Validating cortical surface analysis of medial prefrontal cortex.

This paper describes cortical analysis of 19 high resolution MRI subvolumes of medial prefrontal cortex (MPFC), a region that has been implicated in major depressive disorder. An automated Bayesian segmentation is used to delineate the MRI subvolumes into cerebrospinal fluid (CSF), gray matter (GM), white matter (WM), and partial volumes of either CSF/GM or GM/WM. The intensity value at which there is equal probability of GM and GM/WM partial volume is used to reconstruct MPFC cortical surfaces based on a 3-D isocontouring algorithm. The segmented data and the generated surfaces are validated by comparison with hand segmented data and semiautomated contours, respectively. The L(1) distances between Bayesian and hand segmented data are 0.05-0.10 (n = 5). Fifty percent of the voxels of the reconstructed surface lie within 0.12-0.28 mm (n = 14) from the semiautomated contours. Cortical thickness metrics are generated in the form of frequency of occurrence histograms for GM and WM labelled voxels as a function of their position from the cortical surface. An algorithm to compute the surface area of the GM/WM interface of the MPFC subvolume is described. These methods represent a novel approach to morphometric chacterization of regional cortex features which may be important in the study of psychiatric disorders such as major depression.     

Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification

Accurate reconstruction of the inner and outer cortical surfaces of the human cerebrum is a critical objective for a wide variety of neuroimaging analysis purposes, including visualization, morphometry, and brain mapping. The Anatomic Segmentation using Proximity (ASP) algorithm, previously developed by our group, provides a topology-preserving cortical surface deformation method that has been extensively used for the aforementioned purposes. However, constraints in the algorithm to ensure topology preservation occasionally produce incorrect thickness measurements due to a restriction in the range of allowable distances between the gray and white matter surfaces. This problem is particularly prominent in pediatric brain images with tightly folded gyri. This paper presents a novel method for improving the conventional ASP algorithm by making use of partial volume information through probabilistic classification in order to allow for topology preservation across a less restricted range of cortical thickness values. The new algorithm also corrects the classification of the insular cortex by masking out subcortical tissues. For 70 pediatric brains, validation experiments for the modified algorithm, Constrained Laplacian ASP (CLASP), were performed by three methods: (i) volume matching between surface-masked gray matter (GM) and conventional tissue-classified GM, (ii) surface matching between simulated and CLASP-extracted surfaces, and (iii) repeatability of the surface reconstruction among 16 MRI scans of the same subject. In the volume-based evaluation, the volume enclosed by the CLASP WM and GM surfaces matched the classified GM volume 13% more accurately than using conventional ASP. In the surface-based evaluation, using synthesized thick cortex, the average difference between simulated and extracted surfaces was 4.6 +/- 1.4 mm for conventional ASP and 0.5 +/- 0.4 mm for CLASP. In a repeatability study, CLASP produced a 30% lower RMS error for the GM surface and a 8% lower RMS error for the WM surface compared with ASP.     

Consistent reconstruction of cortical surfaces from longitudinal brain MR images

Accurate and consistent reconstruction of cortical surfaces from longitudinal human brain MR images is of great importance in studying longitudinal subtle change of the cerebral cortex. This paper presents a novel deformable surface method for consistent and accurate reconstruction of inner, central and outer cortical surfaces from longitudinal brain MR images. Specifically, the cortical surfaces of the group-mean image of all aligned longitudinal images of the same subject are first reconstructed by a deformable surface method, which is driven by a force derived from the Laplace's equation. And then the longitudinal cortical surfaces are consistently reconstructed by jointly deforming the cortical surfaces of the group-mean image to all longitudinal images. The proposed method has been successfully applied to two sets of longitudinal human brain MR images. Both qualitative and quantitative experimental results demonstrate the accuracy and consistency of the proposed method. Furthermore, the reconstructed longitudinal cortical surfaces are used to measure the longitudinal changes of cortical thickness in both normal and diseased groups, where the overall decline trend of cortical thickness has been clearly observed. Meanwhile, the longitudinal cortical thickness also shows its potential in distinguishing different clinical groups.     

