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

Surface-constrained volumetric brain registration using harmonic mappings

In order to compare anatomical and functional brain imaging data across subjects, the images must first be registered to a common coordinate system in which anatomical features are aligned. Intensity-based volume registration methods can align subcortical structures well, but the variability in sulcal folding patterns typically results in misalignment of the cortical surface. Conversely, surface-based registration using sulcal features can produce excellent cortical alignment but the mapping between brains is restricted to the cortical surface. Here we describe a method for volumetric registration that also produces an accurate one-to-one point correspondence between cortical surfaces. This is achieved by first parameterizing and aligning the cortical surfaces using sulcal landmarks. We then use a constrained harmonic mapping to extend this surface correspondence to the entire cortical volume. Finally, this mapping is refined using an intensity-based warp. We demonstrate the utility of the method by applying it to T1-weighted magnetic resonance images (MRIs). We evaluate the performance of our proposed method relative to existing methods that use only intensity information; for this comparison we compute the intersubject alignment of expert-labeled subcortical structures after registration.

Mouse behavioral mutants have neuroimaging abnormalities

Impaired cognitive, memory, or motor performance is a distinguishing characteristic of neurological diseases. Although these symptoms are frequently the most evident in human patients, additional markers of disease are critical for proper diagnosis and staging. Noninvasive neuroimaging methods have become essential in this capacity and provide means of evaluating disease and tracking progression. These imaging methods are also becoming available to scientists in the research laboratory for assessment of animal models of neurological disease. Imaging in mouse models of neurological disease is of particular interest, owing to the availability of inbred strains and genetic manipulation tools that permit detailed investigation of the roles of various genes and gene products in disease pathogenesis. However, the relative prevalence of neuroimaging abnormalities in mice exhibiting neurological symptoms has not been reported. This prevalence has both theoretical and practical value because it is influenced by both the sensitivity of macroscopic anatomical measures to underlying genetic and disease processes and by the efficiency of neuroimaging in detecting and characterizing these effects. In this paper, we describe a meta-analysis of studies involving behavioral mouse mutants at our laboratory. In summary, we have evaluated 15 different mutant genotypes, of which 13 showed abnormal neuroimaging findings. This indicates a surprisingly high prevalence of neuroimaging abnormalities (87%) and suggests that disease processes affecting behavior generally alter neuroanatomy as well. As a consequence, neuroimaging provides a highly sensitive marker of neurological disease in mice exhibiting abnormal behavior.

Statistical properties of Jacobian maps and the realization of unbiased large-deformation nonlinear image registration

Maps of local tissue compression or expansion are often computed by comparing magnetic resonance imaging (MRI) scans using nonlinear image registration. The resulting changes are commonly analyzed using tensor-based morphometry to make inferences about anatomical differences, often based on the Jacobian map, which estimates local tissue gain or loss. Here, we provide rigorous mathematical analyses of the Jacobian maps, and use themto motivate a new numerical method to construct unbiased nonlinear image registration. First, we argue that logarithmic transformation is crucial for analyzing Jacobian values representing morphometric differences. We then examine the statistical distributions of log-Jacobian maps by defining the Kullback-Leibler (KL) distance on material density functions arising in continuum-mechanical models. With this framework, unbiased image registration can be constructed by quantifying the symmetric KL-distance between the identity map and the resulting deformation. Implementation details, addressing the proposed unbiased registration as well as the minimization of symmetric image matching functionals, are then discussed and shown to be applicable to other registration methods, such as inverse consistent registration. In the results section, we test the proposed framework, as well as present an illustrative application mapping detailed 3-D brain changes in sequential magnetic resonance imaging scans of a patient diagnosed with semantic dementia. Using permutation tests, we show that the symmetrization of image registration statistically reduces skewness in the log-Jacobian map.

Magnetic resonance imaging as a tool for in vivo and ex vivo anatomical phenotyping in experimental genetic models

This article describes a suite of computational approaches suitable for deriving various quantitative phenotypes from structural magnetic resonance (MR) images obtained in rodents and used subsequently in genetic studies of complex traits. We begin by introducing the basic principles of genetic studies of complex traits in experimental models. We then illustrate the use of MR-based computational anatomy in vivo and ex vivo, and in combination with histology. This work was carried out in two inbred strains of rats, namely spontaneously hypertensive rats and Brown Norway rats; these are parental strains of the only existing panel of recombinant inbred strains of rats. The rats were scanned in vivo at two time points (at 8 and 12 weeks of age) and ex vivo (at 12 weeks of age). We describe between-strain differences and across-time changes in brain and kidney volumes, as well as regional variations in brain structure using surface- and deformation-based approaches. We conclude by discussing the power of the population-based computational analysis of MR images, and their fusion with histology, in studies of complex traits.

Live imaging of neuronal connections by magnetic resonance: Robust transport in the hippocampal-septal memory circuit in a mouse model of Down syndrome

Connections from hippocampus to septal nuclei have been implicated in memory loss and the cognitive impairment in Down syndrome (DS). We trace these connections in living mice by Mn(2+) enhanced 3D MRI and compare normal with a trisomic mouse model of DS, Ts65Dn. After injection of 4 nl of 200 mM Mn(2+) into the right hippocampus, Mn(2+) enhanced circuitry was imaged at 0.5, 6, and 24 h in each of 13 different mice by high resolution MRI to detect dynamic changes in signal over time. The pattern of Mn(2+) enhanced signal in vivo correlated with the histologic pattern in fixed brains of co-injected 3kD rhodamine-dextran-amine, a classic tracer. Statistical parametric mapping comparing intensity changes between different time points revealed that the dynamics of Mn(2+) transport in this pathway were surprisingly more robust in DS mice than in littermate controls, with statistically significant intensity changes in DS appearing at earlier time points along expected pathways. This supports reciprocal alterations of transport in the hippocampal-forebrain circuit as being implicated in DS and argues against a general failure of transport. This is the first examination of in vivo transport dynamics in this pathway and the first report of elevated transport in DS.

