Detection and mapping of abnormal brain structure with a probabilistic atlas of cortical surfaces

Regional specificity of hippocampal volume reductions in first-episode schizophrenia

Hippocampal volume reductions are widely observed in schizophrenia. Some studies suggest anterior hippocampal regions are more susceptible and associated with frontal lobe dysfunctions, while others implicate posterior regions. Using high-resolution MR images and novel computational image analysis methods, we identified the hippocampal subregions most vulnerable to disease processes in 62 (45 m/17 f) first-episode schizophrenia patients compared to 60 (30 m/30 f) healthy controls, similar in age. The hippocampi were traced on coronal brain slices and hemispheric volumes were compared between diagnostic groups. Regional structural abnormalities were identified by comparing distances, measured from homologous hippocampal surface points to the central core of each individual's hippocampal surface model, between groups in 3D. CSF concentrations were also compared statistically at homologous hippocampal surface points to localize corresponding gray matter reductions. Significant bilateral hippocampal volume reductions were observed in schizophrenia irrespective of brain size corrections. Statistical mapping results, confirmed by permutation testing, showed pronounced left hemisphere shape differences in anterior and midbody CA1 and CA2 regions in patients. Significant CSF increases surrounding the hippocampus were observed in a similar spatial pattern in schizophrenia. Results confirm that hippocampal volume reductions are a robust neuroanatomical correlate of schizophrenia and are present by first episode. Mid- to antero-lateral hippocampal regions show pronounced volume changes and complementary increases in peri-hippocampal CSF, suggesting that these hippocampal regions are more susceptible to disease processes in schizophrenia. Targeting regional hippocampal abnormalities may help dissociate schizophrenia patients from other groups exhibiting global hippocampal volume changes, and better focus systems-level pathophysiological hypotheses.

MRI-based morphometric of typical and atypical brain development

The neuroinformatics landscape in which human brain morphometry occurs has advanced dramatically over the past few years. Rapid advancement in image acquisition methods, image analysis tools and interpretation of morphometric results make the study of in vivo anatomic analysis both challenging and rewarding. This has revolutionized our expectations for current and future diagnostic and investigative work with the developing brain. This paper will briefly cover the methods of morphometric analysis available for neuroanatomic analysis, and tour some sample results from a prototype retrospective database of neuroanatomic volumetric information. From these observations, issues regarding the anatomic variability of developmental maturation of neuroanatomic structures in both typically and atypically developing populations can be discussed.

Unsupervised learning of an atlas from unlabeled point-sets

One of the key challenges in deformable shape modeling is the problem of estimating a meaningful average or mean shape from a set of unlabeled shapes. We present a new joint clustering and matching algorithm that is capable of computing such a mean shape from multiple shape samples which are represented by unlabeled point-sets. An iterative bootstrap process is used wherein multiple shape sample point-sets are nonrigidly deformed to the emerging mean shape, with subsequent estimation of the mean shape based on these nonrigid alignments. The process is entirely symmetric with no bias toward any of the original shape sample point-sets. We believe that this method can be especially useful for creating atlases of various shapes present in medical images. We have applied the method to create mean shapes from nine hand-segmented 2D corpus callosum data sets and 10 hippocampal 3D point-sets.

Temporal dynamics of brain anatomy

The brain changes profoundly in structure and function during development and as a result of diseases such as the dementias, schizophrenia, multiple sclerosis, and tumor growth. Strategies to measure, map, and visualize these brain changes are of immense value in basic and clinical neuroscience. Algorithms that map brain change with sufficient spatial and temporal sensitivity can also assess drugs that aim to decelerate or arrest these changes. In neuroscience studies, these tools can reveal subtle brain changes in adolescence and old age and link these changes with measurable differences in brain function and cognition. Early detection of brain change in patients at risk for dementia; tumor recurrence; or relapsing-remitting conditions, such as multiple sclerosis, is also vital for optimizing therapy. We review a variety of mathematical and computational approaches to detect structural brain change with unprecedented sensitivity, both spatially and temporally. The resulting four-dimensional (4-D) maps of brain anatomy are warehoused in population-based brain atlases. Here, statistical tools compare brain changes across subjects and across populations, adjusting for complex differences in brain structure. Brain changes in an individual can be compared with a normative database comprised of subjects matched for age, gender, and other demographic factors. These dynamic brain maps offer key biological markers for understanding disease progression and testing therapeutic response. The early detection of disease-related brain changes is also critical for possible pre-emptive intervention before the ravages of disease have set in.

Computational anatomy and neuropsychiatric disease: probabilistic assessment of variation and statistical inference of group difference, hemispheric asymmetry, and time-dependent change

Three components of computational anatomy (CA) are reviewed in this paper: (i) the computation of large-deformation maps, that is, for any given coordinate system representations of two anatomies, computing the diffeomorphic transformation from one to the other; (ii) the computation of empirical probability laws of anatomical variation between anatomies; and (iii) the construction of inferences regarding neuropsychiatric disease states. CA utilizes spatial-temporal vector field information obtained from large-deformation maps to assess anatomical variabilities and facilitate the detection and quantification of abnormalities of brain structure in subjects with neuropsychiatric disorders. Neuroanatomical structures are divided into two types: subcortical structures-gray matter (GM) volumes enclosed by a single surface-and cortical mantle structures-anatomically distinct portions of the cerebral cortical mantle layered between the white matter (WM) and cerebrospinal fluid (CSF). Because of fundamental differences in the geometry of these two types of structures, image-based large-deformation high-dimensional brain mapping (HDBM-LD) and large-deformation diffeomorphic metric matching (LDDMM) were developed for the study of subcortical structures and labeled cortical mantle distance mapping (LCMDM) was developed for the study of cortical mantle structures. Studies of neuropsychiatric disorders using CA usually require the testing of hypothesized group differences with relatively small numbers of subjects per group. Approaches that increase the power for testing such hypotheses include methods to quantify the shapes of individual structures, relationships between the shapes of related structures (e.g., asymmetry), and changes of shapes over time. Promising preliminary studies employing these approaches to studies of subjects with schizophrenia and very mild to mild Alzheimer's disease (AD) are presented.

Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia

This paper describes algorithms that can identify patterns of brain structure and function associated with Alzheimer's disease, schizophrenia, normal aging, and abnormal brain development based on imaging data collected in large human populations. Extraordinary information can be discovered with these techniques: dynamic brain maps reveal how the brain grows in childhood, how it changes in disease, and how it responds to medication. Genetic brain maps can reveal genetic influences on brain structure, shedding light on the nature-nurture debate, and the mechanisms underlying inherited neurobehavioral disorders. Recently, we created time-lapse movies of brain structure for a variety of diseases. These identify complex, shifting patterns of brain structural deficits, revealing where, and at what rate, the path of brain deterioration in illness deviates from normal. Statistical criteria can then identify situations in which these changes are abnormally accelerated, or when medication or other interventions slow them. In this paper, we focus on describing our approaches to map structural changes in the cortex. These methods have already been used to reveal the profile of brain anomalies in studies of dementia, epilepsy, depression, childhood- and adult-onset schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder, fetal alcohol syndrome, Tourette syndrome, Williams syndrome, and in methamphetamine abusers. Specifically, we describe an image analysis pipeline known as cortical pattern matching that helps compare and pool cortical data over time and across subjects. Statistics are then defined to identify brain structural differences between groups, including localized alterations in cortical thickness, gray matter density (GMD), and asymmetries in cortical organization. Subtle features, not seen in individual brain scans, often emerge when population-based brain data are averaged in this way. Illustrative examples are presented to show the profound effects of development and various diseases on the human cortex. Dynamically spreading waves of gray matter loss are tracked in dementia and schizophrenia, and these sequences are related to normally occurring changes in healthy subjects of various ages.

Hippocampal deformities in the unaffected siblings of schizophrenia subjects

BACKGROUND

Neuroanatomical abnormalities have been reported in schizophrenia subjects and their relatives and may be related to genetic vulnerability. The objective of this study was to further elucidate hippocampal deformities as a marker of genetic vulnerability for schizophrenia.

METHODS

Magnetic resonance scans were collected in 13 pairs of schizophrenics and their unaffected siblings from families with multiple affected members, in 12 schizophrenics from families without another affected member, and in 10 healthy controls. Hippocampal volume and shape were compared using large-deformation high-dimensional brain mapping.

RESULTS

Decreases in hippocampal volume, covaried for total cerebral volume, were observed in the schizophrenia subjects from families with multiple affected members, as well as in their unaffected siblings. Shape analysis demonstrated that both groups of schizophrenia subjects, as well as the unaffected siblings, could be distinguished from the controls; however, no significant difference in hippocampal shape was found between the schizophrenia subjects and their unaffected siblings. Visualization of the pattern of hippocampal shape deformity in both groups of schizophrenia subjects and in the unaffected siblings showed inward deformities of the head of the hippocampus.

CONCLUSIONS

Deformations of hippocampal anatomy may be related to the genetic vulnerability of acquiring schizophrenia.

Positron emission tomography imaging of the aging brain

PET imaging provides a vital means to study the human brain in vivo in aging and early disease states. PET studies using selective markers for brain metabolism and neurotransmitter function have uncovered a wealth of information on healthy and pathologic brain aging, and its relationship to behavior and mood states. Recognition of inherent potential confounds in the use of PET in aging studies is essential to the proper interpretation of these data.

Evaluation of automated and semi-automated skull-stripping algorithms using similarity index and segmentation error

The skull-stripping in the MR brain image appears to be a key issue in neuroimage analysis. In this paper, we evaluated the accuracy and efficiency of both automated and semi-automated skull-stripping methods. The evaluation was performed on both simulated and real data with the ground truth in skull-stripping. Although automated method showed better efficient results, it should require additional intervention. In contrast to that, semi-automated method showed better accurate results, but it was time consuming and prone to operator bias. Therefore, it might be practical that the semi-automated method was used as the post-processing of the automated one.

Interactive specification of regions of interest on brain surfaces

We describe Surface Editor-a tool for interactive specification of regions of interest (ROIs) on brain surfaces. The tool allows users to define subsurfaces by tracing around areas within a triangle-mesh brain surface. The input to the program is a triangle-mesh representation of a brain volume and a set of user-defined input points on the mesh. The program connects each pair of successive input points with a polyline that results from the intersection of the mesh with a plane that is approximately normal to the mesh. The polyline comprises coplanar line segments. The boundary of an ROI is a connected set of polylines that intersects triangle edges to form a continuous path. To validate Surface Editor we demonstrated that the program could be used to interactively delineate gyri on brain surfaces, and we showed that paths that the program generated were comparable to paths that a user generated and to shortest paths.

Zen and the art of medical image registration: correspondence, homology, and quality

Nonrigid registration (NRR) is routinely used in the study of neuroanatomy and function and is a standard component of analysis packages such as SPM. There remain many unresolved correspondence problems that arise from attempts to associate functional areas with specific neuroanatomy and to compare both function and anatomy across patient groups. Problems can result from ignorance of the underlying neurology which is then compounded by unjustified inferences drawn from the results of NRR. Usually the magnitude, distribution, and significance of errors in NRR are unknown so the errors in correspondences determined by NRR are also unknown and their effect on experimental results cannot easily be quantified. In this paper we review the principles by which the presumed correspondence and homology of structures is used to drive registration and identify the conceptual and algorithmic areas where current techniques are lacking. We suggest that for applications using NRR to be robust and achieve their potential, context-specific definitions of correspondence must be developed which properly characterise error. Prior knowledge of image content must be utilised to monitor and guide registration and gauge the degree of success. The use of NRR in voxel-based morphometry is examined from this context and found wanting. We conclude that a move away from increasingly sophisticated but context-free registration technology is required and that the veracity of studies that rely on NRR should be keenly questioned when the error distribution is unknown and the results are unsupported by other contextual information.