A robust method for extraction and automatic segmentation of brain images

A new protocol is introduced for brain extraction and automatic tissue segmentation of MR images. For the brain extraction algorithm, proton density and T2-weighted images are used to generate a brain mask encompassing the full intracranial cavity. Segmentation of brain tissues into gray matter (GM), white matter (WM), and cerebral spinal fluid (CSF) is accomplished on a T1-weighted image after applying the brain mask. The fully automatic segmentation algorithm is histogram-based and uses the Expectation Maximization algorithm to model a four-Gaussian mixture for both global and local histograms. The means of the local Gaussians for GM, WM, and CSF are used to set local thresholds for tissue classification. Reproducibility of the extraction procedure was excellent, with average variation in intracranial capacity (TIC) of 0.13 and 0.66% TIC in 12 healthy normal and 33 Alzheimer brains, respectively. Repeatability of the segmentation algorithm, tested on healthy normal images, indicated scan-rescan differences in global tissue volumes of less than 0.30% TIC. Reproducibility at the regional level was established by comparing segmentation results within the 12 major Talairach subdivisions. Accuracy of the algorithm was tested on a digital brain phantom, and errors were less than 1% of the phantom volume. Maximal Type I and Type II classification errors were low, ranging between 2.2 and 4.3% of phantom volume. The algorithm was also insensitive to variation in parameter initialization values. The protocol is robust, fast, and its success in segmenting normal as well as diseased brains makes it an attractive clinical application.     

CRUISE: cortical reconstruction using implicit surface evolution

Segmentation and representation of the human cerebral cortex from magnetic resonance (MR) images play an important role in neuroscience and medicine. A successful segmentation method must be robust to various imaging artifacts and produce anatomically meaningful and consistent cortical representations. A method for the automatic reconstruction of the inner, central, and outer surfaces of the cerebral cortex from T1-weighted MR brain images is presented. The method combines a fuzzy tissue classification method, an efficient topology correction algorithm, and a topology-preserving geometric deformable surface model (TGDM). The algorithm is fast and numerically stable, and yields accurate brain surface reconstructions that are guaranteed to be topologically correct and free from self-intersections. Validation results on real MR data are presented to demonstrate the performance of the method.     

Diffusion tensor imaging-based tissue segmentation: validation and application to the developing child and adolescent brain

We present and validate a novel diffusion tensor imaging (DTI) approach for segmenting the human whole-brain into partitions representing grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF). The approach utilizes the contrast among tissue types in the DTI anisotropy vs. diffusivity rotational invariant space. The DTI-based whole-brain GM and WM fractions (GMf and WMf) are contrasted with the fractions obtained from conventional magnetic resonance imaging (cMRI) tissue segmentation (or clustering) methods that utilized dual echo (proton density-weighted (PDw)), and spin-spin relaxation-weighted (T2w) contrast, in addition to spin-lattice relaxation weighted (T1w) contrasts acquired in the same imaging session and covering the same volume. In addition to good correspondence with cMRI estimates of brain volume, the DTI-based segmentation approach accurately depicts expected age vs. WM and GM volume-to-total intracranial brain volume percentage trends on the rapidly developing brains of a cohort of 29 children (6-18 years). This approach promises to extend DTI utility to both micro and macrostructural aspects of tissue organization.     

White matter lesion extension to automatic brain tissue segmentation on MRI

A fully automated brain tissue segmentation method is optimized and extended with white matter lesion segmentation. Cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM) are segmented by an atlas-based k-nearest neighbor classifier on multi-modal magnetic resonance imaging data. This classifier is trained by registering brain atlases to the subject. The resulting GM segmentation is used to automatically find a white matter lesion (WML) threshold in a fluid-attenuated inversion recovery scan. False positive lesions are removed by ensuring that the lesions are within the white matter. The method was visually validated on a set of 209 subjects. No segmentation errors were found in 98% of the brain tissue segmentations and 97% of the WML segmentations. A quantitative evaluation using manual segmentations was performed on a subset of 6 subjects for CSF, GM and WM segmentation and an additional 14 for the WML segmentations. The results indicated that the automatic segmentation accuracy is close to the interobserver variability of manual segmentations.     

Dynamic programming generation of boundaries of local coordinatized submanifolds in the neocortex: application to the planum temporale

