Im Gegensatz zum peripheren Nervensystem (PNS) ist die Regenerationsfähigkeit im adulten zentralen Nervensystem (ZNS) von Vertebraten sehr eingeschränkt. Diese eingeschrängte Regenerationsfähigkeit wird im Wesentlichen durch das Vorhandensein von Myelin im adulten ZNS determiniert. Einerseits ist dieses Lipid für die Stabilisierung und Ernährung von Axonen sowie für die schnelle Reizweiterleitung unbedingt notwendig, andererseits stellt es den größten Inhibitor axonaler Regeneration dar. Myelin ist außerdem Zielstruktur diverser ZNS Pathologien, wie z.B. der Multiplen Sklerose. Für das Verständnis dieser Pathologien sowie der auswachsinhibitorischen Wirkung von Myelin wurde der Hippocampus als eine der plastischten ZNS Regionen gewählt. Dazu waren genaue Kenntnisse der Myeloarchitektur dieses Gebietes notwendig. Nach Etablierung einer zuverlässigen Detektierung für Myelin konnten in der vorliegenden Arbeit detailliert Myelinisierungsvorgänge im sich entwickelnden, im adulten und im deafferenzierten Hippocampus der Ratte analysiert werden. Während der Entwicklung erreichen die ersten entorhinale Axone den Hippocampus bereits am embryonal Tag 17 (E17); Myelin kann jedoch erst am postnatal Tag 17 (P17) lichtmikrokopisch nachgewiesen werden. Die Anzahl myelinisierter Fasern erreicht um den P25 ein Verteilungsmuster, welches dem von adulten Tieren gleicht. Nach Entorhinaler Cortex Läsion (ECL), bei der die Durchtrennung des Tractus perforans (PP) eine Denervation des Hippocampus bewirkt, kommt es zu einem langanhaltenden Verlust von Myelin. Zehn Tage nach Läsion (10 dal), also zum Zeitpunkt maximaler Aussprossung (Sprouting), kommt es zu einem Wiederkehren myelinisierter Fasern. Mehrere myelin-assoziierte Proteine, mit wachstumshemmenden Eigenschaften sind bekannt, wie z.B. die Familie der Nogo Gene (Nogo; englisch, kein Durchkommen). Diese werden ganz entschieden für den Verlust der Regenerationsfähigkeit des adulten ZNS verantwortlich gemacht. In der vorliegenden Arbeit wird die Expression der drei Nogo Gene (Nogo-A, -B, - C) und deren Rezeptor (Ng66R) während der postnatalen Entwicklung, im adulten ZNS sowie nach Läsion beschrieben. Ein erster überraschender Befund war die neuronale Expression der Nogos, die bisher nur in Oligodendrocyten nachgewiesen worden war. Zu einem Zeitpunkt, an dem entorhinale Fasern bereits in den Hippocampus eingewachsen, aber noch nicht myelinisiert sind (P0), wird Nogo-A, -B und Ng66R mRNA mit Ausnahme der Körnerzellschicht des Gyrus dentatus in allen Zellschichten des sich entwickelnden Hippocampus detektiert. Nogo-C und myelin basic protein (MBP) mRNA, werden erst am P15 expremiert, zu einem Zeitpunkt also, an dem myelinisierte Fasern erstmalig im Hippocampus auftreten. MBP wird ausschließlich in glialen, Nogo-C hingegen hauptsächlich in neuronalen Zellen exprimiert. Nach Deafferenzierung zeigt sich eine dynamische und Isoform- spezifische Regulation aller Nogo Transkripte. So zeigen die als erste von der Deafferenzierung betroffenen Körnerzellen zu Beginn der Waller`schen Degeneration sowie der neuronalen und glialen Antwort, eine starke Erhöhung aller Nogo Transkripte. Zum Zeitpunkt der maximalen Aussprossung kam es zu einem signifikanten Abfall der Nogo-C und Ng66R mRNA Expression, währendessen Nogo-A und Nogo-B bereits wieder das Kontrollniveau erreicht hatten. Vor allem im contralateralen Hippocampus, dem Hauptquellgebiet sproutender Fasern, imponierte die Runterregulation von Ng66R mRNA und zeigte erst nach Abschluß von axonalen Sproutingprozessen und der Synapsenformation wieder vergleichbare Werte mit den Kontrolltieren. Diese Korrelation der erniedrigten Ng66R Expression im contralateralen Hippocampus und dem axonalen Einwachsen in den deafferenzierten Hippocampus, läßt eine reduzierte axonale Ansprechbarkeit auf den Neuriten-Auswachshemmer Nogo-A vermuten, da bekannt ist, dass Axone, die kein Ng66R exprimieren, nicht durch die Nogo Gene im Wachstum gehemmt werden. Zusammenfassend kommt es während der Entwicklung und in der Reorganisationsphase zu einer spezifischen und geordneten Myelinisierung im Hippocampus. Die neuronale Expression von Nogo- A, -B und -C in einer so