Gender and age effects in structural brain asymmetry as measured by MRI texture analysis

Effects of gender and age on structural brain asymmetry were studied by 3D texture analysis in 380 adults. Asymmetry is detected by comparing the complex 3D gray-scale image patterns in the left and right cerebral hemispheres as revealed by anatomical T1-weighted MRI datasets. The Talairach and Tournoux parcellation system was applied to study the asymmetry on five levels: the whole cerebrum, nine coronal sections, 12 axial sections, boxes resulting from both coronal and axial subdivisions, and by a sliding spherical window of 9 mm diameter. The analysis revealed that the brain asymmetry increases in the anterior-posterior direction starting from the central region onward. Male brains were found to be more asymmetric than female. This gender-related effect is noticeable in all brain areas but is most significant in the superior temporal gyrus, Heschl's gyrus, the adjacent white matter regions in the temporal stem and the knee of the optic radiation, the thalamus, and the posterior cingulate. The brain asymmetry increases significantly with age in the inferior frontal gyrus, anterior insula, anterior cingulate, parahippocampal gyrus, retrosplenial cortex, coronal radiata, and knee region of the internal capsule. Asymmetry decreases with age in the optic radiation, precentral gyrus, and angular gyrus. The texture-based method reported here is based on extended multisort cooccurrence matrices that employ intensity, gradient, and anisotropy features in a uniform way. It is sensitive, simple to reproduce, robust, and unbiased in the sense that segmentation of brain compartments and spatial transformations are not necessary. Thus, it should be considered as another tool for digital morphometry in neuroscience.

Generalized 3D nonlinear transformations for medical imaging: an object-oriented implementation in VTK

We have contributed an efficient, object-oriented implementation of 3D nonlinear transformations to the Visualization Toolkit that can be applied to a wide variety of data types, including images and polygonal meshes. The transformations are performed via thin-plate splines or via interpolation of a regular lattice of displacement vectors, and are part of a framework that is easily extensible to other nonlinear transformation types. In this paper we demonstrate application of these transformations in medical imaging in general and image-guided surgery in particular, and present a series of performance benchmarks.

A unified non-rigid feature registration method for brain mapping

This paper describes the design, implementation and results of a unified non-rigid feature registration method for the purposes of anatomical MRI brain registration. An important characteristic of the method is its ability to fuse different types of anatomical features into a single point-set representation. We demonstrate the application of the method using two different types of features: the outer cortical surface and major sulcal ribbons. Non-rigid registration of the combined feature point-sets is then performed using a new robust non-rigid point matching algorithm. The point matching algorithm implements an iterative joint clustering and matching (JCM) strategy which effectively reduces the computational complexity without sacrificing accuracy. We have conducted carefully designed synthetic experiments to gauge the effect of using different types of features either separately or together. A validation study examining the accuracy of non-rigid alignment of many brain structures is also presented. Finally, we present anecdotal results on the alignment of two subject MRI brain data.

Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe

Probabilistic atlases of neuroanatomy are more representative of population anatomy than single brain atlases. They allow anatomical labeling of the results of group studies in stereotaxic space, automated anatomical labeling of individual brain imaging datasets, and the statistical assessment of normal ranges for structure volumes and extents. No such manually constructed atlas is currently available for the frequently studied group of young adults. We studied 20 normal subjects (10 women, median age 31 years) with high-resolution magnetic resonance imaging (MRI) scanning. Images were nonuniformity corrected and reoriented along both the anterior-posterior commissure (AC-PC) line horizontally and the midsagittal plane sagittally. Building on our previous work, we have expanded and refined existing algorithms for the subdivision of MRI datasets into anatomical structures. The resulting algorithm is presented in the Appendix. Forty-nine structures were interactively defined as three-dimensional volumes-of-interest (VOIs). The resulting 20 individual atlases were spatially transformed (normalized) into standard stereotaxic space, using SPM99 software and the MNI/ICBM 152 template. We evaluated volume data for all structures both in native space and after spatial normalization, and used the normalized superimposed atlases to create a maximum probability map in stereotaxic space, which retains quantitative information regarding inter-subject variability. Its potential applications range from the automatic labeling of new scans to the detection of anatomical abnormalities in patients. Further data can be extracted from the atlas for the detailed analysis of individual structures.

Construction of an abdominal probabilistic atlas and its application in segmentation

There have been significant efforts to build a probabilistic atlas of the brain and to use it for many common applications, such as segmentation and registration. Though the work related to brain atlases can be applied to nonbrain organs, less attention has been paid to actually building an atlas for organs other than the brain. Motivated by the automatic identification of normal organs for applications in radiation therapy treatment planning, we present a method to construct a probabilistic atlas of an abdomen consisting of four organs (i.e., liver, kidneys, and spinal cord). Using 32 noncontrast abdominal computed tomography (CT) scans, 31 were mapped onto one individual scan using thin plate spline as the warping transform and mutual information (MI) as the similarity measure. Except for an initial coarse placement of four control points by the operators, the MI-based registration was automatic. Additionally, the four organs in each of the 32 CT data sets were manually segmented. The manual segmentations were warped onto the "standard" patient space using the same transform computed from their gray scale CT data set and a probabilistic atlas was calculated. Then, the atlas was used to aid the segmentation of low-contrast organs in an additional 20 CT data sets not included in the atlas. By incorporating the atlas information into the Bayesian framework, segmentation results clearly showed improvements over a standard unsupervised segmentation method.

