Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis

Kernelized fuzzy c-means method in fast segmentation of demyelination plaques in multiple sclerosis

Fuzzy c-means method (FCM) is a popular tool for a fuzzy data processing. In the current study, a FCM-based method of fuzzy clustering in a kernel space has been implemented. First, a "kernel trick" is applied to the fuzzy c-means algorithm. Then, the new method is employed for a fast automated segmentation of demyelination plaques in Multiple Sclerosis (MS). The clusters in a Gaussian kernel space are analysed in the histogram context and used during the initial classification of the brain tissue. Received classification masks are then used to detect the region of interest, eliminate false positives and label MS lesions.

Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine

RATIONALE AND OBJECTIVES

Brain lesions, especially white matter lesions (WMLs), are associated with cardiac and vascular disease, but also with normal aging. Quantitative analysis of WML in large clinical trials is becoming more and more important.

MATERIALS AND METHODS

In this article, we present a computer-assisted WML segmentation method, based on local features extracted from multiparametric magnetic resonance imaging (MRI) sequences (ie, T1-weighted, T2-weighted, proton density-weighted, and fluid attenuation inversion recovery MRI scans). A support vector machine classifier is first trained on expert-defined WMLs, and is then used to classify new scans.

RESULTS

Postprocessing analysis further reduces false positives by using anatomic knowledge and measures of distance from the training set.

CONCLUSIONS

Cross-validation on a population of 35 patients from three different imaging sites with WMLs of varying sizes, shapes, and locations tests the robustness and accuracy of the proposed segmentation method, compared with the manual segmentation results from two experienced neuroradiologists.

Segmentation of age-related white matter changes in a clinical multi-center study

Age-related white matter changes (WMC) are thought to be a marker of vascular pathology, and have been associated with motor and cognitive deficits. In the present study, an optimized artificial neural network was used as an automatic segmentation method to produce probabilistic maps of WMC in a clinical multi-center study. The neural network uses information from T1- and T2-weighted and fluid attenuation inversion recovery (FLAIR) magnetic resonance (MR) scans, neighboring voxels and spatial location. Generalizability of the neural network was optimized by including the Optimal Brain Damage (OBD) pruning method in the training stage. Six optimized neural networks were produced to investigate the impact of different input information on WMC segmentation. The automatic segmentation method was applied to MR scans of 362 non-demented elderly subjects from 11 centers in the European multi-center study Leukoaraiosis And Disability (LADIS). Semi-manually delineated WMC were used for validating the segmentation produced by the neural networks. The neural network segmentation demonstrated high consistency between subjects and centers, making it a promising technique for large studies. For WMC volumes less than 10 ml, an increasing discrepancy between semi-manual and neural network segmentation was observed using the similarity index (SI) measure. The use of all three image modalities significantly improved cross-center generalizability compared to neural networks using the FLAIR image only. Expert knowledge not available to the neural networks was a minor source of discrepancy, while variation in MR scan quality constituted the largest source of error.

Fully automatic segmentation of multiple sclerosis lesions in brain MR FLAIR images using adaptive mixtures method and Markov random field model

In this paper, an approach is proposed for fully automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images. The proposed approach, based on a Bayesian classifier, utilizes the adaptive mixtures method (AMM) and Markov random field (MRF) model to obtain and upgrade the class conditional probability density function (CCPDF) and the a priori probability of each class. To compare the performance of the proposed approach with those of previous approaches including manual segmentation, the similarity criteria of different slices related to 20 MS patients were calculated. Also, volumetric comparison of lesions volume between the fully automated segmentation and the gold standard was performed using correlation coefficient (CC). The results showed a better performance for the proposed approach, compared to those of previous works.

Prefrontal cortex atrophy predicts dementia over a six-year period

The present study investigated prefrontal cortex (PFC) atrophy as a possible predictor of dementia. Eighty-eight older participants of the Maastricht Aging Study (MAAS) were administered for neuropsychological tests at baseline and after three years (t(3)). Magnetic resonance images were acquired at t(3) and nine years after baseline all participants were screened for dementia. Three groups were distinguished: (1) participants who did not develop dementia or cognitive decline, (2) participants who did not develop dementia but did show significant cognitive decline, and (3) participants who developed dementia. Gray matter volume of structures in the PFC and medial temporal lobe (MTL) was measured. Prefrontal volume was significantly smaller in group 3 than in the other two groups, and PFC volume was significantly better than MTL volume in distinguishing between groups 2 and 3. These findings suggest that PFC atrophy is highly associated with dementia and can be considered an important predictor of the disease. It may even be a better predictor than the MTL atrophy that has been found in earlier studies.

Identification of genetically mediated cortical networks: a multivariate study of pediatric twins and siblings

Structural magnetic resonance imaging data from 308 twins, 64 singleton siblings of twins, and 228 singletons were analyzed using structural equation modeling and selected multivariate methods to identify genetically mediated intracortical associations. Principal components analyses (PCA) of the genetic correlation matrix indicated a single factor accounting for over 60% of the genetic variability in cortical thickness. When covaried for mean global cortical thickness, PCA, cluster analyses, and graph models identified genetically mediated fronto-parietal and occipital networks. Graph theoretical models suggest that the observed genetically mediated relationships follow small world architectural rules. These findings are largely concordant with other multivariate studies of brain structure and function, the twin literature, and current understanding on the role of genes in cortical neurodevelopment.

Functional organization of human visual cortex in occipital polymicrogyria

Polymicrogyrias (PMG) are cortical malformations resulting from developmental abnormalities. In animal models PMG has been associated with abnormal anatomy, function, and organization. The purpose of this study was to describe the function and organization of human polymicrogyric cortex using functional magnetic resonance imaging. Three patients with epilepsy and bilateral parasagittal occipital polymicrogyri were studied. They all had normal vision as tested by Humphrey visual field perimetry. The functional organization of the visual cortex was reconstructed using phase-encoded retinotopic mapping analysis. This method sequentially stimulates each point in the visual field along the axes of a polar-coordinate system, thereby reconstructing the representation of the visual field on the cortex. We found normal cortical responses and organization of early visual areas (V1, V2, and V3/VP). The locations of these visual areas overlapped substantially with the PMG. In five out of six hemispheres the reconstructed primary visual cortex completely fell within polymicrogyric areas. Our results suggest that human polymicrogyric cortex is not only organized in a normal fashion, but is also actively involved in processing of visual information and contributes to normal visual perception.