plastischen ZNS- Region unterstützt die Hypothese, dass den Nogo- Genen neben der reinen Hemmung von axonalen Auswachsen weitere Funktionen zuzuordnen sind. So scheinen sie vor allem während der Entwicklung und während der Stabilisierungsphase der hippocampalen Reorganisation eine wichtige Rolle einzunehmen. Die hier dargestellten Daten zeigen auf, dass vor einem therapeutischen Einsatz von Nogo- Antagonisten nach Schädigung deren Verträglichkeit bzw. unerwünschte Nebeneffekte ausgeschlossen werden müssen. / Compared to the peripheral neuronal system (PNS) the reorganisation capacity in the adult central neuronal system (CNS) is highly restricted. One important reason for the lack of reorganisation is the existence of myelin in the CNS. Myelin is crucial for the stabilization of axonal projections in the developing and adult mammalian brain. However, myelin components also act as a non-permissive and repellent substrate of outgrowing axons. In these thesis the appearance of mature, fully myelinated axons during hippocampal development and following entorhinal cortex lesion with the myelin-specific marker Black Gold is reported. Althrough entorhinal axons enter the hippocampal formation at the embryonic day 17, light and ultrastructural analysis revealed that mature myelinated fibres in the hippocampus occur in the second postnatal week. During postnatal development, increasing numbers of myelinated fibers appear and the distribution of myelinated fibers at postnatal day 25 was similar to that found in the adult. After entorhinal cortex lesion, a specific anterograde denervation in the hippocampus takes place, accompanied by a long- lasting loss of myelin. Quantitative analysis of myelin and myelin breakdown products at different time points after lesion revealed a temporally close correlation to the degeneration and reorganisation phases in the hippocampus. In conclusion, it could be shown that the appearance of mature axons in the hippocampus is temporally regulated during development. Reappearing mature axons were found in the hippocampus following axonal sprouting. Various myelin-associated proteins, with neurite inhibition properties are known. One is the family of Nogo genes (no go). They are distinctly responsible for the lack of reorganisation. In these thesis the expression pattern of Nogo-A, Nogo-B, Nogo-C and Nogo-66 receptor (Ng66R) mRNA during hippocampal development and lesion induced axonal sprouting is reported. The first surprising result was the neuronal expression of all Nogos, who were supposed to be only expressed by oligodendrocytes. Nogo-A, Nogo-B and Ng66R transcrips preceded the process of myelination and were highly expressed at postnatal day zero (P0) in all principal hippocampal cell layers, with the exception of dentate granule cells. Only a slight Nogo-C expression was found at P0 in the principal cell layers of the hippocampus. During adulthood, all Nogo splice variants and their receptor were expressed in the neuronal cell layers of the hippocampus, in contrast to the myelin basic protein mRNA expression pattern, which revealed a neuronal source of Nogo gene expression in addition to oligodendrocytes. After hippocampal denervation, the Nogo genes showed an isoform-specific temporal regulation. All Nogo genes were strongly regulated in the hippocampal cell layers, wheras the Ng66R transcrips showed a significant increase in the contralateral cortex. These data could be confirmed on protein levels. Futhermore, Nogo-A expression was up-regulated after kainat- induced seizure. These data show that neurons express Nogo genes with a clearly distinguishable pattern during development. This expression is further dynamically and isoform-specifically altered after lesioning during the early phase of structural rearrangements. Thus, these results indicate a role for Nogo-A, -B and –C during development and during stabilisation phase of hippocampal reorganization. Taken together with these data, the findings that neurons in a highly plastic brain region express Nogo genes supports the hypothesis that Nogo may function beyond its known neuronal growth inhibition activity in shaping neuronal circuits.

