Modeling of brain activity is often seen as requiring great computing power. However in the special case of modeling scalp EEG it is possible to adopt a continuum approximation for the cortex, and then to use the techniques of wave physics to describe its consequent large-scale dynamics. The model incorporates the following critical components: two classes of neurons (excitatory and inhibitory), the typical number and strength of connections between these two classes, the corresponding connections within the thalamus and between the thalamus and cortex, the time constants and basic physiology of neurons, and the propagation of activity between neurons. Representing the immense intricacy of brain anatomy and physiology with suitable summary equations and average parameter values has meant that the model is able to capture the essential characteristics of EEG and ERPs, and to do so in a computationally manageable way.

Modeling of brain activity is often seen as requiring great computing power. However in the special case of modeling scalp EEG it is possible to adopt a continuum approximation for the cortex, and then to use the techniques of wave physics to describe its consequent large-scale dynamics. The model incorporates the following critical components: two classes of neurons (excitatory and inhibitory), the typical number and strength of connections between these two classes, the corresponding connections within the thalamus and between the thalamus and cortex, the time constants and basic physiology of neurons, and the propagation of activity between neurons. Representing the immense intricacy of brain anatomy and physiology with suitable summary equations and average parameter values has meant that the model is able to capture the essential characteristics of EEG and ERPs, and to do so in a computationally manageable way.

Machine Learning for Analysis of Brain Signals / Event-Related Potential (ERP) measures derived from the electroencephalogram (EEG) have been widely used in outcome prediction of brain disorders. Recently, the ERPs that are transient (EEG) responses to auditory, visual, or tactile stimuli, have been introduced as useful predictors of a positive coma outcome (i.e. emergence from coma). In this study, machine learning techniques were applied for detecting the Mismatch Negativity (MMN) component, which is a transient EEG response to auditory stimuli, and its existence has a high correlation with coma awakening, through analyzing ERPs signals recorded from healthy control brain signals. To this end, two different dimensionality reduction methods, Localized Feature Selection (LFS) and minimum-redundancy maximum-relevance (mRMR) were employed, where a localized classifier and the support vector machine (SVM) with radial basis function (RBF) kernel are used as classifiers. We trained both LFS and mRMR algorithms using signals of healthy brains and evaluated their performance for MMN detection on both healthy subjects and coma patients. The evaluation on healthy subjects, using leave-one-subject-out cross-validation technique, shows the detection accuracy performance of 86.6% (using LFS) and 86.5% (using mRMR). In addition to analyzing brain signals for MMN detection, we also implemented a machine learning algorithm for discriminating healthy subjects from those who have experienced TBI. The EEG signals used in the TBI study were recorded using an ERP paradigm. However, we treated the recorded signals as resting state signals. To this end, we used the mRMR feature selection method and fed the selected features into the SVM classifier that outputs the estimated class labels. This method gives us a poor performance compared to the methods that directly used ERP components (without considering them as resting signals.). We conclude that our hypothesis of treating ERP data as resting data is not valid for TBI detection. / Thesis / Master of Applied Science (MASc)