Normalization in PET group comparison studies--the importance of a valid reference region

INTRODUCTION

In positron emission tomography (PET) studies of cerebral blood flow (CBF) and metabolism, the large interindividual variation commonly is minimized by normalization to the global mean prior to statistical analysis. This approach requires that no between-group or between-state differences exist in the normalization region. Given the variability typical of global CBF and the practical limit on sample size, small group differences in global mean easily elude detection, but still bias the comparison, with profound consequences for the physiological interpretation of the results.

MATERIALS AND METHODS

Quantitative [15O]H2O PET recordings of CBF were obtained in 45 healthy subjects (21-81 years) and 14 patients with hepatic encephalopathy (HE). With volume-of-interest (VOI) and voxel-based statistics, we conducted regression analyses of CBF as function of age in the healthy group, and compared the HE group to a subset of the controls. We compared absolute CBF values, and CBF normalized to the gray matter (GM) and white matter (WM) means. In additional simulation experiments, we manipulated the cortical values of 12 healthy subjects and compared these to unaltered control data.

RESULTS

In healthy aging, CBF was shown to be unchanged in WM and central regions. In contrast, with normalization to the GM mean, CBF displayed positive correlation with age in the central regions. Very similar artifactual increases were seen in the HE comparison and also in the simulation experiment.

CONCLUSION

Ratio normalization to the global mean readily elevates CBF in unchanged regions when a systematic between-group difference exists in gCBF, also when this difference is below the detection threshold. We suggest that the routine normalization to the global mean in earlier studies resulted in spurious interpretations of perturbed CBF. Normalization to central WM yields less biased results in aging and HE and could potentially serve as a normalization reference region in other disorders as well.

An a contrario approach for change detection in 3D multimodal images: application to multiple sclerosis in MRI

Estimating significant changes between two images remains a challenging problem in medical image processing. This paper proposes a non-parametric region based method to detect significant changes in 3D multimodal Magnetic Resonance (MR) sequences. The proposed approach relies on an a contrario model which defines significant changes as events with very low probability. We adapt the a contrario framework to deal with multimodal images from which are extracted measures related to intensity and volume changes. Two fusion rules are carefully designed to handle a set of decision thresholds and a set of image measures. The final decision is taken using multiple testing procedures. The efficiency of the algorithm is demonstrated in the context of multiple sclerosis (MS) lesion analysis over time in multimodal MR sequences. We evaluate the proposed method on synthetic images using the Brainweb simulator. Finally, promising results on multimodal sequences on clinical data are presented.

Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation

There is controversy over the nature of the disturbance in brain development that underpins attention-deficit/hyperactivity disorder (ADHD). In particular, it is unclear whether the disorder results from a delay in brain maturation or whether it represents a complete deviation from the template of typical development. Using computational neuroanatomic techniques, we estimated cortical thickness at >40,000 cerebral points from 824 magnetic resonance scans acquired prospectively on 223 children with ADHD and 223 typically developing controls. With this sample size, we could define the growth trajectory of each cortical point, delineating a phase of childhood increase followed by adolescent decrease in cortical thickness (a quadratic growth model). From these trajectories, the age of attaining peak cortical thickness was derived and used as an index of cortical maturation. We found maturation to progress in a similar manner regionally in both children with and without ADHD, with primary sensory areas attaining peak cortical thickness before polymodal, high-order association areas. However, there was a marked delay in ADHD in attaining peak thickness throughout most of the cerebrum: the median age by which 50% of the cortical points attained peak thickness for this group was 10.5 years (SE 0.01), which was significantly later than the median age of 7.5 years (SE 0.02) for typically developing controls (log rank test chi(1)(2) = 5,609, P < 1.0 x 10(-20)). The delay was most prominent in prefrontal regions important for control of cognitive processes including attention and motor planning. Neuroanatomic documentation of a delay in regional cortical maturation in ADHD has not been previously reported.

Cortical thinning in obsessive compulsive disorder

Although studies of obsessive-compulsive disorder (OCD) over the last 20 years have suggested abnormalities in frontal-subcortical circuitry, evidences of structural abnormalities in those areas are still imperfect and contradictory. With recent advances in neuroimaging technology, it is now possible to study cortical thickness based on cortical surfaces, which offers a direct quantitative index of cortical mass. Using the constrained Laplacian-based automated segmentation with proximities (CLASP) algorithm, we measured cortical thickness of 55 patients with OCD (33 men and 22 women) and 52 age- and sex-matched healthy volunteers (32 men and 20 women). We found multiple regions of cortical thinning in OCD patients compared to the normal control group. Patients with OCD had thinner left inferior frontal, left middle frontal, left precentral, left superior temporal, left parahippocampal, left orbitofrontal, and left lingual cortices. Most thinned regions were located in the left ventral cortex system, providing a new perspective that this ventral cortical system may be involved in the pathophysiology of OCD.

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 Alzheimer's 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.

A hierarchical algorithm for MR brain image parcellation

We introduce an algorithm for segmenting brain magnetic resonance (MR) images into anatomical compartments such as the major tissue classes and neuro-anatomical structures of the gray matter. The algorithm is guided by prior information represented within a tree structure. The tree mirrors the hierarchy of anatomical structures and the subtrees correspond to limited segmentation problems. The solution to each problem is estimated via a conventional classifier. Our algorithm can be adapted to a wide range of segmentation problems by modifying the tree structure or replacing the classifier. We evaluate the performance of our new segmentation approach by revisiting a previously published statistical group comparison between first-episode schizophrenia patients, first-episode affective psychosis patients, and comparison subjects. The original study is based on 50 MR volumes in which an expert identified the brain tissue classes as well as the superior temporal gyrus, amygdala, and hippocampus. We generate analogous segmentations using our new method and repeat the statistical group comparison. The results of our analysis are similar to the original findings, except for one structure (the left superior temporal gyrus) in which a trend-level statistical significance (p = 0.07) was observed instead of statistical significance.

