Background: Cognitive behavioral therapy (CBT) is an effective treatment for panic disorder with agoraphobia (PD/AG). It is unknown, how variants of CBT differentially modulate brain networks involved in PD/AG. This study was aimed to evaluate the effects of therapist-guided (T+) versus selfguided (T–) exposure on the neural correlates of fear conditioning in PD/AG. Method: In a randomized, controlled multicenter clinical trial in medication-free patients with PD/AG who were treated with 12 sessions of manualized CBT, functional magnetic resonance imaging (fMRI) was used during fear conditioning before (t1) and after CBT (t2). Quality-controlled fMRI data from 42 patients and 42 healthy subjects (HS) were obtained. Patients were randomized to two variants of CBT (T+, n = 22, and T–, n = 20). Results: The interaction of diagnosis (PD/AG, HS), treatment group (T+, T–), time point (t1, t2) and stimulus type (conditioned stimulus: yes, no) revealed activation in the left hippocampus and the occipitotemporal cortex. The T+ group demonstrated increased activation of the hippocampus at t2 (t2 > t1), which was positively correlated with treatment outcome, and a decreased connectivity between the left inferior frontal gyrus and the left hippocampus across time (t1 > t2). Conclusion: After T+ exposure, contingency-encoding processes related to the posterior hippocampus are augmented and more decoupled from processes of the left inferior frontal gyrus, previously shown to be dysfunctionally activated in PD/AG. Linking single procedural variants to neural substrates offers the potential to inform about the optimization of targeted psychotherapeutic interventions.

This thesis deals with functional magnetic resonance imaging (fMRI), in particular with dynamic causal modelling (DCM) as one of the methods for effective brain connectivity analysis. It has been studied the effect of signal coordinates selection, which was used as an input of DCM analysis, on its results based on simulated data testing. For this purpose, a data simulator has been created and described in this thesis. Furthermore, the methodology of testing the influence of the coordinates selection on DCM results has been reported. The coordinates shift rate has been simulated by adding appropriate levels of various types of noise signals to the BOLD signal. Consequently, the data has been analyzed by DCM. The program has been supplemented by a graphical user interface. To determine behaviour of the model, Monte Carlo simulations have been applied. Results in the form of dependence of incorrectly estimated connections between brain areas on the level of the noise signals have been processed and discussed.

This thesis attempts to analyze spatial perception for navigation in a virtual arena and to cover neuronal basics of distance estimation. For this purpose, we created a virtual version of Hidden goal task which is an analogy to Morris water maze. The thesis presents results of the experiment with removing orientation cues in a circular arena. The aim of the experiment was to determine, if the assumption of Cognitive mapping theory about orientation cues equivalence is valid for our arena. Experiment outcome indicates that the accuracy of goal position estimation is not only influenced by the number of cues but also by the individual hierarchy of the cues. The hierarchy emerges from the distance of the cue from the goal, although in some cases it can be affected by an outstanding identity of the particular cue. These findings are a basis for the experiment utilizing the functional magnetic resonance method to determine neuronal basics for estimating distances in virtual arena in both the egocentric and allocentric reference frame. Results support the findings of the cited papers about the participation of occipital and parietal lobe in estimating object distance in space. Comparison of the two reference frames showed that whereas the egocentric estimation is related to activity in premotor cortex,...