Background: The present study was designed to examine prefrontal cortical processes in anxious children that mediate cognitive regulation in response to emotion-eliciting stimuli, and the changes that occur after anxious children participate in a cognitive behavioral therapy treatment program. Methods: Electroencephalographic activity was recorded from clinically anxious children and typically developing children at pre- and post-treatment sessions. Event-related potential components were recorded while children performed a go/no-go task using facial stimuli depicting angry, calm, and happy expressions. Results: At pre-treatment, anxious children had significantly greater posterior P1 and frontal N2 amplitudes than typically developing children, components associated with attention/arousal and cognitive control, respectively. For the anxious group only, there were no differences in neural activation between face (emotion) types or trial (Go vs. No-go) types. Anxious children who did not improve with treatment showed increased cortical activation within the time window of the P1 at pre-treatment relative to comparison and improver children. From pre- to post-treatment, only anxious children who improved with treatment showed increased cortical activation within the time window of the N2. Conclusions: At pre-treatment, anxious children appeared to show increased cortical activation regardless of the emotional content of the stimuli. Anxious children also showed greater medial-frontal activity regardless of task demands and response accuracy. These findings suggest indiscriminate cortical processes that may underlie the hypervigilant regulatory style seen in clinically anxious individuals. Neural activation patterns following treatment suggest that heightened perceptual vigilance, as represented by increased P1 amplitudes for non-improvers, may have prevented these anxious children from learning the treatment strategies, leading to poorer outcomes. Increased cognitive control, as represented by increased N2 amplitudes for improvers, may have enabled these anxious children to implement treatment strategies more effectively, leading to improved treatment outcomes. Hence, P1 activation may serve as a predictor of treatment outcome, while N2 activation may serve as an indicator of treatment-related outcome. These findings point to the cortical processes that maintain maladaptive functioning versus the cortical processes that underlie successful intervention in clinically anxious children.

Background: The present study was designed to examine prefrontal cortical processes in anxious children that mediate cognitive regulation in response to emotion-eliciting stimuli, and the changes that occur after anxious children participate in a cognitive behavioral therapy treatment program. Methods: Electroencephalographic activity was recorded from clinically anxious children and typically developing children at pre- and post-treatment sessions. Event-related potential components were recorded while children performed a go/no-go task using facial stimuli depicting angry, calm, and happy expressions. Results: At pre-treatment, anxious children had significantly greater posterior P1 and frontal N2 amplitudes than typically developing children, components associated with attention/arousal and cognitive control, respectively. For the anxious group only, there were no differences in neural activation between face (emotion) types or trial (Go vs. No-go) types. Anxious children who did not improve with treatment showed increased cortical activation within the time window of the P1 at pre-treatment relative to comparison and improver children. From pre- to post-treatment, only anxious children who improved with treatment showed increased cortical activation within the time window of the N2. Conclusions: At pre-treatment, anxious children appeared to show increased cortical activation regardless of the emotional content of the stimuli. Anxious children also showed greater medial-frontal activity regardless of task demands and response accuracy. These findings suggest indiscriminate cortical processes that may underlie the hypervigilant regulatory style seen in clinically anxious individuals. Neural activation patterns following treatment suggest that heightened perceptual vigilance, as represented by increased P1 amplitudes for non-improvers, may have prevented these anxious children from learning the treatment strategies, leading to poorer outcomes. Increased cognitive control, as represented by increased N2 amplitudes for improvers, may have enabled these anxious children to implement treatment strategies more effectively, leading to improved treatment outcomes. Hence, P1 activation may serve as a predictor of treatment outcome, while N2 activation may serve as an indicator of treatment-related outcome. These findings point to the cortical processes that maintain maladaptive functioning versus the cortical processes that underlie successful intervention in clinically anxious children.