Structural brain magnetic resonance imaging of pediatric twins

To explore the relative impact of genetic and nongenetics factors on human brain anatomy during childhood and adolescence development, a collaborative team from the Child Psychiatry Branch of the National Institute of Mental Health and Virginia Commonwealth University is applying structural equation modeling to brain morphometric data acquired via magnetic resonance imaging from a large sample of monozygotic and dizygotic pediatric subjects. In this report, we discuss methodologic issues related to pediatric neuroimaging twin studies and synthesize results to date from the project. Current sample size from the ongoing longitudinal study is approximately 150 twin pairs. Consistent themes are: (1) heritability is high and shared environmental effects low for most brain morphometric measures; (2) the cerebellum has a distinct heritability profile; (3) genetic and environmental factors contribute to the development of the cortex in a regional and age specific manner; and (4) shared genetic effects account for more of the variance than structure specific effects. Understanding of influences on trajectories of brain development may shed light on the emergence of psychopathology during childhood and adolescence and ultimately may guide therapeutic interventions.

Cortical morphology in children and adolescents with different apolipoprotein E gene polymorphisms: an observational study

BACKGROUND

Alleles of the apolipoprotein E (APOE) gene modulate risk for Alzheimer's disease, with carriers of the epsilon4 allele being at increased risk and carriers of the epsilon2 allele possibly at decreased risk compared with non-carriers. Our aim was to determine whether possession of an epsilon4 allele would confer children with a neural substrate that might render them at risk for Alzheimer's disease, and whether carriers of the epsilon2 allele might have a so-called protective cortical morphology.

METHODS

239 healthy children and adolescents were genotyped and had repeated neuroanatomic MRI (total 530 scans). Mixed model regression was used to determine whether the developmental trajectory of the cortex differed by genotype.

FINDINGS

Cortical thickness of the left entorhinal region was significantly thinner in epsilon4 carriers than it was in non-epsilon4 carriers (3.79 [SE 0.06] mm, range 1.54-5.24 vs 3.94 [0.03] mm, 2.37-6.11; p=0.03). There was a significant stepwise increase in cortical thickness in the left entorhinal regions, with epsilon4 carriers having the thinnest cortex and epsilon2 carriers the thickest, with epsilon3 homozygotes occupying an intermediate position (left beta 0.11 [SE 0.05], p=0.02). Neuroanatomic effects seemed fixed and non-progressive, with no evidence of accelerated cortical loss in young healthy epsilon4 carriers.

INTERPRETATION

Alleles of the apolipoprotein E gene have distinct neuroanatomic signatures, identifiable in childhood. The thinner entorhinal cortex in individuals with the epsilon4 allele might contribute to risk of Alzheimer's disease.

Genes, maternal smoking, and the offspring brain and body during adolescence: design of the Saguenay Youth Study

The search for genes of complex traits is aided by the availability of multiple quantitative phenotypes collected in geographically isolated populations. Here we provide rationale for a large-scale study of gene-environment interactions influencing brain and behavior and cardiovascular and metabolic health in adolescence, namely the Saguenay Youth Study (SYS). The SYS is a retrospective study of long-term consequences of prenatal exposure to maternal cigarette smoking (PEMCS) in which multiple quantitative phenotypes are acquired over five sessions (telephone interview, home, hospital, laboratory, and school). To facilitate the search for genes that modify an individual's response to an in utero environment (i.e. PEMCS), the study is family-based (adolescent sibships) and is carried out in a relatively geographically isolated population of the Saguenay Lac-Saint-Jean (SLSJ) region in Quebec, Canada. DNA is acquired in both biological parents and in adolescent siblings. A genome-wide scan will be carried out with sib-pair linkage analyses, and fine mapping of identified loci will be done with family-based association analyses. Adolescent sibships (12-18 years of age; two or more siblings per family) are recruited in high schools throughout the SLSJ region; only children of French-Canadian origin are included. Based on a telephone interview, potential participants are classified as exposed or nonexposed prenatally to maternal cigarette smoking; the two groups are matched for the level of maternal education and the attended school. A total of 500 adolescent participants in each group will be recruited and phenotyped. The following types of datasets are collected in all adolescent participants: (1) magnetic resonance images of brain, abdominal fat, and kidneys, (2) standardized and computer-based neuropsychological tests, (3) hospital-based cardiovascular, body-composition and metabolic assessments, and (4) questionnaire-derived measures (e.g. life habits such as eating and physical activity; drug, alcohol use and delinquency; psychiatric symptoms; personality; home and school environment; academic and vocational attitudes). Parents complete a medical questionnaire, home-environment questionnaire, a handedness questionnaire, and a questionnaire about their current alcohol and drug use, depression, anxiety, and current and past antisocial behavior. To date, we have fully phenotyped a total of 408 adolescent participants. Here we provide the description of the SYS and, using the initial sample, we present information on ascertainment, demographics of the exposed and nonexposed adolescents and their parents, and the initial MRI-based assessment of familiality in the brain size and the volumes of grey and white matter.

The associations among hippocampal volume, cortisol reactivity, and memory performance in healthy young men

In aged and pathological populations, reduced hippocampal volume is frequently described in association with impairment of hippocampus-dependent cognitive processes and chronically elevated cortisol levels. Recent studies in young healthy subjects show a negative association between hippocampal volume and memory. The aim of the present study was to investigate the associations among hippocampal volume, cortisol levels and memory performance in a group of healthy young men. Hippocampal volume was determined by manual segmentation of high-resolution 3D Magnetic Resonance Images from 13 subjects. Stress-induced cortisol levels in response to the "Trier Social Stress Test" (TSST) as well as the cortisol response to awakening (CRA) over four weeks were assessed. Declarative memory performance was tested before and after exposure to the TSST. The results show that larger hippocampal volume was associated with a significantly stronger cortisol increase in response to the TSST and a significantly greater CRA. Moreover, larger hippocampal volume was associated with significantly lower memory performance before the TSST. Our results challenge the direction of the frequently observed relationships among hippocampal volume, cortisol reactivity and memory performance and question the relevance of findings in clinical and aged subjects for young healthy populations.

