Caracterización de señales electroencefalográficas utilizando la transformada wavelet discreta como herramienta para apoyar el diagnóstico del trastorno por déficit de atención e hiperactividad TDAH

Despite arising in childhood, attention deficit hyperactivity disorder (ADHD) can persist into adulthood, compromising the individual’s social skills. ADHD diagnosis is a real chal- lenge due to its dependence on the clinical observation of the patient, the information provided by parents and teachers, and the clinicians’ expertise..

Residual Deficits Observed In Athletes Following Concussion: Combined Eeg And Cognitive Study

The neurocognitive sequelae of a sport-related concussion and its management are poorly defined. Emerging evidence suggests that the residual deficits can persist one year or more following a brain injury. Detecting and quantifying the residual deficits are vital in making a decision about the treatment plan and may prevent further damage. For example, improper return to play (RTP) decisions in sports such as football have proven to be associated with the further chance of recurring injury, long-term neurophysiological impairments, and worsening of brain functional activity. The reliability of traditional cognitive assessment tools is debatable, and thus attention has turned to assessments based on electroencephalogram (EEG) to evaluate subtle post-concussive alterations. In this study, we calculated neurocognitive deficits in two different datasets. One dataset contains a combination of EEG analysis with three standard post-concussive assessment tools. The data for this dataset were collected for all testing modalities from 21 adolescent athletes (seven concussive and fourteen healthy) in three different trials. Another dataset contains post-concussion eyes closed EEG signal for twenty concussed and twenty age-matched controls. For EEG assessment, along with linear frequency-based features, we introduced a set of time-frequency and nonlinear features for the first time to explore post-concussive deficits. In conjunction with traditional frequency band analysis, we also presented a new individual frequency based approach for EEG assessment. A set of linear, time-frequency and nonlinear EEG markers were found to be significantly different in the concussed group compared to their matched peers in the healthy group. Although EEG analysis exhibited discrepancies, none of the cognitive assessment resulted in significant deficits. Therefore, the evidence from the study highlight that our proposed EEG analysis and markers are more efficient at deciphering post-concussion residual neurocognitive deficits and thus has a potential clinical utility of proper concussion assessment and management. Moreover, a number of studies have clearly demonstrated the feasibility of supervised and unsupervised pattern recognition algorithms to classify patients with various health-related issues. Inspired by these studies, we hypothesized that a set of robust features would accurately differentiate concussed athletes from control athletes. To verify it, features such as power spectral, statistical, wavelet, and other nonlinear features were extracted from the EEG signal and were used as an input to various classification algorithms to classify the concussed individuals. Various techniques were applied to classify control and concussed athletes and the performance of the classifiers was compared to ensure the best accuracy. Finally, an automated approach based on meaningful feature detection and efficient classification algorithm were presented to systematically identify concussed athletes from healthy controls with a reasonable accuracy. Thus, the study provides sufficient evidence that the proposed analysis is useful in evaluating the post-concussion deficits and may be incorporated into clinical assessments for a standard evaluation of athletes after a concussion

The functional neuroanatomy of auditory sensory gating and its behavioural implications