Dynamic programming is used to define boundaries of cortical submanifolds with focus on the planum temporale (PT) of the superior temporal gyrus (STG), which has been implicated in a variety of neuropsychiatric disorders. To this end, automated methods are used to generate the PT manifold from 10 high-resolution MRI subvolumes ROI masks encompassing the STG. A procedure to define the subvolume ROI masks from original MRI brain scans is developed. Bayesian segmentation is then used to segment the subvolumes into cerebrospinal fluid, gray matter (GM), and white matter (WM). 3D isocontouring using the intensity value at which there is equal probability of GM and WM is used to reconstruct the triangulated graph representing the STG cortical surface, enabling principal curvature at each point on the graph to be computed. Dynamic programming is used to delineate the PT manifold by tracking principal curves from the retro-insular end of the Heschl's gyrus (HG) to the STG, along the posterior STG up to the start of the ramus and back to the retro-insular end of the HG. A coordinate system is then defined on the PT manifold. The origin is defined by the retro-insular end of the HG and the y-axis passes through the point on the posterior STG where the ramus begins. Automated labeling of GM in the STG is robust with L(1) distances between Bayesian and manual segmentation in the range 0.001-0.12 (n = 20). PT reconstruction is also robust with 90% of the vertices of the reconstructed PT within about 1 voxel (n = 20) from semiautomated contours. Finally, the reliability index (based on interrater intraclass correlation) for the surface area derived from repeated reconstructions is 0.96 for the left PT and 0.94 for the right PT, thus demonstrating the robustness of dynamic programming in defining a coordinate system on the PT. It provides a method with potential significance in the study of neuropsychiatric disorders.     

Brain tissue segmentation based on DTI data

We present a method for automated brain tissue segmentation based on the multi-channel fusion of diffusion tensor imaging (DTI) data. The method is motivated by the evidence that independent tissue segmentation based on DTI parametric images provides complementary information of tissue contrast to the tissue segmentation based on structural MRI data. This has important applications in defining accurate tissue maps when fusing structural data with diffusion data. In the absence of structural data, tissue segmentation based on DTI data provides an alternative means to obtain brain tissue segmentation. Our approach to the tissue segmentation based on DTI data is to classify the brain into two compartments by utilizing the tissue contrast existing in a single channel. Specifically, because the apparent diffusion coefficient (ADC) values in the cerebrospinal fluid (CSF) are more than twice that of gray matter (GM) and white matter (WM), we use ADC images to distinguish CSF and non-CSF tissues. Additionally, fractional anisotropy (FA) images are used to separate WM from non-WM tissues, as highly directional white matter structures have much larger fractional anisotropy values. Moreover, other channels to separate tissue are explored, such as eigenvalues of the tensor, relative anisotropy (RA), and volume ratio (VR). We developed an approach based on the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm that combines these two-class maps to obtain a complete tissue segmentation map of CSF, GM, and WM. Evaluations are provided to demonstrate the performance of our approach. Experimental results of applying this approach to brain tissue segmentation and deformable registration of DTI data and spoiled gradient-echo (SPGR) data are also provided.     

Infant brain probability templates for MRI segmentation and normalization

Spatial normalization and segmentation of infant brain MRI data based on adult or pediatric reference data may not be appropriate due to the developmental differences between the infant input data and the reference data. In this study we have constructed infant templates and a priori brain tissue probability maps based on the MR brain image data from 76 infants ranging in age from 9 to 15 months. We employed two processing strategies to construct the infant template and a priori data: one processed with and one without using a priori data in the segmentation step. Using the templates we constructed, comparisons between the adult templates and the new infant templates are presented. Tissue distribution differences are apparent between the infant and adult template, particularly in the gray matter (GM) maps. The infant a priori information classifies brain tissue as GM with higher probability than adult data, at the cost of white matter (WM), which presents with lower probability when compared to adult data. The differences are more pronounced in the frontal regions and in the cingulate gyrus. Similar differences are also observed when the infant data is compared to a pediatric (age 5 to 18) template. The two-pass segmentation approach taken here for infant T1W brain images has provided high quality tissue probability maps for GM, WM, and CSF, in infant brain images. These templates may be used as prior probability distributions for segmentation and normalization; a key to improving the accuracy of these procedures in special populations.     

A fast,model-independent method for cerebral cortical thickness estimation using MRI

Several algorithms for measuring the cortical thickness in the human brain from MR image volumes have been described in the literature, the majority of which rely on fitting deformable models to the inner and outer cortical surfaces. However, the constraints applied during the model fitting process in order to enforce spherical topology and to fit the outer cortical surface in narrow sulci, where the cerebrospinal fluid (CSF) channel may be obscured by partial voluming, may introduce bias in some circumstances, and greatly increase the processor time required.In this paper we describe an alternative, voxel based technique that measures the cortical thickness using inversion recovery anatomical MR images. Grey matter, white matter and CSF are identified through segmentation, and edge detection is used to identify the boundaries between these tissues. The cortical thickness is then measured along the local 3D surface normal at every voxel on the inner cortical surface. The method was applied to 119 normal volunteers, and validated through extensive comparisons with published measurements of both cortical thickness and rate of thickness change with age. We conclude that the proposed technique is generally faster than deformable model-based alternatives, and free from the possibility of model bias, but suffers no reduction in accuracy. In particular, it will be applicable in data sets showing severe cortical atrophy, where thinning of the gyri leads to points of high curvature, and so the fitting of deformable models is problematic.     

