Cortical Surface Registration and Shape Analysis

A population analysis of human cortical morphometry is critical for insights into brain development or degeneration. Such an analysis allows for investigating sulcal and gyral folding patterns. In general, such a population analysis requires both a well-established cortical correspondence and a well-defined quantification of the cortical morphometry. The highly folded and convoluted structures render a reliable and consistent population analysis challenging. Three key challenges have been identified for such an analysis: 1) consistent sulcal landmark extraction from the cortical surface to guide better cortical correspondence, 2) a correspondence establishment for a reliable and stable population analysis, and 3) quantification of the cortical folding in a more reliable and biologically meaningful fashion. The main focus of this dissertation is to develop a fully automatic pipeline that supports a population analysis of local cortical folding changes. My proposed pipeline consists of three novel components I developed to overcome the challenges in the population analysis: 1) automatic sulcal curve extraction for stable/reliable anatomical landmark selection, 2) group-wise registration for establishing cortical shape correspondence across a population with no template selection bias, and 3) quantification of local cortical folding using a novel cortical-shape-adaptive kernel. To evaluate my methodological contributions, I applied all of them in an application to early postnatal brain development. I studied the human cortical morphological development using the proposed quantification of local cortical folding from neonate age to 1 year and 2 years of age, with quantitative developmental assessments. This study revealed a novel pattern of associations between the cortical gyrification and cognitive development.Doctor of Philosoph

Cell Nuclear Morphology Analysis Using 3D Shape Modeling, Machine Learning and Visual Analytics

Quantitative analysis of morphological changes in a cell nucleus is important for the understanding of nuclear architecture and its relationship with cell differentiation, development, proliferation, and disease. Changes in the nuclear form are associated with reorganization of chromatin architecture related to altered functional properties such as gene regulation and expression. Understanding these processes through quantitative analysis of morphological changes is important not only for investigating nuclear organization, but also has clinical implications, for example, in detection and treatment of pathological conditions such as cancer. While efforts have been made to characterize nuclear shapes in two or pseudo-three dimensions, several studies have demonstrated that three dimensional (3D) representations provide better nuclear shape description, in part due to the high variability of nuclear morphologies. 3D shape descriptors that permit robust morphological analysis and facilitate human interpretation are still under active investigation. A few methods have been proposed to classify nuclear morphologies in 3D, however, there is a lack of publicly available 3D data for the evaluation and comparison of such algorithms. There is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wide analyses. In this work, we address a number of these existing limitations. First, we present a largest publicly available, to-date, 3D microscopy imaging dataset for cell nuclear morphology analysis and classification. We provide a detailed description of the image analysis protocol, from segmentation to baseline evaluation of a number of popular classification algorithms using 2D and 3D voxel-based morphometric measures. We proposed a specific cross-validation scheme that accounts for possible batch effects in data. Second, we propose a new technique that combines mathematical modeling, machine learning, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. Employing robust and smooth surface reconstruction methods to accurately approximate 3D object boundary enables the establishment of homologies between different biological shapes. Then, we compute geometric morphological measures characterizing the form of cell nuclei and nucleoli. We combine these methods into a highly parallel computational pipeline workflow for automated morphological analysis of thousands of nuclei and nucleoli in 3D. We also describe the use of visual analytics and deep learning techniques for the analysis of nuclear morphology data. Third, we evaluate proposed methods for 3D surface morphometric analysis of our data. We improved the performance of morphological classification between epithelial vs mesenchymal human prostate cancer cells compared to the previously reported results due to the more accurate shape representation and the use of combined nuclear and nucleolar morphometry. We confirmed previously reported relevant morphological characteristics, and also reported new features that can provide insight in the underlying biological mechanisms of pathology of prostate cancer. We also assessed nuclear morphology changes associated with chromatin remodeling in drug-induced cellular reprogramming. We computed temporal trajectories reflecting morphological differences in astroglial cell sub-populations administered with 2 different treatments vs controls. We described specific changes in nuclear morphology that are characteristic of chromatin re-organization under each treatment, which previously has been only tentatively hypothesized in literature. Our approach demonstrated high classification performance on each of 3 different cell lines and reported the most salient morphometric characteristics. We conclude with the discussion of the potential impact of method development in nuclear morphology analysis on clinical decision-making and fundamental investigation of 3D nuclear architecture. We consider some open problems and future trends in this field.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147598/1/akalinin_1.pd

