Simultaneous Cortical Surface Labeling and Sulcal Curve Extraction

A new 3D myeloarchitectonic map of the human neocortex based on data from the Vogt-Vogt school

During the period extending from 1900 to 1970, Oskar and Cécile Vogt and their numerous collaborators ('the Vogt-Vogt school') published a large number of studies on the myeloarchitecture of the human cerebral cortex. During the last decade, we have concerned ourselves with a detailed meta-analysis of these now almost totally forgotten studies, with the aim to bringing them into the modern era of science. This scrutiny yielded inter alia a myeloarchitectonic map of the human neocortex, showing a parcellation into 182 areas (Nieuwenhuys et al. in Brain Struct Funct 220:2551-2573, 2015; Erratum in Brain Struct Funct 220: 3753-3755, 2015). This map, termed 2D'15, which is based on data derived from all of the 20 publications constituting the myeloarchitectonic legacy of the Vogt-Vogt school, has the limitation that it is two-dimensional i.e. it shows only the parts of the cortex exposed at the free surface of the cerebral hemispheres and not the extensive stretches of cortex hidden in the cortical sulci. However, a limited set of data, derived from four of the 20 publications available, has enabled us to create a 3D map, showing the myeloarchitectonic parcellation of the entire human neocortex. This map, designated as 3D'23, contains 182 areas: 64 frontal, 30 parietal, 6 insular, 19 occipital and 63 temporal. We have also prepared a 2D version (2D'23), of this 3D'23 map to serve as a link between the latter and our original 2D'15 map. Detailed comparison of the parcellations visualized in our three maps (2D'15, 2D'23 and 3D'23) warrants the conclusion that our new 3D'23 map may be considered as representative for the entire myeloarchitectural legacy of the Vogt-Vogt School. Hence it is now possible to compare the rich amount of myeloarchitectonic data assembled by that school directly with the results of current 3D analyses of the architecture of the human cortex, such as the meticulous quantitative cyto- and receptor architectonic studies of Zilles, Amunts and their numerous associates (Amunts et al. in Science 369:988-992, 2020), and the multimodal parcellation of the human cortex based on magnetic resonance images from the Human Connectome Project, performed by Glasser et al. in Nature 536:171-178, 2016).

Landmark Based Shape Analysis for Cerebellar Ataxia Classification and Cerebellar Atrophy Pattern Visualization

Cerebellar dysfunction can lead to a wide range of movement disorders. Studying the cerebellar atrophy pattern associated with different cerebellar disease types can potentially help in diagnosis, prognosis, and treatment planning. In this paper, we present a landmark based shape analysis pipeline to classify healthy control and different ataxia types and to visualize the characteristic cerebellar atrophy patterns associated with different types. A highly informative feature representation of the cerebellar structure is constructed by extracting dense homologous landmarks on the boundary surfaces of cerebellar sub-structures. A diagnosis group classifier based on this representation is built using partial least square dimension reduction and regularized linear discriminant analysis. The characteristic atrophy pattern for an ataxia type is visualized by sampling along the discriminant direction between healthy controls and the ataxia type. Experimental results show that the proposed method can successfully classify healthy controls and different ataxia types. The visualized cerebellar atrophy patterns were consistent with the regional volume decreases observed in previous studies, but the proposed method provides intuitive and detailed understanding about changes of overall size and shape of the cerebellum, as well as that of individual lobules.

A toolbox to visually explore cerebellar shape changes in cerebellar disease and dysfunction

The cerebellum plays an important role in motor control and is also involved in cognitive processes. Cerebellar function is specialized by location, although the exact topographic functional relationship is not fully understood. The spinocerebellar ataxias are a group of neurodegenerative diseases that cause regional atrophy in the cerebellum, yielding distinct motor and cognitive problems. The ability to study the region-specific atrophy patterns can provide insight into the problem of relating cerebellar function to location. In an effort to study these structural change patterns, we developed a toolbox in MATLAB to provide researchers a unique way to visually explore the correlation between cerebellar lobule shape changes and function loss, with a rich set of visualization and analysis modules. In this paper, we outline the functions and highlight the utility of the toolbox. The toolbox takes as input landmark shape representations of subjects' cerebellar substructures. A principal component analysis is used for dimension reduction. Following this, a linear discriminant analysis and a regression analysis can be performed to find the discriminant direction associated with a specific disease type, or the regression line of a specific functional measure can be generated. The characteristic structural change pattern of a disease type or of a functional score is visualized by sampling points on the discriminant or regression line. The sampled points are used to reconstruct synthetic cerebellar lobule shapes. We showed a few case studies highlighting the utility of the toolbox and we compare the analysis results with the literature.

COVARIANCE SHRINKING IN ACTIVE SHAPE MODELS WITH APPLICATION TO GYRAL LABELING OF THE CEREBRAL CORTEX

Active shape models (ASMs) have been widely used in segmentation tasks in medical image analysis. Complex structures and a limited number of training samples can, however, result in the failure to capture the complete range of shape variations. Various modifications to the point distribution model (PDM) have been proposed to increase the flexibility of the model. Still model parameters are often determined empirically without respect to the underlying data structure. We explore shrinkage covariance estimation in building a PDM by combining the sample covariance matrix with a target covariance matrix estimated from a low-dimensional constrained model. Instead of using a global shrinkage intensity, we apply a spatially varying shrinkage intensity field to better adapt to the spatially varying characteristic of a complex shape. The parameters of the constrained model and the amount of shrinkage are determined in a data-driven fashion, so that the resulting distribution is optimized in representing the underlying data. The PDM, which we call SC-PDM, shows an increased flexibility in fitting new shapes and at the same time, is robust to noise. We demonstrate the effectiveness of using SC-PDM to label gyral regions on the human cerebral cortex.