Introduction: The aim of the project was to investigate the effects of antidepressants on brain networks whilst at rest. My hypothesis was that antidepressants work by reversing persistent activity in the brain’s default mode network (DMN). The DMN is implicated in self-reflection and rumination in MDD. The methodologies and results of studies of resting state networks in MDD and the effects of antidepressants are reviewed in the thesis. Increasing evidence implicates glutamate in the action of antidepressant drugs. Whether there are illness related changes in glutamate function is unresolved, largely because of the lack of techniques for assessing it. Ketamine and other NMDA antagonists have improved MDD symptoms within 24 hours though the effects are short lasting. The molecular neural networks involved in ketamine’s putative antidepressant effects are unclear. The thesis reviews the evidence. Much evidence implicates ACC as a site of action of antidepressant effects but whether this is through its regulation of the DMN or other networks is not known. This thesis compares the effect of ketamine and citalopram on ACC-related systems. Method: The thesis combines two systematic reviews of the effects of MDD and antidepressant drugs on i) resting state networks (53 studies) and ii) glutamate neurotransmission (45 studies of clinical efficacy of ketamine). There are two experimental chapters. The first describes investigation into two rapid acting antidepressant drugs acting via glutamate mechanisms. 54 unmedicated cMDD were scanned across two centres on 3T MRI scanners while being infused with placebo (0.5% saline), 0.5mg/kg ketamine or 100mg AZD6765 over 1 hour. fMRI resting state data between drug treatments was compared for the final 25 minutes of the drug infusion and for a 25 minute resting state scan a day later. The second experimental chapter examines whether these effects were shared by citalopram, a standard antidepressant. 67 unmedicated cMDD, rMDD and HC were administered citalopram 7.5mg i.v. and scanned on a 1.5T MRI scanner. In a second study 63 cMDD and HC were administered i.v. citalopram 7.5mg or placebo (0.5% saline). fMRI resting state data for the final 12 ½ minutes following drug infusion was compared. Independent Component Analysis was performed using the Group ICA for fMRI toolbox. The resting component with the highest spatial correlation to the ACC was used. Brain maps of the intensity of the selected component were constructed for each individual. Group averages were calculated and compared using SPM. Regional analysis was performed using Marseille Boite a Regions d'interet. Results: On day 1 AZD6765 significantly increased mean intensity of ACC resting component in the right insula, right IPL and left cingulate gyrus greater than ketamine or placebo. Ketamine increased mean intensity of ACC resting component greater than placebo in the right lentiform nucleus and left mFG. Significantly decreased mean intensity of ACC resting component in the left insula in the AZD6765 group compared to placebo was noted. On day 2 AZD6765 increased mean intensity of ACC resting component greater than ketamine and placebo in the left and right lentiform nuclei. AZD6765 reduced mean intensity of the ACC resting component in the left and right MFG. The first citalopram study revealed reduced mean intensity of ACC resting component in cMDD compared to rMDD and HC in PCC. rMDD had reduced mean intensity of ACC resting component in the precuneus compared to HC. In the second study, citalopram had no effect in HC but normalised precuneus activity in cMDD producing a significant drug x group interaction. Conclusions: The acute antidepressant effects of citalopram are modulated by changes in the bilateral precuneus. The precuneus is central to connectivity with other regions in MDD. It has a prominent role in the DMN and is linked to rumination. The mechanism of the antidepressant effects of AZD6765 is different from those of ketamine and citalopram. The insula, IPL, MFG, cingulate gyrus and lentiform nuclei are all regions implicated in MDD suggesting antidepressant effects. The rapid antidepressant effects of AZD6765 are possibly due to a resetting of the interface between DMN and salience networks.