L'insula a longtemps été considérée essentiellement comme une partie du « cerveau viscéral » du fait de son rôle dans le traitement des réponses physiologiques et viscérales. Or, depuis l’avènement de l’imagerie cérébrale fonctionnelle, son implication dans divers aspects du fonctionnement neuropsychologique est devenue bien établie. De plus en plus d’études suggèrent que le cortex insulaire joue un rôle clé dans les circuits responsables de la prise de décision risquée. L’hypothèse des marqueurs somatiques suggère que les émotions influencent nos décisions aux moyens de changements physiologiques internes et viscéraux. Il a été proposé que l'insula participe à la prise de décision risquée en représentant les états somatiques de la situation chargée émotionnellement et en projetant ces informations au cortex préfrontal ventro-médian, constituant ainsi une structure clé dans les circuits responsables de la prise de décision. Les théories actuelles avancent que l’insula serait davantage impliquée dans la prise de risque lorsque l’individu fait face à une perte potentielle plutôt qu'à un gain. Toutefois, bien que plusieurs études supportent un rôle dans le processus décisionnel, la contribution spécifique du cortex insulaire demeure énigmatique. Les études qui composent cette thèse visent à mieux comprendre la façon dont l'insula participe à la prise de risque aux moyens de tâches neuropsychologiques de gambling qui permettent de simuler des situations de prise de décision de la vie quotidienne. La première étude neurocomportementale examine les conséquences d’une résection au cortex insulaire sur la capacité à prendre des décisions face à un risque potentiel, chez des patients épileptiques réfractaires à la médication qui ont subi une résection unilatérale de cette région. Leurs performances à deux tâches de gambling sont comparées à celles d’un groupe de patients ayant subi une chirurgie de l'épilepsie du lobe temporal (épargnant l’insula) et d’un groupe d’individus contrôles en santé. Les résultats mettent en évidence une altération du patron de prise de risque chez les patients avec résection insulaire, qui se traduit par une difficulté à ajuster leur choix en fonction de la valeur attendue (EV) (c’est-à-dire le ratio entre la magnitude et les probabilités des résultats possibles) de l’option risquée en condition de perte. Cette étude appuie l’idée selon laquelle la prise de décision risquée implique différents processus neuronaux selon si le risque implique un gain ou une perte potentielle. La seconde visée de cette thèse porte sur l’évaluation spécifique de la valence, de l’ampleur, de la probabilité et de l’EV de l’option risquée à l’activité insulaire au cours d’une prise de décision. Au moyen de l’imagerie par résonnance magnétique fonctionnelle, l’activité cérébrale d’individus en santé a été enregistrée, alors qu’ils complétaient une tâche de jeu de hasard. Les résultats de l’étude suggèrent un rôle prédominant de l’insula dans l’ajustement des décisions risquées en fonction de l’EV. De plus, l’activité de l’insula pendant la prise de décision était influencée par la sensibilité à la punition des participants. En somme, les données de cette thèse contribuent à une meilleure compréhension du rôle spécifique de l’insula à la prise de décision risquée et conduisent à une réflexion sur l’évaluation neuropsychologique des atteintes insulaires. / The insula has long been considered primarily as part of the « visceral brain » because of its role in the treatment of physiological and visceral responses. However, since the advent of functional brain imaging, its involvement in various aspects of neuropsychological functioning has become well established. More and more studies suggest that the insular cortex plays a key role in the circuits responsible for risky decision-making. The somatic marker hypothesis suggests that emotions influence our decisions by means of internal and visceral physiological changes. It has been proposed that the insula participates in risky decision-making by representing the somatic states of the emotionally charged situation and projecting this information to the ventromedian prefrontal cortex, thus constituting a key structure in the circuits responsible for decision. Current theories argue that the insula would be more involved in risk taking when the individual faces a potential loss rather than a gain. However, although several studies support a role in the decision-making process, the specific contribution of the insular cortex remains enigmatic. The studies that make up this thesis aim to better understand how the insula participates in risk taking with neuropsychological tasks of gambling that can simulate decision-making situations of everyday life. The first neurobehavioral study examines the consequences of insular cortex resections on the ability to make decisions about potential risk in drug-refractory epileptic patients who have undergone unilateral resection of this region. Their performance in two gambling tasks is compared to a group of patients who had surgery for temporal lobe epilepsy (sparing the insula) and a group of healthy control. The results highlight an alteration of risk taking in patients with insular resection, which results in difficulty in adjusting their choice according to the expected value (EV) (i.e. the ratio between the magnitude and probabilities of possible outcomes) of the risky option in the loss condition. This study supports the idea that risky decision making involves different neural processes depending on whether the risk involves a potential gain or loss. The second aim of this thesis deals with the specific assessment of the valence, magnitude, probability and EV of the risky option to insula activity during a decision-making process. Using functional magnetic resonance imaging, the brain activity of healthy individuals was recorded as they completed a gambling task. The results of the study suggest a predominant role of the insula in adjusting risky decisions based on EV. In addition, the activity of the insular cortex during decision-making was influenced by the participants' sensitivity to punishment. In sum, the data from this thesis contribute to a better understanding of the specific role of the insula in risky decision-making and lead to a reflection on the neuropsychological evaluation of insular lesions.

