Le but de ce travail de thèse a été de caractériser in vivo chez l’Homme, la connectivité cérébrale sur son versant fonctionnel, par le biais de deux techniques: la stéréoélectroencéphalographie (SEEG) et l’IRM fonctionnelle (IRMf) de repos. Ces travaux se sont intégrés dans le cadre du bilan préchirurgical des épilepsies du lobe temporal pharmacorésistantes, dont le but est de déterminer la zone épileptogène à réséquer pour traiter ces patients. Alors que plusieurs études en électrophysiologie ont montré que durant les crises, il existait une synchronisation anormalement élevée entre les structures impliquées dans les processus épileptogènes, aucune donnée de connectivité n’était disponible en période intercritique. Pourtant, la période intercritique est le siège d’anomalies interictales enregistrées en EEG, de profonds remaniements morphologiques, et est associée à des troubles cognitifs. Nous apportons avec ce travail, grâce au recueil et au traitement de signaux SEEG et IRMf enregistrés durant la période intercritique, de nouvelles connaissances i) sur l’organisation de la connectivité fonctionnelle basale (CFB) chez les sujets sains ; ii) sur les altérations de la CFB chez des groupes de patients mais également au niveau individuel ; iii) sur le rapport entre ces anomalies de CFB et les troubles cognitifs observés chez ces patients ; iv) enfin, sur les différences et les similitudes de la CFB évaluée par SEEG et IRMf chez les mêmes sujets, ouvrant ainsi de nouvelles perspectives dans la compréhension des relations entre le signal BOLD et le signal EEG. / The aim of this thesis was to characterize the Human brain functional connectivity in vivo based on signals recorded using stereoelectroencephalography (SEEG) and resting-state functional MRI (fMRI). This work was conducted during the presurgical assessment of drug resistant temporal lobe epilepsy, which aims at determining the epileptogenic zone to be removed to treat these patients. While several electrophysiological studies have shown high synchronization between structures involved in the epileptogenic process during seizure, no similar connectivity data was available during inter-critical period. However, the interictal period is characterized by spikes recorded on EEG, morphological alterations and cognitive impairment. By analyzing fMRI and SEEG signals recorded during the interictal period, this work provides new insights into, i) basal functional connectivity (BFC) organization in healthy subjects, ii) BFC alterations in patients groups but also at the individual level, iii) the relationship between these BFC abnormalities and cognitive impairment observed in these patients; iv) the differences and similarities of BFC evaluated by SEEG and fMRI in the same subjects, thus opening up new perspectives in better understanding of relationships between BOLD and SEEG signal coupling.

Les capacités attentionnelles sont nécessaires à la plupart des tâches de la vie quotidienne. Au cours du vieillissement normal, ces habiletés se modifient. De même, les études suggèrent que l’activité neurofonctionnelle du réseau fronto-pariétal qui sous-tend les capacités attentionnelles diffère entre les individus âgés et de jeunes adultes. Par contre, les changements en contexte du vieillissement du réseau fronto-pariétal ventral, aussi appelé le réseau attentionnel ventral, ont été peu investigués. Une telle question doit être soulevée dans le contexte où les plus récents modèles décrivant les changements fonctionnels associés au vieillissement rapportent que des possibles transformations neurofonctionnelles peuvent survenir au niveau intrahémisphérique et interhémisphérique. Le but de cet ouvrage est de déterminer comment le vieillissement normal affecte le réseau attentionnel ventral et de décrire la nature des changements qui peuvent survenir sur les axes intra et interhémisphériques. Pour y parvenir, la méthode de connectivité fonctionnelle fut privilégiée puisqu’elle permet de quantifier l’interaction neurofonctionnelle entre diverses régions composant un réseau fonctionnel. La première étude de cette thèse a permis de décrire les modifications de connectivité fonctionelle intrahémisphériques du réseau attentionnel ventral en comparant des adultes jeunes et âgés lorsqu’ils réalisent une tâche d’attention sélective en imagerie par résonance magnétique. Sur le plan comportemental, les individus âgés répondaient significativement plus lentement et commettaient davantage d’erreurs que le groupe composé de jeunes adultes. Les résultats de connectivité fonctionnelle montrent que le degré d’intégration de la connectivité fonctionnelle intrahémisphérique est globalement plus élevé chez les individus âgés dans l’ensemble des régions fronto-pariétales composant ce réseau. De plus, il semble que les aires antérieures du réseau, soit