Statistical shape analysis for bio-structures : local shape modelling, techniques and applications

A Statistical Shape Model (SSM) is a statistical representation of a shape obtained from data to study variation in shapes. Work on shape modelling is constrained by many unsolved problems, for instance, difficulties in modelling local versus global variation. SSM have been successfully applied in medical image applications such as the analysis of brain anatomy. Since brain structure is so complex and varies across subjects, methods to identify morphological variability can be useful for diagnosis and treatment. The main objective of this research is to generate and develop a statistical shape model to analyse local variation in shapes. Within this particular context, this work addresses the question of what are the local elements that need to be identified for effective shape analysis. Here, the proposed method is based on a Point Distribution Model and uses a combination of other well known techniques: Fractal analysis; Markov Chain Monte Carlo methods; and the Curvature Scale Space representation for the problem of contour localisation. Similarly, Diffusion Maps are employed as a spectral shape clustering tool to identify sets of local partitions useful in the shape analysis. Additionally, a novel Hierarchical Shape Analysis method based on the Gaussian and Laplacian pyramids is explained and used to compare the featured Local Shape Model. Experimental results on a number of real contours such as animal, leaf and brain white matter outlines have been shown to demonstrate the effectiveness of the proposed model. These results show that local shape models are efficient in modelling the statistical variation of shape of biological structures. Particularly, the development of this model provides an approach to the analysis of brain images and brain morphometrics. Likewise, the model can be adapted to the problem of content based image retrieval, where global and local shape similarity needs to be measured

Proceedings of the Second International Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08) - Geometrical and Statistical Methods for Modelling Biological Shape Variability

International audienceThe goal of computational anatomy is to analyze and to statistically model the anatomy of organs in different subjects. Computational anatomic methods are generally based on the extraction of anatomical features or manifolds which are then statistically analyzed, often through a non-linear registration. There are nowadays a growing number of methods that can faithfully deal with the underlying biomechanical behavior of intra-subject deformations. However, it is more difficult to relate the anatomies of different subjects. In the absence of any justified physical model, diffeomorphisms provide a general mathematical framework that enforce topological consistency. Working with such infinite dimensional space raises some deep computational and mathematical problems, in particular for doing statistics. Likewise, modeling the variability of surfaces leads to rely on shape spaces that are much more complex than for curves. To cope with these, different methodological and computational frameworks have been proposed (e.g. smooth left-invariant metrics, focus on well-behaved subspaces of diffeomorphisms, modeling surfaces using courants, etc.) The goal of the Mathematical Foundations of Computational Anatomy (MFCA) workshop is to foster the interactions between the mathematical community around shapes and the MICCAI community around computational anatomy applications. It targets more particularly researchers investigating the combination of statistical and geometrical aspects in the modeling of the variability of biological shapes. The workshop aims at being a forum for the exchange of the theoretical ideas and a source of inspiration for new methodological developments in computational anatomy. A special emphasis is put on theoretical developments, applications and results being welcomed as illustrations. Following the very successful first edition of this workshop in 2006 (see http://www.inria.fr/sophia/asclepios/events/MFCA06/), the second edition was held in New-York on September 6, in conjunction with MICCAI 2008. Contributions were solicited in Riemannian and group theoretical methods, Geometric measurements of the anatomy, Advanced statistics on deformations and shapes, Metrics for computational anatomy, Statistics of surfaces. 34 submissions were received, among which 9 were accepted to MICCAI and had to be withdrawn from the workshop. Each of the remaining 25 paper was reviewed by three members of the program committee. To guaranty a high level program, 16 papers only were selected

Statistical shape analysis for bio-structures : local shape modelling, techniques and applications

