The bed nucleus of the Stria Terminalis:Connections, genetics, & trait associations

This thesis examines the functional and structural connections of the Bed Nucleus of the Stria Terminalis (BNST). The principal motivation in doing so stems from the documented gap in our knowledge between the prolific pre-clinical animal BNST research, and that of human BNST research (Lebow & Chen, 2016). Understanding the human BNST may prove to be clinically important, as animal models often implicate this structure as being key in processes underlying the stress-response, disorders of negative affect, and in substance misuse- particularly related to alcohol (Herman et al., 2020; Maita et al., 2021). Therefore I further set out to test BNST connectivity relationships with related psychological phenotypes and examine any genetic associations. Chapter 1 provides an overview of the relevant BNST literature and a brief summary of the methods used in this thesis. In Chapter 2 I use the Human Connectome Project young human adults sample (n = ~1000) to map the intrinsic connectivity network of the BNST. In addition, I compare this network to that of the central nucleus of the amygdala, an area anatomically and functionally associated with the BNST (Alheid, 2009). Next, I test for associations across this network with self-report traits relating to dispositional negativity and alcohol use. Finally, I examine the heritability of specific BNST- amygdala sub-region functional connectivity, and co-heritability with the selfreport traits. In Chapter 3 I use the large UK biobank sample (n = ~ 19,000) to run a genome-wide association analysis, aiming to uncover specific common genetic variants that may be linked with BNST – amygdala sub-region functional connectivity. In Chapter 4, I focus on structural connectivity and use a mixture of macaque tracttracing analysis, and human and macaque diffusion MRI probabilistic tractography to examine the evidence for a connection between the subiculum and the BNST. As well, I test for associations between measures of white-matter microstructure and self-report dispositional negativity and alcohol-use phenotypes. Finally, in the Discussion, I bring together the findings of the research, noting their implications within the wider BNST literature and making several suggestions for improving similar analysis in future

The Organization of Corticostriatal Connectivity in the Human Brain

Neurological and psychiatric disorders reveal that the basal ganglia subserve diverse functional domains, including movement, reward, and cognitive disorders (e.g., Parkinson's disease, addiction, schizophrenia). Monkey anatomical studies show that the striatum, the input structure of the basal ganglia, receives projections from nearly the entire cerebral cortex with a broad topography of motor, limbic, and association zones. However, until recently, non-invasive methods have not been available to conduct the complete mapping of the cortex to the striatum in humans. The development of functional connectivity magnetic resonance imaging (fcMRI) now allows the identification of functional connections in humans. The present dissertation reports two studies that first create a complete map of corticostriatal connectivity and then more closely examine striatal connectivity with association networks underlying cognition

