationary and time-varying connectivity between different regions of the human brain during specific complex cognitive tasks. Such activity involved two main aspects: i) the development of a stable, consistent and reproducible procedure for functional connectivity estimation with a high impact on neuroscience field and ii) its application to real data from healthy volunteers eliciting specific cognitive processes (attention and memory). In particular the methodological issues addressed in the present thesis consisted in finding out an approach to be applied in neuroscience field able to: i) include all the cerebral sources in connectivity estimation process; ii) to accurately describe the temporal evolution of connectivity networks; iii) to assess the significance of connectivity patterns; iv) to consistently describe relevant properties of brain networks. The advancement provided in this thesis allowed finding out quantifiable descriptors of cognitive processes during a high resolution EEG experiment involving subjects performing complex cognitive tasks.

Mental states monitoring through passive Brain-Computer Interface systems

The monitoring of cognitive functions aims at gaining information about the current cognitive state of the user by decoding brain signals. In recent years, this approach allowed to acquire valuable information about the cognitive aspects regarding the interaction of humans with external world. From this consideration, researchers started to consider passive application of brain–computer interface (BCI) in order to provide a novel input modality for technical systems solely based on brain activity. The objective of this thesis is to demonstrate how the passive Brain Computer Interfaces (BCIs) applications can be used to assess the mental states of the users, in order to improve the human machine interaction. Two main studies has been proposed. The first one allows to investigate whatever the Event Related Potentials (ERPs) morphological variations can be used to predict the users’ mental states (e.g. attentional resources, mental workload) during different reactive BCI tasks (e.g. P300-based BCIs), and if these information can predict the subjects’ performance in performing the tasks. In the second study, a passive BCI system able to online estimate the mental workload of the user by relying on the combination of the EEG and the ECG biosignals has been proposed. The latter study has been performed by simulating an operative scenario, in which the occurrence of errors or lack of performance could have significant consequences. The results showed that the proposed system is able to estimate online the mental workload of the subjects discriminating three different difficulty level of the tasks ensuring a high reliability.

Innovative Man Machine Interfaces In Aeronautics

The research activity focused on the study, design and evaluation of innovative human-machine interfaces based on virtual three-dimensional environments. It is based on the brain electrical activities recorded in real time through the electrical impulses emitted by the brain waves of the user. The achieved target is to identify and sort in real time the different brain states and adapt the interface and/or stimuli to the corresponding emotional state of the user. The setup of an experimental facility based on an innovative experimental methodology for “man in the loop" simulation was established. It allowed involving during pilot training in virtually simulated flights, both pilot and flight examiner, in order to compare the subjective evaluations of this latter to the objective measurements of the brain activity of the pilot. This was done recording all the relevant information versus a time-line. Different combinations of emotional intensities obtained, led to an evaluation of the current situational awareness of the user. These results have a great implication in the current training methodology of the pilots, and its use could be extended as a tool that can improve the evaluation of a pilot/crew performance in interacting with the aircraft when performing tasks and procedures, especially in critical situations. This research also resulted in the design of an interface that adapts the control of the machine to the situation awareness of the user. The new concept worked on, aimed at improving the efficiency between a user and the interface, and gaining capacity by reducing the user’s workload and hence improving the system overall safety. This innovative research combining emotions measured through electroencephalography resulted in a human-machine interface that would have three aeronautical related applications: • An evaluation tool during the pilot training; • An input for cockpit environment; • An adaptation tool of the cockpit automation.

Neurodegenerative diseases: molecular mechanisms and new strategies for neuroprotection

The term neurodegeneration defines numerous conditions that modify neuron’s normal functions in the human brain where is possible to observe a progressive and consistent neuronal loss. The mechanisms involved in neurodegenerative chronic and acute diseases evolution are not completely understood yet, however they share common characteristics such as misfolded proteins, oxidative stress, inflammation, excitotoxicity, and neuronal loss. Many studies have shown the frequency to develop neurodegenerative chronic diseases several years after an acute brain injury. In addition, many patients show, after a traumatic brain injury, motor and cognitive manifestations that are close to which are observed in neurodegenerative chronic patients. For this reason it is evident how is fundamental the concept of neuroprotection as a way to modulate the neurodegenerative processes evolution. Neuroinflammation, oxidative stress and the apoptotic process may be functional targets where operate to this end. Taking into account these considerations, the aim of the present study is to identify potential common pathogenetic pathways in neurodegenerative diseases using an integrated approach of preclinical studies. The goal is to delineate therapeutic strategies for the prevention of neuroinflammation, neurodegeneration and dysfunctions associated to Parkinson’s disease (PD) and cerebral ischemia. In the present study we used a murine model of PD treated with an isothiocyanate, 6-MSITC, able to quench ROS formation, restore the antioxidant GSH system, slow down the apoptotic neuronal death and counteract motor dysfunction induced by 6-OHDA. In the second study we utilized a transgenic mouse model knockout for CD36 receptor to investigate the inflammation involvement in a long term study of MCAo, which shows a better outcome after the damage induced. In conclusion, results in this study allow underlying the connection among these pathologies, and the importance of a neuroprotective strategy able to restore neurons activity where current drugs therapies have shown palliative but not healing abilities.

