Patients with MDD demonstrate impairments in various components of affective processing, which are believed to persist in the remitted phase of the illness and are believed to underlie the vulnerability for future relapse. Despite advances in neuropsychiatry, the neuroanatomical site of action of various treatment modalities remains unclear, leaving clinicians without an algorithm to guide optimal treatment selection for individual patients. This thesis sought to characterize differences in brain activation during affective processing between MDD treatment responders (RS) and non-responders (NR) by combining clinical and neuroimaging variables in a repeat-measure functional magnetic resonance imaging (fMRI) investigation. We induced increases in positive and negative affect using visual stimuli under fMRI conditions in 21 MDD subjects and 18 healthy controls (HC). Based on previous neuroimaging investigations and preclinical animal data, we hypothesized that increased activation of the amygdala and the pregenual cingulate during negative affect induction (NAI), and decreased activity of the ventral striatum during positive affect induction (PAI), would differentiate ultimate NR from RS. Following the first scan, treatment with fluoxetine and olanzapine was initiated in the MDD group, with follow-up scans at one- and six-weeks thereafter. We hypothesized that decreases in depressive symptoms would be associated with decreased activation of the ventromedial prefrontal cortex (PFC) and amygdala during NAI and increased activation of the hippocampus during PAI. Eleven MDD subjects met criteria for clinical remission at study endpoint. Based on trait differences between MDD and HC, we hypothesized that differences observed during NAI would be limited to brain regions involved in regulation of the affective state, including the dorsolateral PFC and the anterior midcingulate cortex. The results of the analyses confirmed the a-prior hypotheses and additionally demonstrated differential activation of the insular, medial temporal, and premotor cortex during repeat PAI and NAI between HC, RS, and NR. These findings provide: i) a neuroanatomical target of successful antidepressant therapy during PAI/NAI; ii) a differential effect of depressive symptoms and dispositional affect on brain activation during PAI/NAI; and iii) an a-prior method to differentiate RS from NR, and iv) demonstrate the need for additional treatment to prevent relapse in the remitted state.

Functional magnetic resonance imaging (fMRI) has emerged as the method of choice to non-invasively investigate brain function in humans. Though brain is known to act as a nonlinear system, here has not been much effort to explore the applicability of nonlinear analysis techniques to fMRI data. Also, recent trends have suggested that functional localization as a model of brain function is incomplete and efforts are being made to develop models based on networks of regions to understand brain function. Therefore this thesis attempts to introduce the twin concepts of nonlinear dynamics and network analysis into a broad spectrum of fMRI data analysis techniques. First, we characterized the nonlinear univariate dynamics of fMRI noise using the concept of embedding to explain the origin of tissue-specific differences of baseline activity in the brain. The embedding concept was extended to the multivariate case to study nonlinear functional connectivity in the distributed motor network during resting state and continuous motor task. The results showed that the nonlinear method may be more sensitive to the desired gray matter signal. Subsequently, the scope of connectivity was extended to include directional interactions using Granger causality. An integrated approach was developed to alleviate the confounding effect of the spatial variability of the hemodynamic response and graph theory was employed to characterize the network topology. This methodology proved effective in characterizing the dynamics of cortical networks during motor fatigue. The nonlinear extension of Granger causality showed that it was more robust in the presence of confounds such as baseline drifts. Finally, we utilized the integration of the spatial correlation function to study connectivity in local brain networks. We showed that our method is robust and can reveal interesting information including the default mode network during resting state. Application of this technique to anesthesia data showed dose dependent suppression of local connectivity in the default mode network, particularly in the frontal areas. Given the body of evidence emerging from our studies, nonlinear and network characterization of fMRI data seems to provide novel insights into brain function.

