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

The effect of preterm birth on thalamic development based on shape and structural covariance analysis

Acting as a central hub in regulating brain functions, the thalamus plays a pivotal role in controlling high-order brain functions. Considering the impact of preterm birth on infant brain development, traditional studies focused on the overall development of thalamus other than its subregions. In this study, we compared the volumetric growth and shape development of the thalamic hemispheres between the infants born preterm and full-term (Left volume: P = 0.027, Left normalized volume: P < 0.0001; Right volume: P = 0.070, Right normalized volume: P < 0.0001). The ventral nucleus region, dorsomedial nucleus region, and posterior nucleus region of the thalamus exhibit higher vulnerability to alterations induced by preterm birth. The structural covariance (SC) between the thickness of thalamus and insula in preterm infants (Left: corrected P = 0.0091, Right: corrected P = 0.0119) showed significant increase as compared to full-term controls. Current findings suggest that preterm birth affects the development of the thalamus and has differential effects on its subregions. The ventral nucleus region, dorsomedial nucleus region, and posterior nucleus region of the thalamus are more susceptible to the impacts of preterm birth.

Preterm Birth Alters the Regional Development and Structural Covariance of Cerebellum at Term-Equivalent Age

Preterm birth is associated with increased risk for a spectrum of neurodevelopmental disabilities. The cerebellum is implicated in a wide range of cognitive functions extending beyond sensorimotor control and plays an increasingly recognized role in brain development. Morphometric studies based on volume analyses have revealed impaired cerebellar development in preterm infants. However, the structural covariance between the cerebellum and cerebral cortex has not been studied during the neonatal period, and the extent to which structural covariance is affected by preterm birth remains unknown. In this study, using the structural MR images of 52 preterm infants scanned at term-equivalent age and 312 full-term controls from the Developing Human Connectome Project, we compared volumetric growth, local cerebellum shape development and cerebello-cerebral structural covariance between the two groups. We found that although there was no significant difference in the overall volume measurements between preterm and full-term infants, the shape measurements were different. Compared with the control infants, preterm infants had significantly larger thickness in the vermis and lower thickness in the lateral portions of the bilateral cerebral hemispheres. The structural covariance between the cerebellum and frontal and parietal lobes was significantly greater in preterm infants than in full-term controls. The findings in this study suggested that cerebellar development and cerebello-cerebral structural covariance may be affected by premature birth.

Morphological development of the human fetal striatum during the second trimester

The morphological development of the fetal striatum during the second trimester has remained poorly described. We manually segmented the striatum using 7.0-T MR images of the fetal specimens ranging from 14 to 22 gestational weeks. The global development of the striatum was evaluated by volume measurement. The absolute volume (Vabs) of the caudate nucleus (CN) increased linearly with gestational age, while the relative volume (Vrel) showed a quadratic growth. Both Vabs and Vrel of putamen increased linearly. Through shape analysis, the changes of local structure in developing striatum were specifically demonstrated. Except for the CN tail, the lateral and medial parts of the CN grew faster than the middle regions, with a clear rostral-caudal growth gradient as well as a distinct "outside-in" growth gradient. For putamen, the dorsal and ventral regions grew obviously faster than the other regions, with a dorsal-ventral bidirectional developmental pattern. The right CN was larger than the left, whereas there was no significant hemispheric asymmetry in the putamen. By establishing the developmental trajectories, spatial heterochrony, and hemispheric dimorphism of human fetal striatum, these data bring new insight into the fetal striatum development and provide detailed anatomical references for future striatal studies.

Hippocampal Asymmetry of Regional Development and Structural Covariance in Preterm Neonates

Premature birth is associated with a high prevalence of neurodevelopmental impairments in surviving infants. The hippocampus is known to be critical for learning and memory, yet the putative effects of hippocampal dysfunction remain poorly understood in preterm neonates. In particular, while asymmetry of the hippocampus has been well noted both structurally and functionally, how preterm birth impairs hippocampal development and to what extent the hippocampus is asymmetrically impaired by preterm birth have not been well delineated. In this study, we compared volumetric growth and shape development in the hippocampal hemispheres and structural covariance (SC) between hippocampal vertices and cortical thickness in cerebral cortex regions between two groups. We found that premature infants had smaller volumes of the right hippocampi only. Lower thickness was observed in the hippocampal head in both hemispheres for preterm neonates compared with full-term peers, though preterm neonates exhibited an accelerated age-related change of hippocampal thickness in the left hippocampi. The SC between the left hippocampi and the limbic lobe of the premature infants was severely impaired compared with the term-born neonates. These findings suggested that the development of the hippocampus during the third trimester may be altered following early extrauterine exposure with a high degree of asymmetry.