Dystonia is a neurological syndrome characterized by the involuntary contraction of opposing muscles, causing twisting movements or abnormal postures (modified by Fahn, 1987). Writer's cramp is the most common form of task-specific focal dystonia. In the first study, patients with writer's cramp were evaluated for differences in cortical activation during movements likely to induce cramps (complex movements) and movements which rarely lead to dystonia (simple movements). Although complex patient movements during fMRI were never associated with dystonic cramps, they exhibited abnormally decreased cortical activity. This was not observed in simple movements and was unrelated to the character of handwriting or the presence/absence of visual feedback. Our results support the theory of dualistic sensorimotor system behavior in writer's cramp. As the somatosensory system is believed to be affected in focal dystonia, we focused on modulation of the primary somatosensory cortex (SI) induced by repetitive transcranial magnetic stimulation (rTMS) in the second study, in order to improve writer's cramp. In conclusion, 1 Hz rTMS of the SI cortex can improve manifestations of writer's cramp while increasing cortical activity in both hemispheres. Handwriting as well as subjective assessment improved in most...

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本論文では人間の感性のモデルを関数と仮定し,最適化手法によってその感性の最適点を求めることで情報推薦を行うフレームワークについて研究を行った.論文中では,人間の感性に基づく関数の特性について明らかにし,人間の多様な感性への対応できる最適化アルゴリズムを開発した.また,感性に基づく探索に適した設計変数空間の半自動生成手法を開発し,脳機能情報を用いた感性のモデル化について検討した. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

Recently, Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention mainly for two reasons. First, it is one of the most commonly found childhood behavioral disorders. Around 5-10% of the children all over the world are diagnosed with ADHD. Second, the root cause of the problem is still unknown and therefore no biological measure exists to diagnose ADHD. Instead, doctors need to diagnose it based on the clinical symptoms, such as inattention, impulsivity and hyperactivity, which are all subjective. Functional Magnetic Resonance Imaging (fMRI) data has become a popular tool to understand the functioning of the brain such as identifying the brain regions responsible for different cognitive tasks or analyzing the statistical differences of the brain functioning between the diseased and control subjects. ADHD is also being studied using the fMRI data. In this dissertation we aim to solve the problem of automatic diagnosis of the ADHD subjects using their resting state fMRI (rs-fMRI) data. As a core step of our approach, we model the functions of a brain as a connectivity network, which is expected to capture the information about how synchronous different brain regions are in terms of their functional activities. The network is constructed by representing different brain regions as the nodes where any two nodes of the network are connected by an edge if the correlation of the activity patterns of the two nodes is higher than some threshold. The brain regions, represented as the nodes of the network, can be selected at different granularities e.g. single voxels or cluster of functionally homogeneous voxels. The topological differences of the constructed networks of the ADHD and control group of subjects are then exploited in the classification approach. We have developed a simple method employing the Bag-of-Words (BoW) framework for the classification of the ADHD subjects. We represent each node in the network by a 4-D feature vector: node degree and 3-D location. The 4-D vectors of all the network nodes of the training data are then grouped in a number of clusters using K-means; where each such cluster is termed as a word. Finally, each subject is represented by a histogram (bag) of such words. The Support Vector Machine (SVM) classifier is used for the detection of the ADHD subjects using their histogram representation. The method is able to achieve 64% classification accuracy. The above simple approach has several shortcomings. First, there is a loss of spatial information while constructing the histogram because it only counts the occurrences of words ignoring the spatial positions. Second, features from the whole brain are used for classification, but some of the brain regions may not contain any useful information and may only increase the feature dimensions and noise of the system. Third, in our study we used only one network feature, the degree of a node which measures the connectivity of the node, while other complex network features may be useful for solving the proposed problem. In order to address the above shortcomings, we hypothesize that only a subset of