Anatomical and functional phenotyping of mice models of Alzheimer's disease by MR microscopy

The wide variety of transgenic mouse models of Alzheimer's disease (AD) reflects the search for specific genes that influence AD pathology and the drive to create a clinically relevant animal model. An ideal AD mouse model must display hallmark AD pathology such as amyloid plaques, neurofibrillary tangles, reactive gliosis, dystrophic neurites, neuron and synapse loss, and brain atrophy and in parallel behaviorally mimic the cognitive decline observed in humans. Magnetic resonance (MR) microscopy (MRM) can detect amyloid plaque load, development of brain atrophy, and acute neurodegeneration. MRM examples of AD pathology will be presented and discussed. What has lagged behind in preclinical research using transgenic AD mouse models is functional phenotyping of the brain; in other words, the ability to correlate a specific genotype with potential aberrant brain activation patterns. This lack of information is caused by the technical challenges involved in performing functional MRI (fMRI) in mice including the effects of anesthetic agents and the lack of relevant "cognitive" paradigms. An alternative approach to classical fMRI using external stimuli as triggers of brain activation in rodents is to electrically or pharmacologically stimulate regions directly while simultaneously locally tracking the activated interconnected regions of rodents using, for example, the manganese-enhanced MRI (MEMRI) technique. Finally, transgenic mouse models, MRM, and future AD research would be strengthened by the ability to screen for AD-like pathology in other non-AD transgenic mouse models. For example, molecular biologists may focus on cardiac or pulmonary pathologies in transgenic mice models and as an incidental finding discover behavioral AD phenotypes. We will present MRM data of brain and cardiac phenotyping in transgenic mouse models with behavioral deficits.

Displacement of brain regions in preterm infants with non-synostotic dolichocephaly investigated by MRI

Regional investigations of newborn MRI are important to understand the appearance and consequences of early brain injury. Previously, regionalization in neonates has been achieved with a Talairach parcellation, using internal landmarks of the brain. Non-synostotic dolichocephaly defines a bi-temporal narrowing of the preterm infant's head caused by pressure on the immature skull. The impact of dolichocephaly on brain shape and regional brain shift, which may compromise the validity of the parcellation scheme, has not yet been investigated. Twenty-four preterm and 20 fullterm infants were scanned at term equivalent. Skull shapes were investigated by cephalometric measurements and population registration. Brain tissue volumes were calculated to rule out brain injury underlying skull shape differences. The position of Talairach landmarks was evaluated. Cortical structures were segmented to determine a positional shift between both groups. The preterm group displayed dolichocephalic head shapes and had similar brain volumes compared to the mesocephalic fullterm group. In preterm infants, Talairach landmarks were consistently positioned relative to each other and to the skull base, but were displaced with regard to the calvarium. The frontal and superior region was enlarged; central and temporal gyri and sulci were shifted comparing preterm and fullterm infants. We found that, in healthy preterm infants, dolichocephaly led to a shift of cortical structures, but did not influence deep brain structures. We concluded that the validity of a Talairach parcellation scheme is compromised and may lead to a miscalculation of regional brain volumes and inconsistent parcel contents when comparing infant populations with divergent head shapes.

Brain functional localization: a survey of image registration techniques

Functional localization is a concept which involves the application of a sequence of geometrical and statistical image processing operations in order to define the location of brain activity or to produce functional/parametric maps with respect to the brain structure or anatomy. Considering that functional brain images do not normally convey detailed structural information and, thus, do not present an anatomically specific localization of functional activity, various image registration techniques are introduced in the literature for the purpose of mapping functional activity into an anatomical image or a brain atlas. The problems addressed by these techniques differ depending on the application and the type of analysis, i.e., single-subject versus group analysis. Functional to anatomical brain image registration is the core part of functional localization in most applications and is accompanied by intersubject and subject-to-atlas registration for group analysis studies. Cortical surface registration and automatic brain labeling are some of the other tools towards establishing a fully automatic functional localization procedure. While several previous survey papers have reviewed and classified general-purpose medical image registration techniques, this paper provides an overview of brain functional localization along with a survey and classification of the image registration techniques related to this problem.

Geometrically accurate topology-correction of cortical surfaces using nonseparating loops

In this paper, we focus on the retrospective topology correction of surfaces. We propose a technique to accurately correct the spherical topology of cortical surfaces. Specifically, we construct a mapping from the original surface onto the sphere to detect topological defects as minimal nonhomeomorphic regions. The topology of each defect is then corrected by opening and sealing the surface along a set of nonseparating loops that are selected in a Bayesian framework. The proposed method is a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on the magnetic resonance imaging (MRI) intensity profile and the expected local curvature. Applied to real data, our method provides topological corrections similar to those made by a trained operator.

Anatomical equivalence class: a morphological analysis framework using a lossless shape descriptor

Methods of computational anatomy are typically based on a spatial transformation that maps a template to an individual anatomy and vice versa. However, important morphological characteristics are frequently not captured by this transformation, thereby leading to lossy representations. We extend this formulation by incorporating residual anatomical information, i.e., information that is not captured by the shape transformation but is necessary in order to fully and exactly reconstruct the anatomy under measurement. We, therefore, arrive at a lossless morphological representation. By virtue of being lossless, this representation allows us to represent the same anatomy by an infinite number of pairs [transformation, residual], since different residuals correspond to different transformations. We treat these pairs as members of an anatomical equivalence class (AEC), which we approximate using principal component analysis. We show that projection onto the orthogonal to the AEC subspace produces measurements that allow us to better detect morphological abnormalities by eliminating variation in the data that is irrelevant and confounds underlying subtle morphological characteristics. Finally, we show that higher classification rates between a group of normal brains and a group of brains with localized atrophy are obtained if we use nonmetric distances between AECs instead of conventional Euclidean distances between individual morphological measurements. The results confirm that this representation can improve the results compared to conventional analysis, but also highlight limitations of the current approach and point to directions of further development of this general morphological analysis framework.

Anatomical phenotyping in the brain and skull of a mutant mouse by magnetic resonance imaging and computed tomography

Since genetically modified mice have become more common in biomedical research as models of human disease, a need has also grown for efficient and quantitative methods to assess mouse phenotype. One powerful means of phenotyping is characterization of anatomy in mutant vs. normal populations. Anatomical phenotyping requires visualization of structures in situ, quantification of complex shape differences between mouse populations, and detection of subtle or diffuse abnormalities during high-throughput survey work. These aims can be achieved with imaging techniques adapted from clinical radiology, such as magnetic resonance imaging and computed tomography. These imaging technologies provide an excellent nondestructive method for visualization of anatomy in live individuals or specimens. The computer-based analysis of these images then allows thorough anatomical characterizations. We present an automated method for analyzing multiple-image data sets. This method uses image registration to identify corresponding anatomy between control and mutant groups. Within- and between-group shape differences are used to map regions of significantly differing anatomy. These regions are highlighted and represented quantitatively by displacements and volume changes. This methodology is demonstrated for a partially characterized mouse mutation generated by N-ethyl-N-nitrosourea mutagenesis that is a putative model of the human syndrome oculodentodigital dysplasia, caused by point mutations in the gene encoding connexin 43.