les aires préfrontales et insulaires, sont moins intégrées chez les individus âgés, alors que les zones pariétales, temporales et cérébelleuses le sont davantage. Le degré d’intégration de la connectivité est également plus élevé chez les adultes âgés entre les régions postérieures et antérieures. Ainsi, les résultats de cette étude suggèrent que la dynamique des régions antérieures et postérieures du réseau attentionnel ventral est modifiée au cours du vieillissement normal et que les régions postérieures occupent au sein de ce réseau un rôle plus important avec l’âge. Cette hyperconnectivité des aires pariétales pourrait représenter une stratégie de compensation intrahémisphérique (i.e. recrutement de régions additionnelles en postérieur) qui aurait cependant atteint un certain plateau puisque bien que les âgés réussissent à réaliser la tâche, ils performent significativement plus faiblement que de jeunes adultes. La seconde étude s’est intéressée aux modifications de connectivité interhémisphériques du même réseau fonctionnel en comparant le degré de connectivité fonctionnelle entre des individus jeunes et âgés. De manière similaire à l’étude 1, sur le plan comportemental les individus âgés répondaient significativement plus lentement et commettaient plus d’erreurs que les jeunes adultes. En ce qui concerne la dimension inter-hémisphérique du réseau, les résultats des analyses de connectivité montrent que le degré d’intégration des régions hémisphériques gauches fronto-pariétales et temporales est plus faible pour les participants âgés que pour les participants jeunes. Au contraire, les régions frontales, pariétales, temporales et sous-corticales de l’hémisphère droit sont plus intégrées. Par ailleurs, les résultats montrent également que le degré d’intégration interhémisphérique est plus élevé chez les individus âgés. Ainsi, cette étude suggère que le degré de connectivité fonctionnelle entre les régions hémisphériques droites du réseau attentionnel ventral augmente au cours du vieillissement, suggérant ainsi une amplification de la latéralisation de ce réseau vers l’hémisphère droit avec l’âge. Cette étude montre également que malgré une augmentation de la latéralisation du VAN à droite, celle-ci s’accompagne d’une augmentation du degré de connectivité fonctionnelle interhémisphérique qui pourrait être envisagée comme une tentative de compensation interhémisphérique (i.e. recrutement des régions homologues) qui aurait atteint toutefois un certain plateau car même si les âgés réussissent à réaliser la tâche, leur niveau de performance reste significativement plus faible que les jeunes. En somme, ce travail a permis de contribuer à notre compréhension de l’impact du vieillissement sur le réseau attentionnel ventral sur l’axe intrahémisphérique et interhémisphérique. Cet ouvrage lance de nouvelles pistes d’investigation dans ce domaine et pourrait éventuellement mener à l’élaboration d’interventions susceptibles de promouvoir une santé cognitive optimale lors du vieillissement. / Attention is necessary for most of daily life’s tasks. During aging, these cognitive abilities are changed. Studies suggest that the neurofunctional activity of the frontoparietal network, which upholds the attentional capacities, differ between young and older adults. However, age-related changes of the ventral frontoparietal network, also called the ventral attention network, have been less investigated. Such question has to be raised in context of recent models of neurofunctional changes in aging, who report possible functional transformation that could occur both at the intrahemispheric and interhemispheric levels. The goal of the present thesis is to determine how aging affects the ventral attention network and describe the nature of such changes that can occur on the intrahemispheric and interhemispheric axis. To do so, functional connectivity methods were favoured because of their capacity to measure the neurofunctional interaction between the regions of a network. The first study of the present thesis has allowed describing the age-related intrahemispheric modifications of functional connectivity in this network by comparing young and older adults while they respond on a selective attention task during a functional magnetic resonance imagery scan. On the task, aged adults performed significantly slower and made more errors than the young adults. At the functional connectivity level, the results show higher level of the functional connectivity between all frontoparietal regions of this network for the older group. Further, the integration level of functional connectivity in anterior regions of the network seems to be less integrated for the older participants, while posterior regions have more neurofunctional signal dependency. Also, the level of integration of functional connectivity