Auditory sensory gating (ASG) is the ability in individuals to suppress incoming irrelevant sensory input, indexed by evoked response to paired auditory stimuli. ASG is impaired in psychopathology such as schizophrenia, in which it has been proposed as putative endophenotype. This study aims to characterise electrophysiological properties of the phenomenon using MEG in time and frequency domains as well as to localise putative networks involved in the process at both sensor and source level. We also investigated the relationship between ASG measures and personality profiles in healthy participants in the light of its candidate endophenotype role in psychiatric disorders. Auditory evoked magnetic fields were recorded in twenty seven healthy participants by P50 ‘paired-click’ paradigm presented in pairs (conditioning stimulus S1- testing stimulus S2) at 80dB, separated by 250msec with inter trial interval of 7-10 seconds. Gating ratio in healthy adults ranged from 0.5 to 0.8 suggesting dimensional nature of P50 ASG. The brain regions active during this process were bilateral superior temporal gyrus (STG) and bilateral inferior frontal gyrus (IFG); activation was significantly stronger in IFG during S2 as compared to S1 (at p<0.05). Measures of effective connectivity between these regions using DCM modelling revealed the role of frontal cortex in modulating ASG as suggested by intracranial studies, indicating major role of inhibitory interneuron connections. Findings from this study identified a unique event-related oscillatory pattern for P50 ASG with alpha (STG)-beta (IFG) desynchronization and increase in cortical oscillatory gamma power (IFG) during S2 condition as compared to S1. These findings show that the main generator for P50 response is within temporal lobe and that inhibitory interneurons and gamma oscillations in the frontal cortex contributes substantially towards sensory gating. Our findings also show that ASG is a predictor of personality profiles (introvert vs extrovert dimension)

Hand (Motor) Movement Imagery Classification of EEG Using Takagi-Sugeno-Kang Fuzzy-Inference Neural Network

Approximately 20 million people in the United States suffer from irreversible nerve damage and would benefit from a neuroprosthetic device modulated by a Brain-Computer Interface (BCI). These devices restore independence by replacing peripheral nervous system functions such as peripheral control. Although there are currently devices under investigation, contemporary methods fail to offer adaptability and proper signal recognition for output devices. Human anatomical differences prevent the use of a fixed model system from providing consistent classification performance among various subjects. Furthermore, notoriously noisy signals such as Electroencephalography (EEG) require complex measures for signal detection. Therefore, there remains a tremendous need to explore and improve new algorithms. This report investigates a signal-processing model that is better suited for BCI applications because it incorporates machine learning and fuzzy logic. Whereas traditional machine learning techniques utilize precise functions to map the input into the feature space, fuzzy-neuro system apply imprecise membership functions to account for uncertainty and can be updated via supervised learning. Thus, this method is better equipped to tolerate uncertainty and improve performance over time. Moreover, a variation of this algorithm used in this study has a higher convergence speed. The proposed two-stage signal-processing model consists of feature extraction and feature translation, with an emphasis on the latter. The feature extraction phase includes Blind Source Separation (BSS) and the Discrete Wavelet Transform (DWT), and the feature translation stage includes the Takagi-Sugeno-Kang Fuzzy-Neural Network (TSKFNN). Performance of the proposed model corresponds to an average classification accuracy of 79.4 % for 40 subjects, which is higher than the standard literature values, 75%, making this a superior model

EEG-Biofeedback and epilepsy: concept, methodology and tools for (neuro)therapy planning and objective evaluation