Micro-, Meso- and Macro-Connectomics of the Brain

Neurosciences, Neurolog

GAMMA KNIFE RADIOSURGERY OF THE VIM: FROM THE LESIONAL EFFECT TOWARDS NEUROMODULATION

Gamma Knife radiosurgery (GKR) is a neurosurgical stereotactic procedure, combining image guidance, with high-precision convergence of multiple gamma rays, currently emitted by 192 sources of Cobalt-60 (Leksell Gamma Knife ICON®, Elekta Instruments, AB, Sweden). The intimate mechanisms of action are not all very well understood and vary according to the treated pathological condition. In functional disorders, GKR is used either to target a specific anatomical point [e.g. thalamus- ventro-intermediate nucleus (Vim) for tremor] or to target a larger zone, such as an epileptic focus. The present thesis focuses on Vim GKR for drug-resistant essential tremor (ET). Essential tremor is the most common movement disorder, with the predominant clinical finding being kinetic tremor of the arms. Radiosurgery (RS) has several limitations in this indication: (1) indirect targeting (Vim is not visible on current MR acquisitions), with (2) no intraoperative confirmation of the target, (3) delayed clinical effect, (4) inability to predict the radiological response and a (5) lack of understanding of its radiobiological effect. Moreover, despite a standard radiosurgical procedure, there is a variability of clinical effect, with a lower efficacy rate as compared to standard deep-brain stimulation, the reference technique. Gamma Knife radiosurgery has no access to tissue analysis, and targeting and follow-up evaluation are based only on neuroimaging. We addressed the limitation of the indirect targeting by using high-field 7 Tesla (T) MRI, and combining multimodal imaging for Vim definition, at both 3 and 7 T. The central core of this thesis was the understanding of radiobiology of RS for tremor, using both structural [e.g. T1 weighted (T1-w), voxel-based morphometry (VBM)] and functional resting- state functional MRI (rs-fMRI). We aimed for a direct Vim visualization using ultra-high field 7 T. The former allows an increased signal to noise ratio, an improved spatial resolution, as well as a superior sensitivity to magnetic susceptibility engendered contrast. Susceptibility-weighted images (SWI) might be an important step to allow a direct visualization of thalamic subparts (including the Vim). We explored 7T SWI advantages, which were done in a qualitative manner. We combined several different methodologies for Vim definition (in healthy subjects of different ages): manual delineation on 7T, quadrilatere of Guiot used in common clinical practice and automated segmentation based on diffusion weighted imaging and atlases (last two performed by and in collaboration with Dr Najdenovska). We concluded that although 7T SWI, alone or in combination with other neuroimaging modalities, is useful, several limitations need to be overcome yet, precluding a standardization of a direct Vim visualization, with the current state-of- the art. The T1-w and rs-fMRI based studies analyzed the radiobiology effects of Vim GKR for intractable tremor and led to several important contributions. The most relevant and novel was the presence of a visually-sensitive structural and functional network, involved in tremor generation and further arrest after Vim GKR. The patients with this network more integrated pretherapeutically benefited more from RS. The candidate had shaped the term “cerebello- thalamo-cortical” into the “cerebello-thalamo-visuo-motor” network, as a step forward in the understanding of essential tremor’s pathophysiology. Two structures were proposed as main calibrators of this network, in the light of the present thesis: the cerebellum (as the most probable) versus the thalamus itself. Moreover, a more classical basal ganglia network, interconnected with a salience one, as well as a cerebellar, interconnected with the motor and visual one, were reported. Other longitudinal changes involved dorsal attention, insular or supplementary motor area circuitries. Particular phenotypes of ET, including patients with head tremor, were analyzed and discussed. As a perspective and future work, in progress, the dynamics of the extrastriate cortex was further analyzed, using co-activation patterns. -- La radio-neurochirurgie par Gamma Knife (GK) est une procédure de neurochirurgie stéréotaxique, combinant l’utilisation d’une imagerie multimodale, avec la convergence de multiples rayons Gamma émis par 192 sources of Cobalt-60 (Leksell Gamma Knife ICON®, Elekta Instruments, AB, Suède). Ses mécanismes pathophysiologiques ne sont pas complètement élucidés et varient selon la condition traitée. Lors des procédures fonctionnelles, le GK est utilisé pour irradier avec une haute précision, soit un point précis (par exemple, le noyau ventro- intermediare, Vim, du thalamus pour le tremblement), soit une zone plus large, comme un foyer d’épilepsie. La présente thèse a comme sujet principal la radiochirugie du Vim (RC du Vim) pour le tremblement essentiel (TE). Le TE est un des mouvements anormaux le plus commun, manifesté principalement avec un tremblement d’action de la main. Toutefois, la RC du Vim a plusieurs limitations: (1) le ciblage est indirect (le Vim n’est pas visible sur les séquences IRM classiques), (2) elle ne permet pas la confirmation électrophysiologique de la cible, (3) l’effet clinique est délayé dans le temps, (4) la réponse radiologique est difficile à prédire et, (5) il manque une compréhension claire de son effet radiobiologique. De plus, malgré le fait que la procédure soit standardisée, il y a une variabilité de son effet clinique. La RC ne permet pas d’analyser le tissu et, le ciblage ainsi que le suivi, sont réalisés uniquement sur la base de la neuroimagerie. Nous avons analysé la limitation du ciblage indirect en utilisant l’IRM à haut champs [7 Tesla (T)] et en la combinant avec une imagerie multimodale, incluant des séquences 3T et 7T, pour la définition du Vim. La partie centrale de la thèse se focalise sur la compréhension de l’effet radiobiologique de la RC du Vim dans le TE. Cette partie se base tant de l’analyse de l’imagerie structurelle (séquence classique T1) que sur l’imagerie fonctionnelle (IRM de repos). Le but de la première partie de la thèse est la visualisation directe du Vim en utilisant l’IRM 7T, qui a plusieurs avantages par rapport à l’IRM 3T, y compris une meilleure résolution spatiale. Notamment, la séquence SWI a un intérêt particulier, mais elle n’avait encore jamais été explorée que de manière quantitative au niveau du thalamus (qui contient le Vim). Nous avons combinée plusieurs modalités pour définir le Vim (chez des sujets sains de différents âges): visualisation directe sur la 7T, quadrilatère de Guiot tel qu’utilisé en pratique clinique courante, ainsi que segmentation automatique en imagerie de diffusion ou par des atlas (ces dernières deux approches ont été réalisées par, et en collaboration avec, Dr Najdenovska). Nous avons conclu que la séquence 7T SWI, malgré certains avantages, et utilisée seule ou combinée avec d’autres modalités, présente certaines limitations qui ne permettent pas, à l’heure actuelle, de l’utiliser d’une manière standardisée, tant chez les sujets sains que chez les patients atteints de TE. Dans la deuxième partie, l’étude de la radiobiologie de la radiochirugie pour le TE a permis d’apporter plusieurs contributions. La plus importante est la mise en évidence d’un « réseau visuel » structurel et fonctionnel, impliqué dans la genèse du tremblement et dans son amélioration après une RC du Vim. Les patients dont ce réseau est mieux intégré avant la procédure ont de meilleures chances d’amélioration clinique du TE. Dans ce contexte, nous avons proposé d’adapter le terme classique d’ «axe cérébello-thalamo-moteur» en le modifiant en « axe cérébello-thalamo-visuo-moteur», ce qui pourrait aider à une meilleure compréhension de la pathophysiologie du TE. Nous proposons également que deux structures puissent jouer le rôle de neuromodulateur de ce réseau, le cervelet et le thalamus. Une autre contribution est la description de l’interconnexion entre le réseau classique impliquant les noyaux de la base et celui l’attention, ainsi que de l’interconnexion entre le réseau cérébelleux et celui des cortex moteur primaire et visuel associatif. Des phénotypes particuliers du tremblement ont été analysés, incluant par exemple des tremblements du chef. Des travaux en cours incluent l’étude de la dynamique du cortex extra-strié en utilisant de nouvelles approches, comme les patterns de co-activation