Accelerated brain gray matter loss in fibromyalgia patients: premature aging of the brain?

Fibromyalgia is an intractable widespread pain disorder that is most frequently diagnosed in women. It has traditionally been classified as either a musculoskeletal disease or a psychological disorder. Accumulating evidence now suggests that fibromyalgia may be associated with CNS dysfunction. In this study, we investigate anatomical changes in the brain associated with fibromyalgia. Using voxel-based morphometric analysis of magnetic resonance brain images, we examined the brains of 10 female fibromyalgia patients and 10 healthy controls. We found that fibromyalgia patients had significantly less total gray matter volume and showed a 3.3 times greater age-associated decrease in gray matter than healthy controls. The longer the individuals had had fibromyalgia, the greater the gray matter loss, with each year of fibromyalgia being equivalent to 9.5 times the loss in normal aging. In addition, fibromyalgia patients demonstrated significantly less gray matter density than healthy controls in several brain regions, including the cingulate, insular and medial frontal cortices, and parahippocampal gyri. The neuroanatomical changes that we see in fibromyalgia patients contribute additional evidence of CNS involvement in fibromyalgia. In particular, fibromyalgia appears to be associated with an acceleration of age-related changes in the very substance of the brain. Moreover, the regions in which we demonstrate objective changes may be functionally linked to core features of the disorder including affective disturbances and chronic widespread pain.

On evaluating brain tissue classifiers without a ground truth

In this paper, we present a set of techniques for the evaluation of brain tissue classifiers on a large data set of MR images of the head. Due to the difficulty of establishing a gold standard for this type of data, we focus our attention on methods which do not require a ground truth, but instead rely on a common agreement principle. Three different techniques are presented: the Williams' index, a measure of common agreement; STAPLE, an Expectation Maximization algorithm which simultaneously estimates performance parameters and constructs an estimated reference standard; and Multidimensional Scaling, a visualization technique to explore similarity data. We apply these different evaluation methodologies to a set of eleven different segmentation algorithms on forty MR images. We then validate our evaluation pipeline by building a ground truth based on human expert tracings. The evaluations with and without a ground truth are compared. Our findings show that comparing classifiers without a gold standard can provide a lot of interesting information. In particular, outliers can be easily detected, strongly consistent or highly variable techniques can be readily discriminated, and the overall similarity between different techniques can be assessed. On the other hand, we also find that some information present in the expert segmentations is not captured by the automatic classifiers, suggesting that common agreement alone may not be sufficient for a precise performance evaluation of brain tissue classifiers.

Probabilistic brain atlas encoding using Bayesian inference

This paper addresses the problem of creating probabilistic brain atlases from manually labeled training data. We propose a general mesh-based atlas representation, and compare different atlas models by evaluating their posterior probabilities and the posterior probabilities of their parameters. Using such a Baysian framework, we show that the widely used "average" brain atlases constitute relatively poor priors, partly because they tend to overfit the training data, and partly because they do not allow to align corresponding anatomical features across datasets. We also demonstrate that much more powerful representations can be built using content-adaptive meshes that incorporate non-rigid deformation field models. We believe extracting optimal prior probability distributions from training data is crucial in light of the central role priors play in many automated brain MRI analysis techniques.

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.

The fidelity of the cortical retinotopic map in human amblyopia

To delineate the fidelity of the functional cortical organization in humans with amblyopia, we undertook an investigation into how spatial information is mapped across the visual cortex in amblyopic observers. We assessed whether the boundaries of the visual areas controlled by the amblyopic and fellow fixing eye are in the same position, the fidelity of the retinotopic map within different cortical areas and the average receptive field size in different visual areas. The functional organization of the visual cortex was reconstructed using a fMRI phase-encoded retinotopic mapping analysis. This method sequentially stimulates each point in the visual field along the axes of a polar-coordinate system, thereby reconstructing the representation of the visual field on the cortex. We found that the cortical areas were very similar in normals and amblyopes, with only small differences in boundary positions of different visual areas between fixing and fellow amblyopic eye activation. Within these corresponding visual areas, we did find anomalies in retinotopy in some but not all amblyopes that were not simply a consequence of the poorer functional responses and affected central and peripheral field regions. Only a small increase in the average (or collective) receptive field size was found for full-field representation in amblyopes and none at all for central field representation. The former may simply be a consequence of the poorer functional responses.

A multivariate analysis of neuroanatomic relationships in a genetically informative pediatric sample

An important component of brain mapping is an understanding of the relationships between neuroanatomic structures, as well as the nature of shared causal factors. Prior twin studies have demonstrated that much of individual differences in human anatomy are caused by genetic differences, but information is limited on whether different structures share common genetic factors. We performed a multivariate statistical genetic analysis on volumetric MRI measures (cerebrum, cerebellum, lateral ventricles, corpus callosum, thalamus, and basal ganglia) from a pediatric sample of 326 twins and 158 singletons. Our results suggest that the great majority of variability in cerebrum, cerebellum, thalamus and basal ganglia is determined by a single genetic factor. Though most (75%) of the variability in corpus callosum was explained by additive genetic effects these were largely independent of other structures. We also observed relatively small but significant environmental effects common to multiple neuroanatomic regions, particularly between thalamus, basal ganglia, and lateral ventricles. These findings are concordant with prior volumetric twin studies and support radial models of brain evolution.

Small-World Anatomical Networks in the Human Brain Revealed by Cortical Thickness from MRI

An important issue in neuroscience is the characterization for the underlying architectures of complex brain networks. However, little is known about the network of anatomical connections in the human brain. Here, we investigated large-scale anatomical connection patterns of the human cerebral cortex using cortical thickness measurements from magnetic resonance images. Two areas were considered anatomically connected if they showed statistically significant correlations in cortical thickness and we constructed the network of such connections using 124 brains from the International Consortium for Brain Mapping database. Significant short- and long-range connections were found in both intra- and interhemispheric regions, many of which were consistent with known neuroanatomical pathways measured by human diffusion imaging. More importantly, we showed that the human brain anatomical network had robust small-world properties with cohesive neighborhoods and short mean distances between regions that were insensitive to the selection of correlation thresholds. Additionally, we also found that this network and the probability of finding a connection between 2 regions for a given anatomical distance had both exponentially truncated power-law distributions. Our results demonstrated the basic organizational principles for the anatomical network in the human brain compatible with previous functional networks studies, which provides important implications of how functional brain states originate from their structural underpinnings. To our knowledge, this study provides the first report of small-world properties and degree distribution of anatomical networks in the human brain using cortical thickness measurements.