Semi-automatic brain region extraction (SABRE) reveals superior cortical and deep gray matter atrophy in MS

In multiple sclerosis (MS), atrophy occurs in various cortical and subcortical regions. However, it is unclear whether this is mostly due to gray (GM) or white matter (WM) loss. Recently, a new semi-automatic brain region extraction (SABRE) technique was developed to quantify parenchyma volume in 13 hemispheric regions. This study utilized SABRE and tissue segmentation to examine whether regional brain atrophy in MS is mostly due to GM or WM loss, correlated with disease duration, and moderated by disease course. We studied 68 MS patients and 39 normal controls with 1.5 T brain MRI. As expected, MS diagnosis was associated with significantly lower (P < 0.001) regional brain parenchymal fractions (RBPFs). While significant findings emerged in 11 GM comparisons, only four WM comparisons were significant. The largest mean RBPF percent differences between groups (MS < NC) were in the posterior basal ganglia/thalamus region (-19.3%), superior frontal (-15.7%), and superior parietal (-14.3%) regions. Logistic regression analyses showed GM regions were more predictive of MS diagnosis than WM regions. Eight GM RBPFs were significantly correlated (P < 0.001) with disease duration compared to only one WM region. Significant trends emerged for differences in GM, but not WM between secondary progressive (SP) and relapsing-remitting MS patients. Percent differences in GM between the two groups were largest in superior frontal (-9.9%), medial superior frontal (-6.5%), and superior parietal (-6.1%) regions, with SP patients having lower volumes. Overall, atrophy in MS is diffuse and mostly related to GM loss particularly in deep GM and superior frontal-parietal regions.     

Evidence for cervical cord tissue disorganisation with aging by diffusion tensor MRI

This study investigated the influence of normal aging on cervical cord volumetry and diffusivity changes and assessed whether magnetic resonance imaging (MRI) abnormalities of the aging cervical cord and brain are associated. Conventional and diffusion tensor (DT) MRI of the brain and cervical cord were acquired from 96 healthy subjects (age range=13-70 years). Cross-sectional area, mean diffusivity (MD) and fractional anisotropy (FA) of the cervical cord were measured. Volumetry and diffusivity metrics were also obtained for the brain white matter (WM) and grey matter (GM) (overall and cortical). No cervical cord lesions were seen on conventional MR images from all subjects. Degenerative vertebral column changes (not associated to cord compression) were found in 41 subjects (43%). Average FA of the cervical cord, but not average MD and cross-sectional area, was correlated with age (r=-0.70, p<0.001). Additionally, T2 brain lesion volume, normalised brain volume (NBV), normalised global and cortical brain GM volumes and average MD of the brain GM and WM also correlated with age (r values ranging from -0.83 to 0.62). Only brain WM average FA was weakly correlated with cervical cord average FA (r=0.25, p=0.02). The final multivariate model retained cord average FA (r=-0.37, p<0.001), normalised cortical GM volume (r=-0.56, p<0.001) and NBV (r=-0.22, p=0.04) as independent correlates of age (r2=0.76). Cervical cord is vulnerable to aging. The decrease of FA, in the absence of atrophy and MD changes, suggests gliosis as the most likely pathological feature of the aging cord.     

Genetic and environmental influences of white and gray matter signal contrast: a new phenotype for imaging genetics?

