Variability in neural activity has historically been treated as noise, in favour of deriving estimates based on central tendency (e.g., mean). Recently, researchers have shown that variability and mean confer different sources of information and that increased variability in neural activity is associated with superior behavioural performance and that it decreases during late-life. Although mounting evidence suggests that neural variability is beneficial, it is less clear whether these findings are driven by within- or between-person factors and whether they are apparent during higher-order cognitive tasks. Further, variability can be derived in several different ways, drawing into question its congruence across operationalizations. The present investigation sought to separate within- and between-person sources of variance in order to ascertain what was driving any observable effects in three operationalizations of cerebral oxygenation, computed based on central tendency (mean), variability (standard deviation) and signal complexity (multivariate multiscale entropy). 25 older adults (71-81 years of age) completed two tasks of executive functions while undergoing assessment using functional near infrared spectroscopy. Time-varying covariation models were employed to estimate the effects of cerebral oxygenation on behavioural performance, as well as the moderating effects of age and fall status. Findings suggest that mean and variability are differentially associated with behavioural performance and are increased in older adults at greater fall risk. Whereas mean based computations were positively associated with more accurate and faster responding, variability based computations were primarily associated with faster responding only and occurred in non-overlapping regions of prefrontal cortex. Future studies of neural variability may consider examining within- and between-person factors and operationalizing signal complexity in cerebral oxygenation over longer time periods to examine its effects over multiple time scales. / Graduate / drewh@uvic.ca

Explorations into the organisation of spontaneous activity within the brain have demonstrated the existence of networks of temporally correlated activity, consisting of brain areas that share similar cognitive or sensory functions. These so-called resting state networks (RSNs) emerge spontaneously during rest and disappear in response to overt stimuli or cognitive demands. In recent years, the study of RSNs has emerged as a valuable tool for probing brain function, both in the healthy brain and in disorders such as schizophrenia, Alzheimer’s disease and Parkinson’s disease. However, analyses of these networks have so far been limited, in part due to assumptions that the patterns of neuronal activity that underlie these networks remain constant over time. Moreover, the majority of RSN studies have used functional magnetic resonance imaging (fMRI), in which slow fluctuations in the level of oxygen in the blood are used as a proxy for the activity within a given brain region. In this thesis we develop the use of magnetoencephalography (MEG) to study resting state functional connectivity. Unlike fMRI, MEG provides a direct measure of neuronal activity and can provide novel insights into the temporal dynamics that underlie resting state activity. In particular, we focus on the application of non- stationary analysis methods, which are able to capture fast temporal changes in activity. We first develop a framework for preprocessing MEG data and measuring interactions within different RSNs (Chapter 3). We then extend this framework to assess temporal variability in resting state functional connectivity by applying time- varying measures of interactions and show that within-network functional connectivity is underpinned by non-stationary temporal dynamics (Chapter 4). Finally we develop a data driven approach based on a hidden Markov model for inferring short lived connectivity states from resting state and task data (Chapter 5). By applying this approach to data from multiple subjects we reveal transient states that capture short lived patterns of neuronal activity (Chapter 6).

Clinical and behavioural evidence has shown that the threat value of pain biases decisions about whether a stimulus is perceived as painful or not, and if yes, how intense is the sensation. This thesis aims to investigate the neural mechanisms underlying the effect of perceived threat on perceptual decisions about pain. The first study investigates the neural mechanisms underlying the effect of threat on the decision about the quality of the sensation, i.e., whether it is perceived as painful or not. The perception of pain (relative to no pain) was associated with activation in the anterior insula as well as an increased connectivity between this region and the mid-cingulate cortex (MCC). Activity in the MCC was correlated with the threat-related bias to perceived pain. In the second study, probabilistic tractography was performed with diffusion tensor imaging to investigate the structural connectivity between subdivisions of the insula and other pain-related regions. Additional analyses revealed that the structural connectivity between the anterior insula and the MCC, and between the posterior insula and somatosensory cortices, is positively correlated with the threat-related bias toward pain. In the third study, a multivariate pattern analysis (MVPA) was performed to investigate whether pain can be decoded from functional neuroimaging data acquired during the anticipation and during the receipt of pain. The results show that pain can be predicted by the pattern of neural activity in the right anterior insula during anticipation and stimulation. The fourth study investigated the effect of uncertainty about the stimulation intensity as a form of threat on the perceived intensity of pain. Uncertainty was found to be associated with an increased activation in the anterior insula. Overall, these findings suggest that a neural network consisting of the anterior insula and the MCC plays a key role in decisions about the quality and the quantity of nociceptive sensation. Results from the MVPA analysis support the notion that perceptual decisions are encoded by a distributed network of brain regions. The variability in anatomical connections between these regions may account for the individual differences in the susceptibility to a threat-mediated bias toward pain.