Dynamics of gray matter loss in Alzheimer's disease

We detected and mapped a dynamically spreading wave of gray matter loss in the brains of patients with Alzheimer's disease (AD). The loss pattern was visualized in four dimensions as it spread over time from temporal and limbic cortices into frontal and occipital brain regions, sparing sensorimotor cortices. The shifting deficits were asymmetric (left hemisphere > right hemisphere) and correlated with progressively declining cognitive status (p < 0.0006). Novel brain mapping methods allowed us to visualize dynamic patterns of atrophy in 52 high-resolution magnetic resonance image scans of 12 patients with AD (age 68.4 +/- 1.9 years) and 14 elderly matched controls (age 71.4 +/- 0.9 years) scanned longitudinally (two scans; interscan interval 2.1 +/- 0.4 years). A cortical pattern matching technique encoded changes in brain shape and tissue distribution across subjects and time. Cortical atrophy occurred in a well defined sequence as the disease progressed, mirroring the sequence of neurofibrillary tangle accumulation observed in cross sections at autopsy. Advancing deficits were visualized as dynamic maps that change over time. Frontal regions, spared early in the disease, showed pervasive deficits later (>15% loss). The maps distinguished different phases of AD and differentiated AD from normal aging. Local gray matter loss rates (5.3 +/- 2.3% per year in AD v 0.9 +/- 0.9% per year in controls) were faster in the left hemisphere (p < 0.029) than the right. Transient barriers to disease progression appeared at limbic/frontal boundaries. This degenerative sequence, observed in vivo as it developed, provides the first quantitative, dynamic visualization of cortical atrophic rates in normal elderly populations and in those with dementia.

Very high-resolution morphometry using mass-preserving deformations and HAMMER elastic registration

This article presents a very high-resolution voxel-based morphometric method, by using a mass-preserving deformation mechanism and a fully automated spatial normalization approach, referred to as HAMMER. By using a hierarchical attribute-based deformation strategy, HAMMER partly overcomes limitations of several existing spatial normalization methods, and it achieves a level of accuracy that makes possible morphometric measurements of spatial specificity close to the voxel dimensions. The proposed method is validated by a series of experiments, with both simulated and real brain images.

An algorithm for rapid calculation of a probabilistic functional atlas of subcortical structures from electrophysiological data collected during functional neurosurgery procedures

The paper introduces an optimal algorithm for rapid calculation of a probabilistic functional atlas (PFA) of subcortical structures from data collected during functional neurosurgery procedures. The PFA is calculated based on combined intraoperative electrophysiology, pre- and intraoperative neuroimaging, and postoperative neurological verification. The algorithm converts the coordinates of the neurologically most effective contacts into probabilistic functional maps taking into account the geometry of a stimulating electrode. The PFA calculation comprises the reconstruction of the contact coordinates from two orthogonal projections, normalizing (warping) the contacts modeled as cylinders, voxelizing the contact models, calculating the atlas, and computing probability. In addition, an analytical representation of the PFA is formulated based on Gaussian modeling. The initial PFA has been calculated from the data collected during the treatment of 274 Parkinson's disease patients, most of them operated bilaterally (487 operated hemispheres). It contains the most popular stereotactic targets, the subthalamic nucleus, globus pallidus internus, and ventral intermedius nucleus. The key application of the algorithm is targeting in stereotactic and functional neurosurgery, and it also can be employed in human and animal brain research.

A framework for callosal fiber distribution analysis

This paper presents a framework for analyzing the spatial distribution of neural fibers in the brain, with emphasis on interhemispheric fiber bundles crossing through the corpus callosum. The proposed approach combines methodologies for fiber tracking and spatial normalization and is applied on diffusion tensor images and standard magnetic resonance images.

Imaging databases and neuroscience

Brain atlases are equivalent to neuroimage databases provided an appropriate coordinate system to enable multisubject comparisons, along with comprehensive descriptions of the data, are included. Warping tools, visualization, and statistical analyses that accommodate the various neuroimaging modalities can be used to integrate diverse data and form comprehensive maps describing a particular subpopulation's brain structure and function. By linking task performance and genetic information to brain morphology, complex interrelations between genotype, phenotype, and behavior can be established. Several examples of these multimodal, multisubject atlases, including those that are dynamic, are presented.

A twin study of genetic contributions to hippocampal morphology in schizophrenia

Our goal was to establish whether altered hippocampal morphology represents a trait marker for genetic vulnerability in schizophrenia. We outlined the hippocampi on high-resolution MR images obtained from matched samples of control and discordant monozygotic and dizygotic co-twins (N = 40 pairs). Hippocampal measures were used in statistical tests specifically designed to identify disease-associated genetic and nongenetic influences on morphology. 3D surface average maps of the hippocampus were additionally compared in biological risk groups. Smaller hippocampal volumes were confirmed in schizophrenia. Dizygotic affected co-twins showed smaller left hippocampi compared to their healthy siblings. Disease-associated effects were not present between monozygotic discordant co-twins. Monozygotic, but not dizygotic, unaffected co-twins exhibited smaller left hippocampi compared to control twins, supporting genetic influences. Surface areas and posterior volumes similarly revealed schizophrenia and genetic liability effects. Results suggest that hippocampal volume reduction may be a trait marker for identifying individuals possessing a genetic predisposition for schizophrenia.

Spatial normalization of spine MR images for statistical correlation of lesions with clinical symptoms

An image analysis method was developed for spatial normalization of spine magnetic resonance images. A deformable shape model of the spine is first constructed, and it is subsequently used by an automated algorithm to find a shape transformation that places patient data into a stereotactic space. Very good agreement with manual segmentations was observed. The main application of this method is in lesion-deficit analysis for determining associations between structural damage and clinical symptoms.

BrainSuite: an automated cortical surface identification tool

We describe a new magnetic resonance (MR) image analysis tool that produces cortical surface representations with spherical topology from MR images of the human brain. The tool provides a sequence of low-level operations in a single package that can produce accurate brain segmentations in clinical time. The tools include skull and scalp removal, image nonuniformity compensation, voxel-based tissue classification, topological correction, rendering, and editing functions. The collection of tools is designed to require minimal user interaction to produce cortical representations. In this paper we describe the theory of each stage of the cortical surface identification process. We then present classification validation results using real and phantom data. We also present a study of interoperator variability.