A Statistical Shape Model (SSM) is a statistical representation of a shape obtained from data to study variation in shapes. Work on shape modelling is constrained by many unsolved problems, for instance, difficulties in modelling local versus global variation. SSM have been successfully applied in medical image applications such as the analysis of brain anatomy. Since brain structure is so complex and varies across subjects, methods to identify morphological variability can be useful for diagnosis and treatment. The main objective of this research is to generate and develop a statistical shape model to analyse local variation in shapes. Within this particular context, this work addresses the question of what are the local elements that need to be identified for effective shape analysis. Here, the proposed method is based on a Point Distribution Model and uses a combination of other well known techniques: Fractal analysis; Markov Chain Monte Carlo methods; and the Curvature Scale Space representation for the problem of contour localisation. Similarly, Diffusion Maps are employed as a spectral shape clustering tool to identify sets of local partitions useful in the shape analysis. Additionally, a novel Hierarchical Shape Analysis method based on the Gaussian and Laplacian pyramids is explained and used to compare the featured Local Shape Model. Experimental results on a number of real contours such as animal, leaf and brain white matter outlines have been shown to demonstrate the effectiveness of the proposed model. These results show that local shape models are efficient in modelling the statistical variation of shape of biological structures. Particularly, the development of this model provides an approach to the analysis of brain images and brain morphometrics. Likewise, the model can be adapted to the problem of content based image retrieval, where global and local shape similarity needs to be measured.EThOS - Electronic Theses Online ServiceConsejo Nacional de Ciencia y Tecnología (Mexico) (CONACYT)GBUnited Kingdo

Atlas Construction for Measuring the Variability of Complex Anatomical Structures

RÉSUMÉ La recherche sur l'anatomie humaine, en particulier sur le cœur et le cerveau, est d'un intérêt particulier car leurs anomalies entraînent des pathologies qui sont parmi les principales causes de décès dans le monde et engendrent des coûts substantiels. Heureusement, les progrès en imagerie médicale permettent des diagnostics et des traitements autrefois impossibles. En contrepartie, la quantité phénoménale de données produites par ces technologies nécessite le développement d'outils efficaces pour leur traitement. L'objectif de cette thèse est de proposer un ensemble d'outils permettant de normaliser des mesures prélevées sur différents individus, essentiels à l'étude des caractéristiques de structures anatomiques complexes. La normalisation de mesures consiste à rassembler une collection d'images dans une référence commune, aussi appelée construction d'atlas numériques, afin de combiner des mesures provenant de différents patients. Le processus de construction inclut deux étapes principales; la segmentation d'images pour trouver des régions d'intérêts et le recalage d'images afin de déterminer les correspondances entres régions d'intérêts. Les méthodes actuelles de constructions d'atlas peuvent nécessiter des interventions manuelles, souvent fastidieuses, variables, et sont en outre limitées par leurs mécanismes internes. Principalement, le recalage d'images dépend d'une déformation incrémentales d'images sujettes a des minimums locaux. Le recalage n'est ainsi pas optimal lors de grandes déformations et ces limitations requièrent la nécessite de proposer de nouvelles approches pour la construction d'atlas. Les questions de recherche de cette thèse se concentrent donc sur l'automatisation des méthodes actuelles ainsi que sur la capture de déformations complexes de structures anatomiques, en particulier sur le cœur et le cerveau. La méthodologie adoptée a conduit à trois objectifs de recherche spécifiques. Le premier prévoit un nouveau cadre de construction automatise d'atlas afin de créer le premier atlas humain de l'architecture de fibres cardiaques. Le deuxième vise à explorer une nouvelle approche basée sur la correspondance spectrale, nommée FOCUSR, afin de capturer une grande variabilité de formes sur des maillages. Le troisième aboutit finalement à développer une approche fondamentalement différente pour le recalage d'images à fortes déformations, nommée les démons spectraux. Le premier objectif vise plus particulièrement à construire un atlas statistique de l'architecture des fibres cardiaques a partir de 10 cœurs ex vivo humains. Le système développé a mené à deux contributions techniques et une médicale, soit l'amélioration de la segmentation de structures cardiaques et l'automatisation du calcul de forme moyenne, ainsi que notamment la première étude chez l'homme de la variabilité de l'architecture des fibres cardiaques. Pour résumer les principales conclusions, les fibres du cœur humain moyen varient de +- 12 degrés, l'angle d'helix s'étend entre -41 degrés (+- 26 degrés) sur l'épicarde à +66 degrés (+- 15 degrés) sur l'endocarde, tandis que l'angle transverse varie entre +9 degrés (+- 12 degrés) et +34 degrés (+- 29 degrés) à travers le myocarde. Ces résultats sont importants car ces fibres jouent un rôle clef dans diverses fonctions