Patients with both depression and anxiety show an increased tendency to deploy attention towards negative information. Cognitive models of the illnesses predict that these negative attentional biases are causally related to the symptoms of the disorders. Consistent with this, modifying attentional bias using either antidepressant medication or simple, computer based training tasks has previously been associated with altered symptomatology in both non-clinical and clinical populations. The current thesis aimed to investigate the mechanisms by which attentional bias training tasks alter attention. The investigations were conducted within an experimental neuroscience framework which has previously been successfully deployed in studies of antidepressant medication. The thesis then sought to use these initial results to improve the basic understanding of attentional control processes and, ultimately, guide the development of novel treatment strategies. The initial studies of the thesis characterised the behavioural and neural effects of attentional bias training. Behaviourally, a high degree of generalisation of the training effect was found across a range of emotional stimuli. Neurally, training was found to alter activity in a network of prefrontal regions known to be involved in the control of attention. Further analysis, utilising a computational learning model, suggested that the attentional control systems identified in this study could be understood in terms of expectation based processes. These studies therefore indicated that, in contrast to the predominately limbic effects of antidepressant medication, training initially altered the response of frontal control circuitry. The later studies of the thesis investigated possible strategies for extending the use of attentional bias training. Firstly, combining training with antidepressant medication was found to produce an interference effect on emotional memory suggesting that administering both interventions concurrently is likely to erode their cognitive impact. Lastly, attentional bias training was found not to alter attention in patients with bipolar disorder, with the results of the study indicating that standard assessments of attentional bias in this clinical population are likely to be unreliable. Overall, these studies indicate that attentional bias training may be used to alter the top-down control of attention to emotional information and suggest that such effects may interfere with the bottom-up effects of antidepressant drugs. More generally the work demonstrates the utility of using a cognitive-neuroscientific framework to explore the mechanisms and impact of novel therapeutic strategies.

How is information organized in the brain during natural reading? Where and when do the required processes occur, such as the perception of individual words and the construction of sentence meanings. How are semantics, syntax and higher-level narrative structure represented? Answering these questions is core to understanding how the brain processes language and organizes complex information. However, due to the complexity of language processing, most brain imaging studies focus only on one of these questions using highly controlled stimuli which may not generalize beyond the experimental setting. This thesis proposes an alternative framework to study language processing. We acquire data using a naturalistic reading paradigm, annotate the presented text using natural language processing tools and predict brain activity with machine learning techniques. Finally, statistical testing is used to form rigorous conclusions. We also suggest the use of direct non-parametric hypothesis tests that do not rely on any model assumptions, and therefore do not suffer from model misspecification. Using our framework, we construct a brain reading map from functional magnetic resonance imaging data of subjects reading a chapter of a popular book. This map represents regions that our model reveals to be representing syntactic, semantic, visual and narrative information. Using this single experiment, our approach replicates many results from a wide range of classical studies that each focus on one aspect of language processing. We extend our brain reading map to include temporal dynamics as well as spatial information by using magnetoencephalography. We obtain a spatio-temporal picture of how successive words are processed by the brain. We show the progressive perception of each word in a posterior to anterior fashion. For each region along this pathway we show a differentiation of the word properties that best explain its activity.