EEG-Biofeedback and Epilepsy: Concept, Methodology and Tools for (Neuro)therapy Planning and Objective Evaluation ABSTRACT Objective diagnosis and therapy evaluation are still challenging tasks for many neurological disorders. This is highly related to the diversity of cases and the variety of treatment modalities available. Especially in the case of epilepsy, which is a complex disorder not well-explained at the biochemical and physiological levels, there is the need for investigations for novel features, which can be extracted and quantified from electrophysiological signals in clinical practice. Neurotherapy is a complementary treatment applied in various disorders of the central nervous system, including epilepsy. The method is subsumed under behavioral medicine and is considered an operant conditioning in psychological terms. Although the application areas of this promising unconventional approach are rapidly increasing, the method is strongly debated, since the neurophysiological underpinnings of the process are not yet well understood. Therefore, verification of the efficacy of the treatment is one of the core issues in this field of research. Considering the diversity in epilepsy and its various treatment modalities, a concept and a methodology were developed in this work for increasing objectivity in diagnosis and therapy evaluation. The approach can also fulfill the requirement of patient-specific neurotherapy planning. Neuroprofile is introduced as a tool for defining a structured set of quantifiable measures which can be extracted from electrophysiological signals. A set of novel quantitative features (i.e., percentage epileptic pattern occurrence, contingent negative variation level difference measure, direct current recovery index, heart rate recovery ratio, and hyperventilation heart rate index) were defined, and the methods were introduced for extracting them. A software concept and the corresponding tools (i.e., the neuroprofile extraction module and a database) were developed as a basis for automation to support the methodology. The features introduced were investigated through real data, which were acquired both in laboratory studies with voluntary control subjects and in clinical applications with epilepsy patients. The results indicate the usefulness of the introduced measures and possible benefits of integrating the indices obtained from electroencephalogram (EEG) and electrocardiogram for diagnosis and therapy evaluation. The applicability of the methodology was demonstrated on sample cases for therapy evaluation. Based on the insights gained through the work, synergetics was proposed as a theoretical framework for comprehending neurotherapy as a complex process of learning. Furthermore, direct current (DC)-level in EEG was hypothesized to be an order parameter of the brain complex open system. For future research in this field, investigation of the interactions between higher cognitive functions and the autonomous nervous system was proposed. Keywords: EEG-biofeedback, epilepsy, neurotherapy, slow cortical potentials, objective diagnosis, therapy evaluation, epileptic pattern quantification, fractal dimension, contingent negative variation, hyperventilation, DC-shifts, instantaneous heart rate, neuroprofile, database system, synergetics.Die Epilepsie ist eine komplexe neurologische Erkrankung, die auf biochemischer und physiologischer Ebene nicht ausreichend geklärt ist. Die Vielfalt der epileptischen Krankheitsbilder und der Behandlungsmodalitäten verursacht ein Defizit an quantitativen Kenngrößen auf elektrophysiologischer Basis, die die Objektivität und die Effizienz der Diagnose und der Therapieevaluierung signifikant erhöhen können. Die Neurotherapie (bzw. EEG-Biofeedback) ist eine komplementäre Behandlung, die bei Erkrankungen, welche in Zusammenhang mit Regulationsproblemen des Zentralnervensystems stehen, angewandt wird. Obwohl sich die Applikationen dieser unkonventionellen Methode erweitern, wird sie nach wie vor stark diskutiert, da deren neuro- und psychophysiologischen Mechanismen wenig erforscht sind. Aus diesem Grund ist die Ermittlung von Kenngrößen als elektrophysiologische Korrelaten der ablaufenden Prozesse zur objektiven Einstellung und Therapievalidierung eines der Kernprobleme des Forschungsgebietes und auch der vorliegenden Arbeit. Unter Berücksichtigung der aktuellen neurologischen Erkenntnisse und der durch Untersuchungen an Probanden, sowie an Epilepsie-Patienten gewonnenen Ergebnisse, wurden ein Konzept und eine Methodologie entwickelt, um die Objektivität in der Diagnose und Therapieevaluierung zu erhöhen. Die Methodologie basiert auf einem Neuroprofil, welches als ein signalanalytisches mehrdimensionales Modell eingeführt wurde. Es beschreibt einen strukturierten Satz quantifizierbarer Kenngrößen, die aus dem Elektroenzephalogramm (EEG), den ereignisbezogenen Potentialen und dem Elektrokardiogramm extrahiert werden können. Als Komponenten des Neuroprofils wurden neuartige quantitative Kenngrößen (percentage epileptic pattern occurrence, contingent negative variation level difference measure, direct current recovery index, heart rate recovery ratio, hyperventilation heart rate index) definiert und die Methoden zu deren Berechnung algorithmisiert. Die Anwendbarkeit der Methodologie wurde beispielhaft für die Evaluierung von Neurotherapien an Epilepsie-Patienten demonstriert. Als Basis für eine zukünftige Automatisierung wurden ein Softwarekonzept und entsprechende Tools (neuroprofile extraction module und die Datenbank ?NeuroBase?) entwickelt. Der Ansatz erfüllt auch die Anforderungen der patientenspezifischen Therapieplanung und kann auf andere Krankheitsbilder übertragen werden. Durch die neu gewonnenen Erkenntnisse wurde die Synergetik als ein theoretischer Rahmen für die Analyse der Neurotherapie als komplexer Lernprozess vorgeschlagen. Es wurde die Hypothese aufgestellt, dass das Gleichspannungsniveau im EEG ein Ordnungsparameter des Gehirn ist, wobei das Gehirn als ein komplexes offenes System betrachtet wird. Für zukünftige Forschungen auf dem Gebiet wird empfohlen, die Wechselwirkungen zwischen den höheren kognitiven Funktionen und dem autonomen Nervensystem in diesem Kontext zu untersuchen

