Alex P. Zijdenbos

Brain development during childhood and adolescence: a longitudinal MRI study

Pediatric neuroimaging studies, up to now exclusively cross sectional, identify linear decreases in cortical gray matter and increases in white matter across ages 4 to 20. In this large-scale longitudinal pediatric neuroimaging study, we confirmed linear increases in white matter, but demonstrated nonlinear changes in cortical gray matter, with a preadolescent increase followed by a postadolescent decrease. These changes in cortical gray matter were regionally specific, with developmental curves for the frontal and parietal lobe peaking at about age 12 and for the temporal lobe at about age 16, whereas cortical gray matter continued to increase in the occipital lobe through age 20.

A nonparametric method for automatic correction of intensity nonuniformity in MRI data

A novel approach to correcting for intensity nonuniformity in magnetic resonance (MR) data is described that achieves high performance without requiring a model of the tissue classes present. The method has the advantage that it can be applied at an early stage in an automated data analysis, before a tissue model is available. Described as nonparametric nonuniform intensity normalization (N3), the method is independent of pulse sequence and insensitive to pathological data that might otherwise violate model assumptions. To eliminate the dependence of the field estimate on anatomy, an iterative approach is employed to estimate both the multiplicative bias field and the distribution of the true tissue intensities. The performance of this method is evaluated using both real and simulated MR data.

Morphometric analysis of white matter lesions in MR images: method and validation

The analysis of MR images is evolving from qualitative to quantitative. More and more, the question asked by clinicians is how much and where, rather than a simple statement on the presence or absence of abnormalities. The authors present a study in which the results obtained with a semiautomatic, multispectral segmentation technique are quantitatively compared to manually delineated regions. The core of the semiautomatic image analysis system is a supervised artificial neural network classifier augmented with dedicated preand postprocessing algorithms, including anisotropic noise filtering and a surface-fitting method for the correction of spatial intensity variations. The study was focused on the quantitation of white matter lesions in the human brain. A total of 36 images from six brain volumes was analyzed twice by each of two operators, under supervision of a neuroradiologist. Both the intra- and interrater variability of the methods were studied in terms of the average tissue area detected per slice, the correlation coefficients between area measurements, and a measure of similarity derived from the kappa statistic. The results indicate that, compared to a manual method, the use of the semiautomatic technique not only facilitates the analysis of the images, but also has similar or lower intra- and interrater variabilities.

Sexual dimorphism of brain developmental trajectories during childhood and adolescence

Human total brain size is consistently reported to be approximately 8-10% larger in males, although consensus on regionally specific differences is weak. Here, in the largest longitudinal pediatric neuroimaging study reported to date (829 scans from 387 subjects, ages 3 to 27 years), we demonstrate the importance of examining size-by-age trajectories of brain development rather than group averages across broad age ranges when assessing sexual dimorphism. Using magnetic resonance imaging (MRI) we found robust male/female differences in the shapes of trajectories with total cerebral volume peaking at age 10.5 in females and 14.5 in males. White matter increases throughout this 24-year period with males having a steeper rate of increase during adolescence. Both cortical and subcortical gray matter trajectories follow an inverted U shaped path with peak sizes 1 to 2 years earlier in females. These sexually dimorphic trajectories confirm the importance of longitudinal data in studies of brain development and underline the need to consider sex matching in studies of brain development.

Induction of a non-encephalitogenic type 2 T helper-cell autoimmune response in multiple sclerosis after administration of an altered peptide ligand in a placebo- controlled, randomized phase II trial

In this ‘double-blind’, randomized, placebo-controlled phase II trial, we compared an altered peptide ligand of myelin basic protein with placebo, evaluating their safety and influence on magnetic resonance imaging in relapsing–remitting multiple sclerosis. A safety board suspended the trial because of hypersensitivity reactions in 9% of the patients. There were no increases in either clinical relapses or in new enhancing lesions in any patient, even those with hypersensitivity reactions. Secondary analysis of those patients completing the study showed that the volume and number of enhancing lesions were reduced at a dose of 5 mg. There was also a regulatory type 2 T helper-cell response to altered peptide ligand that cross-reacted with the native peptide.

Fast and robust parameter estimation for statistical partial volume models in brain MRI.

Due to the finite spatial resolution of imaging devices, a single voxel in a medical image may be composed of mixture of tissue types, an effect known as partial volume effect (PVE). Partial volume estimation, that is, the estimation of the amount of each tissue type within each voxel, has received considerable interest in recent years. Much of this work has been focused on the mixel model, a statistical model of PVE. We propose a novel trimmed minimum covariance determinant (TMCD) method for the estimation of the parameters of the mixel PVE model. In this method, each voxel is first labeled according to the most dominant tissue type. Voxels that are prone to PVE are removed from this labeled set, following which robust location estimators with high breakdown points are used to estimate the mean and the covariance of each tissue class. Comparisons between different methods for parameter estimation based on classified images as well as expectation--maximization-like (EM-like) procedure for simultaneous parameter and partial volume estimation are reported. The robust estimators based on a pruned classification as presented here are shown to perform well even if the initial classification is of poor quality. The results obtained are comparable to those obtained using the EM-like procedure, but require considerably less computation time. Segmentation results of real data based on partial volume estimation are also reported. In addition to considering the parameter estimation problem, we discuss differences between different approximations to the complete mixel model. In summary, the proposed TMCD method allows for the accurate, robust, and efficient estimation of partial volume model parameters, which is crucial to a variety of brain MRI data analysis procedures such as the accurate estimation of tissue volumes and the accurate delineation of the cortical surface.