Tetrahedral spectral feature-Based bayesian manifold learning for grey matter morphometry: Findings from the Alzheimer's disease neuroimaging initiative

Structural and anatomical analyses of magnetic resonance imaging (MRI) data often require a reconstruction of the three-dimensional anatomy to a statistical shape model. Our prior work demonstrated the usefulness of tetrahedral spectral features for grey matter morphometry. However, most of the current methods provide a large number of descriptive shape features, but lack an unsupervised scheme to automatically extract a concise set of features with clear biological interpretations and that also carries strong statistical power. Here we introduce a new tetrahedral spectral feature-based Bayesian manifold learning framework for effective statistical analysis of grey matter morphology. We start by solving the technical issue of generating tetrahedral meshes which preserve the details of the grey matter geometry. We then derive explicit weak-form tetrahedral discretizations of the Hamiltonian operator (HO) and the Laplace-Beltrami operator (LBO). Next, the Schrödinger's equation is solved for constructing the scale-invariant wave kernel signature (SIWKS) as the shape descriptor. By solving the heat equation and utilizing the SIWKS, we design a morphometric Gaussian process (M-GP) regression framework and an active learning strategy to select landmarks as concrete shape descriptors. We evaluate the proposed system on publicly available data from the Alzheimers Disease Neuroimaging Initiative (ADNI), using subjects structural MRI covering the range from cognitively unimpaired (CU) to full blown Alzheimer's disease (AD). Our analyses suggest that the SIWKS and M-GP compare favorably with seven other baseline algorithms to obtain grey matter morphometry-based diagnoses. Our work may inspire more tetrahedral spectral feature-based Bayesian learning research in medical image analysis.

Valproic acid-induced changes of 4D nuclear morphology in astrocyte cells

Histone deacetylase inhibitors, such as valproic acid (VPA), have important clinical therapeutic and cellular reprogramming applications. They induce chromatin reorganization that is associated with altered cellular morphology. However, there is a lack of comprehensive characterization of VPA-induced changes of nuclear size and shape. Here, we quantify 3D nuclear morphology of primary human astrocyte cells treated with VPA over time (hence, 4D). We compared volumetric and surface-based representations and identified seven features that jointly discriminate between normal and treated cells with 85% accuracy on day 7. From day 3, treated nuclei were more elongated and flattened and then continued to morphologically diverge from controls over time, becoming larger and more irregular. On day 7, most of the size and shape descriptors demonstrated significant differences between treated and untreated cells, including a 24% increase in volume and 6% reduction in extent (shape regularity) for treated nuclei. Overall, we show that 4D morphometry can capture how chromatin reorganization modulates the size and shape of the nucleus over time. These nuclear structural alterations may serve as a biomarker for histone (de-)acetylation events and provide insights into mechanisms of astrocytes-to-neurons reprogramming.

Selective morphological and volumetric alterations in the hippocampus of children exposed in utero to gestational diabetes mellitus

Prior epidemiological studies have found that in utero exposure to gestational diabetes mellitus (GDM) is associated with increased risk for neurodevelopmental disorders. However, brain alterations associated with GDM are not known. The hippocampus is pivotal for cognition and emotional regulation. Therefore, we assessed relationships between in utero exposure to GDM and hippocampal morphology and subfield structure during childhood. One hundred seventeen children aged 7–11 years (57% girls, 57% exposed to GDM), born at Kaiser Permanente Southern California, participated in the BrainChild Study. Maternal GDM status was determined from electronic medical records. Children underwent brain magnetic resonance imaging. Freesurfer 6.0 was used to measure hippocampal and individual hippocampal subfield gray matter volume (mm³). Morphological analyses on the hippocampal surface were carried out using shape analysis. GDM‐exposed children exhibited reduced radial thickness in a small, spatially‐restricted portion of the left inferior body of the hippocampus that corresponds to the CA1 subfield. There was a significant interaction between GDM‐exposure and sex on the right hippocampal CA1 subfield. GDM‐exposed boys had reduced right CA1 volume compared to unexposed boys, but this association was no longer significant after controlling for age. No significant group differences were observed in girls. Our results suggest that GDM‐exposure impacts shape of the left hippocampal CA1 subfield in both boys and girls and may reduce volume of right hippocampal CA1 only in boys. These in‐depth findings illuminate the unique properties of the hippocampus impacted by prenatal GDM‐exposure and could have important implications for hippocampal‐related functions.

The effect of body mass index on hippocampal morphology and memory performance in late childhood and adolescence

Childhood obesity is associated with negative physiological and cognitive health outcomes. The hippocampus is a diverse subcortical structure involved in learned feeding behaviors and energy regulation, and research has shown that the hippocampus is vulnerable to the effects of excess adiposity. Previous studies have demonstrated reduced hippocampal volume in overweight and obese children; however, it is unclear if certain subregions are selectively affected. The purpose of this study was to determine how excess body weight influences regional hippocampal surface morphology and memory performance in a large cross-sectional cohort of 588 children and adolescents between 8.33 and 19.92 years of age using body mass index expressed as a percentage of the 95th percentile cutoff (%BMIp95). We demonstrate %BMIp95 is associated with reduced radial thickness in the superior anterior region of the left hippocampus, and this relationship is predominantly driven by children younger than 14 years. We also found %BMIp95 was associated with worse performance on a spatial episodic memory task and this relationship was partially mediated by the radial thickness of the significant shape cluster. These results demonstrate the differential influence of excess body weight on regional hippocampal structure and hippocampal-dependent behavior in children and adolescents.

