Automated graph-based analysis and correction of cortical volume topology

FreeSurfer

FreeSurfer is a suite of tools for the analysis of neuroimaging data that provides an array of algorithms to quantify the functional, connectional and structural properties of the human brain. It has evolved from a package primarily aimed at generating surface representations of the cerebral cortex into one that automatically creates models of most macroscopically visible structures in the human brain given any reasonable T1-weighted input image. It is freely available, runs on a wide variety of hardware and software platforms, and is open source.

Cortical atrophy in experimental autoimmune encephalomyelitis: in vivo imaging

There are strong correlations between cortical atrophy observed by MRI and clinical disability and disease duration in multiple sclerosis (MS). The objective of this study was to evaluate the progression of cortical atrophy over time in vivo in experimental autoimmune encephalomyelitis (EAE), the most commonly used animal model for MS. Volumetric changes in brains of EAE mice and matched healthy controls were quantified by collecting high-resolution T2-weighted magnetic resonance images in vivo and labeling anatomical structures on the images. In vivo scanning permitted us to evaluate brain structure volumes in individual animals over time and we observed that though brain atrophy progressed differently in each individual animal, all mice with EAE demonstrated significant atrophy in whole brain, cerebral cortex, and whole cerebellum compared to normal controls. Furthermore, we found a strong correlation between cerebellar atrophy and cumulative disease score in mice with EAE. Ex vivo MRI showed a significant decrease in brain and cerebellar volume and a trend that did not reach significance in cerebral cortex volume in mice with EAE compared to controls. Cross modality correlations revealed a significant association between neuronal loss on neuropathology and in vivo atrophy of the cerebral cortex by neuroimaging. These results demonstrate that longitudinal in vivo imaging is more sensitive to changes that occur in neurodegenerative disease models than cross-sectional ex vivo imaging. This is the first report of progressive cortical atrophy in vivo in a mouse model of MS.

Longitudinally guided level sets for consistent tissue segmentation of neonates

Quantification of brain development as well as disease-induced pathologies in neonates often requires precise delineation of white matter, grey matter and cerebrospinal fluid. Unlike adults, tissue segmentation in neonates is significantly more challenging due to the inherently lower tissue contrast. Most existing methods take a voxel-based approach and are limited to working with images from a single time-point, even though longitudinal scans are available. We take a different approach by taking advantage of the fact that the pattern of the major sulci and gyri are already present in the neonates and generally preserved but fine-tuned during brain development. That is, the segmentation of late-time-point image can be used to guide the segmentation of neonatal image. Accordingly, we propose a novel longitudinally guided level-sets method for consistent neonatal image segmentation by combining local intensity information, atlas spatial prior, cortical thickness constraint, and longitudinal information into a variational framework. The minimization of the proposed energy functional is strictly derived from a variational principle. Validation performed on both simulated and in vivo neonatal brain images shows promising results.

Learning-based meta-algorithm for MRI brain extraction

Multiple-segmentation-and-fusion method has been widely used for brain extraction, tissue segmentation, and region of interest (ROI) localization. However, such studies are hindered in practice by their computational complexity, mainly coming from the steps of template selection and template-to-subject nonlinear registration. In this study, we address these two issues and propose a novel learning-based meta-algorithm for MRI brain extraction. Specifically, we first use exemplars to represent the entire template library, and assign the most similar exemplar to the test subject. Second, a meta-algorithm combining two existing brain extraction algorithms (BET and BSE) is proposed to conduct multiple extractions directly on test subject. Effective parameter settings for the meta-algorithm are learned from the training data and propagated to subject through exemplars. We further develop a level-set based fusion method to combine multiple candidate extractions together with a closed smooth surface, for obtaining the final result. Experimental results show that, with only a small portion of subjects for training, the proposed method is able to produce more accurate and robust brain extraction results, at Jaccard Index of 0.956 +/- 0.010 on total 340 subjects under 6-fold cross validation, compared to those by the BET and BSE even using their best parameter combinations.

Consistent reconstruction of cortical surfaces from longitudinal brain MR images

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

A computational growth model for measuring dynamic cortical development in the first year of life

Human cerebral cortex develops extremely fast in the first year of life. Quantitative measurement of cortical development during this early stage plays an important role in revealing the relationship between cortical structural and high-level functional development. This paper presents a computational growth model to simulate the dynamic development of the cerebral cortex from birth to 1 year old by modeling the cerebral cortex as a deformable elastoplasticity surface driven via a growth model. To achieve a high accuracy, a guidance model is also incorporated to estimate the growth parameters and cortical shapes at later developmental stages. The proposed growth model has been applied to 10 healthy subjects with longitudinal brain MR images acquired at every 3 months from birth to 1 year old. The experimental results show that our proposed method can capture the dynamic developmental process of the cortex, with the average surface distance error smaller than 0.6 mm compared with the ground truth surfaces, and the results also show that 1) the curvedness and sharpness decrease from 2 weeks to 12 months and 2) the frontal lobe shows rapidly increasing cortical folding during this period, with relatively slower increase of the cortical folding in the occipital and parietal lobes.

WITHDRAWN: Anatomic differences in first episode schizophrenia: a deformation-based morphometry MRI Study

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Topology preserving warping of 3-D binary images according to continuous one-to-one mappings

The estimation of one-to-one mappings is one of the most intensively studied topics in the research field of nonrigid registration. Although the computation of such mappings can be now accurately and efficiently performed, the solutions for using them in the context of binary image deformation is much less satisfactory. In particular, warping a binary image with such transformations may alter its discrete topological properties if common resampling strategies are considered. In order to deal with this issue, this paper proposes a method for warping such images according to continuous and bijective mappings while preserving their discrete topological properties (i.e., their homotopy type). Results obtained in the context of the atlas-based segmentation of complex anatomical structures highlight the advantages of the proposed approach.