This thesis uses a combination of cognitive-neurophysiological and genetically-sensitive longitudinal designs to study the associations of attention-deficit/hyperactivity disorder (ADHD) with bipolar disorder (BD) and preterm birth, as well as with the risk or protective factors IQ and physical activity. Previous research on preterm-born individuals and individuals with BD suggests ADHD-like symptoms and cognitive impairments, but direct comparisons are limited. Here, we first examine how cortical activity patterns differ between women with adult ADHD and women with BD during rest and task conditions to identify impairments that are specific to or shared between the disorders. The findings provide evidence for commonalities in brain dysfunction between ADHD and BD: frontal theta power may play a role as a marker of neurobiological processes in both disorders. Second, we investigate whether the ADHD-like symptoms and cognitive-neurophysiological impairments seen in preterm-born adolescents are identical to those in ADHD by directly comparing ADHD symptom scores and performance on a cognitive-neurophysiological test battery sensitive to impairments in ADHD across preterm-born adolescents, term-born adolescents with ADHD and term-born controls. We find that ADHD symptoms are increased in the preterm group compared to controls. The analyses further indicate similarities in brain function between ADHD and preterm birth, as well as unique impairments in the preterm group. Taken together, these results suggest that preterm birth may present a risk factor for both ADHD and additional impairments. Third, using twin data we carry out a developmental-genetic analysis of the association between ADHD and IQ, showing that ADHD symptoms and IQ scores significantly predict each other over time. Finally, we explore a putative protective factor for ADHD by investigating the effect of physical activity on ADHD symptoms. Using a population-based sample of twins, we show that physical activity is inversely associated with ADHD symptoms, even after adjusting for unmeasured confounding. Overall, we demonstrate certain commonalities in brain dysfunction between ADHD and BD. Whereas preterm birth and lower IQ present risk factors for ADHD, physical activity emerges as a potential protective factor.

<p>U radu je predložena metoda merenja moždanih ERP potencijala zasnovana na merenju harmonika epohe. Predložena metoda je zasnovana na pristupu takozvanog stohastičkog digitalnog merenja na intervalu (SDMI), a hardver sa kojim se ova metoda može implementirati je zasnovan na brzim A/D konvertorima i FPGA strukturi. Metoda je ispitana brojnim simulacijama i eksperimentima i pokazano je da SDMI manjeg broja epoha, sa zadovoljavajućom tačnošću, meri latenciju ERP-a, što je korisno kod vremenski kraćih merenja, kada tačno merenje amplitude ERP komponente nije od značaja.</p>

Attention uses sensory inputs and goals to select information from our environment. Monkey electrophysiological literature demonstrates that visuo-tactile bimodal neurons (respond to visual and tactile stimuli presented on/near the hand) facilitate multisensory integration. Human behavioral studies show that hand position/function bias visual attention. Event-related potentials (ERPs) reveal the cortical dynamics coordinating visual inputs, body position, and action goals. Early, sensory ERPs (N1) indicate multisensory integration. Later, cognitive ERPs (P3) reflect task-related processing. Study 1 investigates a discrepancy between monkey and human literatures. Monkey studies demonstrate bimodal neuron responses equidistantly around the whole hand, but human studies demonstrate attentional bias for grasping space. In a visual detection paradigm, participants positioned their hand so target and non-target stimuli appeared near the palm or back of the hand; ERPs were measured. N1 components indicated no amplitude differences between Palm vs. Back conditions, but P3 components revealed greater target vs. non-target differentiation for Palm conditions. Results suggest cortical timing underlies grasping vs. whole hand bias differences: early processing does not differentiate using hand function, but cognitive processing does when stimuli are discriminated for action. Study 2 investigates whether proprioceptive inputs facilitate visual processing. In a visual detection paradigm, participants viewed stimuli presented between occluders blocking view of a hand positioned either near or far from the stimuli. N1 amplitudes were similar for near and far conditions, but P3 amplitudes for target/non-target differences were accentuated for near conditions. Proprioceptive effects emerge later in processing. ERP reveals the cortical dynamics underlying hand position effects on vision.