Sexual dimorphism revealed in the structure of the mouse brain using three-dimensional magnetic resonance imaging

A large variety of sexual dimorphisms have been described in the brains of many vertebrate species, including humans. Naturally occurring sexual dimorphism has been implicated in the risk, progression and recovery from numerous neurological disorders, including head injury, multiple sclerosis and stroke. Genetically altered mice are a key tool in the study of structure-function relationships in the mammalian central nervous system and serve as models for human neuropsychiatric and neurological disorders. However, there are a limited number of quantitative three-dimensional analyses of the adult mouse brain structures. In order to address limitations in our knowledge of anatomical differences, a comprehensive study was undertaken using full 3D magnetic resonance imaging (MRI) to examine sexual dimorphisms in the C57BL/6J whole mouse brain. An expected difference in overall brain size between the sexes was found, where male brains were 2.5% larger in volume than female brains. Beyond the overall brain size differences in the sexes, the following significantly different regions were found: males were larger in the thalamus, primary motor cortex and posterior hippocampus, while females were larger in posterior hypothalamic area, entorhinal cortex and anterior hippocampus. Using high-definition 3D MRI on a normal inbred mouse strain, we have mapped in detail many sex-associated statistically significant differences in brain structures.

Brain atlases and neuroanatomic imaging

Quantifying the effect of a genetic manipulation or disease is a complicated process in a population of animals. Probabilistic brain atlases can capture population variability and be used to quantify those variations in anatomy as measured by structural imaging. Minimum deformation atlases (MDAs), a subclass of probabilistic atlases, are intensity-based averages of a collection of scans in a common space unbiased by selection of a single target image. Here, we describe a method for generating an MDA from a set of magnetic resonance microscopy images. First, the images are segmented to remove any non-brain tissue and bias field corrected to remove field inhomogeneities. The corrected images are then linearly aligned to a representative scan, the geometric mean of all the transformations is calculated, and a minimum deformation target (MDT) is produced by averaging the volumes in this new space. The brains are then non-linearly aligned to the MDT to produce the MDA. Finally, the images are linearly aligned to the MDA using a full-affine transformation to spatially and intensity normalize them, removing global differences in size, shape, and position but retaining anatomically significant differences.

COMPARE: classification of morphological patterns using adaptive regional elements

This paper presents a method for classification of structural brain magnetic resonance (MR) images, by using a combination of deformation-based morphometry and machine learning methods. A morphological representation of the anatomy of interest is first obtained using a high-dimensional mass-preserving template warping method, which results in tissue density maps that constitute local tissue volumetric measurements. Regions that display strong correlations between tissue volume and classification (clinical) variables are extracted using a watershed segmentation algorithm, taking into account the regional smoothness of the correlation map which is estimated by a cross-validation strategy to achieve robustness to outliers. A volume increment algorithm is then applied to these regions to extract regional volumetric features, from which a feature selection technique using support vector machine (SVM)-based criteria is used to select the most discriminative features, according to their effect on the upper bound of the leave-one-out generalization error. Finally, SVM-based classification is applied using the best set of features, and it is tested using a leave-one-out cross-validation strategy. The results on MR brain images of healthy controls and schizophrenia patients demonstrate not only high classification accuracy (91.8% for female subjects and 90.8% for male subjects), but also good stability with respect to the number of features selected and the size of SVM kernel used.

A review of diffusion tensor imaging studies in schizophrenia

Both post-mortem and neuroimaging studies have contributed significantly to what we know about the brain and schizophrenia. MRI studies of volumetric reduction in several brain regions in schizophrenia have confirmed early speculations that the brain is disordered in schizophrenia. There is also a growing body of evidence suggesting that a disturbance in connectivity between different brain regions, rather than abnormalities within the separate regions themselves, are responsible for the clinical symptoms and cognitive dysfunctions observed in this disorder. Thus an interest in white matter fiber tracts, subserving anatomical connections between distant, as well as proximal, brain regions, is emerging. This interest coincides with the recent advent of diffusion tensor imaging (DTI), which makes it possible to evaluate the organization and coherence of white matter fiber tracts. This is an important advance as conventional MRI techniques are insensitive to fiber tract direction and organization, and have not consistently demonstrated white matter abnormalities. DTI may, therefore, provide important new information about neural circuitry, and it is increasingly being used in neuroimaging studies of psychopathological disorders. Of note, in the past five years 18 DTI studies in schizophrenia have been published, most describing white matter abnormalities. Questions still remain, however, regarding what we are measuring that is abnormal in this disease, and how measures obtained using one method correspond to those obtained using other methods? Below we review the basic principles involved in MR-DTI, followed by a review of the different methods used to evaluate diffusion. Finally, we review MR-DTI findings in schizophrenia.

Towards multimodal atlases of the human brain

Atlases of the human brain have an important impact on neuroscience. The emergence of ever more sophisticated imaging techniques, brain mapping methods and analytical strategies has the potential to revolutionize the concept of the brain atlas. Atlases can now combine data describing multiple aspects of brain structure or function at different scales from different subjects, yielding a truly integrative and comprehensive description of this organ. These integrative approaches have provided significant impetus for the human brain mapping initiatives, and have important applications in health and disease.

3D pattern of brain atrophy in HIV/AIDS visualized using tensor-based morphometry

35% of HIV-infected patients have cognitive impairment, but the profile of HIV-induced brain damage is still not well understood. Here we used tensor-based morphometry (TBM) to visualize brain deficits and clinical/anatomical correlations in HIV/AIDS. To perform TBM, we developed a new MRI-based analysis technique that uses fluid image warping, and a new alpha-entropy-based information-theoretic measure of image correspondence, called the Jensen-Rényi divergence (JRD).

METHODS

3D T1-weighted brain MRIs of 26 AIDS patients (CDC stage C and/or 3 without HIV-associated dementia; 47.2+/-9.8 years; 25M/1F; CD4+ T-cell count: 299.5+/-175.7/microl; log10 plasma viral load: 2.57+/- 1.28 RNA copies/ml) and 14 HIV-seronegative controls (37.6+/-12.2 years; 8M/6F) were fluidly registered by applying forces throughout each deforming image to maximize the JRD between it and a target image (from a control subject). The 3D fluid registration was regularized using the linearized Cauchy-Navier operator. Fine-scale volumetric differences between diagnostic groups were mapped. Regions were identified where brain atrophy correlated with clinical measures.