A space for measuring mind and brain: interdisciplinarity and digital tools in the development of brain mapping and functional imaging, 1980-1990

Brain mapping is said to have opened up the possibility of a new collaboration between the sciences of mind and the sciences of the brain, potentially leading to a new kind of scientist, sometimes called "cognitive neuroscientist." This article traces the recent history of brain mapping and analyzes the processes that have led to a new "close working relationship" between the sciences of mind and brain. A key part of the working relationship is shown to be constituted through the development of the Talairach system, a digital space in which to measure structure and function. The development of meaningful brain mapping data involves the creation of measurement spaces that allow interdisciplinary collaboration and is not the result solely of theoretical developments or of the application of a technology.

Genetic contributions to altered callosal morphology in schizophrenia

Patients with schizophrenia exhibit abnormalities in midsagittal corpus callosum area, shape, and/or displacement. Our goal was to confirm these findings and to establish the genetic and nongenetic contributions to altered callosal morphology in schizophrenia. Relationships between ventricular enlargements potentially contributing to callosal displacements were assessed as a secondary goal. High-resolution magnetic resonance images were obtained from co-twins of monozygotic and dizygotic pairs discordant for schizophrenia and healthy control twins (N = 40 pairs). Investigators blind to group status segmented the corpus callosum and ventricles in native brain volumes aligned using a rigid-body transformation with no scaling. Total and parcellated midsagittal callosal areas and measures indexing vertical displacements of the corpus callosum were used in statistical tests to identify schizophrenia and sex effects and to dissociate genetic and nongenetic influences on morphology. Anatomical mesh modeling methods provided group average and surface variability maps of the callosum. Callosal areas did not differ between groups defined by sex or biological risk. Vertical displacements of the callosum, pronounced in male patients, were confirmed in schizophrenia and observed between dizygotic, but not monozygotic co-twins discordant for schizophrenia. Like their affected twins, however, unaffected monozygotic co-twins of the schizophrenia probands exhibited significant callosal displacements. Lateral and third ventricle enlargements were related to callosal displacements. Results clearly support that genetic rather than disease-specific or shared environmental influences contribute to altered callosal morphology in schizophrenia. An upward bowing of the callosum may thus provide an easily identifiable neuroanatomic marker to screen individuals possessing a biological vulnerability for schizophrenia.

A new rapid landmark-based regional MRI segmentation method of the brain

BACKGROUND

Neurodegenerative and cerebrovascular diseases show a distinct distribution of regional atrophy and subcortical lesions.

OBJECTIVE

To develop an easily applicable landmark-based method for segmentation of the brain into the four cerebral lobes from MRI images.

METHOD

The segmentation method relies on a combination of anatomical landmarks and geometrical definitions. It is applied on the surface reconstruction of the MRI volume. The internal borders between the lobes are defined on the axial slices of the brain. The reliability of this method was determined from MRI scans of 10 subjects. To illustrate the use of the method, it was applied to MRI scans of an independent group of 10 healthy elderly subjects and 10 patients with vascular dementia to determine the regional distribution of white matter hyperintensities (WMH).

RESULTS

The intra-rater relative error (and intra-class correlation coefficient) of the lobe segmentation ranged from 1.6% to 6.9% (from 0.91 to 0.99). The inter-rater relative error (and intra-class correlation coefficient) ranged from 1.4% to 5.2% (from 0.96 to 0.99). Density of WMH was significantly higher in all four lobes in VD patients compared to controls (p<0.05). Within each group, WMH density was significantly higher in frontal and parietal than in temporal and occipital lobes (p<0.05).

CONCLUSION

This landmark based method can accommodate age and disease-related changes in brain morphology. It may be particularly useful for the study of neurodegenerative and cerebrovascular disease and for the validation of template-based automated techniques.

Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain

We present a technique for automatically assigning a neuroanatomical label to each voxel in an MRI volume based on probabilistic information automatically estimated from a manually labeled training set. In contrast to existing segmentation procedures that only label a small number of tissue classes, the current method assigns one of 37 labels to each voxel, including left and right caudate, putamen, pallidum, thalamus, lateral ventricles, hippocampus, and amygdala. The classification technique employs a registration procedure that is robust to anatomical variability, including the ventricular enlargement typically associated with neurological diseases and aging. The technique is shown to be comparable in accuracy to manual labeling, and of sufficient sensitivity to robustly detect changes in the volume of noncortical structures that presage the onset of probable Alzheimer's disease.

Mapping genetic influences on human brain structure

Recent advances in brain imaging and genetics have empowered the mapping of genetic and environmental influences on the human brain. These techniques shed light on the 'nature/nurture' debate, revealing how genes determine individual differences in intelligence quotient (IQ) or risk for disease. They visualize which aspects of brain structure and function are heritable, and to what degree, linking these features with behavioral or cognitive traits or disease phenotypes. In genetically transmitted disorders such as schizophrenia, patterns of brain structure can be associated with increased disease liability, and sites can be mapped where non-genetic triggers may initiate disease. We recently developed a large-scale computational brain atlas, including data components from the Finnish Twin registry, to store information on individual variations in brain structure and their heritability. Algorithms from random field theory, anatomical modeling, and population genetics were combined to detect a genetic continuum in which brain structure is heavily genetically determined in some areas but not others. These algorithmic advances motivate studies of disease in which the normative atlas acts as a quantitative reference for the heritability of structural differences and deficits in patient populations. The resulting genetic brain maps isolate biological markers for inherited traits and disease susceptibility, which may serve as targets for genetic linkage and association studies. Computational methods from brain imaging and genetics can be fruitfully merged, to shed light on the inheritance of personality differences and behavioral traits, and the genetic transmission of diseases that affect the human brain.