H προβολή υποσυνείδητων μηνυμάτων είναι η διαδικασία έκθεσης ερεθισμάτων κάτω από το κατώφλι της συνειδητοποίησης. Με τον τρόπο αυτό μπορεί να επηρεαστούν οι σκέψεις, τα συναισθήματα και ενέργειες του ανθρώπου. Η υποσυνείδητη αντίληψη συμβαίνει όταν οι πληροφορίες αποθηκεύονται στο ανθρώπινο μυαλό, χωρίς ο δέκτης να έχει συνειδητά επίγνωση του προβλήματος. Οι πληροφορίες φτάνουν στο μυαλό, γιατί ενώ δεν είναι συνειδητά αντιληπτές, γίνονται αντιληπτές από το υποσυνείδητο κομμάτι του εγκεφάλου. Αντικείμενο της παρούσας διπλωματικής εργασίας είναι η αποτίμηση των πιθανών διαφορών στις καταγραφές ηλεκτροεγκεφαλογραφήματος (ΕΕG) και προκλητών δυναμικών (ERPs) κατά την υποβολή ενός ατόμου σε οπτικά υποσυνείδητα ερεθίσματα σε σύγκριση με καταγραφές χωρίς ερέθισμα. Στην εργασία χρησιμοποιήθηκε ένα ερευνητικό πρωτόκολλο το οποίο εξετάζει το πώς επηρεάζουν τα υποσυνείδητα ερεθίσματα τη λήψη αποφάσεων και την εγκεφαλική λειτουργία. Στο πρώτο μέρος της εργασίας (κεφάλαια 1 και 2) γίνεται μια συνοπτική αναφορά στις κυριότερες μεθόδους απεικόνισης εγκεφάλου, όπως το ηλεκτροεγκεφαλογράφημα και την λειτουργική απεικόνιση Μαγνητικού Συντονισμού καθώς και στον συνδυασμό τους για πιο ικανοποιητικά αποτελέσματα. Το κεφάλαιο 3 αναφέρεται κυρίως σε θέματα σχετικά τα προκλητά δυναμικά, καθώς και την υποσυνείδητη αντίληψη. Στο κεφάλαιο 4 περιγράφεται η πειραματική διαδικασία και η μετρητική διάταξη που χρησιμοποιήθηκε καθώς και η παρουσίαση της επεξεργασίας των μετρήσεων μέσω του eeglab. Τέλος, στο κεφάλαιο 5 παρουσιάζονται τα αποτελέσματα της επεξεργασίας σε διαγράμματα προκλητών δυναμικών και φασματικής ισχύος καθώς επίσης και τα συμπεράσματα της εργασίας αυτής. / The display of subliminal messages is the process of stimuli exposure below the threshold of awareness. Through this procedure the thoughts, feelings and actions of a human can be influenced. The subliminal perception occurs when information stored in the human mind without the receiver being consciously aware of it. The information reaching the brain is perceived by the subconscious part of the brain. The object of this diploma thesis is to assess the possible differences in electroencephalogram (EEG) and event - related potentials (ERPs) recordings during the presentation of visual subliminal stimuli compared to non – subliminal conditions. A protocol that examines how subliminal stimuli influence the decision making and the cerebral operation is used. In the first part of the thesis (chapters 1 and 2) there is a brief review of the main brain imaging methods such as EEG and fMRI as well as the combination of them. Chapter 3 reveals issues about event - related potentials and mostly about subliminal perception. In chapter 4, the experiment and the measuring devices used are described, and there is also a presentation of the analysis by using the eeglab. Finally, chapter 5 includes the results of analysis on event - related potential and spectral power graphs, as well as the conclusions of this work.