XXY (Klinefelter syndrome): a pediatric quantitative brain magnetic resonance imaging case-control study

OBJECTIVE

An extra X chromosome in males (XXY), known as Klinefelter syndrome, is associated with characteristic physical, cognitive, and behavioral features of variable severity. The objective of this study was to examine possible neuroanatomical substrates of these cognitive and behavioral features during childhood and adolescence.

METHODS

MRI brain scans were acquired for 42 XXY and 87 healthy XY age-matched control males. We compared these 2 groups on regional brain volumes and cortical thickness.

RESULTS

Total cerebral volume and all lobar volumes except parietal white matter were significantly smaller in the XXY group, whereas lateral-ventricle volume was larger. Consistent with the cognitive profile, the cortex was significantly thinner in the XXY group in left inferior frontal, temporal, and superior motor regions.

CONCLUSION

The brain-imaging findings of preferentially affected frontal, temporal, and motor regions and relative sparing of parietal regions are consistent with observed cognitive and behavioral strengths and weaknesses in XXY subjects.

Pattern classification using principal components of cortical thickness and its discriminative pattern in schizophrenia

We proposed pattern classification based on principal components of cortical thickness between schizophrenic patients and healthy controls, which was trained using a leave-one-out cross-validation. The cortical thickness was measured by calculating the Euclidean distance between linked vertices on the inner and outer cortical surfaces. Principal component analysis was applied to each lobe for practical computational issues and stability of principal components. And, discriminative patterns derived at every vertex in the original feature space with respect to support vector machine were analyzed with definitive findings of brain abnormalities in schizophrenia for establishing practical confidence. It was simulated with 50 randomly selected validation set for the generalization and the average accuracy of classification was reported. This study showed that some principal components might be more useful than others for classification, but not necessarily matching the ordering of the variance amounts they explained. In particular, 40-70 principal components rearranged by a simple two-sample t-test which ranked the effectiveness of features were used for the best mean accuracy of simulated classification (frontal: (left(%)|right(%))=91.07|88.80, parietal: 91.40|91.53, temporal: 93.60|91.47, occipital: 88.80|91.60). And, discriminative power appeared more spatially diffused bilaterally in the several regions, especially precentral, postcentral, superior frontal and temporal, cingulate and parahippocampal gyri. Since our results of discriminative patterns derived from classifier were consistent with a previous morphological analysis of schizophrenia, it can be said that the cortical thickness is a reliable feature for pattern classification and the potential benefits of such diagnostic tools are enhanced by our finding.

Focal cortical atrophy in multiple sclerosis: relation to lesion load and disability

Multiple sclerosis (MS) is thought to predominantly affect white matter (WM). Recently, however, loss of cortical gray matter has also been described. Little is known about the cause of cortical atrophy in MS, whether it occurs early in the disease course, and whether it affects all cortical regions equally or if there is a preferential pattern of focal cortical atrophy. An automated method was used to compute the thickness at every vertex of the cortical surface of the brains of 425 early relapsing-remitting MS patients. We correlated cortical thickness with the WM lesion load and the Expanded Disability Status Scale score. Mean cortical thickness correlated with WM lesion load and disability. The correlations of cortical thickness with total lesion load and disability were most significant in cingulate gyrus, insula, and associative cortical regions. Conversely, primary sensory, visual, and motor areas showed a less significant relationship. The highest amount of atrophy per lesion volume or disability scale unit was in the anterior cingulate cortex. This study confirms the relation between cortical atrophy, WM lesion load, and disability in MS, and suggests that cortical atrophy occurs even in MS patients with only mild disability. Most interestingly, we show a specific regional pattern of focal atrophy in MS that is distinctively different from the one in normal aging. The predilection of the atrophic process for areas that are heavily inter-connected with other brain regions suggests that interruption of WM tracts by MS plaques contributes, at least in part, to the development of cortical atrophy.

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 Alzheimer's 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.

A pediatric twin study of brain morphometry

BACKGROUND

Longitudinal pediatric neuroimaging studies have demonstrated increasing volumes of white matter and regionally-specific inverted U shaped developmental trajectories of gray matter volumes during childhood and adolescence. Studies of monozygotic and dyzygotic twins during this developmental period allow exploration of genetic and non-genetic influences on these developmental trajectories.

METHOD

Magnetic resonance imaging brain scans were acquired on a pediatric sample of 90 monozygotic twin pairs, 38 same-sex dyzygotic twin pairs, and 158 unrelated typically developing singletons. Structural equation modeling was used to estimate the additive genetic, common environment, and unique environment effects, as well as age by heritability interactions, on measures of brain volumes from these images.

RESULTS

Consistent with previous adult studies, additive genetic effects accounted for a substantial portion of variability in nearly all brain regions with the notable exception of the cerebellum. Significant age by heritability interactions were observed with gray matter volumes showing a reduction in heritability with increasing age, while white matter volume heritability increased with greater age.

CONCLUSION

Understanding the relative contributions of genetic and nongenetic factors on developmental brain trajectories may have implications for better understanding brain-based disorders and typical cognitive development.

Childhood onset schizophrenia: cortical brain abnormalities as young adults

BACKGROUND

Childhood onset schizophrenia (COS) is a rare but severe form of the adult onset disorder. While structural brain imaging studies show robust, widespread, and progressive gray matter loss in COS during adolescence, there have been no longitudinal studies of sufficient duration to examine comparability with the more common adult onset illness.

METHODS

Neuro-anatomic magnetic resonance scans were obtained prospectively from ages 7 through 26 in 70 children diagnosed with COS and age and sex matched healthy controls. Cortical thickness was measured at 40,962 points across the cerebral hemispheres using a novel, fully automated, validated method. Patterns of patient-control differences in cortical development were compared over a 19-year period.