Working memory deficits in schizophrenia are not necessarily specific or associated with MRI-based estimates of area 46 volumes

Despite substantial evidence that the prefrontal cortex does not function normally in patients diagnosed with schizophrenia, evidence for prefrontal structural abnormalities, as measured by magnetic resonance imaging (MRI), has been inconsistent. Additionally, evidence for relationships between prefrontal structural and functional measures has been limited. The inconsistencies in the MRI literature are, at least in part, due to a lack of standard and specific measurement protocols that allow delineation of functionally distinct cortical regions. In this study, reliable methods for measuring an estimate of area 46 (estimate referred to as area 46(e)), as defined by 'Cereb. Cortex 5 (1995) 323', were developed and used to examine relationships between area 46(e) volumes, working memory, and symptom severity in 23 male patients and 23 healthy male comparison subjects. Patients performed more poorly than healthy reference subjects on all cognitive measures including measures of spatial and non-spatial working memory, but showed no significant corresponding deficits in area 46(e) volumes or whole brain volumes. Moreover, there were no significant relationships between symptom severity and area 46(e) volumes. These findings suggest that the prefrontal functional abnormalities observed in schizophrenia may occur in the absence of prefrontal volume deficits, and may instead involve more widespread brain systems or prefrontal connections with other brain regions.

Voxel-based morphometry using the RAVENS maps: methods and validation using simulated longitudinal atrophy

Statistical analysis of anatomical maps in a stereotaxic space has been shown to be a useful tool in population-based studies for quantifying local anatomical differences or changes, without a priori assumptions about the location and extent of the regions of interest. This paper presents an extension and validation of a previously published methodology, referred to as RAVENS, for characterizing regional atrophy in the brain. A new method for elastic, volume-preserving spatial normalization, which allows for accurate quantification of very localized atrophy, is used. The RAVENS methodology was tested on images with simulated atrophy within two gyri: precentral and superior temporal. It was found to accurately determine the regions of atrophy, despite their localized nature and the interindividual variability of cortical structures. Moreover, it was found to perform substantially better than the voxel-based morphology method of SPM'99. Improved sensitivity was achieved at the expense of human effort involved in defining a number of sulcal curves that serve as constraints on the 3D elastic warping.

Automated graph-based analysis and correction of cortical volume topology

The human cerebral cortex is topologically equivalent to a sheet and can be considered topologically spherical if it is closed at the brain stem. Low-level segmentation of magnetic resonance (MR) imagery typically produces cerebral volumes whose tessellations are not topologically spherical. We present a novel algorithm that analyzes and constrains the topology of a volumetric object. Graphs are formed that represent the connectivity of voxel segments in the foreground and background of the image. These graphs are analyzed and minimal corrections to the volume are made prior to tessellation. We apply the algorithm to a simple test object and to cerebral white matter masks generated by a low-level tissue identification sequence. We tessellate the resulting objects using the marching cubes algorithm and verify their topology by computing their Euler characteristics. A key benefit of the algorithm is that it localizes the change to a volume to the specific areas of its topological defects.

A joint physics-based statistical deformable model for multimodal brain image analysis

A probabilistic deformable model for the representation of multiple brain structures is described. The statistically learned deformable model represents the relative location of different anatomical surfaces in brain magnetic resonance images (MRIs) and accommodates their significant variability across different individuals. The surfaces of each anatomical structure are parameterized by the amplitudes of the vibration modes of a deformable spherical mesh. For a given MRI in the training set, a vector containing the largest vibration modes describing the different deformable surfaces is created. This random vector is statistically constrained by retaining the most significant variation modes of its Karhunen-Loève expansion on the training population. By these means, the conjunction of surfaces are deformed according to the anatomical variability observed in the training set. Two applications of the joint probabilistic deformable model are presented: isolation of the brain from MRI using the probabilistic constraints embedded in the model and deformable model-based registration of three-dimensional multimodal (magnetic resonance/single photon emission computed tomography) brain images without removing nonbrain structures. The multi-object deformable model may be considered as a first step toward the development of a general purpose probabilistic anatomical atlas of the brain.

Cerebral asymmetry and the effects of sex and handedness on brain structure: a voxel-based morphometric analysis of 465 normal adult human brains

We used voxel-based morphometry (VBM) to examine human brain asymmetry and the effects of sex and handedness on brain structure in 465 normal adults. We observed significant asymmetry of cerebral grey and white matter in the occipital, frontal, and temporal lobes (petalia), including Heschl's gyrus, planum temporale (PT) and the hippocampal formation. Males demonstrated increased leftward asymmetry within Heschl's gyrus and PT compared to females. There was no significant interaction between asymmetry and handedness and no main effect of handedness. There was a significant main effect of sex on brain morphology, even after accounting for the larger global volumes of grey and white matter in males. Females had increased grey matter volume adjacent to the depths of both central sulci and the left superior temporal sulcus, in right Heschl's gyrus and PT, in right inferior frontal and frontomarginal gyri and in the cingulate gyrus. Females had significantly increased grey matter concentration extensively and relatively symmetrically in the cortical mantle, parahippocampal gyri, and in the banks of the cingulate and calcarine sulci. Males had increased grey matter volume bilaterally in the mesial temporal lobes, entorhinal and perirhinal cortex, and in the anterior lobes of the cerebellum, but no regions of increased grey matter concentration.

A unified statistical approach to deformation-based morphometry

We present a unified statistical framework for analyzing temporally varying brain morphology using the 3D displacement vector field from a nonlinear deformation required to register a subject's brain to an atlas brain. The unification comes from a single model for structural change, rather than two separate models, one for displacement and one for volume changes. The displacement velocity field rather than the displacement itself is used to set up a linear model to account for temporal variations. By introducing the rate of the Jacobian change of the deformation, the local volume change at each voxel can be computed and used to measure possible brain tissue growth or loss. We have applied this method to detecting regions of a morphological change in a group of children and adolescents. Using structural magnetic resonance images for 28 children and adolescents taken at different time intervals, we demonstrate how this method works.