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Orientador: Prof. Dr. João Ricardo Sato / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Neurociência e Cognição, 2016. / Com o avanço das tecnologias em neuroimagem, em principal da Ressonância Magnética Funcional, baseada no sinal Blood Oxygenation Level Dependent (BOLD), possibilitou-se a realização de estudos que investigam o funcionamento de redes cerebrais. Dentre as diversas redes a Default Mode Network (DMN) e a Task Positive Network (TPN) tem se destacado na literatura. Isso se deve ao fato de que estas redes estão associadas a diferentes funções cognitivas, como por exemplo, no processo da interação social ou durante o raciocínio mecânico. Este estudo teve como objetivo principal, investigar a conectividade funcional obtida do sinal BOLD entre seis regiões pertencentes às estas duas redes, da quais três regiões estão associadas ao processo de interação social e as outras três estão associadas ao raciocínio mecânico. Esta análise foi realizada em 652 indivíduos entre a segunda infância e a pré-adolescência. Além disso, foi investigado também o possível efeito da idade, do sexo (gênero) e do comportamento social na conectividade funcional entre estas regiões. Os resultados mostram a existência de correlação negativa entre todos os pares de conexões de regiões investigados. A partir desta confirmação, verificou-se a possível influência das variáveis idade e sexo na conectividade funcional, e constatou-se o efeito da variável idade em dois pares de conexões: Córtex Pré-frontal Medial com Sulco Pré-frontal Superior, e Córtex Pré-frontal Lateral com Córtex Pré-frontal Medial. A variável sexo não alcançou valores significativos. Por fim, verificou-se a relação do comportamento social na conectividade funcional dos pares de conexões que apresentaram valores significativos na análise da variável idade. Foi encontrada a evidência de que problemas no comportamento social em crianças entre a segunda infância e a pré-adolescência estão associados a anormalidades na conectividade funcional entre um par de conexão: Córtex Pré-frontal Lateral com Córtex Pré-frontal Medial. Este achado sugere que os indivíduos com problemas de comportamento social apresentam uma correlação negativa menor, mais fraca, nesta conexão. / The advance of technology in neuroimaging, in specific Functional Magnetic Resonance Imaging based on the signal Blood Oxygenation Level Dependent (BOLD), allowed to carry out studies investigating the functioning of brain networks. Among the various networks, the Default Mode Network and the Task Positive Network has been highlighted in the literature. This interest is because these networks are related to different cognitive functions, such as social interaction or mechanical reasoning. This study investigates the functional connectivity of the BOLD signal obtained from six regions belonging to these two networks, of which three regions are associated with the process of social interaction, and the other three are related to the mechanical reasoning. This analysis was performed in 652 individuals between middle childhood and preadolescence. We also investigated the potential effect of age, gender and social behavior in the functional connectivity between these regions. The results show a negative correlation between all pairs of connected regions investigated. From these findings, we examined the possible effects of the variables age and gender in the functional connectivity, and we found the effect of the variable age in two pairs of connections: Medial Prefrontal Cortex with Superior Frontal Sulcus and Lateral Prefrontal Cortex with Medial Prefrontal Cortex. The variable gender did not reach significant values. Finally, we investigate the possible effects of the social behavior in the functional connectivity in this pairs of connections that got significant values in the analysis of variable age. We found evidence that problems in the social behavior of children between middle childhood and preadolescence are associated with abnormalities in functional connectivity between a pair of connection: Lateral Prefrontal Cortex with Medial Prefrontal Cortex. These findings suggest that individuals with social behavior problems have a smaller negative correlation in this pair of connection.

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common child-onset neurodevelopment disorders, affecting 5% of children in the United States (American Psychiatric Association, 2013). Treatment matching in ADHD is difficult and unsatisfactory; the same general treatment algorithm is recommended for all children. It is therefore important to consider the development of specialized treatment programs based on a variety of behavioral and neurological biomarkers. Unfortunately, due to its multi-faceted classification, the heterogeneity of this behavioral disorder is under-investigated (Costa Dias et al., 2015). Scientific research in this area is especially limited as the severity of ADHD goes undiagnosed, children tend to have difficulties remaining still in MRI scanners, and the hyperactivity-impulsivity that is associated with ADHD may cause further challenges when trying to remain motionless in the scanner. Furthermore, tasks such as Facial Emotion Perception Task (FEPT) and Theory of Mind (ToM) have not been used to analyze social and behavioral deficits in children with ADHD. More research needs to be allocated to helping uncover the neural substrates underlying the inattention and hyperactivity traits of this disorder. For this reason, we acquired functional magnetic resonance imaging (fMRI) data from five children with ADHD performing the FEPT and ToM tasks. The results showed the children have an easier and quick time correctly identifying happy emotional states, as compared to the fearful, angry, and neutral conditions. Results from the FEPT task also revealed that the participants were thinking and reasoning more (i.e., taking longer to deduce an ending) when identifying emotions than identifying animals. The ToM task showed that the default mode network (DMN) may not be fully suppressed when the children are choosing the correct cartoon ending, and therefore the children may be having lapses in attention. These findings may assist the current hypothesis that the default mode network has reduced network homogeneity in people with ADHD. Overall, the findings presented in this thesis provide a good diving board into discovering the reason(s) for the social cognition and emotion recognition impairments associated with ADHD, but further research is needed in order to one day pinpoint and ultimately correct the regions(s) of dysfunction.