RESULTS

Throughout the age range, the COS group had significantly smaller mean cortical thickness compared to controls. However, the COS brain developmental trajectory appeared to normalize in posterior (parietal) regions, and remained divergent in the anterior regions (frontal and temporal) regions, and the pattern of loss became more like that seen in adults.

CONCLUSIONS

Cortical thickness loss in COS appears to localize with age to prefrontal and temporal regions that are seen for both medication naïve and medicated adult onset patients.

Acquisition-related morphological variability in structural MRI

RATIONALE AND OBJECTIVES

Significant effort has been spent during the past decades to develop innovative image-processing algorithms and improve existing methods in terms of precision, reproducibility, and computational efficiency, but relatively little research was undertaken to find out the extent to which the validity of results obtained with these methods is limited by inherent imperfections of the input images. This observation is especially true for magnetic resonance imaging (MRI)-based morphometry, which aims at precise and highly reproducible determination of geometric properties of anatomic structures, although MRI images are geometrically distorted.

MATERIALS AND METHODS

A method for characterization of site-specific geometric distortions and results of a long-term study designed to find the extent to which imperfections in the data-acquisition process limit the reliable detection of subtle morphological changes in MRI data acquired with state-of-the-art scanners are presented. Because of the long-term character of the study, results include effects resulting from limited hardware stability, as well as from imperfections in patient repositioning.

RESULTS

Maximal relative morphological changes detected in our phantom data series were 1.0 mm positional and 2.0% volumetric difference (relative to a 7600-mm3 cuboid) in a subvolume relevant for whole-brain morphometry. Morphological variability was even greater for human volunteer data (up to 5% in local gray matter volume) because of movements during scan, natural morphological variability, and a presumably less precise segmentation procedure.

CONCLUSION

Imperfections in the MRI data-acquisition process in combination with practical limitations in patient repositioning can substantially confound studies of subtle morphological changes.

Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI

PURPOSE

To automatically segment multiple sclerosis (MS) lesions into three subtypes (i.e., enhancing lesions, T1 "black holes", T2 hyperintense lesions).

MATERIALS AND METHODS

Proton density-, T2- and contrast-enhanced T1-weighted brain images of 12 MR scans were pre-processed through intracranial cavity (IC) extraction, inhomogeneity correction and intensity normalization. Intensity-based statistical k-nearest neighbor (k-NN) classification was combined with template-driven segmentation and partial volume artifact correction (TDS+) for segmentation of MS lesions subtypes and brain tissue compartments. Operator-supervised tissue sampling and parameter calibration were performed on 2 randomly selected scans and were applied automatically to the remaining 10 scans. Results from this three-channel TDS+ (3ch-TDS+) were compared to those from a previously validated two-channel TDS+ (2ch-TDS+) method. The results of both the 3ch-TDS+ and 2ch-TDS+ were also compared to manual segmentation performed by experts.

RESULTS

Intra-class correlation coefficients (ICC) of 3ch-TDS+ for all three subtypes of lesions were higher (ICC between 0.95 and 0.96) than that of 2ch-TDS+ for T2 lesions (ICC = 0.82). The 3ch-TDS+ also identified the three lesion subtypes with high specificity (98.7-99.9%) and accuracy (98.5-99.9%). Sensitivity of 3ch-TDS+ for T2 lesions was 16% higher than with 2ch-TDS+. Enhancing lesions were segmented with the best sensitivity (81.9%). "Black holes" were segmented with the least sensitivity (62.3%).

CONCLUSION

3ch-TDS+ is a promising method for automated segmentation of MS lesion subtypes.

Modulation of V1 Activity by Shape: Image-Statistics or Shape-Based Perception?

It is current dogma that neurons in primary visual cortex extract local edges from the scene from which later visual areas reconstruct more meaningful shapes. Recent neuroimaging studies, however, have shown V1 modulations by the degree of structure in the image (shape). These V1 modulations due to the level of shape coherence have been explained in one of two possible ways: due to changes in image statistics or shape-based perceptual influences from higher visual areas. Here we compare both hypotheses using stimuli composed of Gabor arrays constructed to form circular shapes that can be successively degraded by manipulating the orientations of individual Gabors while maintaining local and global statistics. In a first experiment, we confirm that V1 responses are inversely correlated with the degree of structure in the image. In a second experiment, stimulus predictions are compared based on the degree of circular shape or change in the image statistic varied (orientation variance) in the image. We find that these V1 modulations to shape change are correlated with low-level changes in orientation contrast rather than shape perception per se.

Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI

We introduce MACACC-Mapping Anatomical Correlations Across Cerebral Cortex-to study correlated changes within and across different cortical networks. The principal topic of investigation is whether the thickness of one area of the cortex changes in a statistically correlated fashion with changes in thickness of other cortical regions. We further extend these methods by introducing techniques to test whether different population groupings exhibit significantly varying MACACC patterns. The methods are described in detail and applied to a normal childhood development population (n = 292), and show that association cortices have the highest correlation strengths. Taking Brodmann Area (BA) 44 as a seed region revealed MACACC patterns strikingly similar to tractography maps obtained from diffusion tensor imaging. Furthermore, the MACACC map of BA 44 changed with age, older subjects featuring tighter correlations with BA 44 in the anterior portions of the superior temporal gyri. Lastly, IQ-dependent MACACC differences were investigated, revealing steeper correlations between BA 44 and multiple frontal and parietal regions for the higher IQ group, most significantly (t = 4.0) in the anterior cingulate.

Multiple sclerosis medical image analysis and information management

Magnetic resonance imaging (MRI) has become a central tool for patient management, as well as research, in multiple sclerosis (MS). Measurements of disease burden and activity derived from MRI through quantitative image analysis techniques are increasingly being used. There are many complexities and challenges in building computerized processing pipelines to ensure efficiency, reproducibility, and quality control for MRI scans from MS patients. Such paradigms require advanced image processing and analysis technologies, as well as integrated database management systems to ensure the most utility for clinical and research purposes. This article reviews pipelines available for quantitative clinical MRI research in MS, including image segmentation, registration, time-series analysis, performance validation, visualization techniques, and advanced medical imaging software packages. To address the complex demands of the sequential processes, the authors developed a workflow management system that uses a centralized database and distributed computing system for image processing and analysis. The implementation of their system includes a web-form-based Oracle database application for information management and event dispatching, and multiple modules for image processing and analysis. The seamless integration of processing pipelines with the database makes it more efficient for users to navigate complex, multistep analysis protocols, reduces the user's learning curve, reduces the time needed for combining and activating different computing modules, and allows for close monitoring for quality-control purposes. The authors' system can be extended to general applications in clinical trials and to routine processing for image-based clinical research.

