In this paper, we present a novel method for analysis and segmentation of laminar structure of the cortex based on tissue characteristics whose change across the gray matter facilitates distinction between cortical layers. We develop and analyze features of individual neurons to investigate changes in architectonic differentiation and present a novel high-performance, automated tree-ensemble method trained on data manually labeled by three human investigators. From the location and basic measures of neurons, more complex features are developed and used in machine learning models for automatic segmentation of cortical layers. Tree ensembles are used on data manually labeled by three human experts. The most accurate classification results were obtained by training three models separately and creating another ensemble by combining probability outputs for final neuron layer classification. Measurement of importances of developed neuron features on both global model level and individual prediction level are obtained.
Recent methods for automated laminar analysis developed after the year 2000 have moved on from only using GLI in analysis of cortical profiles. In year 2002 @cite_2 , authors develop statistical features that characterize each GLI profile and its discreet derivative, such as the first four moments about the mean, treating the profile as a frequency distribution, for the total of 10 features for each profile. Authors also bring these features in relation to cytoarchitectonics and discuss different distance measures used to compare profile vectors derived from different brain areas. An important step in development of profile features was made eight years later in @cite_13 by the introduction of automatic methods for estimation of cell counts, thus providing realistic information about neuron density. Authors provide the number and distribution of neurons within several projection columns in rat cortex, reporting an estimate of neuron number per layers. This data is further used to derive the action potential output of projection columns specific for each layer, clearly showing direct link to analysis of the brain function through analysis of its cytoarchitecture.
@cite_13 @cite_2
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This chapter discusses the principles of classical architectonic mapping in the context of recent imaging techniques. It presents an observer-independent approach for a quantitative analysis of cortical areas and their borders, which is based on a multivariate statistical analysis of the cytoarchitecture and illustrates the application of this approach for the cytoarchitectonic mapping of the human visual cortex. Important criteria for cytoarchitectonic mapping are the absolute thickness of cortical layers, the proportionate thickness of a layer relative to the other cortical layers and to the total cortical depth, the presence of clearly recognizable laminar borders and vertical columns, the packing density and size of neuronal cell bodies, the homogeneous or clustered distribution of cell bodies throughout the layers, and the presence of special cell types such as Betz cells. Understanding the regional distributions of neurotransmitter receptors is likely to provide a crucial intermediary level of description between function and structure, since different cytoarchitectonic and functional areas have different mean receptor densities as well as distinct laminar distribution patterns. Another way for a better understanding of brain function and the underlying anatomy is to compare architectonic maps obtained in postmortem brains with activation maps obtained in functional imaging studies in a common spatial reference system. Since these two kinds of maps stem from different subsets of brains, such a comparison must be performed on a probabilistic basis. We describe a new, observer-independent procedure for identifying boundaries between cortical areas. The method is useful for images obtained from sections which provide microstructural information on the cortical laminar pattern, e.g., Nissl-, myelin-, or immunohistochemically stained sections or receptor autoradiographs. The laminar pattern is represented by profile curves extending from the cortical surface to the white matter boundary. These profiles are constructed from digitized images. Digitization is based on the grey level index (Nissl) or densitometry (myelin, immunohistochemistry, receptor autoradiography). The shapes of neighboring profiles are compared by calculating their distances according to feature vectors extracted from the profiles. Profiles derived from a homogeneous area can be expected to be similar in shape and hence show low distance values between each other. Maximum distances can be found between profiles which lie on opposite sides of a structural boundary. The Mahalanobis distance was found to be more sensitive and to yield greater spatial resolution than other distance measures such as the Euclidean distance. Cell-stained sections of the human neocortex were analyzed. The method not only verified boundaries which had been defined by visual inspection, it also revealed new ones which had not been detected visually. The procedure offers an important supplement to the traditional methods based on visual inspection which, for the first time, is based on quantitative data and therefore offers a new level of reproducibility and observer independence. Anatomical atlases based on this procedure thus provide a new tool for the interpretation of structural data obtained from functional imaging techniques. In Alzheimer's disease (AD) diagnosis process, functional brain image modalities such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians. However, the current evaluation of these images entails a succession of manual reorientations and visual interpretation steps, which attach in some way subjectivity to the diagnostic. In this work, a complete computer aided diagnosis (CAD) system for an automatic evaluation of the neuroimages is presented. Principal component analysis (PCA)-based methods are proposed as feature extraction techniques, enhanced by other linear approaches such as linear discriminant analysis (LDA) or the measure of the Fisher discriminant ratio (FDR) for feature selection. The final features allow to face up the so-called small sample size problem and subsequently they are used for the study of neural networks (NN) and support vector machine (SVM) classifiers. The combination of the presented methods achieved accuracy results of up to 96.7 and 89.52 for SPECT and PET images, respectively, which means a significant improvement over the results obtained by the classical voxels-as-features (VAF) reference approach. Abstract Understanding the organization of the human brain requires identification of its functional subdivisions. Clustering schemes based on resting-state functional magnetic resonance imaging (fMRI) data are rapidly emerging as non-invasive alternatives to cytoarchitectonic mapping in postmortem brains. Here, we propose a novel spatio-temporal probabilistic parcellation scheme that overcomes major weaknesses of existing approaches by (i) modeling the fMRI time series of a voxel as a von Mises-Fisher distribution, which is widely used for clustering high dimensional data; (ii) modeling the latent cluster labels as a Markov random field, which provides spatial regularization on the cluster labels by penalizing neighboring voxels having different cluster labels; and (iii) introducing a prior on the number of labels, which helps in uncovering the number of clusters automatically from the data. Cluster labels and model parameters are estimated by an iterative expectation maximization procedure wherein, given the data and current estimates of model parameters, the latent cluster labels, are computed using α-expansion, a state of the art graph cut, method. In turn, given the current estimates of cluster labels, model parameters are estimated by maximizing the pseudo log-likelihood. The performance of the proposed method is validated using extensive computer simulations. Using novel stability analysis we examine the sensitivity of our methods to parameter initialization and demonstrate that the method is robust to a wide range of initial parameter values. We demonstrate the application of our methods by parcellating spatially contiguous as well as non-contiguous brain regions at both the individual participant and group levels. Notably, our analyses yield new data on the posterior boundaries of the supplementary motor area and provide new insights into functional organization of the insular cortex. Taken together, our findings suggest that our method is a powerful tool for investigating functional subdivisions in the human brain.