mécaniques et électrophysiologiques du cœur. Le deuxième objectif cherche à capturer une grande variabilité de formes entre structures anatomiques complexes, plus particulièrement entre cortex cérébraux à cause de l'extrême variabilité de ces surfaces et de leur intérêt pour l'étude de fonctions cognitives. La nouvelle méthode de correspondance surfacique, nommée FOCUSR, exploite des représentations spectrales car l'appariement devient plus facile et rapide dans le domaine spectral plutôt que dans l'espace Euclidien classique. Dans sa forme la plus simple, FOCUSR améliore les méthodes spectrales actuelles par un recalage non rigide des représentations spectrales, toutefois, son plein potentiel est atteint en exploitant des données supplémentaires lors de la mise en correspondance. Par exemple, les résultats ont montré que la profondeur des sillons et de la courbure du cortex cérébral améliore significativement la correspondance de surfaces de cerveaux. Enfin, le troisième objectif vise à améliorer le recalage d'images d'organes ayant des fortes variabilités entre individus ou subis de fortes déformations, telles que celles créées par le mouvement cardiaque. La méthodologie amenée par la correspondance spectrale permet d'améliorer les approches conventionnelles de recalage d'images. En effet, les représentations spectrales, capturant des similitudes géométriques globales entre différentes formes, permettent de surmonter les limitations actuelles des méthodes de recalage qui restent guidées par des forces locales. Le nouvel algorithme, nommé démons spectraux, peut ainsi supporter de très grandes déformations locales et complexes entre images, et peut être tout autant adapté a d'autres approches, telle que dans un cadre de recalage conjoint d'images. Il en résulte un cadre complet de construction d'atlas, nommé démons spectraux multijoints, où la forme moyenne est calculée directement lors du processus de recalage plutôt qu'avec une approche séquentielle de recalage et de moyennage. La réalisation de ces trois objectifs spécifiques a permis des avancées dans l'état de l'art au niveau des méthodes de correspondance spectrales et de construction d'atlas, en permettant l'utilisation d'organes présentant une forte variabilité de formes. Dans l'ensemble, les différentes stratégies fournissent de nouvelles contributions sur la façon de trouver et d'exploiter des descripteurs globaux d'images et de surfaces. D'un point de vue global, le développement des objectifs spécifiques établit un lien entre : a) la première série d'outils, mettant en évidence les défis à recaler des images à fortes déformations, b) la deuxième série d'outils, servant à capturer de fortes déformations entre surfaces mais qui ne reste pas directement applicable a des images, et c) la troisième série d'outils, faisant un retour sur le traitement d'images en permettant la construction d'atlas a partir d'images ayant subies de fortes déformations. Il y a cependant plusieurs limitations générales qui méritent d'être investiguées, par exemple, les données partielles (tronquées ou occluses) ne sont pas actuellement prises en charge les nouveaux outils, ou encore, les stratégies algorithmiques utilisées laissent toujours place à l'amélioration. Cette thèse donne de nouvelles perspectives dans les domaines de l'imagerie cardiaque et de la neuroimagerie, toutefois, les nouveaux outils développés sont assez génériques pour être appliqués a tout recalage d'images ou de surfaces. Les recommandations portent sur des recherches supplémentaires qui établissent des liens avec la segmentation à base de graphes, pouvant conduire à un cadre complet de construction d'atlas où la segmentation, le recalage, et le moyennage de formes seraient tous interdépendants. Il est également recommandé de poursuivre la recherche sur la construction de meilleurs modèles électromécaniques cardiaques à partir des résultats de cette thèse. En somme, les nouveaux outils offrent de nouvelles bases de recherche et développement pour la normalisation de formes, ce qui peut potentiellement avoir un impact sur le diagnostic, ainsi que la planification et la pratique d'interventions médicales.----------ABSTRACT Research on human anatomy, in particular on the heart and the brain, is a primary concern for society since their related diseases are among top killers across the globe and have exploding associated costs. Fortunately, recent advances in medical imaging offer new possibilities for diagnostics and treatments. On the other hand, the growth in data produced by these relatively new technologies necessitates the development of efficient tools for processing data. The focus of this thesis is to provide a set of tools for normalizing measurements across individuals in order to study complex anatomical characteristics. The normalization of measurements consists of bringing a collection of images into a common reference, also known as atlas construction, in order to combine measurements made on different individuals. The process of constructing an atlas involves the topics of segmentation, which finds regions of interest in the data (e.g., an organ, a structure), and registration, which finds correspondences between regions of interest. Current frameworks may require tedious and hardly reproducible user interactions, and are additionally limited by their computational schemes, which rely on slow iterative deformations of images, prone to