RESULTS

Severe atrophy ( approximately 15-20% deficit) was detected bilaterally in the primary and association sensorimotor areas. Atrophy of these regions, particularly in the white matter, correlated with cognitive impairment (P = 0.033) and CD4+ T-lymphocyte depletion (P = 0.005).

CONCLUSION

TBM facilitates 3D visualization of AIDS neuropathology in living patients scanned with MRI. Severe atrophy in frontoparietal and striatal areas may underlie early cognitive dysfunction in AIDS patients, and may signal the imminent onset of AIDS dementia complex.

Novel image processing techniques to better understand white matter disruption in multiple sclerosis

In Multiple Sclerosis (MS) patients, conventional magnetic resonance imaging (MRI) shows a pattern of white matter (WM) disruption but may also overlook some WM damage. Diffusion tensor MRI (DT-MRI) can provide important in-vivo information about fiber direction that is not provided by conventional MRI. The geometry of diffusion tensors can quantitatively characterize the local structure in tissues. The integration of both conventional MRI and DT-MRI measures together with connectivity-based regional assessment provide a better understanding of the nature and the location of WM abnormalities. Image processing and visualization techniques have been developed and applied to study conventional MRI and DT-MRI of MS patients. These include methods of: Image Segmentation for identifying the different areas of the brain as well as to discriminate normal from abnormal WM, Computerized Atlases, which include structural information obtained from a set of subjects, and Tractographies which can aid in the delineation of WM fiber tracts by tracking connected diffusion tensors. These new techniques hold out the promise of improving our understanding of WM architecture and its disruption in diseases such as MS. In the present study, we review the work that has been done in the development of these techniques and illustrate their applications.

A novel quantitative cross-validation of different cortical surface reconstruction algorithms using MRI phantom

Cortical surface reconstruction is important for functional brain mapping and morphometric analysis of the brain cortex. Several methods have been developed for the faithful reconstruction of surface models which represent the true cortical surface in both geometry and topology. However, there has been no explicit comparison study among those methods because each method has its own procedures, file formats, coordinate systems, and use of the reconstructed surface. There has also been no explicit evaluation method except visual inspection to validate the whole-cortical surface models quantitatively. In this study, we presented a novel phantom-based validation method of the cortical surface reconstruction algorithm and quantitatively cross-validated the three most prominent cortical surface reconstruction algorithms which are used in Freesurfer, BrainVISA, and CLASP, respectively. The validation included geometrical accuracy and mesh characteristics such as Euler number, fractal dimension (FD), total surface area, and local density of points. CLASP showed the best geometric/topologic accuracy and mesh characteristics such as FD and total surface area compared to Freesurfer and BrainVISA. In the validation of local density of points, Freesurfer and BrainVISA showed more even distribution of points on the cortical surface compared to CLASP.

Longitudinal stability of MRI for mapping brain change using tensor-based morphometry

Measures of brain change can be computed from sequential MRI scans, providing valuable information on disease progression, e.g., for patient monitoring and drug trials. Tensor-based morphometry (TBM) creates maps of these brain changes, visualizing the 3D profile and rates of tissue growth or atrophy, but its sensitivity depends on the contrast and geometric stability of the images. As part of the Alzheimer's Disease Neuroimaging Initiative (ADNI), 17 normal elderly subjects were scanned twice (at a 2-week interval) with several 3D 1.5 T MRI pulse sequences: high and low flip angle SPGR/FLASH (from which Synthetic T1 images were generated), MP-RAGE, IR-SPGR (N = 10) and MEDIC (N = 7) scans. For each subject and scan type, a 3D deformation map aligned baseline and follow-up scans, computed with a nonlinear, inverse-consistent elastic registration algorithm. Voxelwise statistics, in ICBM stereotaxic space, visualized the profile of mean absolute change and its cross-subject variance; these maps were then compared using permutation testing. Image stability depended on: (1) the pulse sequence; (2) the transmit/receive coil type (birdcage versus phased array); (3) spatial distortion corrections (using MEDIC sequence information); (4) B1-field intensity inhomogeneity correction (using N3). SPGR/FLASH images acquired using a birdcage coil had least overall deviation. N3 correction reduced coil type and pulse sequence differences and improved scan reproducibility, except for Synthetic T1 images (which were intrinsically corrected for B1-inhomogeneity). No strong evidence favored B0 correction. Although SPGR/FLASH images showed least deviation here, pulse sequence selection for the ADNI project was based on multiple additional image analyses, to be reported elsewhere.

Advances in magnetic resonance imaging methods for the evaluation of bipolar disorder

This article reviews the current state of magnetic resonance imaging techniques as applied to bipolar disorder. Addressed are conventional methods of structural neuroimaging and recently developed techniques. This latter group comprises volumetric analysis, voxel-based morphometry, the assessment of T2 white matter hyperintensities, shape analysis, cortical surface-based analysis, and diffusion tensor imaging. Structural analysis methods used in magnetic resonance imaging develop exponentially, and now present opportunities to identify disease-specific neuroanatomic alterations. Greater acuity and complementarity in measuring these alterations has led to the generation of further hypotheses regarding the pathophysiology of bipolar disorder. Included in the summary of findings is consideration of a resulting neuroanatomic model. Integrative issues and future directions in this relatively young field, including multi-modal approaches enabling us to produce more comprehensive results, are discussed.

An MRI study of structural variations in schizophrenia using deformation field morphometry

Magnetic resonance imaging (MRI) has an important role in investigating the changes in brain structure that are associated with schizophrenia. In this study, MRI scans of patients diagnosed with schizophrenia (37 males; 19 females; 17-42 years of age) were compared with those of an age- and sex-matched group of normal subjects (37 males; 19 females; 18-40 years of age). Based on the images of the healthy control subjects, we constructed a representative average brain template. Automated image analysis techniques were used to measure differences in the regional nonlinear deformation fields between the two groups. A deformation field, which measures the spatial transformation to deform a template of brain anatomy to each individual data, was obtained as a three-dimensional displacement vector in each voxel. There was a significantly greater magnitude of the deformation fields in the superior frontal and parietal lobes as well as in the cingulate gyrus connecting both lobes of the patients with schizophrenia than in those of healthy controls, suggesting that these cerebral regions have a significantly higher structural variability in schizophrenia.