Automatic segmentation of neonatal images using convex optimization and coupled level sets

Accurate segmentation of neonatal brain MR images remains challenging mainly due to their poor spatial resolution, inverted contrast between white matter and gray matter, and high intensity inhomogeneity. Most existing methods for neonatal brain segmentation are atlas-based and voxel-wise. Although active contour/surface models with geometric information constraint have been successfully applied to adult brain segmentation, they are not fully explored in the neonatal image segmentation. In this paper, we propose a novel neonatal image segmentation method by combining local intensity information, atlas spatial prior, and cortical thickness constraint in a single level-set framework. Besides, we also provide a robust and reliable tissue surface initialization for the proposed method by using a convex optimization technique. Thus, tissue segmentation, as well as inner and outer cortical surface reconstruction, can be obtained simultaneously. The proposed method has been tested on a large neonatal dataset, and the validation on 10 neonatal brain images (with manual segmentations) shows very promising results.

Topological correction of brain surface meshes using spherical harmonics

Surface reconstruction methods allow advanced analysis of structural and functional brain data beyond what can be achieved using volumetric images alone. Automated generation of cortical surface meshes from 3D brain MRI often leads to topological defects and geometrical artifacts that must be corrected to permit subsequent analysis. Here, we propose a novel method to repair topological defects using a surface reconstruction that relies on spherical harmonics. First, during reparameterization of the surface using a tiled platonic solid, the original MRI intensity values are used as a basis to select either a "fill" or "cut" operation for each topological defect. We modify the spherical map of the uncorrected brain surface mesh, such that certain triangles are favored while searching for the bounding triangle during reparameterization. Then, a low-pass filtered alternative reconstruction based on spherical harmonics is patched into the reconstructed surface in areas that previously contained defects. Self-intersections are repaired using a local smoothing algorithm that limits the number of affected points to less than 0.1% of the total, and as a last step, all modified points are adjusted based on the T1 intensity. We found that the corrected reconstructions have reduced distance error metrics compared with a "gold standard" surface created by averaging 12 scans of the same brain. Ninety-three percent of the topological defects in a set of 10 scans of control subjects were accurately corrected. The entire process takes 6-8 min of computation time. Further improvements are discussed, especially regarding the use of the T1-weighted image to make corrections.

CENTS: cortical enhanced neonatal tissue segmentation

The acquisition of high-quality magnetic resonance (MR) images of neonatal brains is largely hampered by their characteristically small head size and insufficient tissue contrast. As a result, subsequent image processing and analysis, especially brain tissue segmentation, are often affected. To overcome this problem, a dedicated phased array neonatal head coil is utilized to improve MR image quality by augmenting signal-to-noise ratio and spatial resolution without lengthening data acquisition time. In addition, a specialized hybrid atlas-based tissue segmentation algorithm is developed for the delineation of fine structures in the acquired neonatal brain MR images. The proposed tissue segmentation method first enhances the sheet-like cortical gray matter (GM) structures in the to-be-segmented neonatal image with a Hessian filter for generation of a cortical GM confidence map. A neonatal population atlas is then generated by averaging the presegmented images of a population, weighted by their cortical GM similarity with respect to the to-be-segmented image. Finally, the neonatal population atlas is combined with the GM confidence map, and the resulting enhanced tissue probability maps for each tissue form a hybrid atlas is used for atlas-based segmentation. Various experiments are conducted to compare the segmentations of the proposed method with manual segmentation (on both images acquired with a dedicated phased array coil and a conventional volume coil), as well as with the segmentations of two population-atlas-based methods. Results show the proposed method is capable of segmenting the neonatal brain with the best accuracy, and also preserving the most structural details in the cortical regions.

Topological well-composedness and glamorous glue: a digital gluing algorithm for topologically constrained front propagation

We propose a new approach to front propagation algorithms based on a topological variant of well-composedness which contrasts with previous methods based on simple point detection. This provides for a theoretical justification, based on the digital Jordan separation theorem, for digitally "gluing" evolved well-composed objects separated by well-composed curves or surfaces. Additionally, our framework can be extended to more relaxed topologically constrained algorithms based on multisimple points. For both methods this framework has the additional benefit of obviating the requirement for both a user-specified connectivity and a topologically-consistent marching cubes/squares algorithm in meshing the resulting segmentation.

Alzheimer's CSF markers in older schizophrenia patients

OBJECTIVES

Cognitive impairment is prevalent in older schizophrenia patients but its biological basis is unknown. Neuropathological studies have not revealed Alzheimer disease (AD) lesion burden but in vivo data are lacking.

METHOD

We investigated the concentrations of CSF biomarkers of brain amyloidosis (Abeta42) and neurodegeneration (total and p-tau) in a group of older schizophrenia patients and related them to cognitive and MRI measures. Older schizophrenia (n = 11), AD patients (n = 20) and elderly controls (n = 6) underwent cognitive testing, lumbar puncture, and MRI scanning. Abeta42 and total and p-tau concentrations were assayed in the CSF. MRI volumes were assessed using both voxel-based (cortical pattern matching) and region-of-interest analyses.

RESULTS

CSF tau concentration in older schizophrenia patients was within normal limits (total tau 171 ± 51 pg/ml, p-tau 32 ± 8 pg/ml), while CSF Abeta42 (465 ± 112 pg/ml) levels were significantly lower compared to healthy elders (638 ± 130 pg/ml) but higher than in AD patients (352 ± 76 pg/ml). There was a strong positive relationship between CSF total or p-tau levels and MMSE scores in schizophrenia patients but not in AD, where higher concentrations of total tau were correlated with higher volumes in the occipital cortex (r = 0.63, p = 0.036), while in AD a significant correlation was found between lower Abeta42 concentrations and lower gray matter volume in the cingulate and lateral orbital cortices (r > 0.46, p < 0.05).