A probabilistic atlas of locus coeruleus pathways to transentorhinal cortex for connectome imaging in Alzheimer's disease

According to the latest Braak staging of Alzheimer's disease (AD), tau pathology occurs earliest in the brain in the locus coeruleus (LC) of the brainstem, then propagates to the transentorhinal cortex (TEC), and later to other neocortical regions. Recent animal and in vivo human brain imaging research also support the trans-axonal propagation of tau pathology. In addition, neurochemical studies link norepinephrine to behavioral symptoms in AD. It is thus critical to examine the integrity of the LC-TEC pathway in studying the early development of the disease, but there has been limited work in this direction. By leveraging the high-resolution and multi-shell diffusion MRI data from the Human Connectome Project (HCP), in this work we develop a novel method for the reconstruction of the LC-TEC pathway in a cohort of 40 HCP subjects carefully selected based on rigorous quality control of the residual distortion artifacts in the brainstem. A probabilistic atlas of the LC-TEC pathway of both hemispheres is then developed in the MNI152 space and distributed publicly on the NITRC website. To apply our atlas on clinical imaging data, we develop an automated approach to calculate the medial core of the LC-TEC pathway for localized analysis of connectivity changes. In a cohort of 138 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we demonstrate the detection of the decreased fiber integrity in the LC-TEC pathways with increasing disease severity.

Hippocampal Shape Maturation in Childhood and Adolescence

The hippocampus is a subcortical structure critical for learning and memory, and a thorough understanding of its neurodevelopment is important for studying these processes in health and disease. However, few studies have quantified the typical developmental trajectory of the structure in childhood and adolescence. This study examined the cross-sectional age-related changes and sex differences in hippocampal shape in a multisite, multistudy cohort of 1676 typically developing children (age 1-22 years) using a novel intrinsic brain mapping method based on Laplace-Beltrami embedding of surfaces. Significant age-related expansion was observed bilaterally and nonlinear growth was observed primarily in the right head and tail of the hippocampus. Sex differences were also observed bilaterally along the lateral and medial aspects of the surface, with females exhibiting relatively larger surface expansion than males. Additionally, the superior posterior lateral surface of the left hippocampus exhibited an age-sex interaction with females expanding faster than males. Shape analysis provides enhanced sensitivity to regional changes in hippocampal morphology over traditional volumetric approaches and allows for the localization of developmental effects. Our results further support evidence that hippocampal structures follow distinct maturational trajectories that may coincide with the development of learning and memory skills during critical periods of development.

3D Shape Modeling and Analysis of Retinal Microvasculature in OCT-Angiography Images

3D optical coherence tomography angiography (OCT-A) is a novel and non-invasive imaging modality for analyzing retinal diseases. The studies of microvasculature in 2D en face projection images have been widely implemented, but comprehensive 3D analysis of OCT-A images with rich depth-resolved microvascular information is rarely considered. In this paper, we propose a robust, effective, and automatic 3D shape modeling framework to provide a high-quality 3D vessel representation and to preserve valuable 3D geometric and topological information for vessel analysis. Effective vessel enhancement and extraction steps by means of curvelet denoising and optimally oriented flux (OOF) filtering are first designed to produce 3D microvascular networks. Afterwards, a novel 3D data representation of OCT-A microvasculature is reconstructed via advanced mesh reconstruction techniques. Based on the 3D surfaces, shape analysis is established to extract novel shape-based microvascular area distortion via the Laplace-Beltrami eigen-projection. The extracted feature is integrated into a graph-cut segmentation system to categorize large vessels and small capillaries for more precise shape analysis. The proposed framework is validated on a dedicated repeated scan dataset including 260 volume images and shows high repeatability. Statistical analysis using the surface area biomarker is performed on small capillaries to avoid the effect of tailing artifact from large vessels. It shows significant differences ( ) between DR stages on 100 subjects in a OCTA-DR dataset. The proposed shape modeling and analysis framework opens the possibility for further investigating OCT-A microvasculature in a new perspective.

