The current study examined electrical brain activation in adolescent participants playing three different video games. Forty-five school aged children (M=14.3 years, SD=1.5) were randomly assigned to play either a violent game, non-violent game, or a non-violent game specifically designed to "train" the brain. Electroencephalography (EEG) was recorded during video game play. Results revealed an asymmetric right hemisphere activation in the alpha band for participants in violent game group, while those in the non-violent groups exhibited left hemispheric activation. Greater right activation in emotion literature denotes signs of withdrawal or avoidance from undesired stimulus. Implications of this finding as well as other findings related to electrical brain activation during video game play is discussed further in the manuscript.

The aging brain is characterized by alteration in synaptic contacts, which leads to decline of motor and cognitive functions. These changes are reflected in the age related shifts in power spectrum of electroencephalogram (EEG) signals in both wakefulness and sleep. Various non-linear measures have been used to obtain more insights from EEG analysis compared to the conventional spectral analysis. In our study we used Sample Entropy to quantify regularity of the EEG signal. Because elderly subjects arouse from sleep more often than younger subjects, we hypothesized that Entropy of EEG signals from elderly subjects would be higher than that from middle aged subjects, within a sleep stage. We also hypothesized that the entropy increases during and following an arousal and does not return to background levels immediately after an arousal. Our results show that Sample Entropy varies systematically with sleep state in healthy middle-aged and elderly female subjects, reflecting the changing regularity in the EEG. Sample Entropy is significantly higher in elderly in sleep Stage 2 and REM, suggesting that in these two sleep stages the cortical state is closer to wake than in middle-aged women. Sample Entropy is higher in post-arousal compared to the pre-arousal and stays high for a 30 sec period.

Impaired motor function following neurological injury may be overcome through therapies that induce neuroplastic changes in the brain. Therapeutic methods include repetitive exercises that promote use-dependent plasticity (UDP), the benefit of which may be increased by first administering peripheral nerve stimulation (PNS) to activate afferent fibers, resulting in increased cortical excitability. We speculate that PNS delivered only in response to attempted movement would induce timing-dependent plasticity (TDP), a mechanism essential to normal motor learning. Here we develop a brain-machine interface (BMI) to detect movement intent and effort in healthy volunteers (n=5) from their electroencephalogram (EEG). This could be used in the future to promote TDP by triggering PNS in response to a patient’s level of effort in a motor task. Linear classifiers were used to predict state (rest, sham, right, left) based on EEG variables in a handgrip task and to determine between three levels of force applied. Mean classification accuracy with out-of-sample data was 54% (23-73%) for tasks and 44% (21-65%) for force. There was a slight but significant correlation (p<0.001) between sample entropy and force exerted. The results indicate the feasibility of applying PNS in response to motor intent detected from the brain.