CONCLUSIONS

Older schizophrenia patients show a peculiar pattern of CSF Abeta42 and tau concentrations that relates to cognitive and structural markers but is not consistent with neurodegeneration and could be secondary to neurodevelopmental or drug treatment effects.

A comparison of voxel and surface based cortical thickness estimation methods

Cortical thickness estimation performed in-vivo via magnetic resonance imaging is an important technique for the diagnosis and understanding of the progression of neurodegenerative diseases. Currently, two different computational paradigms exist, with methods generally classified as either surface or voxel-based. This paper provides a much needed comparison of the surface-based method FreeSurfer and two voxel-based methods using clinical data. We test the effects of computing regional statistics using two different atlases and demonstrate that this makes a significant difference to the cortical thickness results. We assess reproducibility, and show that FreeSurfer has a regional standard deviation of thickness difference on same day scans that is significantly lower than either a Laplacian or Registration based method and discuss the trade off between reproducibility and segmentation accuracy caused by bending energy constraints. We demonstrate that voxel-based methods can detect similar patterns of group-wise differences as well as FreeSurfer in typical applications such as producing group-wise maps of statistically significant thickness change, but that regional statistics can vary between methods. We use a Support Vector Machine to classify patients against controls and did not find statistically significantly different results with voxel based methods compared to FreeSurfer. Finally we assessed longitudinal performance and concluded that currently FreeSurfer provides the most plausible measure of change over time, with further work required for voxel based methods.

Infant brain atlases from neonates to 1- and 2-year-olds

Background

Studies for infants are usually hindered by the insufficient image contrast, especially for neonates. Prior knowledge, in the form of atlas, can provide additional guidance for the data processing such as spatial normalization, label propagation, and tissue segmentation. Although it is highly desired, there is currently no such infant atlas which caters for all these applications. The reason may be largely due to the dramatic early brain development, image processing difficulties, and the need of a large sample size.

Methodology

To this end, after several years of subject recruitment and data acquisition, we have collected a unique longitudinal dataset, involving 95 normal infants (56 males and 39 females) with MRI scanned at 3 ages, i.e., neonate, 1-year-old, and 2-year-old. State-of-the-art MR image segmentation and registration techniques were employed, to construct which include the templates (grayscale average images), tissue probability maps (TPMs), and brain parcellation maps (i.e., meaningful anatomical regions of interest) for each age group. In addition, the longitudinal correspondences between age-specific atlases were also obtained. Experiments of typical infant applications validated that the proposed atlas outperformed other atlases and is hence very useful for infant-related studies.

Conclusions

We expect that the proposed infant 0–1–2 brain atlases would be significantly conducive to structural and functional studies of the infant brains. These atlases are publicly available in our website, http://bric.unc.edu/ideagroup/free-softwares/.

Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features

Neuroimage measures from magnetic resonance (MR) imaging, such as cortical thickness, have been playing an increasingly important role in searching for biomarkers of Alzheimer's disease (AD). Recent studies show that, AD, mild cognitive impairment (MCI) and normal control (NC) can be distinguished with relatively high accuracy using the baseline cortical thickness. With the increasing availability of large longitudinal datasets, it also becomes possible to study the longitudinal changes of cortical thickness and their correlation with the development of pathology in AD. In this study, the longitudinal cortical thickness changes of 152 subjects from 4 clinical groups (AD, NC, Progressive-MCI and Stable-MCI) selected from Alzheimer's Disease Neuroimaging Initiative (ADNI) are measured by our recently developed 4 D (spatial+temporal) thickness measuring algorithm. It is found that the 4 clinical groups demonstrate very similar spatial distribution of grey matter (GM) loss on cortex. To fully utilize the longitudinal information and better discriminate the subjects from 4 groups, especially between Stable-MCI and Progressive-MCI, 3 different categories of features are extracted for each subject, i.e., (1) static cortical thickness measures computed from the baseline and endline, (2) cortex thinning dynamics, such as the thinning speed (mm/year) and the thinning ratio (endline/baseline), and (3) network features computed from the brain network constructed based on the correlation between the longitudinal thickness changes of different regions of interest (ROIs). By combining the complementary information provided by features from the 3 categories, 2 classifiers are trained to diagnose AD and to predict the conversion to AD in MCI subjects, respectively. In the leave-one-out cross-validation, the proposed method can distinguish AD patients from NC at an accuracy of 96.1%, and can detect 81.7% (AUC = 0.875) of the MCI converters 6 months ahead of their conversions to AD. Also, by analyzing the brain network built via longitudinal cortical thickness changes, a significant decrease (p < 0.02) of the network clustering coefficient (associated with the development of AD pathology) is found in the Progressive-MCI group, which indicates the degenerated wiring efficiency of the brain network due to AD. More interestingly, the decreasing of network clustering coefficient of the olfactory cortex region was also found in the AD patients, which suggests olfactory dysfunction. Although the smell identification test is not performed in ADNI, this finding is consistent with other AD-related olfactory studies.

Robust surface reconstruction via Laplace-Beltrami eigen-projection and boundary deformation

In medical shape analysis, a critical problem is reconstructing a smooth surface of correct topology from a binary mask that typically has spurious features due to segmentation artifacts. The challenge is the robust removal of these outliers without affecting the accuracy of other parts of the boundary. In this paper, we propose a novel approach for this problem based on the Laplace-Beltrami (LB) eigen-projection and properly designed boundary deformations. Using the metric distortion during the LB eigen-projection, our method automatically detects the location of outliers and feeds this information to a well-composed and topology-preserving deformation. By iterating between these two steps of outlier detection and boundary deformation, we can robustly filter out the outliers without moving the smooth part of the boundary. The final surface is the eigen-projection of the filtered mask boundary that has the correct topology, desired accuracy and smoothness. In our experiments, we illustrate the robustness of our method on different input masks of the same structure, and compare with the popular SPHARM tool and the topology preserving level set method to show that our method can reconstruct accurate surface representations without introducing artificial oscillations. We also successfully validate our method on a large data set of more than 900 hippocampal masks and demonstrate that the reconstructed surfaces retain volume information accurately.