Morphometric development of the human fetal cerebellum during the early second trimester

The protracted nature of development makes the cerebellum vulnerable to a broad spectrum of pathologic conditions, especially during the early fetal period. This study aims to characterize normal cerebellar growth in human fetuses during the early second trimester. We manually segmented the fetal cerebellum using 7.0-T high-resolution MR images obtained in 35 specimens with gestational ages ranging from 15 to 22 weeks. Volume measurements and shape analysis were performed to quantitatively evaluate global and regional cerebellar growth. The absolute volume of the fetal cerebellum showed a quadratic growth with increasing gestational age, while the pattern of relative volume changes revealed that the cerebellum grew at a greater pace than the cerebrum after 17 gestational weeks. Shape analysis was used to examine the distinctive development of subregions of the cerebellum. The extreme lateral portions of both cerebellar hemispheres showed the lowest rate of growth. The anterior lobe grew faster than most of the posterior lobe. These findings expand our understanding of the early growth pattern of the human cerebellum and could be further used to assess the developmental conditions of the fetal brain.

3D Surface-Based Geometric and Topological Quantification of Retinal Microvasculature in OCT-Angiography via Reeb Analysis

3D optical coherence tomography angiography (OCT-A) is a novel, non-invasive imaging modality for studying important retina-related diseases. Current works have been mainly focusing on the microvascular analysis of 2D enface OCT-A projections while direct 3D analysis using rich depth-resolved microvascular information is rarely considered. In this work, we aim to set up an innovative 3D microvascular modeling framework via Reeb analysis to explore rich geometric and topological information. We first use effective vessel extraction and surface reconstruction techniques to establish a complete 3D mesh representation of retinal OCT-A microvasculature. We propose to use geodesic distance as a feature function to build level contours with smooth transitions on mesh surface. Intrinsic Reeb graphs are thereby constructed through level contours to represent general OCT-A microvascular topology. Afterwards, specific geometric and topological analysis are performed on Reeb graphs to quantify critical microvascular characteristics. The proposed Reeb analysis framework is evaluated on a clinical DR dataset and shows great advantage in describing 3D microvascular changes. It is able to produce important surface-based microvascular biomarkers with high statistical power for disease studies.

Characterization of lenticulostriate arteries with high resolution black-blood T1-weighted turbo spin echo with variable flip angles at 3 and 7 Tesla

OBJECTIVES

The lenticulostriate arteries (LSAs) with small diameters of a few hundred microns take origin directly from the high flow middle cerebral artery (MCA), making them especially susceptible to damage (e.g. by hypertension). This study aims to present high resolution (isotropic ∼0.5 mm), black blood MRI for the visualization and characterization of LSAs at both 3 T and 7 T.

MATERIALS AND METHODS

T1-weighted 3D turbo spin-echo with variable flip angles (T1w TSE-VFA) sequences were optimized for the visualization of LSAs by performing extended phase graph (EPG) simulations. Twenty healthy volunteers (15 under 35 years old, 5 over 60 years old) were imaged with the T1w TSE-VFA sequences at both 3 T and 7 T. Contrast-to-noise ratio (CNR) was quantified, and LSAs were manually segmented using ITK-SNAP. Automated Reeb graph shape analysis was performed to extract features including vessel length and tortuosity. All quantitative metrics were compared between the two field strengths and two age groups using ANOVA.

RESULTS

LSAs can be clearly delineated using optimized 3D T1w TSE-VFA at 3 T and 7 T, and a greater number of LSA branches can be detected compared to those by time-of-flight MR angiography (TOF MRA) at 7 T. The CNR of LSAs was comparable between 7 T and 3 T. T1w TSE-VFA showed significantly higher CNR than TOF MRA at the stem portion of the LSAs branching off the medial middle cerebral artery. The mean vessel length and tortuosity were greater on TOF MRA compared to TSE-VFA. The number of detected LSAs by both TSE-VFA and TOF MRA was significantly reduced in aged subjects, while the mean vessel length measured on 7 T TSE-VFA showed significant difference between the two age groups.

CONCLUSION

The high-resolution black-blood 3D T1w TSE-VFA sequence offers a new method for the visualization and quantification of LSAs at both 3 T and 7 T, which may be applied for a number of pathological conditions related to the damage of LSAs.

3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification

Quantitative analysis of morphological changes in a cell nucleus is important for the understanding of nuclear architecture and its relationship with pathological conditions such as cancer. However, dimensionality of imaging data, together with a great variability of nuclear shapes, presents challenges for 3D morphological analysis. Thus, there is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wide analysis. We propose a new approach that combines modeling, analysis, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. We used robust surface reconstruction that allows accurate approximation of 3D object boundary. Then, we computed geometric morphological measures characterizing the form of cell nuclei and nucleoli. Using these features, we compared over 450 nuclei with about 1,000 nucleoli of epithelial and mesenchymal prostate cancer cells, as well as 1,000 nuclei with over 2,000 nucleoli from serum-starved and proliferating fibroblast cells. Classification of sets of 9 and 15 cells achieved accuracy of 95.4% and 98%, respectively, for prostate cancer cells, and 95% and 98% for fibroblast cells. To our knowledge, this is the first attempt to combine these methods for 3D nuclear shape modeling and morphometry into a highly parallel pipeline workflow for morphometric analysis of thousands of nuclei and nucleoli in 3D.