Ce travail de thèse a pour principal objectif d'évaluer, au moyen de l'électro-encéphalographie de haute densité (EEG-HD), la plasticité de la représentation somato-sensorielle de la main chez l'Homme induite par neurostimulation non-invasise. Cette étude a pris sa source dans le constat que la stimulation cérébrale du cortex moteur représente une alternative thérapeutique efficace pour les patients qui souffrent de douleurs neuropathiques pharmaco-résistantes. Les mécanismes responsables de l'analgésie induite par la stimulation magnétique trans-crânienne répétitive (rTMS) sont encore mal connus, mais la séduisante hypothèse selon laquelle la stimulation du cortex moteur pouvait induire une plasticité dans le cortex somatosensoriel a été évoquée. Nos travaux de thèse s'attachent à déterminer, chez le sujet sain, les effets de la rTMS réalisée en regard de l'aire motrice de la main sur sa représentation somatotopique dans le cortex somatosensoriel. Dans le premier chapitre de ce document nous décrivons les réseaux anatomiques de transmission et perception somesthésique, avant de revoir dans le Chapitre 2 la littérature pertinente sur la représentation somatotopique du cortex sensoriel primaire (S1). De nombreuses études démontrent que cette représentation n'est nullement figée, mais peut au contraire évoluer suite à des lésions somatosensorielles sous-corticales ou corticales ; la littérature spécifique à ce phénomène de plasticité post-lésionnelle est revue dans le Chapitre 3, ainsi que les données suggérant le rôle de cette plasticité dans le développement de douleurs neuropathiques. Les patients souffrant de ce type de douleurs peuvent avoir recours à la stimulation du cortex moteur, notamment au moyen de techniques non invasives comme la rTMS. Les particularités techniques et les effets physiologiques de cette méthode sont tout d'abord présentés dans le Chapitre 4, avant d'exposer les effets analgésiques de la rTMS. Les travaux réalisés au cours de cette thèse sont exposés sous forme d'articles (Chapitres 5 & 6). Nous avons dans un premier temps cherché à déterminer une méthodologie robuste qui permet d'évaluer la représentation somatotopique de la main à l'aide des Potentiels Evoqués Somesthésiques (PES) obtenus par la stimulation de quatre sites distincts de la main (Auriculaire-Pouce-Nerf Cubital-Nerf Radial). La détermination de la représentation corticale de la main dans S1 était plus reproductible et robuste sur les réponses précoces de l'aire 3b (N20/P20) que sur celle des aires 1-2 (P45). Une estimation adéquate de l'étendue de la main basée sur le couple " pouce - nerf cubital " était possible avec 64 et 128 électrodes. Lorsque l'étendue de la main était considérée avec le couple de stimulation standard " auriculaire - pouce ", le plus haut niveau d'échantillonnage spatial était nécessaire. C'est pourquoi nous avons choisi le premier couple pour l'étude des possibles changements de la représentation corticale de la main suite à l'application de la rTMS, sous deux modalités distinctes (20 Hz et mode theta-burst intermittent -iTBS). Les deux modes de rTMS entraînent une certaine plasticité de la représentation somatotopique de la main dans S1, avec toutefois quelques nuances : les changements liés au mode " theta burst " étaient variables d'un sujet à l'autre et non significatifs sur la mesure d'étendue de la main. La rTMS à 20 Hz quant à elle, induisait des modifications très significatives et bien reproductibles. De plus, ce mode de stimulation était le seul à induire une augmentation du seuil nociceptif. Ces différences peuvent s'expliquer par des mécanismes d'action différents ou par des différences dans le nombre total de stimuli corticaux administrés. Bien que nos résultats suggèrent que la rTMS à haute fréquence est capable d'induire des modifications plastiques significatives et d'augmenter la représentation corticale de la région stimulée, il reste à déterminer si cette plasticité est à même d'être modifié par la rTMS à 20 Hz chez des patients souffrant de douleurs neuropathiques, et dans ce cas si elle est ou non associée aux effets analgésiques induits par ce type de technique non invasive.