Mindfulness-based meditation practices involve various attentional skills including the ability to sustain and focus ones attention. During a simple mindfulness based breath awareness meditation, sustained attention is required to maintain focus on the breath while meta-cognitive awareness and executive control are required to detect and correct mind wandering. The purpose of this thesis was to investigate whether a simple, mindfulness based breath awareness meditation, administered over a short period to meditation naïve individuals could modulate core attentional functions and associated task related neural activity. Two longitudinal randomised control studies were conducted. The aim of the first study was to establish if said modulations were possible in a sample of healthy adults, meeting a current research need for longitudinal evidence in this field and providing important information regarding a potential mechanism for the salutary effects widely observed from the use of mindfulness based interventions. It was found that short term engagement with a mindfulness based breath awareness meditation can modulate core attentional functions and task related neural activity, with specific modulations found in electrophysiological markers of sustained attention to the goal/task at hand and perceptual stimulus discrimination. In line with current theoretical models it is argued that modulations to such core attentional processes following short term training may provide a platform upon which mindfulness related salutary effects are built. The second study was designed to establish if such modulations were possible in older adults. It is argued that mindfulness training may have utility for increasing cognitive reserve, a potential mechanism by which age related declines in cognitive functions may be mitigated. It was found that both behavioural and electrophysiological markers of core attentional functions were modulated following 8 weeks mindfulness training but not following a matched active control group condition (simple brain training exercises). The reviewed extant evidence and findings of this study suggest that mindfulness meditation may enhance cognitive reserve through the repeated activation of attentional functions and associated neural activity during practice and are consistent with recent theoretical models of cognitive reserve. The potential for mindfulness training to positively modulate core attentional functions in older adults and to potentially impact cognitive ageing demands further investigation.

En associant les récentes techniques de neuro-imagerie (IRMf et Potentiels Evoqués) à la finesse des analyses syntaxiques des approches typologiques et formelles, cette recherche pluridisciplinaire se penche sur la question de la représentation des structures hiérarchiques qui caractérisent l’unité-phrase à travers les langues. La façon dont le cerveau humain représente, construit et l'esprit comprend les diverses structures de phrase, est en effet une des plus importantes questions qui restent encore largement irrésolues dans l’organisation cérébrale du langage. En nous appuyant sur la diversité des langues dans leur organisation syntaxique de l’unité-phrase, nous avons pu isoler différentes dimensions de cette complexité grâce aux propriétés syntaxiques du français dans la formation des questions, ainsi qu'aux spécificités des articulations Topique-Commentaire en chinois mandarin. Suite à une étude du marquage intonationel de la hiérarchie entre Topique et Commentaire, nous avons pu enregistrer les réponses cérébrales (PE) à ce type de constructions en contexte, et ainsi découvrir l’influence de sa signature prosodique sur son traitement en temps réel. Nos deux études d’IRMf apportent quand à elles un éclairage sur les bases neurales de deux dimensions de la complexité syntaxique de la phrase : sa structure hiérarchique et les transformations structurelles dont elle témoigne en cas de dislocation de ses éléments. La première étude, sur les interrogatives en français, met en lumière les corrélats cérébraux de différents types de movements syntaxiques, la seconde, sur les différents phénomènes topicaux du chinois, révèle les représentations et processus qui sont liés à l’activation par le Topique de l’interface entre l’unité-phrase et le niveau du discours. / Combining fine-grained linguistic analyses — from both typological and formal approaches to syntax — with neuro-imaging techniques (fMRI and ERP), this pluri-disciplinary research aims at investigating experimentally the issue of the hierarchical nature and complexity of the linguistic representation of sentence structure and its processing strategies across languages, specifically focusing on the case of Chinese Topic-Comment articulations and on French Interrogative constructions. The question of how the brain achieves sentence structure representation, building and understanding is often seen as one of the most important and unsolved issues of the neural organization of language. Leveraging on cross-linguistic invariance and variability in sentence hierarchical structure organization and building, we found in Chinese and French two exceptional testing grounds to isolate different syntactic complexity dimensions of the sentence-unit encoding. While the on-line auditory comprehension of sentence hierarchical structure in case of minimal intonational cues is investigated thanks to ERP recordings of Topic-Comment articulations in Chinese, two fMRI studies isolate two different syntactic complexity dimensions, respectively reflecting the sentence’s hierarchy and syntactic transformations. The first study, on French interrogative, seeks to isolate the neural correlates of different syntactic movement types. The second study, on Chinese sentence-discourse interface and Topics types, enables us to distinguish word-order surface complexity factors from syntactic movement transformations.