is higher in older adults between anterior and posterior regions. Thus, results from this study suggest that the anterior and posterior regions of the ventral attention network interact differently during aging and that the posterior regions play a more important role with age in this network. This hyperconnectivity in the parietal regions could represent an unsuccessful intrahemispheric compensation attempt (i.e. recruitment of additional regions in posterior part of the brain) since older adults perform significantly less well than younger adults. The second study has investigated interhemispheric alterations of functional connectivity in the same functional network by comparing young and older adults. Like in the first study, younger adults were faster to respond on task and were more accurate. Regarding the neurofunctional lateralization of the network, the degree of functional connectivity is lower in older adults for the left hemisphere’s frontoparietal and temporal regions. However, older adults have a higher degree of functional connectivity in the right frontal, parietal, temporal and subcortical regions of the same network. Also, the results also show that the interhemispheric integration level is superior for the older adults. Thus, this study suggests that the level of functional connectivity with the right hemisphere’s regions of the ventral attention network increases with age, which could suggest an age-related lateralization of this network towards the right hemisphere. In this context, increased interhemispheric functional connectivity could be interpreted as a failed interhemispheric compensation attempt (i.e. recruitment of homologous regions) since the performance of older adults on task was significantly lower than younger adults. In short, this work has allowed contributing to our understanding of the impact of aging on the ventral attention network both on the intrahemispheric and interhemispheric axis. These various results bring up new hypothesis that needs to be investigated in further studies and eventually that could lead to the establishment of intervention that promote an optimal healthy cognitive aging.

For most of the naturally occurring signals, especially biomedical signals, the underlying physical process generating the signal is often not fully known, making it difficult to obtain a parametric model. Therefore, signal processing techniques are used to analyze the signal for non-parametrically characterizing the underlying system from which the signals are produced. Most of the real life systems are nonlinear and time varying, which poses a challenge while characterizing them. Additionally, multiple sensors are used to extract signals from such systems, resulting in multichannel signals which are inherently coupled. In this thesis, we counter this challenge by using Recurrence Plot based techniques for characterizing biomedical systems such as heart or brain, using signals such as heart rate variability (HRV), electroencephalogram(EEG) or functional magnetic resonance imaging (fMRI), respectively, extracted from them. In time series analysis, it is well known that a system can be represented by a trajectory in an N-dimensional state space, which completely represents an instance of the system behavior. Such a system characterization has been done using dynamical invariants such as correlation dimension, Lyapunov exponent etc. Takens has shown that when the state variables of the underlying system are not known, one can obtain a trajectory in ‘phase space’ using only the signals obtained from such a system. The phase space trajectory is topologically equivalent to the state space trajectory. This enables us to characterize the system behavior from only the signals sensed from them. However, estimation of correlation dimension, Lyapunov exponent, etc, are vulnerable to non-stationarities in the signal and require large number of sample points for accurate computation, both of which are important in the case of biomedical signals. Alternatively, a technique called Recurrence Plots (RP) has been proposed, which addresses these concerns, apart from providing additional insights. Measures to characterize RPs of single and two channel data are called Recurrence Quantification Analysis (RQA) and cross RQA (CRQA), respectively. These methods have been applied with a good measure of success in diverse areas. However, they have not been studied extensively in the context of experimental biomedical signals, especially multichannel data. In this thesis, the RP technique and its associated measures are briefly reviewed. Using the computational tools developed for this thesis, RP technique has been applied on select single channel, multichannel and multimodal (i.e. multiple channels derived from different modalities) biomedical signals. Connectivity