To fully understand how the brain works, it is necessary to relate the brain’s function to its anatomy. Cortical anatomy is subject-specific. It is character- ized by the thickness and number of intracortical layers, which differ from one cortical area to the next. Each cortical area fulfills a certain function. With magnetic res- onance imaging (MRI) it is possible to study structure and function in-vivo within the same subject. The resolution of ultra-high field MRI at 7T allows to resolve intracortical anatomy. This opens the possibility to relate cortical function of a sub- ject to its corresponding individual structural area, which is one of the main goals of neuroimaging. To parcellate the cortex based on its intracortical structure in-vivo, firstly, im- ages have to be quantitative and homogeneous so that they can be processed fully- automatically. Moreover, the resolution has to be high enough to resolve intracortical layers. Therefore, the in-vivo MR images acquired for this work are quantitative T1 maps at 0.5 mm isotropic resolution. Secondly, computational tools are needed to analyze the cortex observer-independ- ently. The most recent tools designed for this task are presented in this thesis. They comprise the segmentation of the cortex, and the construction of a novel equi-volume coordinate system of cortical depth. The equi-volume model is not restricted to in- vivo data, but is used on ultra-high resolution post-mortem data from MRI as well. It could also be used on 3D volumes reconstructed from 2D histological stains. An equi-volume coordinate system yields firstly intracortical surfaces that follow anatomical layers all along the cortex, even within areas that are severely folded where previous models fail. MR intensities can be mapped onto these equi-volume surfaces to identify the location and size of some structural areas. Surfaces com- puted with previous coordinate systems are shown to cross into different anatomical layers, and therefore also show artefactual patterns. Secondly, with the coordinate system one can compute cortical traverses perpendicularly to the intracortical sur- faces. Sampling intensities along equi-volume traverses results in cortical profiles that reflect an anatomical layer pattern, which is specific to every structural area. It is shown that profiles constructed with previous coordinate systems of cortical depth disguise the anatomical layer pattern or even show a wrong pattern. In contrast to equi-volume profiles these profiles from previous models are not suited to analyze the cortex observer-independently, and hence can not be used for automatic delineations of cortical areas. Equi-volume profiles from four different structural areas are presented. These pro- files show area-specific shapes that are to a certain degree preserved across subjects. Finally, the profiles are used to classify primary areas observer-independently.

To fully understand how the brain works, it is necessary to relate the brain’s function to its anatomy. Cortical anatomy is subject-specific. It is character- ized by the thickness and number of intracortical layers, which differ from one cortical area to the next. Each cortical area fulfills a certain function. With magnetic res- onance imaging (MRI) it is possible to study structure and function in-vivo within the same subject. The resolution of ultra-high field MRI at 7T allows to resolve intracortical anatomy. This opens the possibility to relate cortical function of a sub- ject to its corresponding individual structural area, which is one of the main goals of neuroimaging. To parcellate the cortex based on its intracortical structure in-vivo, firstly, im- ages have to be quantitative and homogeneous so that they can be processed fully- automatically. Moreover, the resolution has to be high enough to resolve intracortical layers. Therefore, the in-vivo MR images acquired for this work are quantitative T1 maps at 0.5 mm isotropic resolution. Secondly, computational tools are needed to analyze the cortex observer-independ- ently. The most recent tools designed for this task are presented in this thesis. They comprise the segmentation of the cortex, and the construction of a novel equi-volume coordinate system of cortical depth. The equi-volume model is not restricted to in- vivo data, but is used on ultra-high resolution post-mortem data from MRI as well. It could also be used on 3D volumes reconstructed from 2D histological stains. An equi-volume coordinate system yields firstly intracortical surfaces that follow anatomical layers all along the cortex, even within areas that are severely folded where previous models fail. MR intensities can be mapped onto these equi-volume surfaces to identify the location and size of some structural areas. Surfaces com- puted with previous coordinate systems are shown to cross into different anatomical layers, and therefore also show artefactual patterns. Secondly, with the coordinate system one can compute cortical traverses perpendicularly to the