Effetti dell'integrazione visuo-acustica in pazienti con disturbo di campo visivo

Human brain is provided with a flexible audio-visual system, which interprets and guides responses to external events according to spatial alignment, temporal synchronization and effectiveness of unimodal signals. The aim of the present thesis was to explore the possibility that such a system might represent the neural correlate of sensory compensation after a damage to one sensory pathway. To this purpose, three experimental studies have been conducted, which addressed the immediate, short-term and long-term effects of audio-visual integration on patients with Visual Field Defect (VFD). Experiment 1 investigated whether the integration of stimuli from different modalities (cross-modal) and from the same modality (within-modal) have a different, immediate effect on localization behaviour. Patients had to localize modality-specific stimuli (visual or auditory), cross-modal stimulus pairs (visual-auditory) and within-modal stimulus pairs (visual-visual). Results showed that cross-modal stimuli evoked a greater improvement than within modal stimuli, consistent with a Bayesian explanation. Moreover, even when visual processing was impaired, cross-modal stimuli improved performance in an optimal fashion. These findings support the hypothesis that the improvement derived from multisensory integration is not attributable to simple target redundancy, and prove that optimal integration of cross-modal signals occurs in processing stage which are not consciously accessible. Experiment 2 examined the possibility to induce a short term improvement of localization performance without an explicit knowledge of visual stimulus. Patients with VFD and patients with neglect had to localize weak sounds before and after a brief exposure to a passive cross-modal stimulation, which comprised spatially disparate or spatially coincident audio-visual stimuli. After exposure to spatially disparate stimuli in the affected field, only patients with neglect exhibited a shifts of auditory localization toward the visual attractor (the so called Ventriloquism After-Effect). In contrast, after adaptation to spatially coincident stimuli, both neglect and hemianopic patients exhibited a significant improvement of auditory localization, proving the occurrence of After Effect for multisensory enhancement. These results suggest the presence of two distinct recalibration mechanisms, each mediated by a different neural route: a geniculo-striate circuit and a colliculus-extrastriate circuit respectively. Finally, Experiment 3 verified whether a systematic audio-visual stimulation could exert a long-lasting effect on patients’ oculomotor behaviour. Eye movements responses during a visual search task and a reading task were studied before and after visual (control) or audio-visual (experimental) training, in a group of twelve patients with VFD and twelve controls subjects. Results showed that prior to treatment, patients’ performance was significantly different from that of controls in relation to fixations and saccade parameters; after audiovisual training, all patients reported an improvement in ocular exploration characterized by fewer fixations and refixations, quicker and larger saccades, and reduced scanpath length. Similarly, reading parameters were significantly affected by the training, with respect to specific impairments observed in left and right hemisphere–damaged patients. The present findings provide evidence that a systematic audio-visual stimulation may encourage a more organized pattern of visual exploration with long lasting effects. In conclusion, results from these studies clearly demonstrate that the beneficial effects of audio-visual integration can be retained in absence of explicit processing of visual stimulus. Surprisingly, an improvement of spatial orienting can be obtained not only when a on-line response is required, but also after either a brief or a long adaptation to audio-visual stimulus pairs, so suggesting the maintenance of mechanisms subserving cross-modal perceptual learning after a damage to geniculo-striate pathway. The colliculus-extrastriate pathway, which is spared in patients with VFD, seems to play a pivotal role in this sensory compensation.

Development, degeneration and neural network of the bodily self

The question addressed by this dissertation is how the human brain builds a coherent representation of the body, and how this representation is used to recognize its own body. Recent approaches by neuroimaging and TMS revealed hints for a distinct brain representation of human body, as compared with other stimulus categories. Neuropsychological studies demonstrated that body-parts and self body-parts recognition are separate processes sub-served by two different, even if possibly overlapping, networks within the brain. Bodily self-recognition is one aspect of our ability to distinguish between self and others and the self/other distinction is a crucial aspect of social behaviour. This is the reason why I have conducted a series of experiment on subjects with everyday difficulties in social and emotional behaviour, such as patients with autism spectrum disorders (ASD) and patients with Parkinson’s disease (PD). More specifically, I studied the implicit self body/face recognition (Chapter 6) and the influence of emotional body postures on bodily self-processing in TD children as well as in ASD children (Chapter 7). I found that the bodily self-recognition is present in TD and in ASD children and that emotional body postures modulate self and others’ body processing. Subsequently, I compared implicit and explicit bodily self-recognition in a neuro-degenerative pathology, such as in PD patients, and I found a selective deficit in implicit but not in explicit self-recognition (Chapter 8). This finding suggests that implicit and explicit bodily self-recognition are separate processes subtended by different mechanisms that can be selectively impaired. If the bodily self is crucial for self/other distinction, the space around the body (personal space) represents the space of interaction and communication with others. When, I studied this space in autism, I found that personal space regulation is impaired in ASD children (Chapter 9).

Novel investigation methods in Computational Social Dynamics and Complex Systems

In this thesis the evolution of the techno-social systems analysis methods will be reported, through the explanation of the various research experience directly faced. The first case presented is a research based on data mining of a dataset of words association named Human Brain Cloud: validation will be faced and, also through a non-trivial modeling, a better understanding of language properties will be presented. Then, a real complex system experiment will be introduced: the WideNoise experiment in the context of the EveryAware european project. The project and the experiment course will be illustrated and data analysis will be displayed. Then the Experimental Tribe platform for social computation will be introduced . It has been conceived to help researchers in the implementation of web experiments, and aims also to catalyze the cumulative growth of experimental methodologies and the standardization of tools cited above. In the last part, three other research experience which already took place on the Experimental Tribe platform will be discussed in detail, from the design of the experiment to the analysis of the results and, eventually, to the modeling of the systems involved. The experiments are: CityRace, about the measurement of human traffic-facing strategies; laPENSOcosì, aiming to unveil the political opinion structure; AirProbe, implemented again in the EveryAware project framework, which consisted in monitoring air quality opinion shift of a community informed about local air pollution. At the end, the evolution of the technosocial systems investigation methods shall emerge together with the opportunities and the threats offered by this new scientific path.