Retrospective evaluation of a topology preserving non-rigid registration method

This paper proposes a comprehensive evaluation of a monomodal B-spline-based non-rigid registration algorithm allowing topology preservation in 3-D. This article is to be considered as the companion of [Noblet, V., Heinrich, C., Heitz, F., Armspach, J.-P., 2005. 3-D deformable image registration: a topology preservation scheme based on hierarchical deformation models and interval analysis optimization. IEEE Transactions on Image Processing, 14 (5), 553-566] where this algorithm, based on the minimization of an objective function, was introduced and detailed. Overall assessment is based on the estimation of synthetic deformation fields, on average brain construction, on atlas-based segmentation and on landmark mapping. The influence of the model parameters is characterized. Comparison between several objective functions is carried out and impact of their symmetrization is pointed out. An original intensity normalization scheme is also introduced, leading to significant improvements of the registration quality. The comparison benchmark is the popular demons algorithm [Thirion, J.-P., 1998. Image matching as a diffusion process: an analogy with Maxwell's demons. Medical Image Analysis, 2 (3), 243-260], that exhibited best results in a recent comparison between several non-rigid 3-D registration methods [Hellier, P., Barillot, C., Corouge, I., Gibaud, B., Le Goualher, G., Collins, D.L., Evans, A., Malandain, G., Ayache, N., Christensen, G.E., Johnson, H.J., 2003. Retrospective evaluation of intersubject brain registration. IEEE Transactions on Medical Imaging, 22 (9), 1120-1130]. The topology preserving B-spline-based method proved to outperform the commonly available ITK implementation of the demons algorithms on many points. Some limits of intensity-based registration methods are also highlighted through this work.

Interactive segmentation of the cerebral lobes with fuzzy inference in 3T MR images

Measurement of volume and surface area of the frontal, parietal, temporal and occipital lobes from magnetic resonance (MR) images shows promise as a method for use in diagnosis of dementia. This article presents a novel computer-aided system for automatically segmenting the cerebral lobes from 3T human brain MR images. Until now, the anatomical definition of cerebral lobes on the cerebral cortex is somewhat vague for use in automatic delineation of boundary lines, and there is no definition of cerebral lobes in the interior of the cerebrum. Therefore, we have developed a new method for defining cerebral lobes on the cerebral cortex and in the interior of the cerebrum. The proposed method determines the boundaries between the lobes by deforming initial surfaces. The initial surfaces are automatically determined based on user-given landmarks. They are smoothed and deformed so that the deforming boundaries run along the hourglass portion of the three-dimensional shape of the cerebrum with fuzzy rule-based active contour and surface models. The cerebrum is divided into the cerebral lobes according to the boundaries determined using this method. The reproducibility of our system with a given subject was assessed by examining the variability of volume and surface area in three healthy subjects, with measurements performed by three beginners and one expert user. The experimental results show that our system segments the cerebral lobes with high reproducibility.

Regional specificity of cerebrospinal fluid abnormalities in first episode schizophrenia

The timing and regional specificity of cerebrospinal fluid (CSF) enlargements have not been well described in schizophrenia. High-resolution magnetic resonance images and computational image analysis methods were used to localize cross-sectional changes in lateral ventricle and sulcal and subarachnoid CSF in first episode schizophrenia patients (51 males/21 females) and healthy subjects (37 males/41 females). Volumes were obtained for each lateral ventricle horn and regional differences identified by comparing the distances from the ventricular surfaces to the central core at anatomically matched locations. Extra-cortical CSF differences were compared by measuring the proportion of CSF voxels sampled from spatially homologous cortical surface points. Significant extra-cortical CSF enlargements were observed in first episode patients, where regional differences surrounded the temporal, anterior frontal and parietal cortices. Volume and ventricular surface analyses failed to show significant effects of diagnosis. However, interactions indicated dorsal superior horn expansions in female patients compared with same-sex controls. Since ventricular enlargements are widely reported in chronic patients, our observations at first episode suggest ventricular enlargement may progress after disease onset with early changes occurring around the dorsal superior horn. In contrast, sulcal and subarachnoid CSF increases may be manifest near or before the first episode but after brain development is complete, reflecting pronounced reductions in proximal brain tissue.

An introduction to MR imaging-based stroke morphometry

The anatomic description of the stroke lesion is an essential component of clinical diagnosis and treatment and has become an established tool in investigations into underlying stroke pathophysiology. Magnetic resonance (MR) imaging permits quantitative evaluation of the distributed consequences of the pathologic stroke insult. General properties of stroke effects have emerged using these tools. This article surveys the classes of morphometric data that are available from conventional MR images, the methods for extracting quantitative results, and samples of the application of these methods to stroke. These samples highlight anatomic-based considerations regarding the nature of stroke and its repercussions within the brain parenchyma.

Graphical-Model-based Morphometric Analysis

We propose a novel method for voxel-based morphometry (VBM), which we call Graphical-Model-based Morphometric Analysis (GAMMA), to identify morphological abnormalities automatically, and to find complex probabilistic associations among voxels in magnetic-resonance images and clinical variables. GAMMA is a fully automatic, nonparametric morphometric-analysis algorithm, with high sensitivity and specificity. It uses a Bayesian network to represent the associations among voxels and the function variable, and uses a contextual-clustering method based on a Markov random field to find clusters in which all voxels have similar associations with the function variable. We use loopy belief propagation to infer the unobserved label field and belief map. As opposed to voxel-based morphometric methods based on general linear models, GAMMA is capable of identifying nonlinear associations among the function variable and voxels. Compared with our previous approach, a Bayesian morphometry algorithm, GAMMA has greater sensitivity, specificity, and computational efficiency.

Genetics of brain structure and intelligence

Genetic influences on brain morphology and IQ are well studied. A variety of sophisticated brain-mapping approaches relating genetic influences on brain structure and intelligence establishes a regional distribution for this relationship that is consistent with behavioral studies. We highlight those studies that illustrate the complex cortical patterns associated with measures of cognitive ability. A measure of cognitive ability, known as g, has been shown highly heritable across many studies. We argue that these genetic links are partly mediated by brain structure that is likewise under strong genetic control. Other factors, such as the environment, obviously play a role, but the predominant determinant appears to be genetic.