analysis is demonstrated as post-processing of RP analysis on multichannel signals such as EEG and fMRI. Finally, a novel metric, based on the modification of a CRQA measure is proposed, which shows improved results. For the case of single channel signal, we have considered a large database of HRV signals of 112 subjects recorded for both normal and abnormal (anxiety disorder and depression disorder) subjects, in both supine and standing positions. Existing RQA measures, Recurrence Rate and Determinism, were used to distinguish between normal and abnormal subjects with an accuracy of 58.93%. A new measure, MLV has been introduced, using which a classification accuracy of 98.2% is obtained. Correlation between probabilities of recurrence (CPR) is a CRQA measure used to characterize phase synchronization between two signals. In this work, we demonstrate its utility with application to multimodal and multichannel biomedical signals. First, for the multimodal case, we have computed running CPR (rCPR), a modification proposed by us, which allows dynamic estimation of CPR as a function of time, on multimodal cardiac signals (electrocardiogram and arterial blood pressure) and demonstrated that the method can clearly detect abnormalities (premature ventricular contractions); this has potential applications in cardiac care such as assisted automated diagnosis. Second, for the multichannel case, we have used 16 channel EEG signals recorded under various physiological states such as (i) global epileptic seizure and pre-seizure and (ii) focal epilepsy. CPR was computed pair-wise between the channels and a CPR matrix of all pairs was formed. Contour plot of the CPR matrix was obtained to illustrate synchronization. Statistical analysis of CPR matrix for 16 subjects of global epilepsy showed clear differences between pre-seizure and seizure conditions, and a linear discriminant classifier was used in distinguishing between the two conditions with 100% accuracy. Connectivity analysis of multichannel EEG signals was performed by post-processing of the CPR matrix to understand global network-level characterization of the brain. Brain connectivity using thresholded CPR matrix of multichannel EEG signals showed clear differences in the number and pattern of connections in brain connectivity graph between epileptic seizure and pre-seizure. Corresponding brain headmaps provide meaningful insights about synchronization in the brain in those states. K-means clustering of connectivity parameters of CPR and linear correlation obtained from global epileptic seizure and pre-seizure showed significantly larger cluster centroid distances for CPR as opposed to linear correlation, thereby demonstrating the efficacy of CPR. The headmap in the case of focal epilepsy clearly enables us to identify the focus of the epilepsy which provides certain diagnostic value. Connectivity analysis on multichannel fMRI signals was performed using CPR matrix and graph theoretic analysis. Adjacency matrix was obtained from CPR matrices after thresholding it using statistical significance tests. Graph theoretic analysis based on communicability was performed to obtain community structures for awake resting and anesthetic sedation states. Concurrent behavioral data showed memory impairment due to anesthesia. Given the fact that previous studies have implicated the hippocampus in memory function, the CPR results showing the hippocampus within the community in awake state and out of it in anesthesia state, demonstrated the biological plausibility of the CPR results. On the other hand, results from linear correlation were less biologically plausible. In biological systems, highly synchronized and desynchronized systems are of interest rather than moderately synchronized ones. However, CPR is approximately a monotonic function of synchronization and hence can assume values which indicate moderate synchronization. In order to emphasize high synchronization/ desynchronization and de-emphasize moderate synchronization, a new method of Correlation Synchronization Convergence Time (CSCT) is proposed. It is obtained using an iterative procedure involving the evaluation of CPR for successive autocorrelations until CPR converges to a chosen threshold. CSCT was evaluated for 16 channel EEG data and corresponding contour plots and histograms were obtained, which shows better discrimination between synchronized and asynchronized states compared to the conventional CPR. This thesis has demonstrated the efficacy of RP technique and associated measures in characterizing various classes of biomedical signals. The results obtained are corroborated by well known physiological facts, and they provide physiologically meaningful insights into the functioning of the underlying biological systems, with potential diagnostic value in healthcare.