Altered neurocircuitry in the dopamine transporter knockout mouse brain

The plasma membrane transporters for the monoamine neurotransmitters dopamine, serotonin, and norepinephrine modulate the dynamics of these monoamine neurotransmitters. Thus, activity of these transporters has significant consequences for monoamine activity throughout the brain and for a number of neurological and psychiatric disorders. Gene knockout (KO) mice that reduce or eliminate expression of each of these monoamine transporters have provided a wealth of new information about the function of these proteins at molecular, physiological and behavioral levels. In the present work we use the unique properties of magnetic resonance imaging (MRI) to probe the effects of altered dopaminergic dynamics on meso-scale neuronal circuitry and overall brain morphology, since changes at these levels of organization might help to account for some of the extensive pharmacological and behavioral differences observed in dopamine transporter (DAT) KO mice. Despite the smaller size of these animals, voxel-wise statistical comparison of high resolution structural MR images indicated little morphological change as a consequence of DAT KO. Likewise, proton magnetic resonance spectra recorded in the striatum indicated no significant changes in detectable metabolite concentrations between DAT KO and wild-type (WT) mice. In contrast, alterations in the circuitry from the prefrontal cortex to the mesocortical limbic system, an important brain component intimately tied to function of mesolimbic/mesocortical dopamine reward pathways, were revealed by manganese-enhanced MRI (MEMRI). Analysis of co-registered MEMRI images taken over the 26 hours after introduction of Mn²⁺ into the prefrontal cortex indicated that DAT KO mice have a truncated Mn²⁺ distribution within this circuitry with little accumulation beyond the thalamus or contralateral to the injection site. By contrast, WT littermates exhibit Mn²⁺ transport into more posterior midbrain nuclei and contralateral mesolimbic structures at 26 hr post-injection. Thus, DAT KO mice appear, at this level of anatomic resolution, to have preserved cortico-striatal-thalamic connectivity but diminished robustness of reward-modulating circuitry distal to the thalamus. This is in contradistinction to the state of this circuitry in serotonin transporter KO mice where we observed more robust connectivity in more posterior brain regions using methods identical to those employed here.

Topological correction of brain surface meshes using spherical harmonics

A brain surface reconstruction allows advanced analysis of structural and functional brain data that is not possible using volumetric data alone. However, the generation of a brain surface mesh from MRI data often introduces topological defects and artifacts that must be corrected. We show that it is possible to accurately correct these errors using spherical harmonics. Our results clearly demonstrate that brain surface meshes reconstructed using spherical harmonics are free from topological defects and large artifacts that were present in the uncorrected brain surface. Visual inspection reveals that the corrected surfaces are of very high quality. The spherical harmonic surfaces are also quantitatively validated by comparing the surfaces to an "ideal" brain based on a manually corrected average of twelve scans of the same subject. In conclusion, the spherical harmonics approach is a direct, computationally fast method to correct topological errors.

Gyral folding pattern analysis via surface profiling

Folding is an essential shape characteristic of the human cerebral cortex. Descriptors of cortical folding patterns have been studied for decades. However, many previous studies are either based on local shape descriptors such as curvature, or based on global descriptors such as gyrification index or spherical wavelets. This paper proposes a gyrus-scale folding pattern analysis technique via cortical surface profiling. Firstly, we sample the cortical surface into 2D profiles and model them using a power function. This step provides both the flexibility of representing arbitrary shape by profiling and the compactness of representing shape by parametric modeling. Secondly, based on the estimated model parameters, we extract affine-invariant features on the cortical surface, and apply the affinity propagation clustering algorithm to parcellate the cortex into cortical regions with strict hierarchy and smooth transitions among them. Finally, a second-round surface profiling is performed on the parcellated cortical surface, and the number of hinges is detected to describe the gyral folding pattern. We have applied the surface profiling method to two normal brain datasets and a schizophrenia patient dataset. The experimental results demonstrate that the proposed method can accurately classify human gyri into 2-hinge, 3-hinge and 4-hinge patterns. The distribution of these folding patterns on brain lobes and the relationship between fiber density and gyral folding patterns are further investigated. Results from the schizophrenia dataset are consistent with commonly found abnormality in former studies by others, which demonstrates the potential clinical applications of the proposed technique.

High resolution lung airway cast segmentation with proper topology suitable for computational fluid dynamic simulations

Developing detailed lung airway models is an important step towards understanding the respiratory system. While modern imaging and airway casting approaches have dramatically improved the potential detail of such models, challenges have arisen in image processing as the demand for greater detail pushes the image processing approaches to their limits. Airway segmentations with proper topology have neither loops nor invalid voxel-to-voxel connections. Here we describe a new technique for segmenting airways with proper topology and apply the approach to an image volume generated by magnetic resonance imaging of a silicone cast created from an excised monkey lung.

An automated pipeline for cortical sulcal fundi extraction

In this paper, we propose a novel automated pipeline for extraction of sulcal fundi from triangulated cortical surfaces. This method consists of four consecutive steps. Firstly, we adopt a finite difference method to estimate principal curvatures, principal directions and curvature derivatives, along the principal directions, for each vertex. Then, we detect the sulcal fundi segment in each triangle of the cortical surface based on curvatures and curvature derivatives. Afterwards, we link the sulcal fundi segments into continuous curves. Finally, we connect breaking sulcal fundi and smooth bumping sulcal fundi by using the fast marching method on the cortical surface. The proposed method can find the accurate sulcal fundi using curvatures and curvature derivatives without any manual interaction. The method was applied to 10 normal brain MR images on inner cortical surfaces. We quantitatively evaluated the accuracy of the sulcal fundi extraction method using manually labeled sulcal fundi by experts. The average difference between automatically extracted major sulcal fundi and the expert labeled results is consistently around 1.0mm on 10 subject images, indicating the good performance of the proposed method.