local minima. Image registration is, therefore, not optimal with large deformations. Such limitations indicate the need to research new approaches for atlas construction. The research questions are consequently addressing the problems of automating current frameworks and capturing global and complex deformations between anatomical structures, in particular between human hearts and brains. More precisely, the methodology adopted in the thesis led to three specific research objectives. Briefly, the first step aims at developing a new automated framework for atlas construction in order to build the first human atlas of the cardiac fiber architecture. The second step intends to explore a new approach based on spectral correspondence, named FOCUSR, in order to precisely capture large shape variability. The third step leads, finally, to a fundamentally new approach for image registration with large deformations, named the Spectral Demons algorithm. The first objective aims more specifically at constructing a statistical atlas of the cardiac fiber architecture from a unique human dataset of 10 ex vivo hearts. The developed framework made two technical, and one medical, contributions, that are the improvement of the segmentation of cardiac structures, the automation of the shape averaging process, and more importantly, the first human study on the variability of the cardiac fiber architecture. To summarize the main finding, the fiber orientations in human hearts has been found to vary with about +- 12 degrees, the range of the helix angle spans from -41 degrees (+- 26 degrees) on the epicardium to +66 degrees (+- 15 degrees) on the endocardium, while, the range of the transverse angle spans from +9 degrees (+- 12 degrees) to +34 degrees (+- 29 degrees) across the myocardial wall. These findings are significant in cardiology since the fiber architecture plays a key role in cardiac mechanical functions and in electrophysiology. The second objective intends to capture large shape variability between complex anatomical structures, in particular between cerebral cortices due to their highly convoluted surfaces and their high anatomical and functional variability across individuals. The new method for surface correspondence, named FOCUSR, exploits spectral representations since matching is easier in the spectral domain rather than in the conventional Euclidean space. In its simplest form, FOCUSR improves current spectral approaches by refining spectral representations with a nonrigid alignment; however, its full power is demonstrated when using additional features during matching. For instance, the results showed that sulcal depth and cortical curvature improve significantly the accuracy of cortical surface matching. Finally, the third objective is to improve image registration for organs with a high inter-subject variability or undergoing very large deformations, such as the heart. The new approach brought by the spectral matching technique allows the improvement of conventional image registration methods. Indeed, spectral representations, which capture global geometric similarities and large deformations between different shapes, may be used to overcome a major limitation of current registration methods, which are in fact guided by local forces and restrained to small deformations. The new algorithm, named Spectral Demons, can capture very large and complex deformations between images, and can additionally be adapted to other approaches, such as in a groupwise configuration. This results in a complete framework for atlas construction, named Groupwise Spectral Demons, where the average shape is computed during the registration process rather than in sequential steps. The achievements of these three specific objectives permitted advances in the state-of-the-art of spectral matching methods and of atlas construction, enabling the registration of organs with significant shape variability. Overall, the investigation of these different strategies provides new contributions on how to find and exploit global descriptions of images and surfaces. From a global perspective, these objectives establish a link between: a) the first set of tools, that highlights the challenges in registering images with very large deformations, b) the second set of tools, that captures very large deformations between surfaces but are not applicable to images, and c) the third set of tools, that comes back on processing images and allows a natural construction of atlases from images with very large deformations. There are, however, several general remaining limitations, for instance, partial data (truncated or occluded) is currently not supported by the new tools, or also, the strategy for computing and using spectral representations still leaves room for improvement. This thesis gives new perspectives in cardiac and neuroimaging, yet at the same time, the new tools remain general enough for virtually any application that uses surface or image registration. It is recommended to research additional links with graph-based segmentation methods, which may lead to a complete framework for atlas construction where segmentation, registration and shape averaging are all interlinked. It is also recommended to pursue research on building better cardiac electromechanical models from the findings of this thesis. Nevertheless, the new tools provide new grounds for research and application of shape normalization, which may potentially impact diagnostic, as well as planning and performance of medical interventions