Novel measure of olfactory bulb function in health and disease

Present neuroimaging techniques are capable of recording the neural activity from all over the brain but the olfactory bulb (OB). The OB is the first olfactory processing stage of the central nervous system and the site of insult in several neurological disorders, particularly Parkinson’s disease (PD). It has been suggested that the OB has a pivotal role in the olfactory system anal-ogous to primary visual cortex (V1) and thalamus in the visual system. However, due to the existing technical limitations, there has not been any non-invasive technique that can reliably measure the OB function in humans, consequently limiting its functional recording to one in-tracranial study dating back to the 60s. Initially in Study I, a non-invasive method of measuring the function of human OB is devel-oped, so-called electrobulbogram (EBG). In line with previous animal literature as well as the only intracranial study in human OB, it was demonstrated that gamma oscillations on the EBG electrodes occurred shortly after the odor onset. Subsequently, applying source recon-struction analysis provided evidence that observed oscillations were localized to the OB. Ad-ditionally, the OB recording with the EBG method showed a test-retest reliability comparable with visual event related potentials. Notably, the detected gamma oscillations were demon-strated to be insensitive to habituation, the OB’s marked characteristic which has previously been demonstrated in rodents. Last, but not least, assessing the EBG response in an individual who did not have the bilateral OB indicated that the lack of OB results in disappearance of gamma oscillations in the EBG electrodes. Given that Study I determined the possibility of reliably measuring the function of the OB using the EBG, in Study II, I assessed the functional role of OB’s oscillations in the pro-cessing of the odor valence. Odor valence has been suggested to be linked to approach–avoidance responses and therefore, processing of odor valence is thought to be one of the core aspects of odor processing in the olfactory system. Consequently, using combined EBG and EEG recording, OB activity was reconstructed on the source level during processing of odors with different valences. Gamma and beta oscillations were found to be related to va-lence perception in the human OB. Moreover, the early beta oscillations were associated with negative but not positive odors, where these beta oscillations can be linked to preparatory neural responses in the motor cortex. Subsequently, in a separate experiment, negative odors were demonstrated to trigger a whole-body motor avoidance response in the time window overlapping with the valence processes in the OB. These negative odor-elicited motor re-sponses were measured by a force plate as a leaning backward motion. Altogether, the results from Study II indicated that the human OB processes odor valence sequentially in the gamma and beta frequency bands, where the early processing of negative odors in the OB might be facilitating rapid approach-avoidance behaviors. To further evaluate the functional role of the OB in odor processing, in Study III, OB’s communication with its immediate recipient, namely piriform cortex (PC), was assessed. These two areas are critical nodes of the olfactory system which communicate with each other through neural oscillations. The activity of the OB and the PC were reconstructed using a combination of EBG, EEG, and source reconstruction techniques. Subsequently, the cross spectrogram of the OB and the PC was assessed as a measure of functional connectivity where temporal evolution from fast to slow oscillations in the OB–PC connectivity was found during the one second odor processing. Furthermore, the spectrally resolved Granger causal-ity analysis suggested that the afferent connection form the OB to the PC occurred in the gamma and beta bands whereas the efferent connection from the PC to the OB was concen-trated in the theta and delta bands. Notably, odor identity could be deciphered from the low gamma oscillatory pattern in the OB–PC connectivity as early as 100ms after the odor onset. Hence, findings from this study elucidate on our understanding of the bidirectional infor-mation flow in the human olfactory system. Olfactory dysfunction, due to neurodegeneration in the OB, commonly appears several years earlier than the occurrence of the PD-related characteristic motor symptoms. Consequently, a functional measure of the OB may serve as a potential early biomarker of PD. In Study IV, OB function was assessed in PD to answer whether the EBG method can be used to dissociate individuals with a PD diagnosis from healthy age-matched controls. The spectrogram of the EBG signals indicated that there were different values in gamma, beta, and theta for PDs compared with healthy controls. Specifically, six components were found in the EBG re-sponse during early and late time points which together dissociate PDs from controls with a 90% sensitivity and a 100% specificity. Furthermore, these components were linked to med-ication, disease duration and severity, as well as clinical odor identification performance. Overall, these findings support the notion that EBG has a diagnostic value and can be further developed to serve as an early biomarker for PD. In the last study, Study V, the prevalence of COVID-19 was determined using odor intensity ratings as an indication of olfactory dysfunction. Using a large sample data (n = 2440) from a Swedish population, odor intensity ratings of common household items over time were found to be closely associated with prevalence prediction of COVID-19 in the Stockholm region over the same time-period (r = -.83). Impairment in odor intensity rating was further correlated with the number of reported COVID-19 symptoms. Relatedly, individuals who progressed from having no symptoms to having at least one symptom had a marked decline in their odor intensity ratings. The results from this study, given the relatively large sample size, provided a concrete basis for the future studies to further assess the potential association between the deficits in the OB function and olfactory dysfunction in COVID-19. In conclusion, our proposed method for non-invasive measurement of the OB function was shown to provide a reliable recording with a potential as a diagnostic tool for PD. Combining EBG and EEG allowed for reconstruction of the OB signal at the source level, where specific oscillations were found to be critical for odor valence processing and rapid avoidance re-sponse. Moreover, oscillations in different frequency bands were found to be critical for the OB reciprocal communications and transfer of odor identity information to higher order ol-factory subsystems. Finally, COVID-19 was found to be associated with a decline in olfactory acuity which might originate from damage to the patient’s OB. In conclusion, the results from the studies within this thesis provide a new perspective on the functional role of oscillations in the human OB