Lexical access, the matching of auditory information onto lexical representations in the brain, is a crucial component of online language processing. To understand the nature of lexical access, it is important to identify the kind of acoustic information that is stored in the long-term memory and to study how the brain uses such information. This dissertation investigates the contribution of prosodic information to lexical access and examines language-specific processing mechanisms by studying three typologically distinct languages: English, Turkish, and Swedish. The main research objective is to demonstrate the activation of long-term memory traces for words on the sole basis of prosodic information and to test the accuracy of typological phonological descriptions suggested in the literature by studying electrophysiological measurements of brain activation. A secondary research objective is to evaluate three distinct electrophysiological recording systems. The dissertation is based on three papers, each examining neural responses to prosodic changes in one of the three languages with a different recording system. The first two papers deal directly with the interplay between prosody and the lexicon, and investigate whether prosodic changes activate memory traces associated with segmentally identical but prosodically different words; the third paper introduces morphology to this process and investigates whether prosodic changes activate memory traces associated with potential lexical derivations. Neural responses demonstrate that prosodic information indeed activates memory traces associated with words and their potential derivations without any given context. Strongly connected neural networks are argued to guarantee neural activation and implementation of long-term memory traces. Regardless of differences in prosodic typology, all languages exploit prosodic information for lexical processing, although to different extents. The amount of neural activation elicited by a particular piece of prosodic information is positively correlated with the strength of its lexical representation in the brain, which is called lexical specification. This dissertation could serve as a first step towards building an electrophysiological-perceptual taxonomy of prosodic processing based on lexical specification.

Apesar das evidências a favor da automaticidade no processamento de estímulos aversivos, especialmente na amígdala, a sua resposta parece ser dependente da disponibilidade de re- cursos atentivos. Dessa forma, a atenção pode atuar como um mecanismo de regulação emocional, importante para a compreensão de uma série de distúrbios psiquiátricos em que este mecanismo está prejudicado. Nesse estudo, investigamos o processo do regulação e- mocional pela atenção sob uma condição de grande demanda atentiva e com estímulos altamente aversivos. Imagens funcionais por ressonância magnética foram adquiridas de 22 voluntários saudáveis enquanto figuras emocionais (IAPS - International Afective Picture System), neutras e aversivas (corpos mutilados) eram apresentadas enquanto eles realiza- vam três tarefas diferentes, em que a atenção era manipulada. As imagens foram apresenta- das no centro do campo visual, enquanto apareciam duas barras, uma de cada lado da figu- ra. As três tarefas atentivas correspondiam a reconhecer: (1) a valência emocional da figura, (2) a semelhança na orientação das duas barras com diferenças de 0º ou 90º (tarefa fácil) e (3) a semelhança das mesmas com diferença de 0º ou apenas 6º (tarefa muito difícil). Nas análises de regiões de interesse (ROIs) observamos um padrão de regulação emocional, com dimuição da amplitude do sinal BOLD estimado, nas regiões da amígdala, ínsula ante- rior, cíngulo posterior e córtex pré-frontal medial, ventrolateral e orbitofrontal na tarefa fácil. Já na tarefa difícil, esse comportamento se manteve, com excessão do sinal da ínsula e do orbitofrontal, que voltou a subir, talvez, por um efeito de estresse. Verificamos uma maior amplitude do sinal BOLD na região dos córtices pré-frontal dorsolateral, parietal superior e área motora suplementar quando a atenção foi alocada para as tarefas de barras, supostamente, por um efeito da demanda maior de atenção. Nestes resultados, todo um conjunto de estruturas envolvidas no processamento emocional foi regulado pela manipu- lação da atenção nas tarefas. Também observamos uma correlação entre o traço de afeto positivo dos sujeitos e a estimativa do sinal BOLD da amígdala sob a regulação emocional. Estas evidências indicam que, mesmo para estímulos extremamente negativos, a disponibi- lidade de recursos de atenção e, talvez, mecanismos inibitório de controle cognitivos sobre a amígdala sejam fatores condicionantes da resposta emocional. / Because of the biological significance of motional stimuli, their processing is considered largely automatic. However, there are also evidences that processing of emotional stimuli requires some level of attention. Higher attention demands in a task may limit emotional processing which can be interpreted as an emotion regulation strategy. Our experiments utilized highly demanding attention task while also highly aversive and arousing visual stimuli comprising mutilated bodies is presented. The experiment employed a single task, which consisted of determining whether two peripheral bars were like oriented or not, such to evaluate further brain mechanisms involved in emotion regulation by functional mag- netic resonance imaging (fMRI). Our results revealed that task-irrelevant unpleasant images slowed reaction time during the performance of the main easy task, but not at highly de- manding one which is suggesting of emotion suppression. Such modulatory effect was also revealed by ROI analysis showing that many brain regions, including regions of amygdala, anterior insula, posterior cingulate, and medial, ventrolateral and orbitofrontal prefrontal cortex have their processing of emotional visual stimuli reduced by attentional manipula- tions while there were BOLD signal increases in dorsolateral prefrontal and superior parie- tal cortex respective to attention demand. These results suggest that attentional manipula- tions that more fully consume attentional resources in order to demonstrate that the proc- essing of emotional stimuli is limited.