L'électroencéphalographie (EEG) est une technique d'imagerie cérébrale non invasive importante, capable d'enregistrer l'activité neuronale avec une grande résolution temporelle (ms), mais avec une résolution spatiale faible. Le problème inverse en EEG est un problème difficile, fortement sous-déterminé : des contraintes ou des a priori sont nécessaires pour aboutir à une solution unique. Récemment, l'intégration de signaux EEG et d'imagerie par résonance magnétique fonctionnelle (fMRI) a été largement considérée. Les données EEG et fMRI relatives à une tâche donnée, reflètent les activités neuronales des mêmes régions. Nous pouvons donc supposer qu'il existe des cartes spatiales communes entre données EEG et fMRI. En conséquence, résoudre le problème inverse en EEG afin de trouver les cartes spatiales des sources EEG congruentes avec celles obtenues par l'analyse de signaux fMRI semble être une démarche réaliste. Le grand défi reste la relation entre l'activité neuronale électrique (EEG) et l'activité hémodynamique (fMRI), qui n'est pas parfaitement connue à ce jour. La plupart des études actuelles reposent sur un modèle neurovasculaire simpliste par rapport à la réalité. Dans ce travail, nous utilisons des a priori et des faits simples et généraux, qui ne dépendent pas des données ou de l'expérience et sont toujours valides, comme contraintes pour résoudre le problème inverse en EEG. Ainsi, nous résolvons le problème inverse en EEG en estimant les sources spatiales parcimonieuses, qui présentent la plus forte corrélation avec les cartes spatiales obtenues par fMRI sur la même tâche. Pour trouver la représentation parcimonieuse du signal EEG, relative à une tâche donnée, on utilise une méthode (semi-aveugle) de séparation de sources avec référence (RSS), qui extrait les sources dont la puissance est la plus corrélée à la tâche. Cette méthode a été validée sur des simulations réalistes et sur des données réelles d'EEG intracrânienne (iEEG) de patients épileptiques. Cette représentation du signal EEG dans l'espace des sources liées à la tâche est parcimonieuse. En recherchant les fonctions d'activation de fMRI similaires à ces sources, on déduit les cartes spatiales de fMRI très précises de la tâche. Ces cartes fournissent une matrice de poids, qui impose que les voxels activés en fMRI doivent être plus importants que les autres voxels dans la résolution du problème inverse en EEG. Nous avons d'abord validé cette méthode sur des données simulées, puis sur des données réelles relatives à une expérience de reconnaissance de visages. Les résultats montrent en particulier que cette méthode est très robuste par rapport au bruit et à la variabilité inter-sujets. / Electroencephalography (EEG) is an important non-invasive imaging technique as it records the neural activity with high temporal resolution (ms), but it lacks high spatial resolution. The inverse problem of EEG is underdetermined and a constraint or prior information is needed to find a unique solution. Recently, EEG-fMRI integration is widely considered. These methods can be categoraized in three groups. First group uses the EEG temporal sources as the regressors in the generalized linear method (GLM) which is used to analyze the fMRI data. The second group analyzes EEG and fMRI simultaneously which is known as fusion technique. The last one, which we are interested in, uses the fMRI results as prior information in the EEG inverse problem. The EEG and fMRI data of a specific task, eventually reflect the neurological events of the same activation regions. Therefore, we expect that there exist common spatial patterns in the EEG and the fMRI data. Therefore, solving the EEG inverse problem to find the spatial pattern of the EEG sources which is congruent with the fMRI result seems to be close to the reality. The great challenge is the relationship between neural activity (EEG) and hemodynamic changes (fMRI), which is not discovered by now. Most of the previous studies have used simple neurovascular model because using the realistic model is very complicated. Here, we use general and simple facts as constraints to solve the EEG inverse problem which do not rely on the experiment or data and are true for all cases. Therefore, we solve the EEG inverse problem to estimate sparse connected spatial