the nodes of the network possesses important information for the classification of the ADHD subjects. To identify the important nodes of the network we have developed a novel algorithm. The algorithm generates different random subset of nodes each time extracting the features from a subset to compute the feature vector and perform classification. The subsets are then ranked based on the classification accuracy and the occurrences of each node in the top ranked subsets are measured. Our algorithm selects the highly occurring nodes for the final classification. Furthermore, along with the node degree, we employ three more node features: network cycles, the varying distance degree and the edge weight sum. We concatenate the features of the selected nodes in a fixed order to preserve the relative spatial information. Experimental validation suggests that the use of the features from the nodes selected using our algorithm indeed help to improve the classification accuracy. Also, our finding is in concordance with the existing literature as the brain regions identified by our algorithms are independently found by many other studies on the ADHD. We achieved a classification accuracy of 69.59% using this approach. However, since this method represents each voxel as a node of the network which makes the number of nodes of the network several thousands. As a result, the network construction step becomes computationally very expensive. Another limitation of the approach is that the network features, which are computed for each node of the network, captures only the local structures while ignore the global structure of the network. Next, in order to capture the global structure of the networks, we use the Multi-Dimensional Scaling (MDS) technique to project all the subjects from an unknown network-space to a low dimensional space based on their inter-network distance measures. For the purpose of computing distance between two networks, we represent each node by a set of attributes such as the node degree, the average power, the physical location, the neighbor node degrees, and the average powers of the neighbor nodes. The nodes of the two networks are then mapped in such a way that for all pair of nodes, the sum of the attribute distances, which is the inter-network distance, is minimized. To reduce the network computation cost, we enforce that the maximum relevant information is preserved with minimum redundancy. To achieve this, the nodes of the network are constructed with clusters of highly active voxels while the activity levels of the voxels are measured based on the average power of their corresponding fMRI time-series. Our method shows promise as we achieve impressive classification accuracies (73.55%) on the ADHD-200 data set. Our results also reveal that the detection rates are higher when classification is performed separately on the male and female groups of subjects. So far, we have only used the fMRI data for solving the ADHD diagnosis problem. Finally, we investigated the answers of the following questions. Do the structural brain images contain useful information related to the ADHD diagnosis problem? Can the classification accuracy of the automatic diagnosis system be improved combining the information of the structural and functional brain data? Towards that end, we developed a new method to combine the information of structural and functional brain images in a late fusion framework. For structural data we input the gray matter (GM) brain images to a Convolutional Neural Network (CNN). The output of the CNN is a feature vector per subject which is used to train the SVM classifier. For the functional data we compute the average power of each voxel based on its fMRI time series. The average power of the fMRI time series of a voxel measures the activity level of the voxel. We found significant differences in the voxel power distribution patterns of the ADHD and control groups of subjects. The Local binary pattern (LBP) texture feature is used on the voxel power map to capture these differences. We achieved 74.23% accuracy using GM features, 77.30% using LBP features and 79.14% using combined information. In summary this dissertation demonstrated that the structural and functional brain imaging data are useful for the automatic detection of the ADHD subjects as we achieve impressive classification accuracies on the ADHD-200 data set. Our study also helps to identify the brain regions which are useful for ADHD subject classification. These findings can help in understanding the pathophysiology of the problem. Finally, we expect that our approaches will contribute towards the development of a biological measure for the diagnosis of the ADHD subjects.