Convergent findings have elucidated the regions involved in semantic cognition. The anterior temporal lobes (ATL) act as a hub for multimodal semantic processing alongside modality-specific ‘spoke’ regions. In addition, areas of inferior parietal, posterior temporal and frontal cortex are necessary for semantic cognition. However, many questions remain. Little is known about the timing of the ATL or how distributed regions interact in order to perform semantic processing. In order to gain knowledge of the precise spatial and temporal dynamics of the ATL and semantic cognition network, a series of studies was performed. Chapter 3 investigated the time at which the ATL is necessary for a semantic judgement using chronometric TMS. The ATL was found to be necessary for semantic cognition from 400ms post-stimuli presentation. This is known to be a critical time for semantic processing. Processing of items presented in different modalities converges around this time. This supports the role of the ATL in multimodal semantic cognition. Chapter 4 used offline repetitive TMS to investigate the role of ATL subregions and posterior temporal cortex in semantic and phonological processing. However, no significant TMS effects were demonstrated. Chapter 5 employed dual echo fMRI to assess how different types of semantic relationships are instantiated within the brain. Association (spatially and temporally co-occurring concepts) and conceptual similarity (concepts sharing features) were shown to rely on the same cortical regions. This provides evidence against theories suggesting separate representational hubs for these different relationship types. Instead it supports the reliance of both relationship types on the ATL hub. These two kinds of relationship may be more similar than previously thought, with the hub-and-spoke model able to explain both. The semantic network identified here included ATL, posterior temporal, frontal and ventral parietal cortex. This network of semantic regions was shown to be interconnected in Chapter 6 during a semantic task (using a psychophysiological interaction analysis) and during rest (using a seed-based functional connectivity analysis). Differential connectivity was identified between the ventral ATL (to multimodal semantic regions) and the aSTG (to language-related regions). The semantic network overlapped with the default mode network (DMN) and involved regions previously found to constitute the frontoparietal network (FPN).Emergent questions related to the overlap between previously identified network and the semantic network were addressed with preliminary independent component analyses in Chapter 7. This showed the dynamic connectivity of the ATL in task and rest. The semantic network was found to be distinct from but overlapping with the DMN and FPN. The role of this network in semantic cognition was confirmed, whereas the DMN was not found to relate to semantic processing. The anterior DMN component appeared semantic based on activity alone, suggesting prior results relating the DMN to semantic cognition fail to take the dynamic connectivity of the regions in to account. The left FPN overlapped with semantic control regions but appeared to relate to more general control processes. When assessed with dual echo fMRI, the ATL appears to be highly connected in a dynamic fashion and may be an important region currently under-represented within studies of the connectome. Overall, these studies add to the hub-and-spoke model of semantic cognition, elucidating the types of relationship involved, how regions interact and the precise temporal and spatial dynamics of these areas.

No description available.

De récentes études ont montré que l'activité spontanée du cerveau observée au repos permet d'étudier l'organisation fonctionnelle cérébrale en complément de l'information fournie par les protocoles de tâches. A partir de ces signaux, nous allons extraire un atlas fonctionnel du cerveau modélisant la variabilité inter-sujet. La nouveauté de notre approche réside dans l'intégration d'a-prioris neuroscientifiques et de la variabilité inter-sujet directement dans un modèles probabiliste de l'activité de repos. Ces modèles seront appliqués sur de larges jeux de données. Cette variabilité, ignorée jusqu'à présent, cont nous permettre d'extraire des atlas flous, donc limités en terme de résolution. Des challenges à la fois numériques et algorithmiques sont à relever de par la taille des jeux de données étudiés et la complexité de la modélisation considérée. / Recent studies have shown that resting-state spontaneous brain activity unveils intrinsic cerebral functioning and complete information brought by prototype task study. From these signals, we will set up a functional atlas of the brain, along with an across-subject variability model. The novelty of our approach lies in the integration of neuroscientific priors and inter-individual variability in a probabilistic description of the rest activity. These models will be applied to large datasets. This variability, ignored until now, may lead to learning of fuzzy atlases, thus limited in term of resolution. This program yields both numerical and algorithmic challenges because of the data volume but also because of the complexity of modelisation.