Hitting the right target : noninvasive localization of the subthalamic nucleus motor part for specific deep brain stimulation

Deep brain stimulation of the subthalamic nucleus (STN) has gained momentum as a therapy for advanced Parkinson’s disease. The stimulation effectively alleviates the patients’ typical motor symptoms on a long term, but can give rise to cognitive and psychiatric adverse effects as well. Based on primate studies, the STN has been divided into three functionally different parts, which were distinguished by their afferent and efferent connections. The largest part is the motor area, followed by an associative and a limbic area. The serious adverse effects on cognition and behavior occurring after deep brain stimulation are assumed to be caused by electrical current spread to the associative and limbic areas of the STN. Therefore, selective stimulation of the motor part of the STN seems crucial, both to obtain the best possible therapeutic effect on the motor symptoms and to minimize the debilitating effects on cognition and behavior. However, current medical imaging techniques do not yet facilitate the required accurate identification of the STN itself, let alone its different functional areas. The final target for DBS is still often adjusted using intraoperative electrophysiology. Therefore, in this thesis we aimed to improve imaging for deep brain stimulation using noninvasive MRI protocols, in order to identify the STN and its motor part. We studied the advantages and drawbacks of already available noninvasive methods to target the STN. This review did not lead to a straightforward conclusion; identification of the STN motor part remained an open question. In follow-up on this question, we investigated the possibility to distinguish the different functional STN parts based on their connectivity information. Three types of information were carefully analyzed in this thesis. First, we looked into the clustering of local diffusion information within the STN region. We visually inspected the complex diffusion profiles, derived from postmortem rat brain data with high angular resolution, and augmented this manual segmentation method using k-means and graph cuts clustering. Because the weighing of different orders of diffusion information in the traditionally used L2 norm on the orientation distribution functions (ODFs) remained an open issue, we developed a specialized distance measure, the so-called Sobolev norm. This norm does not only take into account the amplitudes of the diffusion profiles, but also their extrema. We showed it to perform better than the L2 norm on synthetic phantom data and real brain (thalamus) data. The research done on this topic facilitates better classification by clustering of gray matter structures in the (deep) brain. Secondly, we were the first to analyze the STN’s full structural connectivity, based on probabilistic fiber tracking in diffusion MRI data of healthy volunteers. The results correspond well to topical literature on STN projections. Furthermore, we assessed the structural connectivity per voxel of the STN seed region and discovered a gradient in connectivity to the premotor cortex within the STN. While going from the medial to the lateral part of the STN, the connectivity increases, confirming the expected lateral location of the STN motor part. Finally, the connectivity analysis produced evidence for the existence of a "hyperdirect" pathway between the motor cortex and the STN in humans, which is very useful for future research into stimulation targets. The results of these experiments indicate that it is possible to find the motor part of the STN as specific target for deep brain stimulation using structural connectivity information acquired in a noninvasive way. Third and last, we studied functional connectivity using resting state functional MRI data of healthy volunteers. The resulting significant clusters provided us with the first complete description of the STN’s resting state functional connectivity, which corresponds with the expectations based on available literature. Moreover, we performed a reverse regression procedure with the average time series signals in motor and limbic areas as principal regressors. The results were analyzed for each STN voxel separately and also showed mediolateral gradients in functional connectivity within the STN. The lateral STN part exhibited more motor connectivity, while the medial part seemed to be more functionally connected to limbic brain areas, as described in neuronal tracer studies. These results show that functional connectivity analysis also is a viable noninvasive method to find the motor part of the STN. The work on noninvasive MRI methods for identification of the STN and its functional parts, as presented in this thesis, thus contributes to future specific stimulation of the motor part of the STN for deep brain stimulation in patients with Parkinson’s disease. This may help to maximize the motor effects and minimize severe cognitive and psychiatric side effects