The side-effects of chemotherapy treatment are an increasingly important research focus as more cancer patients are reaching survivorship. While treatment allows for survival, it can also lead to problems which can significantly affect quality of life. Cognitive impairments after chemotherapy treatment are one such factor. First presented as anecdotal patient reports, over the last decade empirical evidence for this cognitive concern has been obtained. Much attention has been focused on post-chemotherapy research, yet little attention has been granted to these same patients’ cognition before treatment commences. Breast cancer (BC) patients face many obstacles before chemotherapy treatment such as: surgery and side-effects of anesthesia, increased cytokine activity, stress of a new disease diagnosis and upcoming challenges, and emotional burdens such as depression and anxiety. Many of these factors have independently been shown to affect cognitive abilities in both healthy populations as well as other patient groups. Therefore, the pre-treatment (or baseline) BC patient status warrants systematic study. This would then reduce mistakenly attributing carried-over cognitive deficits to side effects of chemotherapy. As well, it is possible that certain confounding variables may have neural manifestations at baseline that could be exacerbated by chemotherapy agents. The following thesis first presents a review paper which critically describes the current literature examining chemotherapy-related cognitive impairments (CRCIs), as well as possible confound variables affecting this population. Subsequently, three original research papers present pre-chemotherapy data showing significant neuroanatomical and neurofunctional differences in BC patients compared to controls. In particular, these neural differences are present in brain regions that have been reported in post-chemotherapy papers. This, as well as the effects of variables such as the number of days since surgery, depression and anxiety scores and more, support the initiative that research attention should increase focus on these patients at baseline in order to better understand their post-chemotherapy results.

Comment peut-on accumuler de la connaissance sur les fonctions cérébrales ? Comment peut-on bénéficier d'années de recherche en IRM fonctionnelle (IRMf) pour analyser des processus cognitifs plus fins et construire un modèle exhaustif du cerveau ? Les chercheurs se basent habituellement sur des études individuelles pour identifier des régions cérébrales recrutées par les processus cognitifs. La comparaison avec l'historique du domaine se fait généralement manuellement pas le biais de la littérature, qui permet de définir des régions d'intérêt dans le cerveau. Les méta-analyses permettent de définir des méthodes plus formelles et automatisables pour analyser la littérature. Cette thèse examine trois manières d'accumuler et d'organiser les connaissances sur le fonctionnement du cerveau en utilisant des cartes d'activation cérébrales d'un grand nombre d'études. Premièrement, nous présentons une approche qui utilise conjointement deux expériences d'IRMf similaires pour mieux conditionner une analyse statistique. Nous montrons que cette méthode est une alternative intéressante par rapport aux analyses qui utilisent des régions d'intérêts, mais demande cependant un travail manuel dans la sélection des études qui l'empêche de monter à l'échelle. A cause de la difficulté à sélectionner automatiquement les études, notre deuxième contribution se focalise sur l'analyse d'une unique étude présentant un grand nombre de conditions expérimentales. Cette méthode estime des réseaux fonctionnels (ensemble de régions cérébrales) et les associe à des profils fonctionnels (ensemble pondéré de descripteurs cognitifs). Les limitations de cette approche viennent du fait que nous n'utilisons qu'une seule étude, et qu'elle se base sur un modèle non supervisé qui est par conséquent plus difficile à valider. Ce travail nous a cependant apporté la notion de labels cognitifs, qui est centrale pour notre dernière contribution. Cette dernière contribution présente une méthode qui a pour objectif d'apprendre des atlas fonctionnels en combinant plusieurs jeux de données. [Henson2006] montre qu'une inférence directe, c.a.d. la probabilité d'une activation étant donné un processus cognitif, n'est souvent pas suffisante pour conclure sur l'engagement de régions cérébrales pour le processus cognitif en question. Réciproquement, [Poldrack 2006] présente l'inférence inverse qui est la probabilité qu'un processus cognitif soit impliqué étant donné qu'une région cérébrale est activée, et décrit le risque de raisonnements fallacieux qui peuvent en