Myeloarchitecture and Intrinsic Functional Connectivity of Auditory Cortex in Musicians with Absolute Pitch

Introduction This dissertation studied structures and functions of auditory cortex in musicians with a rare auditory perception called absolute pitch (AP) using an in-vivo neuroimaging technique magnetic resonance imaging (MRI). The absolute pitch is defined as an ability to recognize pitch chroma, which is musical naming in the twelve-tone equal-temperament (12-TET) system (e.g., “C#”), of any given tonal sound without external references. It has been of interest of many psychologists since the experimental methods have been introduced in psychology over a century. Early behavioral experiments reported many findings that were validated in later studies with computerized measurement of behaviors. Over the recent two decades, in-vivo neuroimaging studies have found alteration in structures and functions of the brains of musicians with AP compared to control musicians without AP. However, quantitative models on the behaviors of neural systems behind the AP have not been suggested yet. Of course, neuronal modeling is a challenging problem in cognitive neuroscience studies in general. In order to generate such models to explain auditory perceptions such as AP, detailed information on structures and functions of neural systems must be obtained. In this context, we examined microarchitecture of the auditory cortex in musicians with AP using ultra- high field MRI that currently enables the highest spatial resolution of in-vivo imaging at the moment. In addition, we examined the functional connectivity between the auditory cortex and the other regions of the whole cortex. In the dissertation, detailed introduction of the pitch chroma perception is given throughout the human auditory systems from peripheral apparatus to non-primary auditory cortex in the Chapter I. In-depth discussion on the in-vivo imaging techniques, image processing, and statistical inferences focusing on the strength and potential pitfalls of the methods and their common practice in the Chapter II. In the Chapter III and IV, I explained MRI studies of the PhD project in details with discussions on the findings. Finally in the Chapter V, I summarized the major findings and discuss possible interpretation based on the framework of ‘dual auditory pathway hypothesis’. Study of Myeloarchitecture In the first study (Chapter III), a novel MRI sequence named magnetization-prepared two rapid gradient echo (MP2RAGE) was used to investigate cortical myelination. Myeloarchitecture of cerebral cortex is the one of the important histological concepts to understand organization of cortical column as well as cytoarchitecture. Neurons in the cortex are not only linked to the other distant neurons through the white matter but also connected vertically and horizontally to adjacent neurons. These short/long-distance axonal connections form myeloarchitecture of the cortex. The MP2RAGE sequence estimates a physical quantity called longitudinal relaxation rates (R1), which is sensitive to myelin concentration of the tissue. When compared to control musicians without AP, we found greater R1 in the anterior part of the right supratemporal plane in the musicians with AP. Given the finding was specific to the middle depth of cortex, the finding is unlikely related to long-distance axonal connections but likely to local connections. The precise location of the group difference was determined as the right planum polare in the template brain as well as in all individual brains. Based on the finding, I speculated that the working principles of the AP processes might be related to the dual auditory pathway hypothesis. In the theory, spatial auditory information is processed along the dorsal pathway (from the primary auditory cortex, to planum temporale, supramarginal gyrus, parietal lobules, and dorsolateral prefrontal cortex) whereas non-spatial auditory information is processed along the ventral pathway (from the primary auditory cortex to planum polare, temporal pole, anterior insular, and ventrolateral prefrontal cortex) in analogous to visual system. Because pitch chroma is spatially invariant property of an auditory object, and also it is less useful for auditory scene segregation compared to separation based on general pitch range (i.e., pitch height), I suggested the observation of cortical myelin in the anterior non-primary auditory cortex might be related to the absolute recognition of pitch chroma in AP listeners. Another potential implication of the heavy myelination is the function of myelination in neural development. In a rat model, it was demonstrated that the myelination of cortex triggers protein interactions that greatly restrict neuroplasticity after the ‘critical period’ of normal development. From genetic studies, it has been found that the onset of musical training is crucial in the acquisition of AP. Since the planum polare is related to pitch chroma processing, the increase of myelination in this region might indicate the preservation of the pitch chroma representation. Study of Intrinsic Functional Connectivity In the second study (Chapter IV), to further test the hypothesis that this highly myelinated planum polare works differently in the auditory networks, analysis of intrinsic functional connectivity