intracortical sur- faces. Sampling intensities along equi-volume traverses results in cortical profiles that reflect an anatomical layer pattern, which is specific to every structural area. It is shown that profiles constructed with previous coordinate systems of cortical depth disguise the anatomical layer pattern or even show a wrong pattern. In contrast to equi-volume profiles these profiles from previous models are not suited to analyze the cortex observer-independently, and hence can not be used for automatic delineations of cortical areas. Equi-volume profiles from four different structural areas are presented. These pro- files show area-specific shapes that are to a certain degree preserved across subjects. Finally, the profiles are used to classify primary areas observer-independently.:1 Introduction p. 1 2 Theoretical Background p. 5 2.1 Neuroanatomy of the human cerebral cortex . . . .p. 5 2.1.1 Macroscopical structure . . . . . . . . . . . .p. 5 2.1.2 Neurons: cell bodies and fibers . . . . . . . .p. 5 2.1.3 Cortical layers in cyto- and myeloarchitecture . . .p. 7 2.1.4 Microscopical structure: cortical areas and maps . .p. 11 2.2 Nuclear Magnetic Resonance . . . . . . . . . . . . . .p. 13 2.2.1 Proton spins in a static magnetic field B0 . . . . .p. 13 2.2.2 Excitation with B1 . . . . . . . . . . . . . . . . .p. 15 2.2.3 Relaxation times T1, T2 and T∗ 2 . . . . . . . . . .p. 16 2.2.4 The Bloch equations . . . . . . . . . . . . . . . . p. 17 2.3 Magnetic Resonance Imaging . . . . . . . . . . . . . .p. 20 2.3.1 Encoding of spatial location and k-space . . . . . .p. 20 2.3.2 Sequences and contrasts . . . . . . . . . . . . . . p. 22 2.3.3 Ultra-high resolution MRI . . . . . . . . . . . . . p. 24 2.3.4 Intracortical MRI: different contrasts and their sources p. 25 3 Image analysis with computed cortical laminae p. 29 3.1 Segmentation challenges of ultra-high resolution images p. 30 3.2 Reconstruction of cortical surfaces with the level set method p. 31 3.3 Myeloarchitectonic patterns on inflated hemispheres . . . . p. 33 3.4 Profiles revealing myeloarchitectonic laminar patterns . . .p. 36 3.5 Standard computational cortical layering models . . . . . . p. 38 3.6 Curvature bias of computed laminae and profiles . . . . . . p. 39 4 Materials and methods p. 41 4.1 Histology . . . . . p. 41 4.2 MR scanning . . . . p. 44 4.2.1 Ultra-high resolution post-mortem data p. 44 4.2.2 The MP2RAGE sequence . . . . . . . . p. 45 4.2.3 High-resolution in-vivo T1 maps . . . .p. 46 4.2.4 High-resolution in-vivo T∗ 2-weighted images p. 47 4.3 Image preprocessing and experiments . . . . . .p. 48 4.3.1 Fully-automatic tissue segmentation . . . . p. 48 4.3.2 Curvature Estimation . . . . . . . . . . . . p. 49 4.3.3 Preprocessing of post-mortem data . . . . . .p. 50 4.3.4 Experiments with occipital pole post-mortem data .p. 51 4.3.5 Preprocessing of in-vivo data . . . . . . . . . . p. 52 4.3.6 Evaluation experiments on in-vivo data . . . . . .p. 56 4.3.7 Application experiments on in-vivo data . . . . . p. 56 4.3.8 Software . . . . . . . . . . . . . . . . . . . . .p. 58 5 Computational cortical layering models p. 59 5.1 Implementation of standard models . .p. 60 5.1.1 The Laplace model . . . . . . . . .p. 60 5.1.2 The level set method . . . . . . . p. 61 5.1.3 The equidistant model . . . . . . .p. 62 5.2 The novel anatomically motivated equi-volume model p. 63 5.2.1 Bok’s equi-volume principle . . . . . .p. 63 5.2.2 Computational equi-volume layering . . p. 66 6 Validation of the novel equi-volume model p. 73 6.1 The equi-volume model versus previous models on post-mortem samples p. 73 6.1.1 Comparing computed surfaces and anatomical layers . . . . . . . . p. 73 6.1.2 Cortical profiles reflecting an anatomical layer . . . . . . . . .p. 79 6.2 The equi-volume model versus previous models on in-vivo data . . . .p. 82 6.2.1 Comparing computed surfaces and anatomical layers . . . . . . . . p. 82 6.2.2 Cortical profiles reflecting an anatomical layer . . . . . . . . .p. 85 6.3 Dependence of computed surfaces on cortical curvature . . . . .p. 87 6.3.1 Within a structural area . . . . . . . . . . . . . . . . . . p. 87 6.3.2 Artifactual patterns on inflated surfaces . . . . . . . . . .p. 87 7 Applying the equi-volume model: Analyzing cortical architecture in-vivo in different structural areas p. 91 7.1 Impact of resolution on cortical profiles . . . . . . . . . . . . . p. 91 7.2 Intersubject variability of cortical profiles . . . . . . . . . . . p. 94 7.3 Myeloarchitectonic patterns on inflated hemispheres . . . . . . .p. 95 7.3.1 Comparison of patterns with inflated labels . . . . . . . . . .p. 97 7.3.2 Patterns at different cortical depths . . . . . . . . . . . . .p. 97 7.4 Fully-automatic primary-area classification using cortical profiles p. 99 8 Discussion p. 105 8.1 The novel equi-volume model . . . . . . . . . . . . . . . . . . . . .p. 105 8.2 Analyzing cortical myeloarchitecture in-vivo with T1 maps . . . . . .p. 109 9 Conclusion and outlook p. 113 Bibliography p. 117 List of Figures p. 127