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
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:13084 |
| Date | 12 May 2014 |
| Creators | Wähnert, Miriam |
| Contributors | Turner, Robert, Shattuck, David W., Universität Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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