L’expression faciale est un vecteur de communication important dans l’expérience de douleur. Cependant, les corrélats neuronaux associés à cette manifestation de la douleur demeurent peu investigués. Le but de ce mémoire était de développer un modèle neurobiologique permettant de prédire l’expression faciale évoquée par des stimuli douloureux afin d’approfondir nos connaissances sur les mécanismes cérébraux de la douleur et de la communication non verbale. La signature cérébrale de l’expression faciale de la douleur a été élaborée sur un jeu de données d’IRMf acquis chez des adultes en santé en utilisant des algorithmes d’apprentissage machine pour prédire des scores d’expression faciale évoquée par des stimulations douloureuses phasiques (c.-à-d. de courtes stimulations) à l’échelle de la population. Les résultats suggèrent qu’il est possible de prédire ces réponses faciales à partir d’un patron d’activation multivoxels. Cette signature cérébrale se distingue, du moins partiellement, de signatures cérébrales prédictives de l’intensité et du caractère déplaisant de la douleur rapportée et de la valeur future de la douleur. Bien que d’autres études soient nécessaires pour examiner la spécificité et la généralisabilité de la signature cérébrale de l’expression faciale de la douleur, ce mémoire souligne l’existence d’une représentation cérébrale spatialement distribuée prédictive des réponses faciales en lien avec la douleur, et suggère l’importance de cette mesure comportementale dans l’expérience de la douleur comme étant complémentaire aux mesures autorapportées de l’intensité perçue. / Facial expression is an important communication vector in the experience of pain. However, the neural correlates associated with this manifestation of pain remain relatively unexplored. The goal of this thesis was to develop a neurobiological model to predict facial expression evoked by painful stimuli in order to expand our knowledge of the brain mechanisms of pain and non-verbal communication. The brain signature of facial expressions of pain was developed on a dataset including healthy adults using machine learning algorithms to predict facial expression scores evoked by phasic painful stimuli (i.e., short stimulation) at the population level. The results suggest that it is possible to predict the facial expression of pain from a multivoxel activation pattern. This brain signature of facial pain expression is at least partially distinct from other brain signatures predictive of reported pain intensity and unpleasantness, and future pain value. Although further studies are needed to examine the specificity and generalizability of the brain signature of facial expression of pain, this master thesis highlights the existence of a spatially distributed brain representation predictive of pain-related facial responses, and suggests the importance of this behavioural measure in the experience of pain as complementary to self-reported measures of pain intensity.

La schizophrénie est un trouble de santé mentale sévère, caractérisée par des altérations neurobiologiques marquées. Un biomarqueur notoire de ce trouble est la dysconnectivité cérébrale fonctionnelle, détectée grâce à l’imagerie par résonance magnétique fonctionnelle. Celle-ci se retrouve principalement au niveau des réseaux neurocognitifs qui sous-tendent les fonctions cérébrales supérieures. Toutefois, les altérations de connectivité fonctionnelle cérébrale ne sont pas uniques à la schizophrénie, elles sont également retrouvées dans plusieurs autres troubles de santé mentale. Le premier objectif de cette thèse doctorale est d’effectuer une méta-analyse sur la dysconnectivité cérébrale fonctionnelle à l’état de repos pour dissocier les altérations spécifiques à la schizophrénie de celles qui sont communes aux troubles de l’humeur. Ce projet novateur basé sur l’extraction de labels neuroanatomiques inclut 428 articles scientifiques. Les résultats confirment des altérations transdiagnostiques des réseaux neurocognitifs et soulignent une dysconnectivité spécifique des réseaux sensorimoteurs dans la schizophrénie. Parmi les altérations des réseaux neurocognitifs, une hyperconnectivité entre le réseau central exécutif (REC) et le réseau du mode par défaut (RMD) est associée à des déficits de mémoire de travail dans la schizophrénie, c’est-à-dire à des difficultés de mémorisation et de manipulation d’informations à court terme. Actuellement, aucun traitement efficace n’existe contre ces déficits. La stimulation magnétique transcrânienne (SMT), déjà approuvée comme traitement dans la dépression, est une piste de thérapie prometteuse. Elle agit en modulant spécifiquement les réseaux de connectivité dysfonctionnels. Jusqu’à présent, les effets de l'application de la SMT pour diminuer les déficits de mémoire de travail dans la schizophrénie sont mitigés. Une explication potentielle s’oriente vers la position de la cible de stimulation sur le cortex qui est standardisée et ne prend pas en compte l'importante variabilité interindividuelle dans l'organisation spatiale des réseaux de connectivité fonctionnelle. D'autres paramètres méthodologiques comme l’intensité de stimulation ont aussi un impact sur l’effet de la stimulation. Le second projet de cette