Holistic Mapping of Striatum Surfaces in the Laplace-Beltrami Embedding Space

In brain shape analysis, the striatum is typically divided into three parts: the caudate, putamen, and accumbens nuclei for its analysis. Recent connectivity and animal studies, however, indicate striatum-cortical inter-connections do not always follow such subdivisions. For the holistic mapping of striatum surfaces, conventional spherical registration techniques are not suitable due to the large metric distortions in spherical parameterization of striatal surfaces. To overcome this difficulty, we develop a novel striatal surface mapping method using the recently proposed Riemannian metric optimization techniques in the Laplace-Beltrami (LB) embedding space. For the robust resolution of sign ambiguities in the LB spectrum, we also devise novel anatomical contextual features to guide the surface mapping in the embedding space. In our experimental results, we compare with spherical registration tools from FreeSurfer and FSL to demonstrate that our novel method provides a superior solution to the striatal mapping problem. We also apply our method to map the striatal surfaces from 211 subjects of the Human Connectome Project (HCP), and use the surface maps to construct a cortical connectivity atlas. Our atlas results show that the striato-cortical connectivity is not distinctive according to traditional structural subdivision of the striatum, and further confirms the holistic approach for mapping striatal surfaces.

Towards a Holistic Cortical Thickness Descriptor: Heat Kernel-Based Grey Matter Morphology Signatures

In this paper, we propose a heat kernel based regional shape descriptor that may be capable of better exploiting volumetric morphological information than other available methods, thereby improving statistical power on brain magnetic resonance imaging (MRI) analysis. The mechanism of our analysis is driven by the graph spectrum and the heat kernel theory, to capture the volumetric geometry information in the constructed tetrahedral meshes. In order to capture profound brain grey matter shape changes, we first use the volumetric Laplace-Beltrami operator to determine the point pair correspondence between white-grey matter and CSF-grey matter boundary surfaces by computing the streamlines in a tetrahedral mesh. Secondly, we propose multi-scale grey matter morphology signatures to describe the transition probability by random walk between the point pairs, which reflects the inherent geometric characteristics. Thirdly, a point distribution model is applied to reduce the dimensionality of the grey matter morphology signatures and generate the internal structure features. With the sparse linear discriminant analysis, we select a concise morphology feature set with improved classification accuracies. In our experiments, the proposed work outperformed the cortical thickness features computed by FreeSurfer software in the classification of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment, on publicly available data from the Alzheimer's Disease Neuroimaging Initiative. The multi-scale and physics based volumetric structure feature may bring stronger statistical power than some traditional methods for MRI-based grey matter morphology analysis.

Conformal invariants for multiply connected surfaces: Application to landmark curve-based brain morphometry analysis

Landmark curves were widely adopted in neuroimaging research for surface correspondence computation and quantified morphometry analysis. However, most of the landmark based morphometry studies only focused on landmark curve shape difference. Here we propose to compute a set of conformal invariant-based shape indices, which are associated with the landmark curve induced boundary lengths in the hyperbolic parameter domain. Such shape indices may be used to identify which surfaces are conformally equivalent and further quantitatively measure surface deformation. With the surface Ricci flow method, we can conformally map a multiply connected surface to the Poincaré disk. Our algorithm provides a stable method to compute the shape index values in the 2D (Poincaré Disk) parameter domain. The proposed shape indices are succinct, intrinsic and informative. Experimental results with synthetic data and 3D MRI data demonstrate that our method is invariant under isometric transformations and able to detect brain surface abnormalities. We also applied the new shape indices to analyze brain morphometry abnormalities associated with Alzheimer' s disease (AD). We studied the baseline MRI scans of a set of healthy control and AD patients from the Alzheimer' s Disease Neuroimaging Initiative (ADNI: 30 healthy control subjects vs. 30 AD patients). Although the lengths of the landmarks in Euclidean space, cortical surface area, and volume features did not differ between the two groups, our conformal invariant based shape indices revealed significant differences by Hotelling' s T² test. The novel conformal invariant shape indices may offer a new sensitive biomarker and enrich our brain imaging analysis toolset for studying diagnosis and prognosis of AD.