Introdução: Déficits de memória episódica constituem o marcador cognitivo mais frequente em pacientes com Comprometimento Cognitivo Leve (CCL).Estudos prévios mostram que déficits de memória episódica podem ser minimizados nestes pacientes por intervenções comportamentais. Entretanto, os mecanismos cerebrais envolvidos nos efeitos do treino cognitivo ainda são pouco explorados. O objetivo deste estudo foi avaliar o correlato neural por ressonância magnética funcional de um treino breve de memória em pacientes com CCL e compará-los com os achados em idosos saudáveis. Foram avaliados 18 pacientes com CCL e 19 idosos controles com a utilização de ressonância magnética funcional (RMf) em uma tarefa de codificação de listas de palavras com diferentes graus de relação semântica antes e após uma sessão de treino de estratégias de memória. Na sessão pré-treino, os participantes foram instruídos a memorizar as palavras durante a sessão de RMf sem qualquer orientação sobre o uso de estratégias de codificação. Após um treino breve no qual estratégias específicas de organização e agrupamento semântico foram exercitadas, os sujeitos foram reconduzidos ao aparelho de ressonância magnética e realizaram a sessão pós-treino, na qual foram instruídos a utilizar a estratégia treinada durante o paradigma de codificação de palavras. Os resultados dos exames de ressonância magnética funcional foram processados e analisados com o programa FSL versão 4.1. Ambos os grupos apresentaram aumento no número de palavras evocadas associado ao uso da estratégia treinada. Em ambos os grupos foi observado um aumento do sinal BOLD após o treino em regiões do córtex pré-frontal dorsolateral esquerdo e do córtex parietal bilateral. No grupo de idosos controles, entretanto, foi observada também uma redução da ativação em regiões do córtex parietal posterior esquerdo e cíngulo posterior bilateral, do córtex pré-frontal medial e cíngulo anterior direitos, do lóbulo parietal inferior e do córtex temporal superior direitos, do córtex pré-frontal dorsolateral direiro e do córtex óribito-frontal bilateral. A interação grupo x tempo foi significativa em áreas do córtex pré-frontal dorsolateral e ventromedial direitos. Estes resultado indicam que existem diferenças no recrutamento de regiões pré-frontais em resposta ao uso de estratégias de codificação em paradigmas de codificação de palavras entre pacientes com CCL e idosos cognitivamente saudáveis / The present studyinvestigated the effects of different applicationsof verbal learning strategies duringepisodicmemory encoding in patients with Mild Cognitive Impairment (MCI) (n=18) and normal controls (n=17) using functional magnetic resonance imaging (fMRI).The main goal of this study was to verify whether externally guided increases in verbal learning strategy application during episodic memory encoding modulate brain activity in memory-related networks in the same level in MCI as in controls. Participantswerescanned twice, using a word-list encoding fMRI paradigm.In the first session, self-initiated encoding strategies were used to intentionally memorize words during encoding. In the second session, participants received an explicit instruction to apply a semantic organization strategy (i.e. semantic clustering)to perform the task. The fMRI word list learning paradigm consisted of alternating blocks of encoding and resting baseline conditions. To perform the spontaneous fMRI session, participants were not instructed about the semantic organization of the words in the lists beforehand or given any practice with related lists. Therefore, any grouping by category observed in the subsequent free recall at the end of this fMRI acquisition was presumed to be self-initiated by the subject. At the end of the spontaneous session, each subject received a brief period of guidance or instructions to apply semantic strategies and organize words in terms of semantic categories during encoding, using a new set of word lists. Immediately after practicing the application of the strategy, participants were scanned again using the same type of paradigm as in the first session, except for the use of new set of word lists and the explicit instruction to apply semantic clustering.Free recall and strategic index scores were assessedafter each session. fMRI brain activation and deactivation during encoding of word lists in memory-related networks were examined across sessions. Results from the fMRI analysis revealed that after the explicit orientation to apply the verbal learning strategy, greater recruitment of frontoparietal network regions were observed in both MCI and control groups in relation to the unconstrained encoding condition. Group-differences in functional deactivations, however, were observed in the medial prefrontal (mPFC) cortex and in the right superior frontal gyrus, two critical nodes of the default mode network, related to the absence of modulation in the activity of the mPFC, along with a lack of suppression of the right superior frontal gyrus in MCI, in response to the increased use of the encoding strategy. A different association between improvement in strategy use and session-related changes in activation of the medial orbitalfrontal cortex between groups was also confirmed. That is, improvements in strategy use in controls contribute to a great extent in the amount of deactivation in OFC, whereas in patients, only a small portion of the increase in activation in this region was predicted by increases in strategy application