sources with the highest correlation with the fMRI spatial map of the same task. For this purpose, we have used sparse decomposition method. For finding sparse representation of the EEG signal, we have projected the data on the uncorrelated temporal sources of the activity. We have proposed a semi-blind source separation method which is called reference-based source separation (R-SS) and extracts discriminative sources between the activity and the background. R-SS method has been verified on a realistic simulation data and the intracranial EEG (iEEG) signal of five epileptic patients. We show that the representation of EEG signal in its task related source space is sparse and then a weighted sparse decomposition method is proposed and used to find the spatial map of the activity. In the weighted sparse decomposition method we put fMRI spatial map in the weighting matrix, such that the activated voxels in fMRI are considered more important than the other voxels in the EEG inverse problem. We validated the proposed method on the simulation data and also we applied the method on the real data of the face perception experiment. The results show that the proposed method is stable against the noise and subject variability.

of the thesis Considerable research attention has been paid to the neural regulation of the lower urinary tract (LUT) in past three decades. The aim of this work is mapping of a brain activity by functional magnetic resonance imaging (fMRI) using refined scanning protocol with synchronously performed urodynamics. We aimed to detect neural activity associated with pelvic floor muscle (PF) contractions, filling of urinary bladder and miction. In addition we evaluated using fMRI brain activity associated with urinary bladder filling in patients with a complete spinal cord injury (SCI). We hypothesized activation of brainstem and forebrain areas in receiving information from the vagal nerves. Adjustments of urodynamic system enabled successful implementation of synchronous filling cystometry with fMRI evaluation of cortical activity. We concluded that synchronous urodynamic examination is a novel feasible method that facilitates and enhance interpretation of fMRI data acquired. The main clusters of brain activation during PF contractions were observed in the medial surface of the frontal lobe (primary motor area) and supplementary motor area (SMA). We detected neural activity associated with filling of urinary bladder and miction in middle and inferior frontal gyrus, angular gyrus, posterior and...

A presença de redes cerebrais funcionais ativadas durante o repouso é bem conhecida e verificada por diferentes técnicas de imagens, como as Imagens por Ressonância Magnética funcionais (IRMf) baseadas no contraste dependente do nível de oxigenação do sangue (BOLD, Blood Oxygenation Level Dependent). Entretanto, apesar de ser atualmente o método não invasivo convencional para tais estudos, o contraste BOLD é sensível a diferentes parâmetros hemodinâmicos (fluxo sanguíneo cerebral, CBF; volume sanguíneo cerebral e extração de oxigênio), cuja relação não é completamente conhecida em diversas patologias. Por outro lado, o método de Marcação dos Spins Arteriais (ASL) é uma técnica de IRM não invasiva que fornece mapas quantitativos de CBF e pode ser usada para avaliar as redes de repouso. Portanto, o objetivo do presente estudo foi investigar a viabilidade de usar sequências de ASL (pulsada e pseudocontínua), disponíveis para o uso na rotina clínica, para o estudo da conectividade funcional do cérebro em estado de repouso. Imagens de ASL e BOLD, de 23 indivíduos jovens e saudáveis, foram adquiridas em um equipamento de 3T. Após o pré-processamento usual das imagens e cálculos dos mapas de perfusão, CBF e pseudo-BOLD (pBOLD), a partir das imagens de ASL, as redes cerebrais de repouso foram obtidas pela Análise de Componentes Independentes (ICA) e pelo método baseado em semente. Utilizando ICA, a análise em grupo conjunta de pBOLD e BOLD identificou cinco redes: rede de modo padrão (DMN), visual, auditiva, saliência e motora. Quando analisados separadamente, os dados de pBOLD mostraram apenas as redes DMN e visual, enquanto os dados de BOLD mostraram também as redes auditiva, saliência, motora, atentiva e frontoparietais direita e esquerda. Para