Early development of sensory perception in Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder

Autism Spectrum Disorders (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) are co-occurring neurodevelopmental disorders emerging early in development. Molecular genetics research suggests that common sensory vulnerabilities underlie the emergence of both disorders, yet no research examined the same sensory markers as potential infant predictors of ASD or ADHD traits in toddlerhood. This thesis examines the early development of sensory perception in infants at elevated likelihood of ASD and/or ADHD and infants at typical likelihood of the disorders. Chapters 1-2 present, respectively, a theoretical introduction and methodological considerations for the investigation of sensory perception in these conditions. Chapter 3 presents evidence from an EEG tactile repetition suppression task administered to 10-month-old infants, prospectively re-assessed at 24 months. Results indicate that reduced repetition suppression is a marker of ASD in infancy and predicts ASD traits in toddlerhood. Results further suggest that early enhanced parent-reported tactile sensory seeking mitigates the association between tactile atypicality and later ASD traits. Chapter 4 presents evidence from an EEG visual task administered to 10-month-old infants, prospectively re-assessed at 24 months. Results indicate that enhanced responsiveness to visual input is a marker of ASD or ADHD in infancy and predicts concurrent parent-reported visual sensory seeking. Results further indicate that enhanced responsiveness to incoming stimulation in infants with later higher ASD traits results from reduced prioritization of ongoing information. Chapter 5 presents a proof-of-concept demonstration that variation in responsiveness to visual input also reflects variation in engagement with ongoing information in an independent cohort of 10-month-old infants at typical likelihood of the conditions. Chapter 6 adopts an individual differences approach and reports on the concurrent/longitudinal associations between markers of information prioritization emerged from Chapter 5 and parent-reported sensory seeking, ASD and ADHD traits in the same participant sample, prospectively re-assessed at 16 months. Chapter 7 discusses contributions and implications for research on the early development of sensory perception in ASD and ADHD