This dissertation is a compendium of multiple research papers that, together, address two main objectives. The first objective and primary research question is to determine whether or not, through a procedure of independent component analysis (ICA)-based data mining, volume-domain validation, and source volume estimation, it is possible to construct a meaningful, objective, and informative model of brain activity from scalpacquired EEG data. Given that a methodology to construct such a model can be created, the secondary objective and research question investigated is whether or not the sources derived from the EEG data can be used to construct a model of complex brain function associated with the spatial navigation and the virtual Morris Water Task (vMWT). The assumptions of the signal and noise characteristics of scalp-acquired EEG data were discussed in the context of what is currently known about functional brain activity to identify appropriate characteristics by which to separate the activities comprising EEG data into parts. A new EEG analysis methodology was developed using both synthetic and real EEG data that encompasses novel algorithms for (1) data-mining of the EEG to obtain the activities of individual areas of the brain, (2) anatomical modeling of brain sources that provides information about the 3-dimensional volumes from which each of the activities separated from the EEG originates, and (3) validation of data mining results to determine if a source activity found via the data-mining step originates from a distinct modular unit inside the head or if it is an artefact. The methodology incorporating the algorithms developed was demonstrated for EEG data collected from study participants while they navigated a computer-based virtual maze environment. The brain activities of participants were meaningfully depicted via brain source volume estimation and representation of the activity relationships of multiple areas of the brain. A case study was used to demonstrate the analysis methodology as applied to the EEG of an individual person. In a second study, a group EEG dataset was investigated and activity relationships between areas of the brain for participants of the group study were individually depicted to show how brain activities of individuals can be compared to the group. The results presented in this dissertation support the conclusion that it is feasible to use ICA-based data mining to construct a physiological model of coordinated parts of the brain related to the vMWT from scalp-recorded EEG data. The methodology was successful in creating an objective and informative model of brain activity from EEG data. Furthermore, the evidence presented indicates that this methodology can be used to provide meaningful evaluation of the brain activities of individual persons and to make comparisons of individual persons against a group. In sum, the main contributions of this body of work are 5 fold. The technical contributions are: (1) a new data mining algorithm tailored for EEG, (2) an EEG component validation algorithm that identifies noise components via their poor representation in a head model, (3) a volume estimation algorithm that estimates the region in the brain from which each source waveform found via data mining originates, (4) a new procedure to study brain activities associated with spatial navigation. The main contribution of this work to the understanding of brain function is (5) evidence of specific functional systems within the brain that are used while persons participate in the vMWT paradigm (Livingstone and Skelton, 2007) examining spatial navigation. / Graduate / 0541 / 0622 / 0623

Age-related declines in hearing abilities are common and can be attributed to changes in the peripheral and central levels of the auditory system. Although central auditory processing is enhanced in younger musicians, the influence of lifelong musicianship on age-related decline in central auditory processing has not yet been investigated. Therefore, the purpose of this dissertation was to investigate whether lifelong musicianship can mitigate age-related decline in central auditory processing. In the first experiment, age-related declines on four hearing assessments were compared between musicians and non-musicians. Speech-in-noise and gap-detection thresholds were found to decline at a slower rate in musicians, providing an increasing advantage with age. Furthermore, musicians had a lifelong advantage in detecting a mistuned harmonic, although the rate of age-related decline was similar for both musicians and non-musicians. Importantly, there was no significant effect of musicianship on pure-tone thresholds, suggesting that lifelong musicianship can mitigate age-related decline in central but not peripheral auditory processing. To test this hypothesis, a second experiment compared auditory evoked responses (AERs) between groups of older and younger musicians and non-musicians. Results indicated that exogenous neural activity was enhanced in musicians, but that age-related changes were similar between musicians and nonmusicians. Furthermore, endogenous, attention-dependent neural activity was enhanced in older adults, suggesting a compensatory cognitive strategy. Importantly, endogenous activity was preferentially enhanced in older musicians, suggesting that lifelong musicianship enhanced cognitive processes related to auditory perception. In the final experiment, the ability to segregate simultaneous sounds was tested in older and younger musicians and non-musicians by using a mistuned harmonic paradigm, where AERs to harmonic complexes were compared to AERs when one of the harmonics was mistuned. Results indicated that musical training in older adults has little effect on early automatic registration of the mistuned harmonic. In contrast, late attention-dependent activity, associated with the perception of the mistuned harmonic as a separate sound, was influenced by musical training in older adults, suggesting that lifelong musicianship preserves or enhances cognitive components of concurrent sound segregation. In summary, musical training was found to reduce age-related decline in hearing abilities due to enhanced central processing of auditory information.

The goal of the study was to use electrical signals from primary motor cortex to generate accurate predictions of the movement onset time of performed movements, for potential use in asynchronous brain-machine interface (BMI) systems. Four subjects, two with electroencephalogram and two with electrocorticogram electrodes, performed various movements while activity from their primary motor cortices was recorded. An analysis program used several criteria (change point, fractal dimension, spectral entropy, sum of differences, bandpower, bandpower integral, phase, and variance), derived from the neural recordings, to generate predictions of movement onset time, which it compared to electromyogram activity onset time, determining prediction accuracy by receiver operating characteristic curve areas. All criteria, excepting phase and change-point analysis, generated accurate predictions in some cases.

