Neurophysiological correlates of psychological attitudes of air traffic controllers during their work

The research proposed in this thesis is part of a European project called NINA (Neurometrics Indicators for Air Traffic Management) funded by Sesar Joint Undertaking, and it involves the participation of Sapienza University of Rome, École Nationale de l’Aviation Civile (ENAC), and Deep Blue srl (Human Factor and Safety Consultant Company). The main goal of the project is to elaborate neurophysiological measurements for real-time assessment and monitoring of the cognitive state in particular professional categories, such as Air Traffic Controllers (ATCOs). The evaluation is performed by using a combination of techniques such as Electroencephalography (EEG), Electrocardiography (EKG) and Electrooculography (EOG), during simulated and realistic working conditions. In the area of ATCOs, the Skill, Rule and Knowledge (S-R-K) taxonomy was developed by Rasmussen to describe the human performance under various circumstances and to integrate a variety of research results coming from human cognition studies (attention, memory, problem solving, decision-making, etc.) under a common framework. It provides a description of human cognition that is functional to the understanding and prediction of behaviour: it specifically deals with how people control their activity and behave in interaction with complex systems. Therefore, by considering the aspect of the cognitive processes in the framework of such taxonomy, it is possible to contextualise them in the work practices. Since to our knowledge there are no corresponding studies in the existing literature, another challenging objective of the project is to develop the SRK concept from a neurophysiological point of view. The focus of the proposed thesis is thus to verify the existence of identifiable neurophysiological features associated to the three levels of cognitive control of behaviour (Skill, Rule and Knowledge), in Air Traffic Management (ATM) context, by using a neurometric able to identify the behaviours of the original taxonomy from a different perspective. To map the neurophysiology of the SRK framework in ATM domain, and to use this methodology, could represent a promising step forward into the analysis of human behaviour, and furthermore, to develop new Human Factors tools able to discriminate the level of operators’ expertise during ecological tasks. In detail, the first part of this work illustrates a brief description of the brain and the Electroencephalographic technique, then an introduction of the NINA project and the literature related to the S-R-K levels of cognitive control are presented. The second section is focused on some additional brain features’ literature and on the experimental phase where several steps were performed as follows: a) the three categories of behaviours were associated with specific cognitive functions (e.g. attention, memory, decision making etc.) already investigated in literature with EEG measurements; b) a link between S-R-K behaviours and expected EEG frequency bands configurations were hypothesized; c) specific events were designed to trigger S, R and K behaviours and integrated into realistic ATM simulations; d) finally, the machine-learning algorithm automatic stop StepWise Linear Discriminant Analysis (asSWLDA) was trained to differentiate the three levels of cognitive control of behaviour by using brain features extracted from the EEG rhythms of different brain areas. Several professional ATCOs from the École Nationale de l’Aviation Civile (ENAC) of Toulouse (France) were involved in the study and the results showed that the classification algorithm was able to discriminate with high reliability the three levels of cognitive control of behaviour during simulated air-traffic scenarios in an ecological ATM environment

Análisis de conectividad funcional de la dinámica neuroenergética del TDAH = Functional Connectivity Analysis of Neuroenergetic Dynamics for ADHD

A fast and economic pilot study for measuring the neuroenergetic dynamics in an ADHD-diagnosed sample is performed. Based in a simplified connectome version, a graph theory application for neural connectivity, the performance and subjective states are linked through brain activity analysis during a behavioral attention test. ADHD is a neurobehavioral disorder related to a deficient filtering of stimuli, inefficacy performing in sustained activities and difficulties responding to unpredictable situations. There are two main strategies to evaluate this disorder: (1) behavioral tests and (2) neural biomarkers. Behavioral tests provide a criterion for classifying responses in a collection of tasks, looking for unstructured and inconsistent responses to given instructions or rules. Hyperactivity, inattention and impulsivity are some criteria analyzed. By the other hand, neural biomarkers are measurable indicators for particular states or diseases set up from EEG data. Since 2013, the theta/beta ratio was accepted as the ADHD biomarker, suggesting a misbalance of electrical brain activity. In this study, brain connectivity on sustained attention task performed by children between 7 to 13 years old from a public school. Ten participants were ADHD-diagnosed and five were selected for the control group to compare EEG signals collected with low-cost neuroheadset. Graphs show different connectivity dynamics in both groups for Theta (4-8 Hz), SMR (12-15 Hz) and Beta (15-20 Hz), indicating connectivity variations in brain regions according to the neuroenergetics theory. The connectivity in the ADHD group is reduced in lower frequencies first (Theta), then SMR and finally Beta. In contrast, the control graphs for Theta and SMR brainwaves are closer to the small-world networks and it can be noticed by comparing the measurements of the different graphs among themselves. The decay process corresponds to the bottom-up approach, where random stimuli trigger transitions from one state to the other, which is in this case the transition from attention to inattention. The declining of resources placed for disposal at the randomized SART stage might imply a limitation regulating the production of the required resources for the tasks fulfillment, as it has been reported in previous studies where other techniques are implemented

Machine Learning and Statistical Analysis of Complex Mathematical Models: An Application to Epilepsy