A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM)

Motivated by the vast amount of information that is rapidly accumulating about the human brain in digital form, we embarked upon a program in 1992 to develop a four-dimensional probabilistic atlas and reference system for the human brain. Through an International Consortium for Brain Mapping (ICBM) a dataset is being collected that includes 7000 subjects between the ages of eighteen and ninety years and including 342 mono- and dizygotic twins. Data on each subject includes detailed demographic, clinical, behavioural and imaging information. DNA has been collected for genotyping from 5800 subjects. A component of the programme uses post-mortem tissue to determine the probabilistic distribution of microscopic cyto- and chemoarchitectural regions in the human brain. This, combined with macroscopic information about structure and function derived from subjects in vivo, provides the first large scale opportunity to gain meaningful insights into the concordance or discordance in micro- and macroscopic structure and function. The philosophy, strategy, algorithm development, data acquisition techniques and validation methods are described in this report along with database structures. Examples of results are described for the normal adult human brain as well as examples in patients with Alzheimer's disease and multiple sclerosis. The ability to quantify the variance of the human brain as a function of age in a large population of subjects for whom data is also available about their genetic composition and behaviour will allow for the first assessment of cerebral genotype-phenotype-behavioural correlations in humans to take place in a population this large. This approach and its application should provide new insights and opportunities for investigators interested in basic neuroscience, clinical diagnostics and the evaluation of neuropsychiatric disorders in patients.

Three-dimensional mapping of temporo-limbic regions and the lateral ventricles in schizophrenia: gender effects

BACKGROUND

Local alterations in morphological parameters are poorly characterized in several brain regions widely implicated in schizophrenia neuropathology.

METHODS

Surface-based anatomical modeling was applied to magnetic resonance data to obtain three-dimensional (3D) average anatomical maps and measures of location, shape, asymmetry, and volume for the lateral ventricles, hippocampus, amygdala, and superior temporal gyrus in schizophrenic (n = 25; 15 male) and normal subjects (n = 28; 15 male) matched for demographic variables. For all regions, intra-group variability was visualized and group differences assessed statistically to discriminate local alterations in anatomy across sex and diagnosis.

RESULTS

Posterior hippocampal volumes, lengths, and widths were reduced in patients. The right amygdala showed volume increases in schizophrenia patients versus controls. Ventricular enlargements, pronounced in the left hemisphere, occurred in the superior and lateral dimensions in patients, and these effects interacted with gender. Superior horn anterior extremes, inferior horn volumes, and hippocampal asymmetries exhibited gender effects. Significant group differences were absent in superior temporal gyrus parameters. Finally, regional variability profiles differed across groups.

CONCLUSIONS

Clear morphometric differences of the lateral ventricles, hippocampus, and amygdala indicate regional displacements and shape distortions in several functional systems in schizophrenia. Alterations in these structures as mapped in 3D may provide the foundation for establishing brain abnormalities not previously defined at such a local level.

A voxel-based morphometric study of ageing in 465 normal adult human brains

Voxel-based-morphometry (VBM) is a whole-brain, unbiased technique for characterizing regional cerebral volume and tissue concentration differences in structural magnetic resonance images. We describe an optimized method of VBM to examine the effects of age on grey and white matter and CSF in 465 normal adults. Global grey matter volume decreased linearly with age, with a significantly steeper decline in males. Local areas of accelerated loss were observed bilaterally in the insula, superior parietal gyri, central sulci, and cingulate sulci. Areas exhibiting little or no age effect (relative preservation) were noted in the amygdala, hippocampi, and entorhinal cortex. Global white matter did not decline with age, but local areas of relative accelerated loss and preservation were seen. There was no interaction of age with sex for regionally specific effects. These results corroborate previous reports and indicate that VBM is a useful technique for studying structural brain correlates of ageing through life in humans.

Mapping cortical asymmetry and complexity patterns in normal children

This study reports the first comprehensive three-dimensional (3D) maps of cortical patterns in children. Using a novel parametric mesh-based analytic technique applied to high-resolution T1-weighted MRI scans, we examined age (6-16 years) and gender differences in cortical complexity (the fractal dimension or complexity of sulcal/gyral convolutions) and asymmetry of 24 primary cortical sulci in normally developing children (N=24). Three-dimensional models of the cerebral cortex were extracted and major sulci mapped in stereotaxic space. Given the documented age-related changes in frontal lobe functions and several neuroimaging studies that have reported accompanying volumetric changes in these regions, we hypothesized that, with age, we would find continued modifications of the cerebrum in frontal cortex. We also predicted that phylogenetically older regions of the cerebrum, such as olfactory cortex, would be less variable in anatomic location across subjects and with age. Age-related increases in cortical complexity were found in both left and right inferior frontal and left superior frontal regions, possibly indicating an increase in secondary branching with age in these regions. Moreover, a significant increase in the length of the left inferior frontal sulcus and a posterior shifting of the left pre-central sulcus was associated with age. Three-dimensional asymmetry and anatomic variability maps revealed a significant left-greater-than-right asymmetry of the Sylvian fissures and superior temporal sulci, and increased variance in dorsolateral frontal and perisylvian areas relative to ventral regions of the cortex. These results suggest increases in cortical complexity and subtle modifications of sulcal topography of frontal lobe regions, likely reflecting ongoing processes such as myelination and synaptic remodeling that continue into the second decade of life. More studies in a larger sample set and/or longitudinal design are needed to address the issues of normal individual variation and sulcal development.