découler. Pour éviter ces problèmes, il ne faut utiliser l'inférence inverse que dans un contexte où l'on suffisamment bien échantillonné l'espace cognitif pour pouvoir faire une inférence pertinente. Nous présentons une méthode qui utilise un «  meta-design » pour décrire des tâches cognitives avec un vocabulaire commun, et qui combine les inférences directe et inverse pour mettre en évidence des réseaux fonctionnels qui sont cohérents à travers les études. Nous utilisons un modèle prédictif pour l'inférence inverse, et effectuons les prédictions sur de nouvelles études pour s'assurer que la méthode n'apprend pas certaines idiosyncrasies des données d'entrées. Cette dernière contribution nous a permis d'apprendre des réseaux fonctionnels, et de les associer avec des concepts cognitifs. Nous avons exploré différentes approches pour analyser conjointement des études d'IRMf. L'une des difficultés principales était de trouver un cadre commun qui permette d'analyser ensemble ces études malgré leur diversité. Ce cadre s'est instancié sous la forme d'un vocabulaire commun pour décrire les tâches d'IRMf. et a permis d'établir un modèle statistique du cerveau à grande échelle et d'accumuler des connaissances à travers des études d'IRM fonctionnelle. / How can we accumulate knowledge on brain functions? How can we leverage years of research in functional MRI to analyse finer-grained psychological constructs, and build a comprehensive model of the brain? Researchers usually rely on single studies to delineate brain regions recruited by mental processes. They relate their findings to previous works in an informal way by defining regions of interest from the literature. Meta-analysis approaches provide a more principled way to build upon the literature. This thesis investigates three ways to assemble knowledge using activation maps from a large amount of studies. First, we present an approach that uses jointly two similar fMRI experiments, to better condition an analysis from a statistical standpoint. We show that it is a valuable data-driven alternative to traditional regions of interest analyses, but fails to provide a systematic way to relate studies, and thus does not permit to integrate knowledge on a large scale. Because of the difficulty to associate multiple studies, we resort to using a single dataset sampling a large number of stimuli for our second contribution. This method estimates functional networks associated with functional profiles, where the functional networks are interacting brain regions and the functional profiles are a weighted set of cognitive descriptors. This work successfully yields known brain networks and automatically associates meaningful descriptions. Its limitations lie in the unsupervised nature of this method, which is more difficult to validate, and the use of a single dataset. It however brings the notion of cognitive labels, which is central to our last contribution. Our last contribution presents a method that learns functional atlases by combining several datasets. [Henson 2006] shows that forward inference, i.e. the probability of an activation given a cognitive process, is often not sufficient to conclude on the engagement of brain regions for a cognitive process. Conversely, [Poldrack 2006] describes reverse inference as the probability of a cognitive process given an activation, but warns of a logical fallacy in concluding on such inference from evoked activity. Avoiding this issue requires to perform reverse inference with a large coverage of the cognitive space. We present a framework that uses a "meta-design" to describe many different tasks with a common vocabulary, and use forward and reverse inference in conjunction to outline functional networks that are consistently represented across the studies. We use a predictive model for reverse inference, and perform prediction on unseen studies to guarantee that we do not learn studies' idiosyncrasies. This final contribution permits to learn functional atlases, i.e. functional networks associated with a cognitive concept. We explored different possibilities to jointly analyse multiple fMRI experiments. We have found that one of the main challenges is to be able to relate the experiments with one another. As a solution, we propose a common vocabulary to describe the tasks. [Henson 2006] advocates the use of forward and reverse inference in conjunction to associate cognitive functions to brain regions, which is only possible in the context of a large scale analysis to overcome the limitations of reverse inference. This framing of the problem therefore makes it possible to establish a large statistical model of the brain, and accumulate knowledge across functional neuroimaging studies.