In vivo neuropathology of cortical changes in elderly persons with schizophrenia

BACKGROUND

Elderly schizophrenia patients frequently develop cognitive impairment of unclear etiology. Magnetic resonance imaging (MRI) studies revealed brain structural abnormalities, but the pattern of cortical gray matter (GM) volume and its relationship with cognitive and behavioral symptoms are unknown.

METHODS

Magnetic resonance scans were taken from elderly schizophrenia patients (n = 20, age 67 +/- 6 SD, Mini-Mental State Examination [MMSE] 23 +/- 4), Alzheimer's disease (AD) patients (n = 20, age 73 +/- 9, MMSE 22 +/- 4), and healthy elders (n = 20, age 73 +/- 8, MMSE 29 +/- 1). Patients were assessed with a comprehensive neuropsychological and behavioral battery. Cortical pattern matching and a region-of-interest analysis, based on Brodmann areas (BAs), were used to map three-dimensional (3-D) profiles of differences in patterns of gray matter volume among groups.

RESULTS

Schizophrenia patients had 10% and 11% lower total left and right GM volume than healthy elders (p < .001) and 7% and 5% more than AD patients (p = .06 and ns). Regions that had both significantly less gray matter than control subjects and gray matter volume as low as AD mapped to the cingulate gyrus and orbitofrontal cortex (BA 30, 23, 24, 32, 25, 11). The strongest correlate of gray matter volume in elderly schizophrenia patients, although nonsignificant, was the positive symptom subscale of the Positive and Negative Syndrome Scale, mapping to the right anterior cingulate area (r = .42, p = .06).

CONCLUSIONS

The orbitofrontal/cingulate region had low gray matter volume in elderly schizophrenia patients. Neither cognitive impairment nor psychiatric symptoms were significantly associated with structural differences, even if positive symptoms tended to be associated with increased gray matter volume in this area.

Neonatal brain image segmentation in longitudinal MRI studies

In the study of early brain development, tissue segmentation of neonatal brain MR images remains challenging because of the insufficient image quality due to the properties of developing tissues. Among various brain tissue segmentation algorithms, atlas-based brain image segmentation can potentially achieve good segmentation results on neonatal brain images. However, their performances rely on both the quality of the atlas and the spatial correspondence between the atlas and the to-be-segmented image. Moreover, it is difficult to build a population atlas for neonates due to the requirement of a large set of tissue-segmented neonatal brain images. To combat these obstacles, we present a longitudinal neonatal brain image segmentation framework by taking advantage of the longitudinal data acquired at late time-point to build a subject-specific tissue probabilistic atlas. Specifically, tissue segmentation of the neonatal brain is formulated as two iterative steps of bias correction and probabilistic-atlas-based tissue segmentation, along with the longitudinal atlas reconstructed by the late time image of the same subject. The proposed method has been evaluated qualitatively through visual inspection and quantitatively by comparing with manual delineations and two population-atlas-based segmentation methods. Experimental results show that the utilization of a subject-specific probabilistic atlas can substantially improve tissue segmentation of neonatal brain images.

Purkinje cell loss in experimental autoimmune encephalomyelitis

Gray matter atrophy observed by brain MRI is an important correlate to clinical disability and disease duration in multiple sclerosis. The objective of this study was to link brain atrophy visualized by neuroimaging to its underlying neuropathology using the MS model, experimental autoimmune encephalomyelitis (EAE). Volumetric changes in brains of EAE mice, as well as matched healthy normal controls, were quantified by collecting post-mortem high-resolution T2-weighted magnetic resonance microscopy and actively stained magnetic resonance histology images. Anatomical delineations demonstrated a significant decrease in the volume of the whole cerebellum, cerebellar cortex, and molecular layer of the cerebellar cortex in EAE as compared to normal controls. The pro-apoptotic marker caspase-3 was detected in Purkinje cells and a significant decrease in Purkinje cell number was found in EAE. Cross modality and temporal correlations revealed a significant association between Purkinje cell loss on neuropathology and atrophy of the molecular layer of the cerebellar cortex by neuroimaging. These results demonstrate the power of using combined population atlasing and neuropathology approaches to discern novel insights underlying gray matter atrophy in animal models of neurodegenerative disease.

A parameterization-based numerical method for isotropic and anisotropic diffusion smoothing on non-flat surfaces

Neuroimaging data, such as 3-D maps of cortical thickness or neural activation, can often be analyzed more informatively with respect to the cortical surface rather than the entire volume of the brain. Any cortical surface-based analysis should be carried out using computations in the intrinsic geometry of the surface rather than using the metric of the ambient 3-D space. We present parameterization-based numerical methods for performing isotropic and anisotropic filtering on triangulated surface geometries. In contrast to existing FEM-based methods for triangulated geometries, our approach accounts for the metric of the surface. In order to discretize and numerically compute the isotropic and anisotropic geometric operators, we first parameterize the surface using a p-harmonic mapping. We then use this parameterization as our computational domain and account for the surface metric while carrying out isotropic and anisotropic filtering. To validate our method, we compare our numerical results to the analytical expression for isotropic diffusion on a spherical surface. We apply these methods to smoothing of mean curvature maps on the cortical surface, a step commonly required for analysis of gyrification or for registering surface-based maps across subjects.