The electroencephalogram (EEG) is a commonly used tool for studying the emergent electrical rhythms of the brain. It has wide utility in psychology, as well as bringing a useful diagnostic aid for neurological conditions such as epilepsy. It is of growing importance to better understand the emergence of these electrical rhythms and, in the case of diagnosis of neurological conditions, to find mechanistic differences between healthy individuals and those with a disease. Mathematical models are an important tool that offer the potential to reveal these otherwise hidden mechanisms. In particular Neural Mass Models (NMMs), which describe the macroscopic activity of large populations of neurons, are increasingly used to uncover large-scale mechanisms of brain rhythms in both health and disease. The dynamics of these models is dependent upon the choice of parameters, and therefore it is crucial to be able to understand how dynamics change when parameters are varied. Despite they are considered low-dimensional in comparison to micro-scale neural network models, with regards to understanding the relationship between parameters and dynamics NMMs are still prohibitively high dimensional for classical approaches such as numerical continuation. We need alternative methods to characterise the dynamics of NMMs in high dimensional parameter spaces. The primary aim of this thesis is to develop a method to explore and analyse the high dimensional parameter space of these mathematical models. We develop an approach based on statistics and machine learning methods called decision tree mapping (DTM). This method is used to analyse the parameter space of a mathematical model by studying all the parameters simultaneously. With this approach, the parameter space can efficiently be mapped in high dimension. We have used measures linked with this method to determine which parameters play a key role in the output of the model. This approach recursively splits the parameter space into smaller subspaces with an increasing homogeneity of dynamics. The concepts of decision tree learning, random forest, measures of importance, statistical tests and visual tools are introduced to explore and analyse the parameter space. We introduce formally the theoretical background and the methods with examples. The DTM approach is used in three distinct studies to: • Identify the role of parameters on the dynamic model. For example, which parameters have a role in the emergence of seizure dynamics? • Constrain the parameter space, such that regions of the parameter space which give implausible dynamic are removed. • Compare the parameter sets to fit different groups. How does the thalamocortical connectivity of people with and without epilepsy differ? We demonstrate that classical studies have not taken into account the complexity of the parameter space. DTM can easily be extended to other fields using mathematical models. We advocate the use of this method in the future to constrain high dimensional parameter spaces in order to enable more efficient, person-specific model calibration

Cholinergic system in sequelae of traumatic brain injury

Background: Traumatic brain injury (TBI) is one of the most significant causes of disability and lowered capacity. TBI cause also a considerable financial burden since the majority of patients are young at the time of injury. Though much scientific work has been conducted, the pathophysiological mechanisms behind the sequelae of TBI are still largely unknown. However, there is evidence emerging from experimental and clinical studies that the cholinergic system seems to be at least partly involved in the cognitive impairment associated with TBI. In the TBI aftermath, patients commonly experience problems with attention, initiative and processing speed, i.e. functions which are mainly regulated by the cholinergic system. Additionally, in particular there are indications that the structures containing acetylcholinecontaining neurons are commonly injured in TBI. Furthermore, there is preliminary evidence that at least some TBI patients may benefit from cholinergic medication. Aims of the study: Our aim was to utilize positron emission tomography (PET) and magnetic resonance imaging (MRI) to evaluate possible alterations in the cholinergic system after TBI. An additional goal was to clarify the association of these structural or functional changes to the patient’s response to cholinergic medication. Patients with moderate-to-severe TBI were compared to healthy controls with PET using the [11C]MP4A tracer. MP4A targets acetylcholinesterase (AChE), which is the pre- and post-synaptic acetylcholine degrading enzyme. The TBI patient group was divided into two depending on their response to rivastigmine (inhibitor of AChE) treatment. These patient groups were imaged with MP4A-PET at baseline (without medication) and after 4 weeks of rivastigmine therapy to compare differences in AChE activity. Cholinergic structures were also investigated with atlas-based MRI morphometry. It was also examined whether the atrophy rates of frontal cholinergic structures were associated with neuropsychological tests results. The subjects filled in a questionnaire to determine whether their smoking histories had any connection to the outcome of TBI. Results: The AChE activity in TBI patients was clearly lowered in cortical regions when compared to controls. Most significantly, AChE activity was reduced in parieto- and occipital-cortices. A comparison of the two TBI patient groups in the primary time point scan showed evidence of lowered AChE activity in frontal cortical structures in rivastigmine responders. However, the inhibitory effect of rivastigmine on AChE activity was similar with patient groups when scanned during drug therapy and there was no longer any significant difference between groups in their AChE activities. MRI morphometry revealed that the higher the atrophy rate in frontal cortical structures, the poorer the performance in neuropsychological tests measuring attention. Smoking history was not associated with TBI outcome. Conclusions: According to the results of this study, it appears that the cholinergic system is altered chronically after TBI. It also seems that these structural alterations and the consequential functional changes in the cholinergic system are connected to the response to cholinergic medication. Additionally, the atrophy rate of frontal cortical structures, which are mainly innervated by cholinergic neurons, appears to have correlation to neuropsychological performance concerning attention. There did not seem to be any link between smoking and TBI outcome

Epilepsy

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well

Mental Disorders

This book brings together an international array of stars of the mental health professions to create a cutting edge volume that sheds light on many important and heretofore poorly understood issues in psychopathology. Mental Disorders-Theoretical and Empirical perspectives will be an important addition to the libraries of scholars and clinicians