ambas as análises, comparações entre as redes de pBOLD e BOLD apresentaram similaridades de moderadas a altas. Entretanto, nenhuma rede foi observada utilizando os dados de perfusão e CBF. Já as análises baseadas em sementes mostraram correlações significativas, para as séries temporais de pBOLD e CBF, entre regiões que constituem algumas redes de repouso conhecidas (DMN, visual, sensorial-motora, atentiva e frontoparietal). Os valores obtidos para a força das conectividades nas redes de pBOLD e CBF se correlacionaram com aqueles obtidos nas redes de BOLD. As diferenças no desempenho de ASL e BOLD devem-se a uma combinação de fatores, como relação sinal ruído e resolução temporal. Além disso, a natureza dos sinais não é a mesma. O sinal BOLD é influenciado por diferentes parâmetros fisiológicos e é proveniente principalmente de grandes veias; enquanto o sinal de ASL é proveniente da rede de capilares, fornecendo especificidade espacial mais alta para a atividade neuronal, além de permitir a quantificação do CBF, que está relacionado mais diretamente ao metabolismo cerebral. Portanto, o presente estudo mostrou ser possível investigar a conectividade funcional do cérebro no estado de repouso com uma sequência comercial, apesar das limitações técnicas da ASL. Além disso, as séries temporais de CBF e BOLD refletem diferentes aspectos do cérebro em repouso, fornecendo informações complementares dos seus processos fisiológicos / The presence of functional brain networks activated during resting state is well known and has been verified by different imaging techniques, such as the functional Magnetic Resonance Imaging (fMRI) based on the Blood Oxygenation Level-Dependent (BOLD) contrast. Although BOLD-fMRI is currently the conventional non invasive method for such studies, BOLD contrast is sensitive to different hemodynamic parameters (Cerebral Blood Flow, CBF; cerebral blood volume and oxygen extraction fraction), whose relationship is not fully understood in several pathologies. In contrast, the Arterial Spin Labeling (ASL) MRI technique is a non invasive tool for CBF quantification and can be used to investigate resting-state networks. Therefore, the goal of the present study was to investigate the feasibility of using ASL sequences (pulsed and pseudocontinuous), available for clinical routine use, for the study of functional connectivity of the brain at rest. ASL and BOLD images of 23 healthy young subjects were acquired in a 3T machine. After the usual image pre-processing and quantification of perfusion, CBF and pseudo-BOLD (pBOLD) maps, from ASL images, resting-state brain networks were obtained by Independent Component Analysis (ICA) and a seed-based method. Five networks were identified in a joint analysis of pBOLD and BOLD: Default Mode Network (DMN), visual, auditory, salience, and motor. When analyzed separately, pBOLD showed only the DMN and visual networks, while BOLD also showed auditory, salience, motor, attentive, right and left frontoparietal networks. For both analyses, comparisons between pBOLD and BOLD networks showed from moderate to high similarities. However, no network was obtained from perfusion and CBF time series. Seed-based analysis showed significant correlations, for pBOLD e CBF time series, between regions that integrate some known networks (DMN, visual, sensorial-motor, attentive and frontoparietal). Functional connectivity strength obtained from pBOLD and CBF networks correlated with the ones from BOLD data. Differences in performance with ASL and BOLD are due to a combination of factors, such as SNR and temporal resolution. Moreover, the nature of the signals is not the same. BOLD signal is influenced by different physiologic parameters and comes mainly from large veins; while ASL signal comes from small capillaries, providing higher spatial specificity regarding neural activity, in addition to allow the quantification of CBF, which is closer related to the cerebral metabolism. In conclusion, the present study showed the feasibility of investigating functional connectivity of the brain at rest using a commercial ASL sequence, even with its technical limitations. Moreover, CBF and BOLD time series reflect different aspects of the resting-state brain and provide complementary information on its physiological processes