A three-dimensional digital atlas database of the adult C57BL/6J mouse brain by magnetic resonance microscopy

A comprehensive three-dimensional digital atlas database of the C57BL/6J mouse brain was developed based on magnetic resonance microscopy images acquired on a 17.6-T superconducting magnet. By using both manual tracing and an atlas-based semi-automatic segmentation approach, T2-weighted magnetic resonance microscopy images of 10 adult male formalin-fixed, excised C57BL/6J mouse brains were segmented into 20 anatomical structures. These structures included the neocortex, hippocampus, amygdala, olfactory bulbs, basal forebrain and septum, caudate-putamen, globus pallidus, thalamus, hypothalamus, central gray, superior colliculi, inferior colliculi, the rest of midbrain, cerebellum, brainstem, corpus callosum/external capsule, internal capsule, anterior commissure, fimbria, and ventricles. The segmentation data were formatted and stored into a database containing three different atlas types: 10 single-specimen brain atlases, an average brain atlas and a probabilistic atlas. Additionally, quantitative group information, such as variations in structural volume, surface area, magnetic resonance microscopy image intensity and local geometry, were computed and stored as an integral part of the database. The database augments ongoing efforts with other high priority strains as defined by the Mouse Phenome Database focused on providing a quantitative framework for accurate mapping of functional, genetic and protein expression patterns acquired by a myriad of technologies and imaging modalities.

Functional MRI BOLD response to Tower of London performance of first-episode schizophrenia patients using cortical pattern matching

Due to its three-dimensional folding pattern, the human neocortex poses a challenge for accurate co-registration of grouped functional brain imaging data. The present study addressed this problem by employing three-dimensional continuum-mechanical image-warping techniques to derive average anatomical representations for co-registration of functional magnetic resonance brain imaging data obtained from 10 male first-episode schizophrenia patients and 10 age-matched male healthy volunteers while they performed a version of the Tower of London task. This novel technique produced an equivalent representation of blood oxygenation level dependent (BOLD) response across hemispheres, cortical regions, and groups, respectively, when compared to intensity average co-registration, using a deformable Brodmann area atlas as anatomical reference. Somewhat closer association of Brodmann area boundaries with primary visual and auditory areas was evident using the gyral pattern average model. Statistically-thresholded BOLD cluster data confirmed predominantly bilateral prefrontal and parietal, right frontal and dorsolateral prefrontal, and left occipital activation in healthy subjects, while patients' hemispheric dominance pattern was diminished or reversed, particularly decreasing cortical BOLD response with increasing task difficulty in the right superior temporal gyrus. Reduced regional gray matter thickness correlated with reduced left-hemispheric prefrontal/frontal and bilateral parietal BOLD activation in patients. This is the first study demonstrating that reduction of regional gray matter in first-episode schizophrenia patients is associated with impaired brain function when performing the Tower of London task, and supports previous findings of impaired executive attention and working memory in schizophrenia.

Parasagittal asymmetries of the corpus callosum

Significant relationships have been reported between midsagittal areas of the corpus callosum and the degree of interhemispheric transfer, functional lateralization and structural brain asymmetries. No study, however, has examined whether parasagittal callosal asymmetries (i.e. those close to the midline of the brain), which may be of specific functional consequence, are present in the human brain. Thus, we applied magnetic resonance imaging and novel computational surface-based methods to encode hemispheric differences in callosal thickness at a very high resolution. Discrete callosal areas were also compared between the hemispheres. Furthermore, acknowledging the frequently reported sex differences in callosal morphology, parasagittal callosal asymmetries were examined within each gender. Results showed significant rightward asymmetries of callosal thickness predominantly in the anterior body and anterior third of the callosum, suggesting a more diffuse functional organization of callosal projections in the right hemisphere. Asymmetries were increased in men, supporting the assumption of a sexually dimorphic organization of male and female brains that involves hemispheric relations and is reflected in the organization and distribution of callosal fibers.

3-D deformable image registration: a topology preservation scheme based on hierarchical deformation models and interval analysis optimization

This paper deals with topology preservation in three-dimensional (3-D) deformable image registration. This work is a nontrivial extension of, which addresses the case of two-dimensional (2-D) topology preserving mappings. In both cases, the deformation map is modeled as a hierarchical displacement field, decomposed on a multiresolution B-spline basis. Topology preservation is enforced by controlling the Jacobian of the transformation. Finding the optimal displacement parameters amounts to solving a constrained optimization problem: The residual energy between the target image and the deformed source image is minimized under constraints on the Jacobian. Unlike the 2-D case, in which simple linear constraints are derived, the 3-D B-spline-based deformable mapping yields a difficult (until now, unsolved) optimization problem. In this paper, we tackle the problem by resorting to interval analysis optimization techniques. Care is taken to keep the computational burden as low as possible. Results on multipatient 3-D MRI registration illustrate the ability of the method to preserve topology on the continuous image domain.

Brain structural mapping using a novel hybrid implicit/explicit framework based on the level-set method

This paper presents a novel approach to feature-based brain image warping, by using a hybrid implicit/explicit framework, which unifies many prior approaches in a common framework. In the first step, we develop links between image warping and the level-set method, and we formulate the fundamental mathematics required for this hybrid implicit/explicit approach. In the second step, we incorporate the large-deformation models into these formulations, leading to a complete and elegant treatment of anatomical structure matching. In this latest approach, exact matching of anatomy is achieved by comparing the target to the warped source structure under the forward mapping and the source to the warped target structure under the backward mapping. Because anatomy is represented nonparametrically, a path is constructed linking the source to the target structure without prior knowledge of their point correspondence. The final point correspondence is constructed based on the linking path with the minimal energy. Intensity-similarity measures can be naturally incorporated in the same framework as landmark constraints by combining them in the gradient descent body forces. We illustrate the approach with two applications: (1) tensor-based morphometry of the corpus callosum in autistic children; and (2) matching cortical surfaces to measure the profile of cortical anatomic variation. In summary, the new mathematical techniques introduced here contribute fundamentally to the mapping of brain structure and its variation and provide a framework that unites feature and intensity-based image registration techniques.

Non-rigid image registration: theory and practice

Image registration is an important enabling technology in medical image analysis. The current emphasis is on development and validation of application-specific non-rigid techniques, but there is already a plethora of techniques and terminology in use. In this paper we discuss the current state of the art of non-rigid registration to put on-going research in context and to highlight current and future clinical applications that might benefit from this technology. The philosophy and motivation underlying non-rigid registration is discussed and a guide to common terminology is presented. The core components of registration systems are described and outstanding issues of validity and validation are confronted.