Correction of intensity variations in MR images for computer-aided tissue classification

A number of supervised and unsupervised pattern recognition techniques have been proposed in recent years for the segmentation and the quantitative analysis of MR images. However, the efficacy of these techniques is affected by acquisition artifacts such as inter-slice, intra-slice, and inter-patient intensity variations. Here a new approach to the correction of intra-slice intensity variations is presented. Results demonstrate that the correction process enhances the performance of backpropagation neural network classifiers designed for the segmentation of the images. Two slightly different versions of the method are presented. The first version fits an intensity correction surface directly to reference points selected by the user in the images. The second version fits the surface to reference points obtained by an intermediate classification operation. Qualitative and quantitative evaluation of both methods reveals that the first one leads to a better correction of the images than the second but that it is more sensitive to operator errors.

In vivo morphometry of the intrasulcal gray matter in the human cingulate, paracingulate, and superior‐rostral sulci: Hemispheric asymmetries, gender differences and probability maps

Volumes of the intrasulcal gray matter were measured in three cerebral sulci located on the medial wall of the human frontal lobe: cingulate sulcus (CS), paracingulate sulcus (PCS), and superior‐rostral sulcus (SRS). The measurements were carried out on T1‐weighted 3‐D high‐resolution magnetic‐resonance (MR) images acquired in 105 young right‐handed volunteers (42 female and 63 male). Before the measurement, the images were transformed into a standardized stereotaxic space (Talairach and Tournoux [1988] Human Brain: 3‐Dimensional Proportional System. An Approach to Cerebral Imaging. Stuttgart, New York: Georg Thieme Verlag), thus removing inter‐individual differences in brain size. The intrasulcal gray matter was segmented in a semi‐automatic manner. Significant gender differences were found in the volume of the CS (female > male) and the PCS (male > female). Hemispheric asymmetries were observed between the left and right volumes of the intrasulcal gray matter in the anterior (right > left) and posterior (left > right) segments of the CS, as well as between the left and right volumes of the PCS (left > right). There was no interaction between the asymmetries and gender. In addition, significant positive correlations were found between the left and right gray‐matter volumes in the anterior (r = 0.43) and posterior (r = 0.66) segments of the CS, whereas significant negative correlations were observed between the gray‐matter volumes of the anterior segment of the CS and those of the PCS (left hemisphere: r = −0.48; right hemisphere: r = −0.42). The observed hemispheric asymmetries in the CS and PCS gray‐matter volumes are consistent with the proposed role of these structures in the integration of emotions with cognition (CS) and in the control of speech/vocalization (PCS). The pattern of inter‐hemispheric correlations in the sulcal gray‐matter points to an increasing asynchrony in the foetal development of primary (CS), secondary (SRS), and tertiary (PCS) sulci, respectively. The presence of negative correlations between the two neighbouring sulci (CS and PCS) suggests that a process of compensation could underlie interactions between adjacent primary and tertiary sulci. Besides the above volumetric analysis, we also provide average (probability) maps of the three sulci; the use of such maps for the parcellation of the medial frontal lobe and localization of “peaks” obtained in blood‐flow activation studies is discussed.

Automated cortical thickness measurements from MRI can accurately separate Alzheimer's patients from normal elderly controls.

We investigated the potential of fully automated measurements of cortical thickness to reproduce the clinical diagnosis in Alzheimers Disease (AD) using 19 patients and 17 healthy controls. Thickness maps were analyzed using three different discriminant techniques to separate patients from controls. All analyses were performed using leave-one-out cross-validation to avoid overtraining of the discriminants. The results show regionally variant patterns of discrimination ability, with over 90% accuracy obtained in the medial temporal lobes and other limbic structures. Multivariate discriminant analysis produced 100% accuracy with six different combinations, all involving the parahippocampal gyrus. We therefore propose automated measurements of cortical thickness as a tool to improve the clinical diagnosis of probable AD, as well as a research method to gain unique insight into the etiology of cortical pathology in the disease.

Gray Matter Differences Correlate with Spontaneous Strategies in a Human Virtual Navigation Task

Young healthy participants spontaneously use different strategies in a virtual radial maze, an adaptation of a task typically used with rodents. Functional magnetic resonance imaging confirmed previously that people who used spatial memory strategies showed increased activity in the hippocampus, whereas response strategies were associated with activity in the caudate nucleus. Here, voxel based morphometry was used to identify brain regions covarying with the navigational strategies used by individuals. Results showed that spatial learners had significantly more gray matter in the hippocampus and less gray matter in the caudate nucleus compared with response learners. Furthermore, the gray matter in the hippocampus was negatively correlated to the gray matter in the caudate nucleus, suggesting a competitive interaction between these two brain areas. In a second analysis, the gray matter of regions known to be anatomically connected to the hippocampus, such as the amygdala, parahippocampal, perirhinal, entorhinal and orbitofrontal cortices were shown to covary with gray matter in the hippocampus. Because low gray matter in the hippocampus is a risk factor for Alzheimers disease, these results have important implications for intervention programs that aim at functional recovery in these brain areas. In addition, these data suggest that spatial strategies may provide protective effects against degeneration of the hippocampus that occurs with normal aging.