Registration of histological images to three-dimensional imaging modalities is an important step in quantitative analysis of brain structure, in architectonic mapping of the brain, and in investigation of the pathology of a brain disease. Reconstruction of histology volume from serial sections is a well-established procedure, but it does not address registration of individual slices from sparse sections, which is the aim of the slice-to-volume approach. This study presents a flexible framework for intensity-based slice-to-volume nonrigid registration algorithms with a geometric transformation deformation field parametrized by various classes of spline functions: thin-plate splines (TPS), Gaussian elastic body splines (GEBS), or cubic B-splines. Algorithms are applied to cross-modality registration of histological and magnetic resonance images of the human brain. Registration performance is evaluated across a range of optimization algorithms and intensity-based cost functions. For a particular case of histological data, best results are obtained with a TPS three-dimensional (3D) warp, a new unconstrained optimization algorithm (NEWUOA), and a correlation-coefficient-based cost function.

Abstract-We present a simple, computationally efficient recognition algorithm that can systematically extract useful information from any large-dimensional neural datasets. The technique is based on classwise Principal Component Analysis, which employs the distribution characteristics of each class to discard non-informative subspace. We propose a two-step procedure, comprising of removal of sparse non-informative subspace of the large-dimensional data, followed by a linear combination of the data in the remaining subspace to extract meaningful features for efficient classification. Our method produces significant improvement over the standard discriminant analysis based methods. The classification results are given for iEEG and EEG signals recorded from the human brain.

The topologically correct and geometrically accurate reconstruction of the cerebral cortex from magnetic resonance (MR) images is an important step in quantitative analysis of the human brain structure, e.g. in cortical thickness measurement studies. Limited resolution of MR images, noise, intensity inhomogeneities, and partial volume effects can all contribute to geometrical inaccuracies and topological errors in the model of cortical surfaces. For example, unresolved touching banks of gray matter (GM) in narrow sulci pose a particular challenge for an automated algorithm, requiring specific steps for the recovery of separating boundaries. We present a method for the automated reconstruction of the cortical compartment from MR images. The method is based on several partial differential equation (PDE) modelling stages. First, a potential field is computed in an electrostatic model with GM posing as an insulating dielectric layer surrounding a charged conductive white matter (WM) object. Second, geodesic distances from WM along the streamlines of the potential field are computed in a Eulerian framework PDE. Third, a digital skeleton surface separating GM sulcal banks is derived by finding shocks in the distance field. At the last stage, a geometric deformable model based on the level set PDE is used to reconstruct the outer cortical surface by advection along the gradient of the distance or potential field. The rule preserving the digital topology, and the skeleton of the distance field resolving fused adjacent banks in sulci, constrain the deformable model evolution. In addition, the deformable model may use the distance field as a constraint on thickness of the reconstructed cortical layer.

Reconstruction of the cerebral cortex from magnetic resonance (MR) images is an important step in quantitative analysis of the human brain structure, for example, in sulcal morphometry and in studies of cortical thickness. Existing cortical reconstruction approaches are typically optimized for standard resolution (~1 mm) data and are not directly applicable to higher resolution images. A new PDE-based method is presented for the automated cortical reconstruction that is computationally efficient and scales well with grid resolution, and thus is particularly suitable for high-resolution MR images with submillimeter voxel size. The method uses a mathematical model of a field in an inhomogeneous dielectric. This field mapping, similarly to a Laplacian mapping, has nice laminar properties in the cortical layer, and helps to identify the unresolved boundaries between cortical banks in narrow sulci. The pial cortical surface is reconstructed by advection along the field gradient as a geometric deformable model constrained by topology-preserving level set approach. The method's performance is illustrated on exvivo images with 0.25–0.35 mm isotropic voxels. The method is further evaluated by cross-comparison with results of the FreeSurfer software on standard resolution data sets from the OASIS database featuring pairs of repeated scans for 20 healthy young subjects.

The architectonic analysis of the human cerebral cortex is presently based on the examination of stained tissue sections. Recent progress in high-resolution magnetic resonance imaging (MRI) promotes the feasibility of an in vivo architectonic analysis. Since the exact relationship between the laminar fine-structure of a cortical MRI signal and histological cyto-and myeloarchitectonic staining patterns is not known, a quantitative study comparing high-resolution MRI to histological ground truth images is necessary for validating a future MRI based architectonic analysis. This communication describes an ongoing study comparing post mortem MR images to a myelin-stained histology of the brain cortex. After establishing a close spatial correspondence between histological sections and MRI using a slice-to-volume nonrigid registration algorithm, transcortical intensity profiles, extracted from both imaging modalities along curved trajectories of a Laplacian vector field, are compared via a cross-correlational analysis.