Impact of Early and Late Visual Deprivation on the Structure of the Corpus Callosum: A Study Combining Thickness Profile with Surface Tensor-Based Morphometry

Blindness represents a unique model to study how visual experience may shape the development of brain organization. Exploring how the structure of the corpus callosum (CC) reorganizes ensuing visual deprivation is of particular interest due to its important functional implication in vision (e.g., via the splenium of the CC). Moreover, comparing early versus late visually deprived individuals has the potential to unravel the existence of a sensitive period for reshaping the CC structure. Here, we develop a novel framework to capture a complete set of shape differences in the CC between congenitally blind (CB), late blind (LB) and sighted control (SC) groups. The CCs were manually segmented from T1-weighted brain MRI and modeled by 3D tetrahedral meshes. We statistically compared the combination of local area and thickness at each point between subject groups. Differences in area are found using surface tensor-based morphometry; thickness is estimated by tracing the streamlines in the volumetric harmonic field. Group differences were assessed on this combined measure using Hotelling's T(2) test. Interestingly, we observed that the total callosal volume did not differ between the groups. However, our fine-grained analysis reveals significant differences mostly localized around the splenium areas between both blind groups and the sighted group (general effects of blindness) and, importantly, specific dissimilarities between the LB and CB groups, illustrating the existence of a sensitive period for reorganization. The new multivariate statistics also gave better effect sizes for detecting morphometric differences, relative to other statistics. They may boost statistical power for CC morphometric analyses.

Feasibility of Structural and Functional MRI Acquisition with Unpowered Implants in Argus II Retinal Prosthesis Patients: A Case Study

PURPOSE

Magnetic resonance imaging (MRI) can measure the effects of vision loss and recovery on brain function and structure. In this case study, we sought to determine the feasibility of acquiring anatomical and functional MRI data in recipients of the Argus II epiretinal prosthesis system.

METHODS

Following successful implantation with the Argus II device, two retinitis pigmentosa (RP) patients completed MRI scans with their implant unpowered to measure primary visual cortex (V1) functional responses to a tactile task, whole-brain morphometry, V1 cortical thickness, and diffusion properties of the optic tract and optic radiation. Measurements in the subjects with the Argus II implant were compared to measurements obtained previously from RP patients and sighted individuals.

RESULTS

The presence of the Argus II implant resulted in artifacts that were localized around the patient's implanted eye and did not extend into cortical regions or white matter tracts associated with the visual system. Structural data on V1 cortical thickness and the retinofugal tract obtained from the two Argus II subjects fell within the ranges of sighted and RP groups. When compared to the RP and sighted subjects, Argus II patients' tactile-evoked cross-modal functional MRI (fMRI) blood oxygen level-dependent (BOLD) responses in V1 also fell within the range of either sighted or RP groups, apparently depending on time since implantation.

CONCLUSIONS

This study demonstrates that successful acquisition and quantification of structural and functional MR images are feasible in the presence of the inactive implant and provides preliminary information on functional changes in the brain that may follow sight restoration treatments.

TRANSITIONAL RELEVANCE

Successful MRI and fMRI acquisition in Argus II recipients demonstrates feasibility of using MRI to study the effect of retinal prosthesis use on brain structure and function.

Automated retinofugal visual pathway reconstruction with multi-shell HARDI and FOD-based analysis

Diffusion MRI tractography provides a non-invasive modality to examine the human retinofugal projection, which consists of the optic nerves, optic chiasm, optic tracts, the lateral geniculate nuclei (LGN) and the optic radiations. However, the pathway has several anatomic features that make it particularly challenging to study with tractography, including its location near blood vessels and bone-air interface at the base of the cerebrum, crossing fibers at the chiasm, somewhat-tortuous course around the temporal horn via Meyer's Loop, and multiple closely neighboring fiber bundles. To date, these unique complexities of the visual pathway have impeded the development of a robust and automated reconstruction method using tractography. To overcome these challenges, we develop a novel, fully automated system to reconstruct the retinofugal visual pathway from high-resolution diffusion imaging data. Using multi-shell, high angular resolution diffusion imaging (HARDI) data, we reconstruct precise fiber orientation distributions (FODs) with high order spherical harmonics (SPHARM) to resolve fiber crossings, which allows the tractography algorithm to successfully navigate the complicated anatomy surrounding the retinofugal pathway. We also develop automated algorithms for the identification of ROIs used for fiber bundle reconstruction. In particular, we develop a novel approach to extract the LGN region of interest (ROI) based on intrinsic shape analysis of a fiber bundle computed from a seed region at the optic chiasm to a target at the primary visual cortex. By combining automatically identified ROIs and FOD-based tractography, we obtain a fully automated system to compute the main components of the retinofugal pathway, including the optic tract and the optic radiation. We apply our method to the multi-shell HARDI data of 215 subjects from the Human Connectome Project (HCP). Through comparisons with post-mortem dissection measurements, we demonstrate the retinotopic organization of the optic radiation including a successful reconstruction of Meyer's loop. Then, using the reconstructed optic radiation bundle from the HCP cohort, we construct a probabilistic atlas and demonstrate its consistency with a post-mortem atlas. Finally, we generate a shape-based representation of the optic radiation for morphometry analysis.