京都大学 / 0048 / 新制・論文博士 / 博士(医学) / 乙第13387号 / 論医博第2219号 / 新制||医||1048(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 溝脇 尚志, 教授 髙橋 良輔, 教授 渡邉 大 / 学位規則第4条第2項該当 / Doctor of Medical Science / Kyoto University / DFAM

Autistic children demonstrate a constellation of traits with varying degrees of severity in areas including language differences, restrictive and repetitive behaviors, and sensory processing differences. However, the relationship between sensory processing and these other behaviors are not well understood especially their neurobiological underpinnings. Therefore, this research examined behavioral measures of semantic language, sensory traits, and associated brain networks in 20 autistic children (ages 6-11) and 22 typically developing (TD) age matched peers. Mann-Whitney U tests revealed a strong correlation between sensory traits and general composite and semantic language in both groups of participants. Sensory seeking traits were most significantly correlated with overall and semantic language scores in our autistic participants. Resting state functional network connectivity was also examined and correlated with behavioral measures. The autistic participants demonstrated three networks of interest that were correlated with semantic language scores. These networks demonstrated both over and underconnectivity, and the brain regions involved provided functions in multisensory integration, language, somatosensory processing, and prediction (among other functions). These findings point to an association between sensory integration and language, especially semantics in both the neurotypical population and autistic individuals. Furthermore, for the autistic population it presents novel information about brain regions and connectivity patterns that may contribute to the relationships between semantic language and sensory differences in the autism.

Childhood maltreatment can have detrimental consequences on individual well-being and cognitive functioning. One type of childhood maltreatment that remains stigmatized and under-researched among men is child sexual abuse (CSA). Research examining the neurophysiological consequences of CSA in males is limited even further. This dissertation presents three original research articles which provide preliminary evidence of the lasting neurophysiological impact of CSA in men. We recruited all male participants, of those who experienced CSA, some had PTSD (CSA+PTSD) others did not have PTSD (CSA-PTSD) allowing for the examination of differences in males with histories of CSA (but no PTSD) versus those who have CSA histories and PTSD. We also recruited control males with no CSA histories nor PTSD. Three functional MRI tasks and one resting state functional scan were obtained. The letter n-back, and an emotional picture n-back task were used in the first study as a measure of working memory and emotional processing. The first study highlights the lasting impact CSA can have on men, regarding brain activity during working memory, and working memory when negative emotional stimuli are involved. The second study examined how negative/traumatic memories are re-experienced. Results from the second study demonstrate that CSA impacts the neurophysiology of autobiographical memory for traumatic experiences. In the final study, resting state functional connectivity was examined within the default mode, salience and limbic networks, and differences in functional connectivity within the networks were observed. Together, these findings highlight the long-term neural impact of CSA and can validate the experience of men who have lived through CSA. They can also guide researchers and clinicians to potential avenues of support for the well-being of these men. These studies highlight the need for more research with men who have experienced CSA so we can fully understand their altered neurophysiological responses, and how this knowledge can be used to support their mental health and continued wellness throughout their lives.