Reward circuitry is perturbed in the absence of the serotonin transporter

The serotonin transporter (SERT) modulates the entire serotonergic system in the brain and influences both the dopaminergic and norepinephrinergic systems. These three systems are intimately involved in normal physiological functioning of the brain and implicated in numerous pathological conditions. Here we use high-resolution magnetic resonance imaging (MRI) and spectroscopy to elucidate the effects of disruption of the serotonin transporter in an animal model system: the SERT knock-out mouse. Employing manganese-enhanced MRI, we injected Mn(2+) into the prefrontal cortex and obtained 3D MR images at specific time points in cohorts of SERT and normal mice. Statistical analysis of co-registered datasets demonstrated that active circuitry originating in the prefrontal cortex in the SERT knock-out is dramatically altered, with a bias towards more posterior areas (substantia nigra, ventral tegmental area, and Raphé nuclei) directly involved in the reward circuit. Injection site and tracing were confirmed with traditional track tracers by optical microscopy. In contrast, metabolite levels were essentially normal in the SERT knock-out by in vivo magnetic resonance spectroscopy and little or no anatomical differences between SERT knock-out and normal mice were detected by MRI. These findings point to modulation of the limbic cortical-ventral striatopallidal by disruption of SERT function. Thus, molecular disruptions of SERT that produce behavioral changes also alter the functional anatomy of the reward circuitry in which all the monoamine systems are involved.

In vivo mapping of incremental cortical atrophy from incipient to overt Alzheimer's disease

Progressive brain atrophy is believed to be the Alzheimer's disease (AD) marker with the greatest evidence for validity. Mapping the topography of cortical atrophy throughout the stages of severity may allow the neural networks affected to be identified. Twenty healthy elderly persons (OH, MMSE 29.1 +/- 1.0), 11 patients with incipient AD (iAD, 26.5 +/- 2.0), 15 with mild AD (miAD, 23.5 +/- 2.2), and 15 with moderate AD (moAD, 16.5 +/- 2.0) underwent 3D magnetic resonance. Cortical pattern matching analysis was performed and maps of percent differences in gray matter distribution were computed between the following groups: iAD versus OH, miAD versus iAD, and moAD versus miAD. Compared to OH, iAD patients exhibited a mean cortical gray matter loss of 9-20% in areas encompassing the polysynaptic hippocampal pathway (posterior cingulate/retrosplenial and medial temporal cortex) and subgenual/orbitofrontal cortices, and a less widespread loss of 5-11% in other neocortical areas. Compared to iAD, miAD featured widespread mean gray matter loss of 14-19% in areas encompassing the direct hippocampal pathway (temporal pole, temporoparietal association cortex, and dorsal prefrontal cortex), sensorimotor, and visual cortex, with a less marked loss (7-9%) in the polysynaptic pathway areas. Compared to miAD, only atrophy in the primary sensorimotor cortex was still relatively marked in moAD, with a mean gray matter loss of 10-11%; the loss in other regions was generally below 10%. These findings suggest that the polysynaptic hippocampal pathway is affected in iAD, the direct pathway and sensorimotor and visual networks are affected in moAD, and the sensorimotor network is affected in moAD.

Brain Tissue Segmentation of Neonatal MR Images Using a Longitudinal Subject-specific Probabilistic Atlas

Brain tissue segmentation of neonate MR images is a challenging task in study of early brain development, due to low signal contrast among brain tissues and high intensity variability especially in white matter. Among various brain tissue segmentation algorithms, the atlas-based segmentation techniques can potentially produce reasonable segmentation results on neonatal brain images. However, their performance on the population-based atlas is still limited due to the high variability of brain structures across different individuals. Moreover, it may be impossible to generate a reasonable probabilistic atlas for neonates without tissue segmentation samples. To overcome these limitations, we present a neonatal brain tissue segmentation method by taking advantage of the longitudinal data available in our study to establish a subject-specific probabilistic atlas. In particular, tissue segmentation of the neonatal brain is formulated as two iterative steps of bias correction and probabilistic atlas based tissue segmentation, along with the guidance of brain tissue segmentation resulted from the later time images of the same subject which serve as a subject-specific probabilistic atlas. The proposed method has been evaluated qualitatively through visual inspection and quantitatively by comparing with manual delineation results. Experimental results show that the utilization of a subject-specific probabilistic atlas can substantially improve tissue segmentation of neonatal brain images.

Decreased white matter integrity in late-myelinating fiber pathways in Alzheimer's disease supports retrogenesis

The retrogenesis model of Alzheimer's disease (AD) posits that white matter (WM) degeneration follows a pattern that is the reverse of myelogenesis. Using diffusion tensor imaging (DTI) to test this model, we predicted greater loss of microstructural integrity in late-myelinating WM fiber pathways in AD patients than in healthy older adults, whereas differences in early-myelinating WM fiber pathways were not expected. We compared 16 AD patients and 14 demographically-matched healthy older adults with a whole-brain approach via tract-based spatial statistics (TBSS), and a region of interest (ROI) approach targeting early-myelinating (posterior limb of internal capsule, cerebral peduncles) and late-myelinating (inferior longitudinal fasciculus [ILF], superior longitudinal fasciculus [SLF]) fiber pathways. Permutation-based voxelwise analysis supported the retrogenesis model. There was significantly lower fractional anisotropy (FA) in AD patients compared to healthy older adults in late-myelinating but not early-myelinating pathways. These group differences appeared to be driven by loss of myelin integrity based on our finding of greater radial diffusion in AD than in healthy elderly. ROI analyses were generally in agreement with whole-brain findings, with significantly lower FA and increased radial diffusion in the ILF in the AD group. Consistent with the retrogenesis model, AD patients showed demonstrable changes in late-myelinating WM fiber pathways. Given greater change in the ILF than the SLF, wallerian degeneration secondary to cortical atrophy may also be a contributing mechanism. Knowledge of the pattern of WM microstructural changes in AD and its underlying mechanisms may contribute to earlier detection and intervention in at-risk groups.

Is total brain volume correlated to cognitive function and education in patients with Alzheimer disease?