Unified approach for multiple sclerosis lesion segmentation on brain MRI

The presence of large number of false lesion classification on segmented brain MR images is a major problem in the accurate determination of lesion volumes in multiple sclerosis (MS) brains. In order to minimize the false lesion classifications, a strategy that combines parametric and nonparametric techniques is developed and implemented. This approach uses the information from the proton density (PD)- and T2-weighted and fluid attenuation inversion recovery (FLAIR) images. This strategy involves CSF and lesion classification using the Parzen window classifier. Image processing, morphological operations, and ratio maps of PD- and T2-weighted images are used for minimizing false positives. Contextual information is exploited for minimizing the false negative lesion classifications using hidden Markov random field-expectation maximization (HMRF-EM) algorithm. Lesions are delineated using fuzzy connectivity. The performance of this algorithm is quantitatively evaluated on 23 MS patients. Similarity index, percentages of over, under, and correct estimations of lesions are computed by spatially comparing the results of present procedure with expert manual segmentation. The automated processing scheme detected 80% of the manually segmented lesions in the case of low lesion load and 93% of the lesions in those cases with high lesion load.

Unified approach for multiple sclerosis lesion segmentation on brain MRI

The presence of large number of false lesion classification on segmented brain MR images is a major problem in the accurate determination of lesion volumes in multiple sclerosis (MS) brains. In order to minimize the false lesion classifications, a strategy that combines parametric and nonparametric techniques is developed and implemented. This approach uses the information from the proton density (PD)- and T2-weighted and fluid attenuation inversion recovery (FLAIR) images. This strategy involves CSF and lesion classification using the Parzen window classifier. Image processing, morphological operations, and ratio maps of PD- and T2-weighted images are used for minimizing false positives. Contextual information is exploited for minimizing the false negative lesion classifications using hidden Markov random field-expectation maximization (HMRF-EM) algorithm. Lesions are delineated using fuzzy connectivity. The performance of this algorithm is quantitatively evaluated on 23 MS patients. Similarity index, percentages of over, under, and correct estimations of lesions are computed by spatially comparing the results of present procedure with expert manual segmentation. The automated processing scheme detected 80% of the manually segmented lesions in the case of low lesion load and 93% of the lesions in those cases with high lesion load.

The NIH MRI study of normal brain development

MRI is increasingly used to study normal and abnormal brain development, but we lack a clear understanding of "normal". Previous studies have been limited by small samples, narrow age ranges and few behavioral measures. This multi-center project conducted epidemiologically based recruitment of a large, demographically balanced sample across a wide age range, using strict exclusion factors and comprehensive clinical/behavioral measures. A mixed cross-sectional and longitudinal design was used to create a MRI/clinical/behavioral database from approximately 500 children aged 7 days to 18 years to be shared with researchers and the clinical medicine community. Using a uniform acquisition protocol, data were collected at six Pediatric Study Centers and consolidated at a Data Coordinating Center. All data were transferred via a web-network into a MYSQL database that allowed (i) secure data transfer, (ii) automated MRI segmentation, (iii) correlation of neuroanatomical and clinical/behavioral variables as 3D statistical maps and (iv) remote interrogation and 3D viewing of database content. A population-based epidemiologic sampling strategy minimizes bias and enhances generalizability of the results. Target accrual tables reflect the demographics of the U.S. population (2000 Census data). Enrolled subjects underwent a standardized protocol to characterize neurobehavioral and pubertal status. All subjects underwent multi-spectral structural MRI. In a subset, we acquired T1/T2 relaxometry, diffusion tensor imaging, single-voxel proton spectroscopy and spectroscopic imaging. In the first of three cycles, successful structural MRI data were acquired in 392 subjects aged 4:6-18:3 years and in 72 subjects aged 7 days to 4:6 years. We describe the methodologies of MRI data acquisition and analysis, using illustrative results. This database will provide a basis for characterizing healthy brain maturation in relationship to behavior and serve as a source of control data for studies of childhood disorders. All data described here will be available to the scientific community from July, 2006.

Intellectual ability and cortical development in children and adolescents

Children who are adept at any one of the three academic 'R's (reading, writing and arithmetic) tend to be good at the others, and grow into adults who are similarly skilled at diverse intellectually demanding activities. Determining the neuroanatomical correlates of this relatively stable individual trait of general intelligence has proved difficult, particularly in the rapidly developing brains of children and adolescents. Here we demonstrate that the trajectory of change in the thickness of the cerebral cortex, rather than cortical thickness itself, is most closely related to level of intelligence. Using a longitudinal design, we find a marked developmental shift from a predominantly negative correlation between intelligence and cortical thickness in early childhood to a positive correlation in late childhood and beyond. Additionally, level of intelligence is associated with the trajectory of cortical development, primarily in frontal regions implicated in the maturation of intelligent activity. More intelligent children demonstrate a particularly plastic cortex, with an initial accelerated and prolonged phase of cortical increase, which yields to equally vigorous cortical thinning by early adolescence. This study indicates that the neuroanatomical expression of intelligence in children is dynamic.

Large-scale morphometric analysis of neuroanatomy and neuropathology

Brain imaging research with MRI spans a wide area, covering both structure and function, and ranging from basic research through clinical research to drug design and clinical trials. In recent years there has been a trend towards the collection of very large MRI databases which can allow for the detection of very small group-dependent effects. However, the logistical challenges of analysing such large datasets presents new challenges. This paper describes the "pipeline" framework developed at the Montreal Neurological Institute for the fully automated morphometric analysis of large brain imaging databases. The potential use of these techniques is illustrated by examples of their applications in multiple sclerosis, Alzheimer's disease, and pediatric development.