Le microcèbe murin (Microcebus murinus) est un primate attirant l’attention de la recherche neuroscientifique. Son anatomie cérébrale est encore mal décrite et ses réseaux cérébraux n'ont jamais été étudiés. Le premier objectif de cette thèse était de développer de nouveaux outils menant à la création d’un atlas numérique 3D du cerveau du microcèbe. Cet atlas est un outil fondamental car pouvant être utilisé pour extraire automatiquement des biomarqueurs cérébraux de diverses neuropathologies. Par la suite, nous avons mis en place des protocoles IRM et informatiques pour analyser la connectivité neuronale du microcèbe murin. Nous avons évalué pour la première fois les réseaux cérébraux de cet animal et révélé que son cerveau est organisé en régions fonctionnelles intégrées dans des réseaux fonctionnels à plus grande échelle. Ces réseaux ont été classés et comparés à des réseaux similaires chez l'homme. Cette comparaison multi-espèces a mis en évidence des règles d'organisation communes mais aussi des divergences. L'imagerie du glutamate par transfert de saturation et par échange chimique (gluCEST) est une méthode permettant de créer des cartes 3D de la distribution du glutamate. Dans une troisième étude, nous avons comparé l’activité neuronale locale, la connectivité fonctionnelle et le contraste gluCEST dans diverses régions du cerveau. Nous avons ainsi mis en évidence différentes associations entre ces trois biomarqueurs. Enfin, l’impact du vieillissement sur la connectivité fonctionnelle, l’activité neuronale locale et le contraste gluCEST a été évalué en comparant deux cohortes de microcèbes murins. / The mouse lemur (Microcebus murinus) is a primate that has attracted attention within neuroscience research. Its cerebral anatomy is still poorly described and its cerebral networks have never been investigated. The first objective of this study was to develop new tools to create a 3D digital atlas of the brain of this model and to use this atlas to automatically follow-up brain characteristics in cohorts of animals. We then implemented protocols to analyze connectivity in mouse lemurs so we could evaluate for the first time the cerebral networks in this species. We revealed that the mouse lemur brain is organised in local functional regions integrated within large scale functional networks. These latter networks were classified and compared to large scale networks in humans. This multispecies comparison highlighted common organization rules but also discrepancies. Additionally, Chemical Exchange Saturation Transfer imaging of glutamate (gluCEST) is a method that allows the creation of 3D maps weighted by the glutamate distribution. In a third study, we compared local neuronal activity, functional connectivity and gluCEST contrast in various brain regions. We highlighted various associations between these three biomarkers. Lastly, the impact of aging on local neuronal activity, functional connectivity and gluCEST has been analyzed by comparing two cohorts of lemurs.

This thesis gives an overview on how to estimate changes in functional brain networks using graph theoretical measures. It explains the assessment and definition of functional brain networks derived from fMRI data. More explicitly, this thesis provides examples and newly developed methods on the measurement and visualization of changes due to pathology, external electrical stimulation or ongoing internal thought processes. These changes can occur on long as well as on short time scales and might be a key to understanding brain pathologies and their development. Furthermore, this thesis describes new methods to investigate and visualize these changes on both time scales and provides a more complete picture of the brain as a dynamic and constantly changing network.:1 Introduction 1.1 General Introduction 1.2 Functional Magnetic Resonance Imaging 1.3 Resting-state fMRI 1.4 Brain Networks and Graph Theory 1.5 White-Matter Lesions and Small Vessel Disease 1.6 Transcranial Direct Current Stimulation 1.7 Dynamic Functional Connectivity 2 Publications 2.1 Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity 2.2 Early small vessel disease affects fronto-parietal and cerebellar hubs in close correlation with clinical symptoms - A resting-state fMRI study 2.3 Dynamic modulation of intrinsic functional connectivity by transcranial direct current stimulation 2.4 Three-dimensional mean-shift edge bundling for the visualization of functional connectivity in the brain 2.5 Dynamic network participation of functional connectivity hubs assessed by resting-state fMRI 3 Summary 4 Bibliography 5. Appendix 5.1 Erklärung über die eigenständige Abfassung der Arbeit 5.2 Curriculum vitae 5.3 Publications 5.4 Acknowledgements

Clinical care for major depressive disorder (MDD) would be greatly improved if we had reliable clinical predictors of individual antidepressant treatment outcome. While, at the present time, no biomarkers have sufficiently proven utility to be ready for clinical application, several neuroimaging modalities have shown promise for such development. Attempts to combine the recently developed modality of resting-state functional Magnetic Resonance Imaging (rs-fMRI) with pharmacological challenges to explore the impact of antidepressants on resting-state brain connectivity have just begun (McCabe et al., 2011a, McCabe et al., 2011b). The aim of the current study was to investigate the effects of a single dose of the SSRI (selective serotonin reuptake inhibitor) escitalopram on resting-state functional connectivity in health.