Unsupervised deep learning of human brain diffusion magnetic resonance imaging tractography data

L'imagerie par résonance magnétique de diffusion est une technique non invasive permettant de connaître la microstructure organisationnelle des tissus biologiques. Les méthodes computationnelles qui exploitent la préférence orientationnelle de la diffusion dans des structures restreintes pour révéler les voies axonales de la matière blanche du cerveau sont appelées tractographie. Ces dernières années, diverses méthodes de tractographie ont été utilisées avec succès pour découvrir l'architecture de la matière blanche du cerveau. Pourtant, ces techniques de reconstruction souffrent d'un certain nombre de défauts dérivés d'ambiguïtés fondamentales liées à l'information orientationnelle. Cela a des conséquences dramatiques, puisque les cartes de connectivité de la matière blanche basées sur la tractographie sont dominées par des faux positifs. Ainsi, la grande proportion de voies invalides récupérées demeure un des principaux défis à résoudre par la tractographie pour obtenir une description anatomique fiable de la matière blanche. Des approches méthodologiques innovantes sont nécessaires pour aider à résoudre ces questions. Les progrès récents en termes de puissance de calcul et de disponibilité des données ont rendu possible l'application réussie des approches modernes d'apprentissage automatique à une variété de problèmes, y compris les tâches de vision par ordinateur et d'analyse d'images. Ces méthodes modélisent et trouvent les motifs sous-jacents dans les données, et permettent de faire des prédictions sur de nouvelles données. De même, elles peuvent permettre d'obtenir des représentations compactes des caractéristiques intrinsèques des données d'intérêt. Les approches modernes basées sur les données, regroupées sous la famille des méthodes d'apprentissage profond, sont adoptées pour résoudre des tâches d'analyse de données d'imagerie médicale, y compris la tractographie. Dans ce contexte, les méthodes deviennent moins dépendantes des contraintes imposées par les approches classiques utilisées en tractographie. Par conséquent, les méthodes inspirées de l'apprentissage profond conviennent au changement de paradigme requis, et peuvent ouvrir de nouvelles possibilités de modélisation, en améliorant ainsi l'état de l'art en tractographie. Dans cette thèse, un nouveau paradigme basé sur les techniques d'apprentissage de représentation est proposé pour générer et analyser des données de tractographie. En exploitant les architectures d'autoencodeurs, ce travail tente d'explorer leur capacité à trouver un code optimal pour représenter les caractéristiques des fibres de la matière blanche. Les contributions proposées exploitent ces représentations pour une variété de tâches liées à la tractographie, y compris (i) le filtrage et (ii) le regroupement efficace sur les résultats générés par d'autres méthodes, ainsi que (iii) la reconstruction proprement dite des fibres de la matière blanche en utilisant une méthode générative. Ainsi, les méthodes issues de cette thèse ont été nommées (i) FINTA (Filtering in Tractography using Autoencoders), (ii) CINTA (Clustering in Tractography using Autoencoders), et (iii) GESTA (Generative Sampling in Bundle Tractography using Autoencoders), respectivement. Les performances des méthodes proposées sont évaluées par rapport aux méthodes de l'état de l'art sur des données de diffusion synthétiques et des données de cerveaux humains chez l'adulte sain in vivo. Les résultats montrent que (i) la méthode de filtrage proposée offre une sensibilité et spécificité supérieures par rapport à d'autres méthodes de l'état de l'art; (ii) le regroupement des tractes dans des faisceaux est fait de manière consistante; et (iii) l'approche générative échantillonnant des tractes comble mieux l'espace de la matière blanche dans des régions difficiles à reconstruire. Enfin, cette thèse révèle les possibilités des autoencodeurs pour l'analyse des données des fibres de la matière blanche, et ouvre la voie à fournir des données de tractographie plus fiables.Abstract : Diffusion magnetic resonance imaging is a non-invasive technique providing insights into the organizational microstructure of biological tissues. The computational methods that exploit the orientational preference of the diffusion in restricted structures to reveal the brain's white matter axonal pathways are called tractography. In recent years, a variety of tractography methods have been successfully used to uncover the brain's white matter architecture. Yet, these reconstruction techniques suffer from a number of shortcomings derived from fundamental ambiguities inherent to the orientation information. This has dramatic consequences, since current tractography-based white matter connectivity maps are dominated by false positive connections. Thus, the large proportion of invalid pathways recovered remains one of the main challenges to be solved by tractography to obtain a reliable anatomical description of the white matter. Methodological innovative approaches are required to help solving these questions. Recent advances in computational power and data availability have made it possible to successfully apply modern machine learning approaches to a variety of problems, including computer vision and image analysis tasks. These methods model and learn the underlying patterns in the data, and allow making accurate predictions on new data. Similarly, they may enable to obtain compact representations of the intrinsic features of the data of interest. Modern data-driven approaches, grouped under the family of deep learning methods, are being adopted to solve medical imaging data analysis tasks, including tractography. In this context, the proposed methods are less dependent on the constraints imposed by current tractography approaches. Hence, deep learning-inspired methods are suit for the required paradigm shift, may open new modeling possibilities, and thus improve the state of the art in tractography. In this thesis, a new paradigm based on representation learning techniques is proposed to generate and to analyze tractography data. By harnessing autoencoder architectures, this work explores their ability to find an optimal code to represent the features of the white matter fiber pathways. The contributions exploit such representations for a variety of tractography-related tasks, including efficient (i) filtering and (ii) clustering on results generated by other methods, and (iii) the white matter pathway reconstruction itself using a generative method. The methods issued from this thesis have been named (i) FINTA (Filtering in Tractography using Autoencoders), (ii) CINTA (Clustering in Tractography using Autoencoders), and (iii) GESTA (Generative Sampling in Bundle Tractography using Autoencoders), respectively. The proposed methods' performance is assessed against current state-of-the-art methods on synthetic data and healthy adult human brain in vivo data. Results show that the (i) introduced filtering method has superior sensitivity and specificity over other state-of-the-art methods; (ii) the clustering method groups streamlines into anatomically coherent bundles with a high degree of consistency; and (iii) the generative streamline sampling technique successfully improves the white matter coverage in hard-to-track bundles. In summary, this thesis unlocks the potential of deep autoencoder-based models for white matter data analysis, and paves the way towards delivering more reliable tractography data