This study aimed to correlate whole brain volume measurements with MRI in patients with Alzheimer disease and the same Mini Mental State Examination (MMSE) but different levels of education. We describe the procedure we used to create 3D models of the brain from MRI images in patients with Alzheimer disease and then took volumetric measurements of the whole brain parenchyma. After this procedure we correlated the total brain volume measurements in patients with six or fewer years of education with those who had at least 12 years of education. Twenty patients with Alzheimer disease were examined with MRI. All of them had an MMSE score between 21 and 24 and were classified as mild Alzheimer disease. Ten of the patients had at least six years of education and the remaining ten had more than 12 years of education. The examinations were done by using a Siemens Expert Plus system of 1T and the MR images were studied using an automatic algorithm. The MRI images were segmented into grey, white matter and CSF. We then measured the volume of each component and classified those in each patient in relation to years of education. The whole procedure was completed successfully in 20 patients. After the volumetric study of the total brain volume by calculating separately grey matter, white matter and CSF, we classified the patients and made the correlation between those with six or fewer years of education and those with twelve or more years of study. Correlating the whole brain volume measurements of patients with Alzheimer disease and the same MMSE but different levels of education showed that there is no significant difference between the total brain volume of the two groups of our study.

Least-square conformal brain mapping with spring energy

The human brain cortex is a highly convoluted sheet. Mapping of the cortical surface into a canonical coordinate space is an important tool for the study of the structure and function of the brain. Here, we present a technique based on least-square conformal mapping with spring energy for the mapping of the cortical surface. This method aims to reduce the metric and area distortion while maintaining the conformal map and computation efficiency. We demonstrate through numerical results that this method effectively controls metric and area distortion, and is computational efficient. This technique is particularly useful for fast visualization of the brain cortex.

Reconstruction of central cortical surface from brain MRI images: method and application

Reconstruction of the central surface representation of the cerebral cortex is an important means to study the structure and function of the human brain. In this paper, we propose a novel method based on an elastic transform vector field to drive a deformable model for the reconstruction of the central cortical surface. Both simulated brain cortexes and real brain images are used to evaluate this approach. We applied the surface reconstruction method and a hybrid volumetric and surface registration algorithm to detect simulated brain atrophy. Experimental results show that the central cortical surface representation has better performance in detecting simulated atrophy than the traditionally used inner or outer cortical surface representations.

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.

Topology correction of segmented medical images using a fast marching algorithm

We present here a new method for correcting the topology of objects segmented from medical images. Whereas previous techniques alter a surface obtained from a binary segmentation of the object, our technique can be applied directly to the image intensities of a probabilistic or fuzzy segmentation, thereby propagating the topology for all isosurfaces of the object. From an analysis of topological changes and critical points in implicit surfaces, we derive a topology propagation algorithm that enforces any desired topology using a fast marching technique. The method has been applied successfully to the correction of the cortical gray matter/white matter interface in segmented brain images and is publicly released as a software plug-in for the MIPAV package.

Dynamic mapping of cortical development before and after the onset of pediatric bipolar illness

BACKGROUND

There are, to date, no pre-post onset longitudinal imaging studies of bipolar disorder at any age. We report the first prospective study of cortical brain development in pediatric bipolar illness for 9 male children, visualized before and after illness onset.

METHOD

We contrast this pattern with that observed in a matched group of healthy children as well as in a matched group of 8 children with 'atypical psychosis' who had similar initial presentation marked by mood dysregulation and transient psychosis (labeled as 'multi-dimensionally impaired' (MDI)) as in the bipolar group, but have not, to date, developed bipolar illness.

RESULTS

Dynamic maps, reconstructed by applying novel cortical pattern matching algorithms, for the children who became bipolar I showed subtle, regionally specific, bilaterally asymmetrical cortical changes. Cortical GM increased over the left temporal cortex and decreased bilaterally in the anterior (and sub genual) cingulate cortex. This was seen most strikingly after the illness onset, and showed a pattern distinct from that seen in childhood onset schizophrenia. The bipolar neurodevelopmental trajectory was generally shared by the children who remained with MDI diagnosis without converting to bipolar I, suggesting that this pattern of cortical development may reflect affective dysregulation (lability) in general.

CONCLUSIONS

These dynamic trajectories of cortical development may explain age-related disparate findings from cross-sectional studies of bipolar illness, and suggest the importance of mood disordered non-bipolar control group in future studies.

Automatic segmentation and reconstruction of the cortex from neonatal MRI

Segmentation and reconstruction of cortical surfaces from magnetic resonance (MR) images are more challenging for developing neonates than adults. This is mainly due to the dynamic changes in the contrast between gray matter (GM) and white matter (WM) in both T1- and T2-weighted images (T1w and T2w) during brain maturation. In particular in neonatal T2w images WM typically has higher signal intensity than GM. This causes mislabeled voxels during cortical segmentation, especially in the cortical regions of the brain and in particular at the interface between GM and cerebrospinal fluid (CSF). We propose an automatic segmentation algorithm detecting these mislabeled voxels and correcting errors caused by partial volume effects. Our results show that the proposed algorithm corrects errors in the segmentation of both GM and WM compared to the classic expectation maximization (EM) scheme. Quantitative validation against manual segmentation demonstrates good performance (the mean Dice value: 0.758+/-0.037 for GM and 0.794+/-0.078 for WM). The inner, central and outer cortical surfaces are then reconstructed using implicit surface evolution. A landmark study is performed to verify the accuracy of the reconstructed cortex (the mean surface reconstruction error: 0.73 mm for inner surface and 0.63 mm for the outer). Both segmentation and reconstruction have been tested on 25 neonates with the gestational ages ranging from approximately 27 to 45 weeks. This preliminary analysis confirms previous findings that cortical surface area and curvature increase with age, and that surface area scales to cerebral volume according to a power law, while cortical thickness is not related to age or brain growth.