using functional MRI (fMRI) measurement acquired during resting was performed. Although spontaneous neural activities during resting was once regarded as Gaussian noise without particular information, extensive researches revealed that the resting-state data (fMRI and also M/EEG) bears substantial information on the subnetworks of brain that subserve various perceptual and cognitive functions. Particularly for the perception of AP, it has been known that spontaneous and unintended recognition of pitch chroma from ambient sounds such as the siren of an ambulance. Thus it is reasonable to assume that the AP-specific network would be constantly active even at rest. From the resting-state fMRI data, greater cross-correlations between the right planum polare, which was found to be highly myelinated, and several cortical areas including the right lateral superior temporal gyrus, the anterior insula, and the left inferior frontal cortex were found in musicians with better AP performance. Moreover, greater cross-coherences between the right planum polare and the medial part of superior frontal gyrus, the anterior cingulate cortex, and the left planum polare were found in musicians with greater AP performance. As speculated, the involvement of the ventral auditory pathway in the AP-specific resting state network was strongly suggested from the tightened functional coupling between anterior supratemporal planes and the left inferior frontal cortex. Interestingly, the right planum polare exhibited greater cross-coherence with the important hub regions of the default mode network, i.e., anterior cingulate cortex and medial parts of the superior frontal cortex and the orbitofrontal cortex, implicating a link between the auditory network and default-mode network in AP listeners. This might be related to constant AP processes in AP listeners, which results in spontaneous and unintentional recognition of AP. Conclusion In the dissertation, novel MRI data from musicians with AP were provided adding knowledge of the myeloarchitectonic characteristics and related intrinsic functional connectivity of the auditory cortex to the current understanding on the neural correlates of AP. The findings were in favor of the proposed involvement of the ventral auditory pathway, which is known for processing spatially invariant properties of auditory objects. Further studies on neural behaviors of the auditory cortex in relation to the myeloarchitecture are needed in developing computational models of AP in the future.Einleitung Diese Dissertation untersucht Strukturen und Funktionen des auditorischen Kortex in Musikern mit einer seltenen auditorischen Wahrnehmen, dem absoluten Gehör (aG), mit Hilfe des in-vivo Bildgebungsfahrens der Magnetresonanztomographie (MRT). Das absolute Gehör bezeichnet die Fähigkeit die Tonklasse (z.B. „C#“) innerhalb des 12-tönigen Systems gleichmäßiger Stimmung (12-TET) ohne externe Referenz benennen zu können. Das Phänomen des absoluten Gehöres ist Gegenstand psychologischer Untersuchungen seitdem die experimentellen Methoden vor über einem Jahrhundert vorgestellt wurden. Erste behaviorale Experimente berichteten zahlreiche Ergebnisse, die später in computer-gestützten Messverfahren validiert werden konnten. In den letzten 20 Jahren konnten Studien, unter Nutzung bildgebender Verfahren, Veränderungen in der Struktur und Funktion in den Gehirnen von Musikern mit absolutem Gehör feststellen. Bisher wurden jedoch noch keine quantitativen Modelle vorgestellt, die das Verhalten neuronaler Systeme beschreiben, die dem absoluten Gehört zugrunde liegen. Die Modellierung neuronaler Systeme stellt ein anspruchsvolles Problem der gesamten kognitiven Neurowissenschaften dar. Detaillierte Informationen bezüglich der Struktur und Funktion des neuronalen Systems müssen gesammelt, um mit Hilfe von Modelle auditorische Empfindungen wie das absolute Gehör erklären zu können. In diesem Zusammenhang haben wir die Mikroarchitektur des auditorischen Kortex von Musiker mit absolutem Gehör mit Hilfe eines ultrahohem Feld-MRTs untersucht; eine Methode mit der derzeit höchsten räumlichen Auflösung aller in-vivo Bildgebungsverfahren. Außerdem wurde die funktionelle Konnektivität zwischen dem auditorischen Kortex und anderen Regionen des gesamten Kortex untersucht. In Kapitel I der Dissertation wird detailliertes Grundwissen zur Empfindung von Tonklassen, vom menschlichen auditorischen System bis zum nicht-primären auditorischen Kortex, vermittelt. Eine vertiefte Diskussion der in-vivo Bildgebungsverfahren, der Bildverarbeitung und den statistischen Rückschlüssen ist Thema von Kapitel II, mit einem Fokus auf der üblichen Verwendung, den Stärken und potentiellen Fehlern der verwendeten Methoden. In den Kapiteln III und IV habe ich die MRT-Studien der Doktorarbeit erklärt und die Ergebnisse diskutiert. Kapitel V fasst die wesentlichen Forschungsergebnisse zusammen und diskutiert eine mögliche Interpretation der Ergebnisse auf Grundlage der Dual Auditory Pathway Hypothese. Untersuchung der Myelinarchitektur In der ersten Studie (Kapitel III) wurde eine neuartige MRT Sequenz, die magnetization-prepared two rapid gradient echo (MP2RAGE) Sequenz, genutzt um die kortikale Myelinisierung zu untersuchen. Die Myelinarchitektur des zerebralen Kortex ist eine der wichtigsten histologischen Konzepte, um sowohl die Organisation einer kortikalen Kolumne als auch die Zytoarchitektur zu verstehen. Die