Superior temporal gyrus differences in childhood-onset schizophrenia

The posterior superior temporal gyrus (STG) is the approximate site of Wernicke's area, a language region, which in previous studies has been reported to be abnormal in adults with schizophrenia. The present study assesses volumetric differences in the superior temporal gyrus of subjects with childhood-onset schizophrenia (COS). MRI scans of 18 subjects diagnosed with childhood-onset schizophrenia and 16 age- and sex-matched normals were analyzed to assess possible volume differences. The COS subjects displayed significant enlargement of the right posterior superior temporal gyrus, showing white matter increases bilaterally in this region. Our findings are consistent with studies that have found increased volumes in temporal lobe regions in COS and may provide a possible neural correlate for the language impairment observed in COS patients.

Geometric strategies for neuroanatomic analysis from MRI

In this paper, we describe ongoing work in the Image Processing and Analysis Group (IPAG) at Yale University specifically aimed at the analysis of structural information as represented within magnetic resonance images (MRI) of the human brain. Specifically, we will describe our applied mathematical approaches to the segmentation of cortical and subcortical structure, the analysis of white matter fiber tracks using diffusion tensor imaging (DTI), and the intersubject registration of neuroanatomical (aMRI) data sets. Many of our methods rally around the use of geometric constraints, statistical (MAP) estimation, and the use of level set evolution strategies. The analysis of gray matter structure and connecting white matter paths combined with the ability to bring all information into a common space via intersubject registration should provide us with a rich set of data to investigate structure and variation in the human brain in neuropsychiatric disorders, as well as provide a basis for current work in the development of integrated brain function-structure analysis.

Classification of structural images via high-dimensional image warping, robust feature extraction, and SVM

This paper presents a method for classification of medical images, using machine learning and deformation-based morphometry. A morphological representation of the anatomy of interest is first obtained using high-dimensional template warping, from which regions that display strong correlations between morphological measurements and the classification (clinical) variable are extracted using a watershed segmentation, taking into account the regional smoothness of the correlation map which is estimated by a cross-validation strategy in order to achieve robustness to outliers. A Support Vector Machine-Recursive Feature Elimination (SVM-RFE) technique is then used to rank computed features from the extracted regions, according to their effect on the leave-one-out error bound. Finally, SVM classification is applied using the best set of features, and it is tested using leave-one-out. The results from a group of 61 brain images of female normal controls and schizophrenia patients demonstrate not only high classification accuracy (91.8%) and steep ROC curves, but also exceptional stability with respect to the number of selected features and the SVM kernel size.

Construction and evaluation of an average CT brain image for inter-subject registration

An average CT brain image is constructed to serve as reference frame for inter-subject registration. A set of 96 clinical CT images is used. Registration includes translation, rotation, and anisotropic scaling. A temporary average based on a subset of 32 images is constructed. This image is used as reference for the iterative construction of the average CT image. This approach is computationally efficient and results in a consistent registration of the 96 images. Registration of new images to the average CT is more consistent than registration to a single CT image. The use of the average CT image is illustrated.

CRUISE: Cortical reconstruction using implicit surface evolution

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

A hybrid approach to the skull stripping problem in MRI

We present a novel skull-stripping algorithm based on a hybrid approach that combines watershed algorithms and deformable surface models. Our method takes advantage of the robustness of the former as well as the surface information available to the latter. The algorithm first localizes a single white matter voxel in a T1-weighted MRI image, and uses it to create a global minimum in the white matter before applying a watershed algorithm with a preflooding height. The watershed algorithm builds an initial estimate of the brain volume based on the three-dimensional connectivity of the white matter. This first step is robust, and performs well in the presence of intensity nonuniformities and noise, but may erode parts of the cortex that abut bright nonbrain structures such as the eye sockets, or may remove parts of the cerebellum. To correct these inaccuracies, a surface deformation process fits a smooth surface to the masked volume, allowing the incorporation of geometric constraints into the skull-stripping procedure. A statistical atlas, generated from a set of accurately segmented brains, is used to validate and potentially correct the segmentation, and the MRI intensity values are locally re-estimated at the boundary of the brain. Finally, a high-resolution surface deformation is performed that accurately matches the outer boundary of the brain, resulting in a robust and automated procedure. Studies by our group and others outperform other publicly available skull-stripping tools.

An MRI study of spatial probability brain map differences between first-episode schizophrenia and normal controls

We created a spatial probability atlas of schizophrenia to provide information about the neuroanatomic variability of brain regions of patients with the disorder. Probability maps of 16 regions of interest (ROIs) were constructed by taking manually parcellated ROIs from subjects' magnetic resonance images (MRIs) and linearly transforming them into Talairach space using the Montreal Neurological Institute (MNI) template. ROIs included temporal, parietal, and prefrontal cortex subregions, with a principal focus on temporal lobe structures. Subject Ns ranged from 11 to 28 for the different ROIs. Our global measure of the spatial distribution of the transformed ROI was the sum of voxels with 50% overlap among subjects. The superior temporal gyrus (STG) and fusiform gyrus (FG) had lower values for schizophrenic subjects than for normal controls, suggestive of greater spatial variability for these ROIs in schizophrenic subjects. For the computation of statistical significance of group differences in portions of the ROI, we used voxel-wise comparisons and Fisher's exact test. First-episode schizophrenic patients compared with controls showed lower probability (P < 0.05) at dorso-posterior areas of planum temporale and Heschl's gyrus, lateral and anterior regions in the left hippocampus (HIPP), and dorsolateral regions of fusiform gyrus. Importantly, most ROIs of schizophrenic subjects showed a significantly lower spatial overlap than controls, even after nonlinear spatial normalization, suggesting a greater heterogeneity in the spatial distribution of ROIs. There is consequently a need for caution in neuroimaging studies where data from schizophrenic subjects are normalized to a particular stereotaxic coordinate system based on healthy controls. Apparent group differences in activation may simply reflect a greater heterogeneity of spatial distribution in schizophrenia.

Why voxel-based morphometric analysis should be used with great caution when characterizing group differences

A variety of voxel-based morphometric analysis methods have been adopted by the neuroimaging community in the recent years. In this commentary we describe why voxel-based statistics, which are commonly used to construct statistical parametric maps, are very limited in characterizing morphological differences between groups, and why the effectiveness of voxel-based statistics is significantly biased toward group differences that are highly localized in space and of linear nature, whereas it is significantly reduced in cases with group differences of similar or even higher magnitude, when these differences are spatially complex and subtle. The complex and often subtle and nonlinear ways in which various factors, such as age, sex, genotype and disease, can affect brain morphology, suggest that alternative, unbiased methods based on statistical learning theory might be able to better quantify brain changes that are due to a variety of factors, especially when relationships between brain networks, rather than individual structures, and disease are examined.