Cognitive deficits in schizophrenia – specific patterns, neural correlates and remediation through training

Research on cognition in people with schizophrenia has provoked a lot of interest for two reasons: It offers a better understanding of the neurobiological components of the disorder and it helps creating effective treatments for cognitive deficits, which limit the possible functional outcome after remission. The three studies presented here are all concerned with cognitive deficits in schizophrenia, but they focus on different levels, from electrophysiology to work ability in a clinical setting. The first two studies addressed the question of an underlying core deficit of the disorder, which might lead to the clinical features of the illness, in particular the commonly observed broad cognitive impairments. In both studies we hypothesized that increased intra-individual variability could be found in a high-functioning sample of patients with schizophrenia. The first study concentrated on response times whereas the second used an electrophysiological measure. The third study directly compared two cognitive trainings which work on different levels – one working with basic cognitive functions like memory and attention and one specifically training planning and problem solving as a part of higher cognitive functioning. The first study did not only find increased intra-individual variability in high-functioning patients with schizophrenia but could show an association between increased variability of response times and poorer work ability. The second study found that on an electrophysiological level increased temporal variability was found when analysing single trials of the N2 component, and that higher variability was linked with a more widespread activation during the N2 time-window. The third study comparing the two trainings did not find a clear advantage of one over the other. Both trainings lead to some improvements in cognitive functioning and work ability. There was an indication that planning ability improved more when trained directly instead of being trained via basic cognitive abilities. The first two studies emphasize the importance of intra-individual variability for schizophrenia and its occurrence on different levels. The association between response variability and work ability further highlights the importance of this measure. The third study indicates that a new training focussing specifically on planning and problem solving had an effect comparable to that of a more conventional training for patients with schizophrenia. Its results show how important it is to directly compare different kinds of training with each other and with a control group. In conjunction, the three studies provide the basis for further research into putative cognitive and neurophysiological core deficits of schizophrenia, which could provide a theoretical basis for the development of cognitive training programs

Validating the use of mobile EEG to investigate neural markers of real-world successful sporting performance in elite athletes

This thesis examines the cognitive and neural processes supporting expert performance in the context of elite sport. We review the existing sports EEG literature, highlighting that it has poor ecological validity. Until recently the findings characterizing sporting performance and expertise have largely arisen from laboratory-based experiments. However, recent technical developments mean that EEG data can now be collected in more ecologically valid field-based settings, during the performance of real sporting behaviour - particularly in target sports where movement is limited. In addition, our literature review led us to identify that most studies investigating sporting expertise performance have employed study designs that compare experts to novices. Although these findings provide insight into the neural mechanisms differentiating experts and novices, they do not necessarily provide information about the neural mechanisms underlying successful and unsuccessful performance within experts. Consequently, the aim of this thesis was to build on the existing literature, investigating the feasibility of recording neural activity in expert athletes in ecologically valid settings, and examining any differences in neural activity relating to successful and unsuccessful sporting performance across a range of sports. Throughout the thesis we assessed neural activity using mobile EEG, employing both group average and N=1 approaches. Time frequency analysis was used to explore the data, providing new understanding of the neuronal changes that occur during performance in expert athletes. Findings demonstrate the feasibility of examining neural activity as a function of performance in ecologically valid settings. The data reveal observable neural signatures that differ as a function of performance levels, that differ between athletes, and that differ across sports. Across the studies presented in this thesis the findings highlight the importance of adopting an individualised approach, and the need to tailor the analysis of EEG data for each athlete. Taken together, the findings provide real-world evidence regarding the neural mechanisms dissociating successful and unsuccessful performance in expert athletes across sports, suggesting that mobile EEG offers exciting new opportunities for understanding and supporting elite sporting performance