Neuronen des Kortex sind nicht nur an entfernte Neuronen über die weiße Substanz gekoppelt, sondern auch durch vertikale und horizontale Verbindungen an unmittelbar benachbarte Neuronen. Diese kurzen und langen axonalen Verbindungen formen die Myelinarchitektur des Kortex. Die MP2RAGE Sequenz bewertet die longitudinalen Relaxations Raten (R1), welche sensitiv für die Myelinkonzentration des untersuchten Gewebes ist. Verglichen mit einer Kontrollgruppe von Musikern ohne aG konnten wir einen höheren R1- Wert im anterioren Teil der rechten supra-temporalen Ebene in Musikern mit aG feststellen. Da das Ergebnis spezifisch für eine mittlere Tiefe des Kortex war ist es wahrscheinlicher, dies auf lokale Verbindungen als auf lange axonale Verbindungen zurückzuführen. Als genauer Ort der Gruppendifferenz wurde das rechte planum polare sowohl in einem idealisierten Gehirn als auch in den individuellen Gehirnen der Probanden festgestellt. Aufgrund dieses Ergebnisses habe ich die Hypothese aufgestellt, dass die Wirkungsweise des absoluten Gehörs mit der Dual Auditory Pathway-Theorie zusammenhängt. Diese Theorie besagt, dass räumliche auditorische Information entlang einer dorsalen Bahn (vom primären auditorischen Kortex zum planum temporale, supramarginalen Gyrus, Parietallappen und dorsolateralen präfrontalen Kortex) und nicht-räumliche Informationen entlang einer ventralen Bahn (vom primären auditorischen Kortex zum planum polare, Temporalpol, anterior insular und ventrolateralen präfrontalen Kortex), ähnlich dem visuellen System, verarbeitet werden. Da die Tonklasse eine räumlich invariante Eigenschaft eines auditorischen Objektes ist und es zudem für die auditorische Szenenunterscheidung weniger bedeutsam ist als die generelle Tonhöhe, habe ich die Vermutung angestellt, dass das kortikale Myelin im anterioren nicht-primären auditorischen Kortex mit dem absoluten Gehört für die Tonklasse im Zusammenhang steht. Eine weitere Implikation der starken Myelinisierung betrifft die Funktion von Myelin in der neuronalen Entwicklung. Im Tiermodell einer Ratte konnte gezeigt werden, dass die Myelinisierung des Kortex Proteininteraktionen auslöst, die die Neuroplastizität nach einer ‚kritischen Periode‘ der normalen Entwicklung erheblich einschränkt. Genetische Studien haben gezeigt, dass der Beginn der musikalischen Ausbildung für die Entwicklung des absoluten Gehöres entscheidend ist. Da das planum polare mit der Verarbeitung von Tonklassen in Verbindung gebracht wird, könnte ein Anstieg der Myelinisierung in diesem Bereich einen Erhalt der Tonklassenrepräsentation bedeuten. Untersuchung der intrinsischen funktionellen Konnektivität In der zweiten Studie (Kapitel IV) wurde die Hypothese, dass das stark myelinisierte planum polare in den auditorischen Netzwerken verschieden wirkt, mittels funktioneller MRT (fMRT) im entspannten Wachzustand weiter untersucht. Spontane Hirnaktivität wurde lange Zeit als Gaußsches Rauschen ohne spezielle Informationen angesehen. Umfangreiche Studien konnten jedoch zeigen, dass Messungen des Ruhezustandes, sowohl fMRT als auch M/EEG, Information bezüglich der Sub-Netzwerke tragen, die Hirnfunktionen der Wahrnehmung und Kognition unterstützen. Besonders in Bezug auf die Wahrnehmung mit absolutem Gehör konnte festgestellt werden, dass Umgebungstöne wie die Sirene eines Krankenwagens unbewusst hinsichtlich der Tonklasse erkannt werden. Diese Erkenntnis stützt die Annahme, dass das aG-Netzwerk auch im Ruhezustand aktiv ist. Mit Hilfe der fMRT-Daten wurde festgestellt, dass die Kreuzkorrelation zwischen dem stark myelinisierten rechten planum polare und weiteren kortikalen Arealen wie dem rechten lateral- superioren temporalen Gyrus, der anterioren insula und dem linken inferior-frontalen Kortex in Musikern mit besserer aG-Performanz erhöht ist. Weiterhin wurde eine erhöhte Kreuzkorrelation zwischen dem rechten planum polare und dem medialen Teil des superior-frontalen Gyrus, dem anterioren cingulate Kortex und dem linken planum polare in Musikern mit noch besser aG- Performanz festgestellt. Die erhöhte funktionelle Kopplung der anterioren supra-temporalen Ebene mit dem linken inferior-frontalen Kortex bekräftigt die Hypothese, dass der ventrale auditorische Pfad in dem aG- spezifischen Netzwerk des Ruhezustands beteiligt ist. Bemerkenswerterweise zeigte das rechte planum polare eine erhöhte Kreuzkorrelation mit wichtigen Hub-regionen des Default-Mode Netzwerkes, also dem anterioren cingulate Kortex und medialen Teilen des superior-frontalen Kortex, sowie dem orbito-frontalen Kortex. Dies bedeutet eine Verknüpfung des auditorischen Netzwerkes und des Default-Mode Netzwerkes in Menschen mit absolutem Gehör und könnte mit aG-Prozessen zusammenhängen, die die spontane und unbewusste Erkennung des absoluten Gehörs erlauben. Schlussfolgerung In dieser Dissertation wurden MRT-Daten von Musikern mit absolutem Gehör untersucht und damit zur Erweiterung des Wissensstandes bezüglich der Myelinarchitektur und der damit zusammenhängenden funktionellen Konnektivität des auditorischen Kortex beigetragen. Die Ergebnisse sprechen zugunsten der Einbindung des ventralen auditorischen Pfades, bekannt für die Verarbeitung räumlich-invarianter Eigenschaften auditorischer Objekte. Weitere Untersuchungen bezüglich des neuronalen Verhaltens des auditorischen Kortex in Verbindung mit der Myelinarchitektur sind notwendig, um quantitative Modelle des absoluten Gehörs entwickeln zu können