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Made available in DSpace on 2014-12-17T15:36:57Z (GMT). No. of bitstreams: 1 JanainaSB.pdf: 2453259 bytes, checksum: d4ce3e2bc8b59c2bee9fa61810a98832 (MD5) Previous issue date: 2009-10-29 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / The thalamus plays an important role in the sensorial processing information, in this particular case, the visual information. Several neuronal groups have been characterized as conductors and processors of important sensorial information to the cerebral cortex. The lateral geniculate complex is one to them, and appears as a group very studied once it is responsible, in almost all totality, for the processing of visual information. Among the nuclei that constitute the lateral geniculate complex we highlight the dorsal lateral geniculate nucleus of the thalamus (DLG), the main thalamic relay for the visual information. This nucleus is located rostral and lateral to medial geniculate nucleus and ventral to thalamic pulvinar nucleus in most of the mammals. In the primates humans and non-humans, it presents as a laminate structure, arranged in layers, when observed in coronal sections. The objective of this work was to do a mapping of the retinal projections and a citoarchictetonic and neurochemical characterization of DLG in the marmoset (Callithrix jacchus), a New World primate. The retinal projections were traced by anterograde transport of subunit b of cholera toxin (CTb), the citoarchicteture was described by Nissl method, and to neurochemical characterization immunohistochemicals technical were used to examine the main neurotransmitters and neuroatives substances present in this neural center. In DGL of marmoset thalamus, in coronal sections labeled by Nissl method, was possible to visualize the division of this nucleus in four layers divided in two portions: magnocellular and parvocellular. The retinal projections were present being visualized fibers and terminals immunorreactives to CTb (IR-CTb) in the DLG ipsilateral and contralateral. And through the immunohistochemicals techniques was observed that DLG contain cells, fibers and/or terminals immunoreactives against neuronal nuclear protein, subunits of AMPA 15 glutamate receptors (GluR1, GluR2/3, GluR4), choline acetyltransferase, serotonin, glutamic acid decarboxylase, binding calcium proteins (calbindin, parvalbumin and calretinin), vasopressin, vasoactive intestinal polypeptide, and an astrocyte protein, glial fibrillary acidic protein. / O t?lamo exerce um importante papel no processamento de informa??es sensoriais, em particular, a informa??o visual. V?rios grupos neuronais j? foram caracterizados como condutores e processadores de informa??es sensoriais importantes para o c?rtex cerebral. O complexo geniculado lateral ? um deles e aparece como um grupo muito estudado uma vez que ? respons?vel, em quase toda sua totalidade, pelo processamento de informa??o visual. Entre os n?cleos que constituem o complexo geniculado lateral destacamos o n?cleo geniculado lateral dorsal do t?lamo (GLD), o principal rel? tal?mico para as informa??es visuais. Este n?cleo se localiza rostral e lateral ao n?cleo geniculado medial e ventral ao n?cleo pulvinar do t?lamo na maioria dos mam?feros. Nos primatas humanos e n?o humanos, apresenta-se como uma estrutura laminar, disposto em camadas, quando observada em sec??es coronais. O objetivo neste trabalho foi fazer um mapeamento da proje??o retiniana e uma caracteriza??o citoarquitet?nica e neuroqu?mica do GLD no Callithrix jacchus (sag?i), um primata do Novo Mundo. As proje??es retinianas foram tra?adas por transporte anter?grado da subunidade B da toxina col?rica (CTb), a citoarquitetura foi descrita atrav?s do m?todo de Nissl, e para a caracteriza??o neuroqu?mica t?cnicas imunoistoqu?micas foram utilizadas para examinar os principais neurotransmissores e subst?ncias neuroativas presentes neste centro neural. No GLD do t?lamo do sag?i, nas sec??es coronais coradas pelo m?todo de Nissl, foi poss?vel visualizar a divis?o desse n?cleo em quatro camadas dividas em duas por??es: magnocelular e parvocelular. As proje??es retinianas estavam presentes visualizando-se fibras e terminais imunorreativos a CTb (CTb- IR) no GLD ipsolateral e contralateral. E atrav?s das t?cnicas imunoistoqu?micas observou-se que o GLD cont?m c?lulas, fibras e/ou terminais 13 imunorreativos a prote?na nuclear neuronal, subunidades dos receptores AMPA de glutamato (GluR1, GluR2/3, GluR4), colina acetiltransferase, serotonina, descarboxilase do ?cido glut?mico, prote?nas ligantes de c?lcio (calbindina, calretinina e parvalbumina), vasopressina, polipept?deo intestinal vasoativo, e uma prote?na astrocit?ria, prote?na ac?dica fibrilar glial.

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