Local landmark-based mapping of human auditory cortex

Mammalian sensory cortex is functionally partitioned into cortical fields that are specialized for different processing operations. In theory, averaging functional and anatomical images across subjects can reveal both the average anatomy and the mean functional organization of sensory regions. However, this averaging process must overcome at least two obstacles: (1) the relative locations and sizes of cortical sensory areas vary in different subjects so that across-subject averaging introduces spatial smearing; (2) the relative locations and sizes of cortical areas vary between hemispheres, making it difficult to compare activations between hemispheres or to combine activations across hemispheres. These difficulties are particularly acute for small cortical regions such as auditory cortex. In whole-brain averaging procedures, considerable intersubject variance in the location and orientation of auditory cortex is introduced by variance of the size and shape of structures outside auditory cortex. Here, we compared these global methods with local landmark-based methods (LLMs) that use warping based on local anatomical landmarks. In comparison to maps made with global methods, LLMs produced anatomical maps of auditory cortex with clearer gyral and sulcal structure, and produce functional maps with improved resolution. These results suggest that LLMs have significant advantages over global mapping procedures in studying the details of auditory cortex organization.

Aging, sexual dimorphism, and hemispheric asymmetry of the cerebral cortex: replicability of regional differences in volume

We examined age-, sex-, and hemisphere-related differences in the cerebral cortex. Volumes of the cerebral hemispheres and 13 regions of interest (ROIs) were measured on magnetic resonance images of 200 healthy adults. The strength of association between age and volume differed across ROIs. The lateral prefrontal cortex exhibited the greatest age-related differences, whereas significantly weaker associations were observed in the prefrontal white matter, sensory-motor, and visual association regions. The hippocampal shrinkage was significant in people in their mid-fifties. The primary visual, anterior cingulate, the inferior parietal cortices, and the parietal white matter showed no age-related differences. The pattern of age-related regional differences replicated the findings previously obtained on an independent sample drawn from the same population. Men evidenced larger volumes in all ROIs except the inferior parietal lobule, even after sexual dimorphism in body size was statistically controlled. In some regions (hippocampus and fusiform gyrus) men exhibited steeper negative age-related trends than women. Although a typical pattern of global hemispheric asymmetry was observed, the direction and magnitude of regional volumetric asymmetry was as inconsistent as in the previous reports. Thus, a pattern of age-related shrinkage suggesting increased vulnerability of the lateral prefrontal cortex to aging appears stable and replicable, whereas little consistency exists in sex-related and hemispheric differences in regional cortical volumes.

Gender differences in the left inferior frontal gyrus in normal children

This study examined frontal lobe subregions in 46 normal children and adolescents (25 females, mean age: 11.08, SD: 3.07; and 21 males, mean age: 10.76, SD: 2.61) to assess the effects of age and gender on volumetric measures as well as hemispheric asymmetries. Superior, middle, inferior, and orbito-frontal gray, white, and cerebrospinal (CSF) volumes were manually delineated in high-resolution magnetic resonance imaging (MRI) data to assess possible morphological changes. We report a significant age-related increase in the white matter of the left inferior frontal gyrus (IFG) in boys (P = 0.007). Additionally, the left IFG was significantly larger in boys compared to girls (P = 0.004). Boys showed increased gray matter volume relative to girls even after correcting for total cerebral volume. Also, boys were found to have significant Right > Left asymmetry patterns with greater right hemispheric volumes for total cerebral volume, total cerebral white matter, MFG white matter, and SFG white matter (P < 0.001). Girls showed significant Right > Left asymmetry patterns in total cerebral and SFG white matter (P < 0.001). These findings suggest continued modification of the IFG during normal development in boys, and significant gender differences in IFG gray matter between boys and girls that may be possibly linked to gender differences in speech development and lateralization of language.

Automated human frontal lobe identification in MR images based on fuzzy-logic encoded expert anatomic knowledge

Identification of human brain structures in MR images comprises an area of increasing interest, which also presents numerous methodological challenges. Here we describe a new knowledge-based automated method designed to identify several major brain sulci and then to define the frontal lobes by using the identified sulci as landmarks. To identify brain sulci, sulcal images were generated by morphologic operations and then separated into different components based on connectivity analysis. Subsequently, the individual anatomic features were evaluated by using fuzzy membership functions. The crisp decisions, i.e., the identification of sulci, were made by taking the maximum of the summation of all the membership functions. The identification was designed in a hierarchical order. The longitudinal fissure was extracted first. The left and right central sulci were then identified based on the left and right hemispheres. Next, the lateral sulci were identified based on the central sulci and hemispheres. Finally, the left and right frontal lobes were defined from the two hemispheres. The method was evaluated by visual inspection, comparison with manual segmentation, and comparison with manually volumetric results in references. The average Jaccard similarities of left and right frontal lobes between the automated and manual segmentation were 0.89 and 0.91, respectively. The average Kappa indices of left and right frontal lobes between the automated and manual segmentation were 0.94 and 0.95, respectively. These results show relatively high accuracy of using this novel method for human frontal lobe identification and segmentation.

A Bayesian morphometry algorithm

Most methods for structure-function analysis of the brain in medical images are usually based on voxel-wise statistical tests performed on registered magnetic resonance (MR) images across subjects. A major drawback of such methods is the inability to accurately locate regions that manifest nonlinear associations with clinical variables. In this paper, we propose Bayesian morphological analysis methods, based on a Bayesian-network representation, for the analysis of MR brain images. First, we describe how Bayesian networks (BNs) can represent probabilistic associations among voxels and clinical (function) variables. Second, we present a model-selection framework, which generates a BN that captures structure-function relationships from MR brain images and function variables. We demonstrate our methods in the context of determining associations between regional brain atrophy (as demonstrated on MR images of the brain), and functional deficits. We employ two data sets for this evaluation: the first contains MR images of 11 subjects, where associations between regional atrophy and a functional deficit are almost linear; the second data set contains MR images of the ventricles of 84 subjects, where the structure-function association is nonlinear. Our methods successfully identify voxel-wise morphological changes that are associated with functional deficits in both data sets, whereas standard statistical analysis (i.e., t-test and paired t-test) fails in the nonlinear-association case.