thèse consiste à réaliser une étude rétrospective chez les sujets sains afin de confirmer l'importance de cibler spécifiquement le REC par la SMT pour induire une modulation fonctionnelle de celui-ci en fonction de l’intensité de stimulation. Nos résultats montrent que la stimulation spécifique du REC induit une diminution de la connectivité entre le REC et le RMD pour des intensités de stimulation plus faibles. Finalement, le troisième projet de cette thèse a pour objectif d'examiner prospectivement, dans la schizophrénie, l’impact d'une SMT personnalisée par rapport à une SMT standard sur la connectivité fonctionnelle à l’état de repos et les performances de mémoire de travail. La cible de la SMT personnalisée a été définie selon les patrons de connectivité individualisée des participants. L’analyse des données a mis en évidence une instabilité prononcée des réseaux de connectivité qui ne nous a pas permis de conclure quant à l’avantage d’une cible personnalisée pour diminuer l’hyperconnectivité entre REC et RMD et les déficits de mémoire de travail dans la schizophrénie. Toutefois, une relation avec la force de la connectivité préstimulation a été identifiée. En conclusion, cette thèse souligne deux points majeurs pour caractériser l’étiologie et le développement de thérapies efficaces pour diminuer les déficits de mémoire de travail dans la schizophrénie. Premièrement, elle confirme la dysconnectivité transdiagnostique des réseaux neurocognitifs dans les troubles psychiatriques et souligne les altérations spécifiques des fonctions sensorimotrices dans la schizophrénie. Deuxièmement, elle démontre l'importance de déterminer des paramètres de stimulation fiables et individualisés pour optimiser l’efficacité de la SMT. / Schizophrenia is a severe mental illness characterized by substantial neurobiological alterations. A notable biomarker of this mental disorder is the functional brain dysconnectivity identified through functional magnetic resonance imaging. The functional brain connectivity alterations are mainly detected within the neurocognitive networks underlying higher brain functions. However, these dysfunctions are not unique to schizophrenia and are also found in other psychiatric disorders. The first objective of this doctoral thesis is to conduct a meta-analysis on resting-state functional brain dysconnectivity in schizophrenia and mood disorders to reveal commonalities and specificities of alterations, based on mean group effects. This innovative project based on the extraction of neuroanatomical labels includes 428 scientific articles. Meta-analytic results confirm transdiagnostic alterations in neurocognitive networks and highlight a specific dysconnectivity in sensorimotor networks in schizophrenia. Among neurocognitive networks alterations, hyperconnectivity between the central executive network (CEN) and the default mode network (DMN) is associated with working memory deficits in schizophrenia, i.e. difficulties to temporarily hold and manipulate information in memory. Currently, there are no effective treatments for these deficits. Transcranial magnetic stimulation (TMS), already approved for depression, is a promising therapeutic approach. This neuromodulation tool operates by modulating dysfunctional connectivity networks. However, TMS effects to decrease working memory deficits in schizophrenia are mixed. A potential explanation lies in the standardized positioning of the stimulation target on the cortex, which does not account for significant inter-individual variability in the spatial organization of functional connectivity networks. Other methodological parameters, such as stimulation intensity, also impact the stimulation's effectiveness. The second project of this thesis aimed to conduct a retrospective study in healthy subjects to confirm the importance of specifically targeting the CEN to induce a functional modulation of these network. The impact of stimulation intensity was also evaluated. Results showed that a specific stimulation of CEN led to a decreased of the functional connectivity between the CEN and the DMN for subthreshold intensities. Finally, a third project aimed to prospectively examine, in schizophrenia, the effects of a personalized TMS compared to a standard TMS on resting-state functional connectivity and working memory performance. Personalized TMS targets were defined based on patients’ individual connectivity patterns. The data analysis revealed a pronounced instability in connectivity networks, which did not allow us to conclude on the advantage of a personalized target to reduce hyperconnectivity between the CEN and the DMN and working memory deficits in schizophrenia. However, a relationship with pre-stimulation connectivity strength was identified. In conclusion, this thesis highlights two major points for characterizing the etiology and the development of optimal TMS to reduce working memory deficits in schizophrenia. First, it confirms the transdiagnostic dysconnectivity of neurocognitive networks in psychiatric disorders and highlights specific alterations of sensorimotor functions in schizophrenia. Second, it demonstrates the importance of identifying reliable and individualized stimulation parameters to optimize the TMS.