Certain electrophysiological markers hold promise in distinguishing individuals with major depressive disorder (MDD) and in predicting antidepressant response, thereby assisting with assessment and optimizing treatment, respectively. This thesis examined resting brain activity via electroencephalographic (EEG) recordings, as well as EEG-derived event-related potentials (ERPs) to auditory stimuli and facial expression presentations in individuals with MDD and controls. Additionally, the utility of resting EEG as well as auditory ERPs (AEPs), and the associated loudness-dependence of AEPs (LDAEP) slope, were assessed in predicating outcome to chronic treatment with one of three antidepressant regimens [escitalopram (ESC); bupropion (BUP); ESC+BUP]. Relative to controls, depressed adults had lower pretreatment cortical activity in regions implicated in approach motives/positive processing. Increased anterior cingulate cortex (ACC)-localized theta was observed, possibly reflecting emotion/cognitive regulation disturbances in the disorder. AEPs and LDAEPs, putative indices of serotonin activity (implicated in MDD etiology), were largely unaltered in MDD. Assessment of ERPs to facial expression processing indicated slightly blunted late preconscious perceptual processing of expressions, and prolonged processing of intensely sad faces in MDD. Faces were rated as sadder overall in MDD, indicating a negative processing bias. Treatment responders (vs. non-responders) exhibited baseline cortical hypoactivity; after a week of treatment, cortical arousal emerged in responders. Increased baseline left fronto-cortical activity and early shifts towards this profile were noted in responders (vs. non-responders). Responders exhibited a steep, and non-responders shallow, baseline N1 LDAEP derived from primary auditory cortex activity. P2 LDAEP slopes (primary auditory cortex-derived) increased after a week of treatment in responders and decreased in non-responders. Consistent with overall findings, ESC responders displayed baseline cortical hypoactivity and steep LDAEP-sLORETA slopes (vs. non-responders). BUP responders also exhibited steep baseline slopes and high ACC theta. These results indicate that specific resting brain activity profiles appear to distinguish depressed from non-depressed individuals. Subtle ERP modulations to simple auditory and emotive processing also existed in MDD. Resting alpha power, ACC theta activity and LDAEP slopes predicted antidepressant response in general, but were limited in predicting outcome to a particular treatment, which may be associated with limited sample sizes.

No description available.

Electroencephalography (EEG) is an electrical signal recorded from a person's scalp, and is used to monitor the neurological state of the patient. This thesis proposes a quantified continuity feature to aid preterm neonatal EEG analysis. The continuity of EEG signals for preterm infants refers to the variation of the EEG amplitude, and is affected by the conceptional age of the infants. Currently, the continuity of the signal is determined largely by visual examination of the raw EEG signal, or by using general guidelines on amplitude-integrated EEG (aEEG), which is a compressed plot of the estimated signal envelope. The proposed parametric feature embodies the statistical distribution parameters of the signal amplitudes. The signal is first segmented into pseudo-stationary segments using Generalized Likelihood Ratio (GLR). These segments are used to construct a vector of amplitude, the distribution of which can be modelled using a log-normal distribution. The mean and standard deviation of the log-normal distribution are used as the continuity feature. This feature is less prone to the effects of local transient activities than the aEEG. This investigation has demonstrated that the degree of continuity corresponds to the major axis of the feature distribution in the feature space, and the minor axis roughly corresponds to the age of the infants in healthy files. Principal component analysis was performed on the feature, with the first coefficient used as a continuity index and the second coefficient as a maturation index. In this research, classifiers were developed to use the continuity feature to produce a qualitative continuity label. It was found that using a linear discriminant analysis based classifier, labelled data can be used as training data to produce labels consistent across all recordings. It was also found that unsupervised classifiers can assist in identifying the intrinsic clusters occurring in the recordings. It was concluded that the proposed continuity feature can be used to aid further research in neonatal EEG analysis. Further work should focus on using the continuity information to provide a context for further feature extraction and analysis.