Structural brain imaging in patients with cognitive impairment in the year 2015

Cognitive impairment, especially in its early stages, is associated with very mild signs and symptoms that are difficult to detect by clinical and neuropsychological assessment. Advanced imaging analysis techniques applied to magnetic resonance images allow the detection of cerebral structural changes in vivo in mildly affected patients, and might be a useful supporting tool in the early diagnosis and treatment of patients with cognitive impairment. The increasing importance of computer science in cognitive neuroscience has led to the dissemination of a new discipline, neuroinformatics, which is crucial for the introduction of research findings into clinical practice. This review describes some advanced imaging analysis techniques aimed at studying brain structural images and how these techniques might benefit clinical practice through image data sharing and remote analysis in order to increase the accuracy of diagnosis in patients with cognitive impairment.

Current concepts of analysis of cerebral white matter hyperintensities on magnetic resonance imaging

Cerebrovascular disease is common and associated with cognitive deficits and increased risk for dementia. Until recently, only limited attention has focused on advances in imaging techniques to better define and quantify the spectrum of asymptomatic cerebrovascular disease commonly seen on magnetic resonance imaging, such as abnormal white matter signals. Abnormal signals in cerebral white matter, although nonspecific, are increased in prevalence and severity in association with aging and cerebrovascular risk factors among older individuals. The ubiquitous occurrence of these abnormal white matter signals commonly referred to as white matter hyperintensities (WMHs) and the association with cerebrovascular risk and cognitive impairment among older individuals make scientific evaluation of WMHs an important and much needed avenue of research. In this section, we review current methods of WMH analysis. Strengths and limitation of both quantitative and qualitative methods are discussed initially, followed by a brief review of current magnetic resonance imaging segmentation and mapping techniques that make it possible to assess the anatomical location of WMHs. We conclude by discussing future analytic methods designed to better understand the pathophysiology and cognitive consequences of WMHs.

Probabilistic segmentation of brain tissue in MR imaging

A new method has been developed for probabilistic segmentation of five different types of brain structures: white matter, gray matter, cerebro-spinal fluid without ventricles, ventricles and white matter lesion in cranial MR imaging. The algorithm is based on information from T1-weighted (T1-w), inversion recovery (IR), proton density-weighted (PD), T2-weighted (T2-w) and fluid attenuation inversion recovery (FLAIR) scans. It uses the K-Nearest Neighbor classification technique that builds a feature space from spatial information and voxel intensities. The technique generates for each tissue type an image representing the probability per voxel being part of it. By application of thresholds on these probability maps, binary segmentations can be obtained. A similarity index (SI) and a probabilistic SI (PSI) were calculated for quantitative evaluation of the results. The influence of each image type on the performance was investigated by alternately leaving out one of the five scan types. This procedure showed that the incorporation of the T1-w, PD or T2-w did not significantly improve the segmentation results. Further investigation indicated that the combination of IR and FLAIR was optimal for segmentation of the five brain tissue types. Evaluation with respect to the gold standard showed that the SI-values for all tissues exceeded 0.8 and all PSI-values exceeded 0.7, implying an excellent agreement.

Fully automatic segmentation of white matter hyperintensities in MR images of the elderly

The role of quantitative image analysis in large clinical trials is continuously increasing. Several methods are available for performing white matter hyperintensity (WMH) volume quantification. They vary in the amount of the human interaction involved. In this paper, we describe a fully automatic segmentation that was used to quantify WMHs in a large clinical trial on elderly subjects. Our segmentation method combines information from 3 different MR images: proton density (PD), T2-weighted and fluid-attenuated inversion recovery (FLAIR) images; our method uses an established artificial intelligent technique (fuzzy inference system) and does not require extensive computations. The reproducibility of the segmentation was evaluated in 9 patients who underwent scan-rescan with repositioning; an inter-class correlation coefficient (ICC) of 0.91 was obtained. The effect of differences in image resolution was tested in 44 patients, scanned with 6- and 3-mm slice thickness FLAIR images; we obtained an ICC value of 0.99. The accuracy of the segmentation was evaluated on 100 patients for whom manual delineation of WMHs was available; the obtained ICC was 0.98 and the similarity index was 0.75. Besides the fact that the approach demonstrated very high volumetric and spatial agreement with expert delineation, the software did not require more than 2 min per patient (from loading the images to saving the results) on a Pentium-4 processor (512 MB RAM).

Mapping brain maturation and cognitive development during adolescence

Non-invasive mapping of brain structure and function with magnetic resonance imaging (MRI) has opened up unprecedented opportunities for studying the neural substrates underlying cognitive development. There is an emerging consensus of a continuous increase throughout adolescence in the volume of white matter, both global and local. There is less agreement on the meaning of asynchronous age-related decreases in the volume of grey matter in different cortical regions; these might equally represent loss ("pruning") or gain (intra-cortical myelination) of tissue. Functional MRI studies have so far focused mostly on executive functions, such as working memory and behavioural inhibition, with very few addressing questions regarding the maturation of social cognition. Future directions for research in this area are discussed in the context of processing biological motion and matching perceptions and actions.

Unified segmentation

A probabilistic framework is presented that enables image registration, tissue classification, and bias correction to be combined within the same generative model. A derivation of a log-likelihood objective function for the unified model is provided. The model is based on a mixture of Gaussians and is extended to incorporate a smooth intensity variation and nonlinear registration with tissue probability maps. A strategy for optimising the model parameters is described, along with the requisite partial derivatives of the objective function.

Quantitative MR imaging of the neocortex

This article provides an overview of novel MR image analysis methods applied to the quantitative assessment of the neocortex in various forms of epilepsy. Postacquisition processing methods, such as voxel-based morphometry and texture analysis, involve the use of computer software to manipulate, enhance, and classify image information in a digital format. These techniques have the potential to demonstrate subtle abnormalities that are not identified by eye because of anatomic variability. Information provided by quantitative MR imaging of the neocortex may be important for the identification of accurate predictors of surgical outcome and may refine the selection of surgical candidates, particularly those with "nonlesional" neocortical epilepsy.