Development of the human fetal hippocampal formation during early second trimester

Development of the fetal hippocampal formation has been difficult to fully describe because of rapid changes in its shape during the fetal period. The aims of this study were to: (1) segment the fetal hippocampal formation using 7.0 T MR images from 41 specimens with gestational ages ranging from 14 to 22 weeks and (2) reveal the developmental course of the fetal hippocampal formation using volume and shape analyses. Differences in hemispheric volume were observed, with the right hippocampi being larger than the left. Absolute volume changes showed a linear increase, while relative volume changes demonstrated an inverted-U shape trend during this period. Together these exhibited a variable developmental rate among different regions of the fetal brain. Different sub-regional growth of the fetal hippocampal formation was specifically observed using shape analysis. The fetal hippocampal formation possessed a prominent medial-lateral bidirectional shape growth pattern during its rotation process. Our results provide additional insight into 3D hippocampal morphology in the assessment of fetal brain development and can be used as a reference for future hippocampal studies.

Hippocampal Shape Modeling Based on a Progressive Template Surface Deformation and its Verification

Accurately recovering the hippocampal shapes against rough and noisy segmentations is as challenging as achieving good anatomical correspondence between the individual shapes. To address these issues, we propose a mesh-to-volume registration approach, characterized by a progressive model deformation. Our model implements flexible weighting scheme for model rigidity under a multi-level neighborhood for vertex connectivity. This method induces a large-to-small scale deformation of a template surface to build the pairwise correspondence by minimizing geometric distortion while robustly restoring the individuals' shape characteristics. We evaluated the proposed method's (1) accuracy and robustness in smooth surface reconstruction, (2) sensitivity in detecting significant shape differences between healthy control and disease groups (mild cognitive impairment and Alzheimer's disease), (3) robustness in constructing the anatomical correspondence between individual shape models, and (4) applicability in identifying subtle shape changes in relation to cognitive abilities in a healthy population. We compared the performance of the proposed method with other well-known methods--SPHARM-PDM, ShapeWorks and LDDMM volume registration with template injection--using various metrics of shape similarity, surface roughness, volume, and shape deformity. The experimental results showed that the proposed method generated smooth surfaces with less volume differences and better shape similarity to input volumes than others. The statistical analyses with clinical variables also showed that it was sensitive in detecting subtle shape changes of hippocampus.

A novel cortical thickness estimation method based on volumetric Laplace-Beltrami operator and heat kernel

Cortical thickness estimation in magnetic resonance imaging (MRI) is an important technique for research on brain development and neurodegenerative diseases. This paper presents a heat kernel based cortical thickness estimation algorithm, which is driven by the graph spectrum and the heat kernel theory, to capture the gray matter geometry information from the in vivo brain magnetic resonance (MR) images. First, we construct a tetrahedral mesh that matches the MR images and reflects the inherent geometric characteristics. Second, the harmonic field is computed by the volumetric Laplace-Beltrami operator and the direction of the steamline is obtained by tracing the maximum heat transfer probability based on the heat kernel diffusion. Thereby we can calculate the cortical thickness information between the point on the pial and white matter surfaces. The new method relies on intrinsic brain geometry structure and the computation is robust and accurate. To validate our algorithm, we apply it to study the thickness differences associated with Alzheimer's disease (AD) and mild cognitive impairment (MCI) on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our preliminary experimental results on 151 subjects (51 AD, 45 MCI, 55 controls) show that the new algorithm may successfully detect statistically significant difference among patients of AD, MCI and healthy control subjects. Our computational framework is efficient and very general. It has the potential to be used for thickness estimation on any biological structures with clearly defined inner and outer surfaces.

A Compact Shape Descriptor for Triangular Surface Meshes

Three-dimensional shape-based descriptors have been widely used in object recognition and database retrieval. In the current work, we present a novel method called compact Shape-DNA (cShape-DNA) to describe the shape of a triangular surface mesh. While the original Shape-DNA technique provides an effective and isometric-invariant descriptor for surface shapes, the number of eigenvalues used is typically large. To further reduce the space and time consumptions, especially for large-scale database applications, it is of great interest to find a more compact way to describe an arbitrary surface shape. In the present approach, the standard Shape-DNA is first computed from the given mesh and then processed by surface area-based normalization and line subtraction. The proposed cShape-DNA descriptor is composed of some low frequencies of the discrete Fourier transform of the processed Shape-DNA. Several experiments are shown to illustrate the effectiveness and efficiency of the cShape-DNA method on 3D shape analysis, particularly on shape comparison and classification.

Metric optimization for surface analysis in the Laplace-Beltrami embedding space

In this paper, we present a novel approach for the intrinsic mapping of anatomical surfaces and its application in brain mapping research. Using the Laplace-Beltrami eigen-system, we represent each surface with an isometry invariant embedding in a high dimensional space. The key idea in our system is that we realize surface deformation in the embedding space via the iterative optimization of a conformal metric without explicitly perturbing the surface or its embedding. By minimizing a distance measure in the embedding space with metric optimization, our method generates a conformal map directly between surfaces with highly uniform metric distortion and the ability of aligning salient geometric features. Besides pairwise surface maps, we also extend the metric optimization approach for group-wise atlas construction and multi-atlas cortical label fusion. In experimental results, we demonstrate the robustness and generality of our method by applying it to map both cortical and hippocampal surfaces in population studies. For cortical labeling, our method achieves excellent performance in a cross-validation experiment with 40 manually labeled surfaces, and successfully models localized brain development in a pediatric study of 80 subjects. For hippocampal mapping, our method produces much more significant results than two popular tools on a multiple sclerosis study of 109 subjects.