The estimation of cortical thickness is in part dependent on the degree of contrast in T1 signal intensity between white matter and gray matter along the cortical mantle. The ratio of white matter to gray matter signal (WM/GM contrast) has been found to vary as a function of age and Alzheimer's disease status, suggesting a biological component to what might otherwise be labeled as a nuisance variable. The aim of the present study was to determine if measures of WM/GM contrast are genetically influenced, as well as the degree to which this phenotype may be related to the genetic and environment determinants of cortical thickness. Participants were 514 male twins (130 monozygotic, 97 dizygotic pairs, and 60 unpaired individuals) from the Vietnam Era Twin Study of Aging. Ages ranged from 51 to 59 years. Measures of WM/GM contrast and cortical thickness were derived for 66 cortical regions of interest (ROI) using FreeSurfer-based methods. Univariate and bivariate twin analyses were used in order to estimate the heritability of WM/GM contrast, as well as the degree of shared genetic and environmental variance between WM/GM contrast and cortical thickness. WM/GM contrast was found to be significantly heritable in the majority of ROIs. The average heritability across individual ROIs was highest in the occipital lobe (.50), and lowest in the cingulate cortex (.24). Significant phenotypic correlations between WM/GM contrast and cortical thickness were observed for most of the ROIs. The majority of the phenotypic correlations were negative, ranging from ?.11 to ?.54. Of the 66 associations, only 17 significant genetic correlations were found, ranging from ?.16 to ?.34, indicating small amounts of shared genetic variance. The majority of the phenotypic correlations were accounted for by small unique environmental effects common between WM/GM contrast and cortical thickness. These findings demonstrate that like cortical thickness, WM/GM contrast is a genetically influenced brain structure phenotype. The lack of significant genetic correlations with cortical thickness suggests that this measure potentially represents a unique source of genetic variance, one that has yet to be explored by the field of imaging genetics.     

Longitudinal changes in patients with traumatic brain injury assessed with diffusion-tensor and volumetric imaging

Traumatic brain injury (TBI) is associated with brain volume loss, but there is little information on the regional gray matter (GM) and white matter (WM) changes that contribute to overall loss. Since axonal injury is a common occurrence in TBI, imaging methods that are sensitive to WM damage such as diffusion-tensor imaging (DTI) may be useful for characterizing microstructural brain injury contributing to regional WM loss in TBI. High-resolution T1-weighted imaging and DTI were used to evaluate regional changes in TBI patients compared to matched controls. Patients received neuropsychological testing and were imaged approximately 2 months and 12.7 months post-injury. Paradoxically, neuropsychological function improved from Visit 1 to Visit 2, while voxel-based analyses of fractional anisotropy (FA), and mean diffusivity (MD) from the DTI images, and voxel-based analyses of the GM and WM probability maps from the T1-weighted images, mainly revealed significantly greater deleterious GM and WM change over time in patients compared to controls. Cross-sectional comparisons of the DTI measures indicated that patients have decreased FA and increased MD compared to controls over large regions of the brain. TBI affected virtually all of the major fiber bundles in the brain including the corpus callosum, cingulum, the superior and inferior longitudinal fascicules, the uncinate fasciculus, and brain stem fiber tracts. The results indicate that both GM and WM degeneration are significant contributors to brain volume loss in the months following brain injury, and also suggest that DTI measures may be more useful than high-resolution anatomical images in assessment of group differences.     

Investigating association of brain volumes with intracranial capacity in schizophrenia

Intracranial volume (ICV) is usually treated as a global or nuisance covariate in almost all volumetric studies of schizophrenia. However, validation for this analytic method has seldom been accomplished. In this study, we aimed to determine the effects of ICV on gray matter (GM) and white matter (WM) volumes. Sixty-three patients with schizophrenia and sixty normal controls were recruited; and high resolution T1 weighted images were obtained by 3T-MRI. After segmentation and normalization of the images into GM, WM, and cerebrospinal fluid (CSF), multiple regression analyses of global GM and WM volumes were performed using explanatory variables such as diagnosis, ICV, and diagnosis-ICV interaction. In addition, associations between regional GM and WM volumes with ICV were also investigated using voxel-based morphometry (VBM). No significant interaction between diagnosis and ICV was found for global GM volume, whereas interactions were detected in restricted GM areas using VBM. On the other hand, an interaction between ICV and diagnosis was found in WM not only for regional volumes, but also for global WM volume. The regression slope of global WM volumes against ICV was steeper in patients with schizophrenia than in healthy controls. These results imply that ICV should be carefully evaluated in the analyses of volumetric studies of schizophrenia, especially when analyzing WM volumes.     