Topology repair of solid models using skeletons

We present a method for repairing topological errors on solid models in the form of small surface handles, which often arise from surface reconstruction algorithms. We utilize a skeleton representation that offers a new mechanism for identifying and measuring handles. Our method presents two unique advantages over previous approaches. First, handle removal is guaranteed not to introduce invalid geometry or additional handles. Second, by using an adaptive grid structure, our method is capable of processing huge models efficiently at high resolutions.

Topology preserving tissue classification with fast marching and topology templates

This paper presents a novel approach for object segmentation in medical images that respects the topological relationships of multiple structures as given by a template. The algorithm combines advantages of tissue classification, digital topology, and level-set evolution into a topology-invariant multiple-object fast marching method. The technique can handle any given topology and enforces object-level relationships with little constraint over the geometry. Applied to brain segmentation, it sucessfully extracts gray matter and white matter structures with the correct spherical topology without topology correction or editing of the subcortical structures.

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.

Topology-preserving tissue classification of magnetic resonance brain images

This paper presents a new framework for multiple object segmentation in medical images that respects the topological properties and relationships of structures as given by a template. The technique, known as topology-preserving, anatomy-driven segmentation (TOADS), combines advantages of statistical tissue classification, topology-preserving fast marching methods, and image registration to enforce object-level relationships with little constraint over the geometry. When applied to the problem of brain segmentation, it directly provides a cortical surface with spherical topology while segmenting the main cerebral structures. Validation on simulated and real images characterises the performance of the algorithm with regard to noise, inhomogeneities, and anatomical variations.

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.

Automated Image Analysis of Fluorescence Microscopic Images to Identify Protein-protein Interactions

The identification of protein-protein interactions along with their spatial and temporal localization is vital data for assigning functional information to proteins. Historically, these data sets obtained from fluorescence microscopy, have been analyzed manually, a process that is both time consuming and tedious. The development of an automated system that can measure the location dynamics of the interaction between two proteins inside a live cell is a high priority. This paper describes an automated image analysis system used to identify the interactions between two proteins of interest fused to either GFP or DIV IVA, a bacterial cell division protein that localizes to the cell poles [1]. Upon the induction of DIV IVA fusion protein expression, the GFP-fusion protein will be recruited to the cell poles if a positive interaction occurs. Advanced image processing and feature extraction algorithms are discussed in detail and a statistical feature set used to quantify the image-based information is developed.

Inter-rater reliability of manual segmentation of the superior, inferior and middle frontal gyri

Precise rules for locating the anatomical boundaries of the dorsolateral prefrontal cortex (DLPFC) or its subdivisions, i.e., superior, inferior and middle frontal gyri (SFG, IFG and MFG) on magnetic resonance images (MRI), have not been defined. The present study describes the inter-rater reliability of manual segmentation of the SFG, IFG and MFG using guidelines based on sulcal-gyral anatomical boundaries as well as the cytoarchitectonic features of the sub-regions of the prefrontal cortex (PFC). Variations in the application of these guidelines in different subjects to account for normal sulcal variability were developed using the atlas of Ono et al. (Ono, M., Kubik, S., Abernathey, C.D., 1990. Atlas of the Cerebral Sulci. Georg Thieme Verlag, New York). Based on previous cytoarchitectonic studies, the coronal plane of the anterior termination of olfactory sulcus (ATOS) was used as a landmark for delimiting the boundary between the frontal pole (FP) and the frontal gyri. The left hemisphere gray-matter volumes of the SFG, IFG and MFG were determined using a set of 10 MRIs (5 normal and 5 schizophrenia subjects) by two trained raters independently. The intra-class correlation coefficients (ICC) for the SFG, IFG and MFG volumes by the two raters were 0.97, 0.94 and 0.93, respectively. Thus, we describe a reliable method of parcellating the SFG, IFG and MFG, which constitute the DLPFC, a brain region involved in a variety of neuropsychiatric conditions.

Topology correction using fast marching methods and its application to brain segmentation

We present here a new method for correcting the topology of objects segmented from medical images. Whereas previous techniques alter a surface obtained from the hard segmentation of the object, our technique works directly in the image domain, propagating the topology for all isosurfaces of the object. From an analysis of topological changes and critical points in implicit surfaces, we introduce a topology progagation algorithm that enforces any desired topology using a fast marching technique. Compared to previous topology correction techniques, the method successfully corrects topology while effecting fewer changes to the original volume.

Estimating linear cortical magnification in human primary visual cortex via dynamic programming

Human primary visual cortex is organized retinotopically, with adjacent locations in cortex representing adjacent locations on the retina. The spatial sampling in cortex is highly nonuniform: the amount of cortex devoted to a unit area of retina decreases with increasing retinal eccentricity. This sampling property can be quantified by the linear cortical magnification factor, which is expressed in terms of millimeters of cortex per degree of visual angle. In this paper, we present a new method using dynamic programming and fMRI retinotopic eccentricity mapping to estimate the linear cortical magnification factor in human primary visual cortex (V1). We localized cortical activity while subjects viewed each of seven stationary contrast- reversing radial checkerboard rings of equal thickness that tiled the visual field from 1.62 to 12.96 degrees of eccentricity. Imaging data from all epochs of each ring were contrasted with data from fixation epochs on a subject-by-subject basis. The resulting t statistic maps were then superimposed on a local coordinate system constructed from the gray/white matter boundary surface of each individual subject's occipital lobe, separately for each ring. Smoothed maps of functional activity on the cortical surface were constructed using orthonormal bases of the Laplace-Beltrami operator that incorporate the geometry of the cortical surface. This allowed us to stably track the ridge of maximum activation due to each ring via dynamic programming optimization over all possible paths on the cortical surface. We estimated the linear cortical magnification factor by calculating geodesic distances between activation ridges on the cortical surface in a population of five normal subjects. The reliability of these estimates was assessed by comparing results based on data from one quadrant to those based on data from the full hemifield along with a split-half reliability analysis.