Les techniques d'acquisition des signaux médicaux sont en constante évolution et fournissent une quantité croissante de données hétérogènes qui doivent être analysées par le médecin. Dans ce contexte, des méthodes automatiques de traitement des signaux médicaux sont régulièrement proposées pour aider l'expert dans l'analyse qualitative et quantitative en facilitant leur interprétation. Ces méthodes doivent tenir compte de la physique de l'acquisition, de l'a priori que nous avons sur ces signaux et de la quantité de données à analyser pour une interprétation plus précise et plus fiable. Dans cette thèse, l'analyse des tissus biologique par spectroscopie RMN et la recherche des activités fonctionnelles cérébrales et leurs connectivités par IRMf sont explorées pour la recherche de nouveaux bio-marqueurs. Chaque information médicale sera caractérisée par un ensemble d'objets que nous cherchons à extraire, à aligner, et à coder. Le regroupement de ces objets par la mesure de leur similitude permettra leur classification et l'identification de bio-marqueurs. C'est ce schéma global d'indexation et de recherche par le contenu d'objets pour la détection des bio-marqueurs que nous proposons. Pour cela, nous nous sommes intéressés dans cette thèse à modéliser et intégrer les connaissances a priori que nous avons sur ces signaux biologiques permettant ainsi de proposer des méthodes appropriées à chaque étape d'indexation et à chaque type de signal. / The medical signal acquisition techniques are constantly evolving in recent years and providing an increasing amount of data which should be then analyzed. In this context, automatic signal processing methods are regularly proposed to assist the expert in the qualitative and quantitative analysis of these images in order to facilitate their interpretation. These methods should take into account the physics of signal acquisition, the a priori we have on the signal formation and the amount of data to analyze for a more accurate and reliable interpretation. In this thesis, we focus on the two-dimensional 2D Heteronuclear Single Quantum Coherence HSQC spectra obtained by High-Resolution Magic Angle Spinning HR-MAS NMR for biological tissue analysis and the functional Magnetic Resonance Imaging fMRI images for functional brain activities analysis. Each processed medical information will be characterized by a set of objects that we seek to extract, align, and code. The clustering of these objects by measuring their similarity will allow their classification and then the identification of biomarkers. It is this global content-based object indexing and retrieval scheme that we propose. We are interested in this thesis to properly model and integrate the a priori knowledge we have on these biological signal allowing us to propose there after appropriate methods to each indexing step and each type of signal.

It has been repeatedly shown that functional magnetic resonance imaging (fMRI) triggers distress and neuroendocrine response systems. Prior studies have revealed that sympathetic arousal increases, particularly at the beginning of the examination. Against this background it appears likely that those stress reactions during the scanning procedure may influence task performance and neural correlates. However, the question how sympathetic arousal elicited by the scanning procedure itself may act as a potential confounder of fMRI data remains unresolved today. Thirty-seven scanner naive healthy subjects performed a simple cued target detection task. Levels of salivary alpha amylase (sAA), as a biomarker for sympathetic activity, were assessed in samples obtained at several time points during the lab visit. SAA increased two times, immediately prior to scanning and at the end of the scanning procedure. Neural activation related to motor preparation and timing as well as task performance was positively correlated with the first increase. Furthermore, the first sAA increase was associated with task induced deactivation (TID) in frontal and parietal regions. However, these effects were restricted to the first part of the experiment. Consequently, this bias of scanner related sympathetic activation should be considered in future fMRI investigations. It is of particular importance for pharmacological investigations studying adrenergic agents and the comparison of groups with different stress vulnerabilities like patients and controls or adolescents and adults.