Body dissatisfaction (BD) is a condition derived from negative thoughts and feelings about one's body and is a core symptom of the eating disorder anorexia nervosa (AN). Beingdissatisfied with one’s body is highly present in women and to some extent men. This might be a result of a skewed ideal in combination with social influences. In recent year, research on neurobiological risk factors as well as neuroscientific and cognitive mappings of AN and BD have gained traction, particularly when it comes to studies using neuroimaging- techniques and cognitive tests. Studies have identified brain regions (insular cortex, anterior cingulate cortex, parietal cortex, amygdala, dorsolateral and orbitofrontal areas of the prefrontal cortex) associated with the processing of body shape as well as dysfunctional processing of self-image and body satisfaction. Structural imaging studies of AN patients using CT and MRI have, in many cases, found reduced cerebral volume, increased spinal fluid (CSF) and enlarged ventricles. Usually, food and water restriction has been seen as the cause, and structural deficits in AN patients have shown to improve with weight gain after long-term recovery.

Submitted by Caroline Xavier (caroline.xavier@pucrs.br) on 2017-06-30T17:22:52Z No. of bitstreams: 1 DIS_ANIBAL_SOLON_HEINSFELD_COMPLETO.pdf: 12807619 bytes, checksum: d11b60094a8bde0d839a6f7a23bbb56c (MD5) / Made available in DSpace on 2017-06-30T17:22:52Z (GMT). No. of bitstreams: 1 DIS_ANIBAL_SOLON_HEINSFELD_COMPLETO.pdf: 12807619 bytes, checksum: d11b60094a8bde0d839a6f7a23bbb56c (MD5) Previous issue date: 2016-03-28 / O Espectro Autista (EA) compreende uma s?rie de desordens no desenvolvimento neurol?gico, caracterizado por defici?ncias sociais e dificuldades de comunica??o, comportamentos repetitivos e atrasos cognitivos. Atualmente, o diagn?stico do EA ? amplamente baseado em medi??es comportamentais, que pode ser demorado, e depende da coopera??o do paciente e da experi?ncia do examinador. Para mitigar esta limita??o, investigamos padr?es neurais que ajudem no diagn?stico de desordens do EA. Nesta disserta??o, usamos t?cnicas de deep learning, a fim de extrair caracter?sticas robustas de neuroimagens de pacientes com autismo. Neuroimagens cont?m cerca de 300.000 pontos espaciais, com aproximadamente 200 medi??es cada. As t?cnicas de deep learning s?o ?teis para extrair caracter?sticas relevantes que diferenciam autistas de n?o-autistas. Ao utilizar denoising autoencoders, uma t?cnica de deep learning espec?fica que visa reduzir a dimensionalidade dos dados, n?s superamos o estado da arte, atingindo 69% de acur?cia, comparado com o melhor resultado encontrado na literatura, com 60% de acur?cia. / Autism Spectrum Disorders (ASD) comprise a range of neurodevelopmental disorders, characterized by social deficits and communication difficulties, repetitive behaviors, and cognitive delays. The diagnosis of ASD is largely based on behavioral measurements, which can be timeconsuming and relies on the patient cooperation and examiner expertise. In order to address this limitation, we aim to investigate neural patterns to help in the diagnosis of ASD. In this dissertation, we use deep learning techniques to extract robust characteristics from neuroimages of autistic subject brain function. Since neuroimage contains about 300,000 spatial points, with approximately 200 temporal measurements each, deep learning techniques are useful in order to extract important features to discriminate ASD subjects from non-ASD. By using denoising autoencoders, a specific deep learning technique that aims to reduce data dimensionality, we surpass the state-of-the-art by achieving 69% of accuracy, compared to 60% using the same dataset.