Detecting disease-specific patterns of brain structure using cortical pattern matching and a population-based probabilistic brain atlas

The rapid creation of comprehensive brain image databases mandates the development of mathematical algorithms to uncover disease specific patterns of brain structure and function in human populations. We describe our construction of probabilistic atlases that store detailed information on how the brain varies across age and gender, across time, in health and disease, and in large human populations. Specifically, we introduce a mathematical framework based on covariant partial differential equations (PDEs), pull-backs of mappings under harmonic flows, and high-dimensional random tensor fields to encode variations in cortical patterning, asymmetry and tissue distribution in a population-based brain image database (N =94 scans). We use this information to detect disease-specific abnormalities in Alzheimer's disease and schizophrenia, including dynamic changes over time. Illustrative examples are chosen to show how group patterns of cortical organization, asymmetry, and disease-specific trends can be resolved that are not apparent in individual brain images. Finally, we create four-dimensional (4D) maps that store probabilistic information on the dynamics of brain change in development and disease. Digital atlases that generate these maps show considerable promise in identifying general patterns of structural and functional variation in diseased populations, and revealing how these features depend on demographic, genetic, clinical and therapeutic parameters.

Deformation-Based Morphometry and Its Relation to Conventional Volumetry of Brain Lateral Ventricles in MRI

Deformation-based morphometry (DBM) is a useful technique to detect morphological differences over the entire brain since it analyses positional differences between every voxel and a standard brain. In this report we compare DBM to semimanual tracing of brain ventricles in a population of 39 patients with schizophrenia. High-resolution T(1)-weighted magnetic resonance images were obtained and processed with DBM and interactive tracing software. We evaluate the validity of the DBM in two different approaches. First, we divide subjects into two groups based on the mean ventricular/brain ratios and compute statistical maps of displacement vectors and their spatial derivatives. This analysis demonstrates a striking consistency of the DBM and visual tracing results. We show that restricting the information about the deformation fields by computing the local Jacobian determinant (as a measure of volume change) provides evidence of the shape of ventricular deformation which is unavailable from ventricular volume measures alone. Second, we compute a mean measure of the Jacobian values over the entire ventricles and observe a correlation of r = 0.962 with visual tracing based ventricular/brain ratios. The results support the usefulness and validity of DBM for the local and global examination of brain morphology.

Magnetic resonance image tissue classification using a partial volume model

We describe a sequence of low-level operations to isolate and classify brain tissue within T1-weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate for image nonuniformities due to magnetic field inhomogeneities by fitting a tricubic B-spline gain field to local estimates of the image nonuniformity spaced throughout the MRI volume. The local estimates are computed by fitting a partial volume tissue measurement model to histograms of neighborhoods about each estimate point. The measurement model uses mean tissue intensity and noise variance values computed from the global image and a multiplicative bias parameter that is estimated for each region during the histogram fit. Voxels in the intensity-normalized image are then classified into six tissue types using a maximum a posteriori classifier. This classifier combines the partial volume tissue measurement model with a Gibbs prior that models the spatial properties of the brain. We validate each stage of our algorithm on real and phantom data. Using data from the 20 normal MRI brain data sets of the Internet Brain Segmentation Repository, our method achieved average kappa indices of kappa = 0.746 +/- 0.114 for gray matter (GM) and kappa = 0.798 +/- 0.089 for white matter (WM) compared to expert labeled data. Our method achieved average kappa indices kappa = 0.893 +/- 0.041 for GM and kappa = 0.928 +/- 0.039 for WM compared to the ground truth labeling on 12 volumes from the Montreal Neurological Institute's BrainWeb phantom.

Maps of the brain

We review recent developments in brain mapping and computational anatomy that have greatly expanded our ability to analyze brain structure and function. The enormous diversity of brain maps and imaging methods has spurred the development of population-based digital brain atlases. These atlases store information on how the brain varies across age and gender, across time, in health and disease, and in large human populations. We describe how brain atlases, and the computational tools that align new datasets with them, facilitate comparison of brain data across experiments, laboratories, and from different imaging devices. The major methods are presented for the construction of probabilistic atlases, which store information on anatomic and functional variability in a population. Algorithms are reviewed that create composite brain maps and atlases based on multiple subjects. We show that group patterns of cortical organization, asymmetry, and disease-specific trends can be resolved that may not be apparent in individual brain maps. Finally, we describe the creation of four-dimensional (4D) maps that store information on the dynamics of brain change in development and disease. Digital atlases that correlate these maps show considerable promise in identifying general patterns of structural and functional variation in human populations, and how these features depend on demographic, genetic, cognitive, and clinical parameters.

Voxel-based morphometry of herpes simplex encephalitis

Voxel-based morphometry (VBM) is a powerful tool for analyzing changes in gray or white matter density of the brain. By using an automated segmentation procedure and standardized parametric statistics it avoids biases inherent in operator-dependent morphological operations (J. Ashburner and K. J. Friston, 2000, NeuroImage 11, 805-821). Since its introduction in 1995, VBM has been used to examine anatomical changes in a variety of diseases associated with neurologic and psychiatric dysfunction. Given the power of this technique for discerning subtle anatomical changes, we wanted to assess its performance on brains with gross structural abnormalities. Such results could have implications regarding the difficulties to be faced when examining other types of distorted brains (e.g., brains with changes due to degenerative disease). This report describes the use of VBM for examining individual and group changes in gray matter concentration in five patients who had recovered from herpes simplex encephalitis (HSE) compared with age- and sex-matched controls. Because HSE tends to affect a specific set of brain regions we thought that this would (1) provide an opportunity to assess the anatomical face validity of VBM, (2) allow us to assess the problems of this technique when used on distorted brains, and (3) provide an in vivo demonstration of the gray matter changes due to HSE. We found that, despite problems in normalizing and segmenting these severely distorted brains, VBM was able to identify correctly a number of the regional gray matter abnormalities in HSE. The results, while consistent with the well-known histopathology of the disease, also demonstrate potential difficulties with this method.