Longitudinal changes in grey and white matter during adolescence

Brain development continues actively during adolescence. Previous MRI studies have shown complex patterns of apparent loss of grey matter (GM) volume and increases in white matter (WM) volume and fractional anisotropy (FA), an index of WM microstructure. In this longitudinal study (mean follow-up = 2.5 ± 0.5 years) of 24 adolescents, we used a voxel-based morphometry (VBM)-style analysis with conventional T1-weighted images to test for age-related changes in GM and WM volumes. We also performed tract-based spatial statistics (TBSS) analysis of diffusion tensor imaging (DTI) data to test for age-related WM changes across the whole brain. Probabilistic tractography was used to carry out quantitative comparisons across subjects in measures of WM microstructure in two fiber tracts important for supporting speech and motor functions (arcuate fasciculus [AF] and corticospinal tract [CST]). The whole-brain analyses identified age-related increases in WM volume and FA bilaterally in many fiber tracts, including AF and many parts of the CST. FA changes were mainly driven by increases in parallel diffusivity, probably reflecting increases in the diameter of the axons forming the fiber tracts. FA values of both left and right AF (but not of the CST) were significantly higher at the end of the follow-up than at baseline. Over the same period, widespread reductions in the cortical GM volume were found. These findings provide imaging-based anatomical data suggesting that brain maturation in adolescence is associated with structural changes enhancing long-distance connectivities in different WM tracts, specifically in the AF and CST, at the same time that cortical GM exhibits synaptic “pruning”.     

Linking white matter tracts to associated cortical grey matter: a tract extension methodology

Quantitative diffusion analysis of white matter (WM) tracts has been utilised in many diseases for determining damage to, and changes in, WM tracts throughout the brain. However, there are limited studies investigating associations between quantitative measures in WM tracts and anatomically linked grey matter (GM), due to the difficulty in determining GM regions connected with a given WM tract.This work describes a straightforward method for extending a WM tract through GM based on geometry. The tract is extended by following a straight line from each point on the tract boundary to the outer boundary of the cortex. A comparison between a multiplanar 2D approach and a 3D method was made. This study also tested an analysis pipeline from tracking WM tracts to quantifying magnetisation transfer ratios (MTR) in the associated cortical GM, and assessed the applicability of the method to healthy control subjects. Tract and associated cortical volumes and MTR values for the cortico-spinal tracts, genu and body of the corpus callosum were extracted; the between-subjects standard deviation was calculated.It was found that a multiplanar 2D approach produced a more anatomically plausible volume of GM than a 3D approach, at the expense of possible overestimation of the GM volume. The between-subjects standard deviation of the tract specific quantitative measurements (from both the WM and GM masks) ranged between 1.2 and 7.3% for the MTR measures, and between 10 and 45% for the absolute volume measures.The results show that the method can be used to produce anatomically plausible extensions of the WM tracts through the GM, and regions defined in this way yield reliable estimates of the MTR from the regions.     

Relating neocortical pathology to disability progression in multiple sclerosis using MRI

Cortical grey matter (cGM) develops a substantial burden of pathology in multiple sclerosis (MS). Previous cross-sectional studies have suggested a relationship between measures of cortical atrophy and disability. Our objective was to develop a method for automatically measuring the apparent cGM thickness as well as the integrity of the interface between cGM and subcortical white matter (GM/WM) both globally and regionally on T(1)-weighted MRI, and use this method in a longitudinal investigation of how these measures differed between patients with stable MS and patients with progressing disability. Measurements were made over the whole brain and for anatomically specified cortical regions, both cross-sectionally at baseline and longitudinally on two MRI scans performed on average 1 year apart. We found a higher average rate of apparent loss of cGM thickness across the whole brain in the group that progressed over the interscan interval compared to the group that remained stable (progressing = -3.13 +/- 2.88%/year, stable = 0.06 +/- 2.31%/year, P = 0.002). This difference was detected with regional measures in parietal and precentral cortex. In contrast, change in the GM/WM interface integrity did not show detectable regional differences, although the group of MS patients whose disability progressed showed a significant decrease in GM/WM interface integrity compared to the stable group (P = 0.003). Regional measures of apparent loss of cGM thickness enhance sensitivity to cortical pathological changes. A measure of integrity offers a new index of disease-associated cortical changes at the GM/WM interface. The results suggest that progression of disability in MS is associated with the progression of MRI-detectable cortical pathology.     

Cortical reconstruction using implicit surface evolution: accuracy and precision analysis

Two different studies were conducted to assess the accuracy and precision of an algorithm developed for automatic reconstruction of the cerebral cortex from T1-weighted magnetic resonance (MR) brain images. Repeated scans of three different brains were used to quantify the precision of the algorithm, and manually selected landmarks on different sulcal regions throughout the cortex were used to analyze the accuracy of the three reconstructed surfaces: inner, central, and pial. We conclude that the algorithm can find these surfaces in a robust fashion and with subvoxel accuracy, typically with an accuracy of one third of a voxel, although this varies with brain region and cortical geometry. Parameters were adjusted on the basis of this analysis in order to improve the algorithm's overall performance.