Although most odorants we encounter in daily life are mixtures of several chemical substances, we still lack significant information on how we perceive and how the brain processes mixtures of odorants. We aimed to investigate the processing of odor mixtures using behavioral measures and functional magnetic resonance imaging (fMRI). The odor mixture contained a target odor (ambroxan) in a concentration at which it could be perceived by half of the subjects (sensitive group); the other half could not perceive the odor (insensitive group). In line with previous findings on multi-component odor mixtures, both groups of subjects were not able to distinguish a complex odor mixture containing or not containing the target odor. However, sensitive subjects had stronger activations than insensitive subjects in chemosensory processing areas such as the insula when exposed to the mixture containing the target odor. Furthermore, the sensitive group exhibited larger brain activations when presented with the odor mixture containing the target odor compared to the odor mixture without the target odor; this difference was smaller, though present for the insensitive group. In conclusion, we show that a target odor presented within a mixture of odors can influence brain activations although on a psychophysical level subjects are not able to distinguish the mixture with and without the target. On the practical side these results suggest that the addition of a certain compound to a mixture of odors may not be detected on a cognitive level; however, this additional odor may significantly change the cerebral processing of this mixture. In this context, FMRI offers unique possibilities to look at the subliminal effects of odors.

The neural mechanisms underlying genetic risk for schizophrenia, a highly heritable psychiatric condition, are still under investigation. New schizophrenia risk genes discovered through genome-wide association studies (GWAS), such as neurogranin (NRGN), can be used to identify these mechanisms. In this study we examined the association of two common NRGN risk single nucleotide polymorphisms (SNPs) with functional and structural brain-based intermediate phenotypes for schizophrenia. We obtained structural, functional MRI and genotype data of 92 schizophrenia patients and 114 healthy volunteers from the multisite Mind Clinical Imaging Consortium study. Two schizophrenia-associated NRGN SNPs (rs12807809 and rs12541) were tested for association with working memory-elicited dorsolateral prefrontal cortex (DLPFC) activity and surface-wide cortical thickness. NRGN rs12541 risk allele homozygotes (TT) displayed increased working memory-related activity in several brain regions, including the left DLPFC, left insula, left somatosensory cortex and the cingulate cortex, when compared to non-risk allele carriers. NRGN rs12807809 non-risk allele (C) carriers showed reduced cortical gray matter thickness compared to risk allele homozygotes (TT) in an area comprising the right pericalcarine gyrus, the right cuneus, and the right lingual gyrus. Our study highlights the effects of schizophrenia risk variants in the NRGN gene on functional and structural brain-based intermediate phenotypes for schizophrenia. These results support recent GWAS findings and further implicate NRGN in the pathophysiology of schizophrenia by suggesting that genetic NRGN risk variants contribute to subtle changes in neural functioning and anatomy that can be quantified with neuroimaging methods.

Background: Panic disorder with agoraphobia is characterized by panic attacks and anxiety in situations where escape might be difficult. However, neuroimaging studies specifically focusing on agoraphobia are rare. Here we used functional magnetic resonance imaging (fMRI) with disorder-specific stimuli to investigate the neural substrates of agoraphobia. Method. We compared the neural activations of 72 patients suffering from panic disorder with agoraphobia with 72 matched healthy control subjects in a 3-T fMRI study. To isolate agoraphobia-specific alterations we tested the effects of the anticipation and perception of an agoraphobia-specific stimulus set. During fMRI, 48 agoraphobia-specific and 48 neutral pictures were randomly presented with and without anticipatory stimulus indicating the content of the subsequent pictures (Westphal paradigm). Results: During the anticipation of agoraphobia-specific pictures, stronger activations were found in the bilateral ventral striatum and left insula in patients compared with controls. There were no group differences during the perception phase of agoraphobia-specific pictures. Conclusions: This study revealed stronger region-specific activations in patients suffering from panic disorder with agoraphobia in anticipation of agoraphobia-specific stimuli. Patients seem to process these stimuli more intensively based on individual salience. Hyperactivation of the ventral striatum and insula when anticipating agoraphobiaspecific situations might be a central neurofunctional correlate of agoraphobia. Knowledge about the neural correlates of anticipatory and perceptual processes regarding agoraphobic situations will help to optimize and evaluate treatments, such as exposure therapy, in patients with panic disorder and agoraphobia.