Fluctuations of attention network activation during the resting state reflect fluctuations in subjective attentional state.

peer reviewe

Individual differences in neural architecture supporting mental time travel

Episodic and semantic memory have been cornerstones of memory research ever since they were first described in a seminal article by Endel Tulving in 1972. Later work by Tulving posited that particularly episodic memory supported mental time travel, a process by which humans could project the self across a conceptual lifespan (however modern research has emphasised the role of semantic memory in the process). Various neurocognitive models have been proposed that attempt to explain the component processes of mental time travel. While it is now recognized that a common ‘core’ brain network underlies memory, prospection, and imagination (Schacter et al., 2017), the neural substrates of the component processes that comprise the core network supporting memory-based simulations, and the extent to which they are dissociable, are still a matter of intense debate. This thesis has demonstrated that combining diffusion MRI-based tractography with interview and self-report measures is a viable method for investigating the associations between interindividual differences in white matter microstructure and cognitive traits or tendencies related to mental time travel. The present findings provide support for the notion that episodic and semantic memory systems are at least partially separate and supported by different structurally instantiated neural pathways. However, it is also clear that they must interact and support each other within episodic construction and mental time travel. Regarding the current models of mental time travel, the results of this thesis do not provide overwhelming support to any single model. However, some evidence has been provided (linking fornix-mediated hippocampal processing to spatial components of memory in particular) that might support the scene construction hypothesis (Hassabis & Maguire, 2007). Further, present findings did not show a significant association between semantic circuitry (mediated by the ILF) and episodic future thinking – which poses a challenge to the semantic scaffolding hypothesis (Irish & Piguet, 2013). However, this is consistent with Tulving’s original notion of episodic and semantic memory (including autobiographical facts) being dissociable but interacting memory systems that are future as well as past directed