Conformal mapping via metric optimization with application for cortical label fusion

In this paper we develop a novel approach for computing conformal maps between anatomical surfaces with the ability of aligning anatomical features and achieving greatly reduced metric distortion. In contrast to conventional approaches that focused on conformal maps to the sphere or plane, our method computes the conformal map between surfaces in the embedding space formed the intrinsically defined Laplace-Beltrami (LB) eigenfunctions. Utilizing the power of LB eigenfunctions as informative descriptors of global geometry, the conformal maps computed by our method can effectively align anatomical features on cortical surfaces. By computing such feature-aware conformal maps to a group-wisely optimal atlas surface, which is also computed with metric optimization in the LB embedding space, we develop a fully automated system for cortical labeling with the fusion of labels on a large number of atlas surfaces. In our experiments, we build our system with 40 labeled surfaces and demonstrate its excellent performance with leave-one-out cross validation. We also applied the automated labeling system to cortical surfaces reconstructed from MR scans of 50 patients with Alzheimer's disease (AD) and 50 normal controls (NC) to illustrate its robustness and effectiveness in clinical data analysis.

Shape abnormalities of subcortical and ventricular structures in mild cognitive impairment and Alzheimer's disease: detecting, quantifying, and predicting

This article assesses the feasibility of using shape information to detect and quantify the subcortical and ventricular structural changes in mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients. We first demonstrate structural shape abnormalities in MCI and AD as compared with healthy controls (HC). Exploring the development to AD, we then divide the MCI participants into two subgroups based on longitudinal clinical information: (1) MCI patients who remained stable; (2) MCI patients who converted to AD over time. We focus on seven structures (amygdala, hippocampus, thalamus, caudate, putamen, globus pallidus, and lateral ventricles) in 754 MR scans (210 HC, 369 MCI of which 151 converted to AD over time, and 175 AD). The hippocampus and amygdala were further subsegmented based on high field 0.8 mm isotropic 7.0T scans for finer exploration. For MCI and AD, prominent ventricular expansions were detected and we found that these patients had strongest hippocampal atrophy occurring at CA1 and strongest amygdala atrophy at the basolateral complex. Mild atrophy in basal ganglia structures was also detected in MCI and AD. Stronger atrophy in the amygdala and hippocampus, and greater expansion in ventricles was observed in MCI converters, relative to those MCI who remained stable. Furthermore, we performed principal component analysis on a linear shape space of each structure. A subsequent linear discriminant analysis on the principal component values of hippocampus, amygdala, and ventricle leads to correct classification of 88% HC subjects and 86% AD subjects.

Cortical surface reconstruction via unified Reeb analysis of geometric and topological outliers in magnetic resonance images

In this paper we present a novel system for the automated reconstruction of cortical surfaces from T1-weighted magnetic resonance images. At the core of our system is a unified Reeb analysis framework for the detection and removal of geometric and topological outliers on tissue boundaries. Using intrinsic Reeb analysis, our system can pinpoint the location of spurious branches and topological outliers, and correct them with localized filtering using information from both image intensity distributions and geometric regularity. In this system, we have also developed enhanced tissue classification with Hessian features for improved robustness to image inhomogeneity, and adaptive interpolation to achieve sub-voxel accuracy in reconstructed surfaces. By integrating these novel developments, we have a system that can automatically reconstruct cortical surfaces with improved quality and dramatically reduced computational cost as compared with the popular FreeSurfer software. In our experiments, we demonstrate on 40 simulated MR images and the MR images of 200 subjects from two databases: the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and International Consortium of Brain Mapping (ICBM), the robustness of our method in large scale studies. In comparisons with FreeSurfer, we show that our system is able to generate surfaces that better represent cortical anatomy and produce thickness features with higher statistical power in population studies.

Unified geometry and topology correction for cortical surface reconstruction with intrinsic reeb analysis

A key challenge in the accurate reconstruction of cortical surfaces is the automated correction of geometric and topological outliers in tissue boundaries. Conventionally these two types of errors are handled separately. In this work, we propose a unified analysis framework for the joint correction of geometric and topological outliers in cortical reconstruction. Using the Reeb graph of intrinsically defined Laplace-Beltrami eigenfunctions, our method automatically locates spurious branches, handles and holes on tissue boundaries and corrects them with image information and geometric regularity derived from paired boundary evolutions. In our experiments, we demonstrate on 200 MR images from two datasets that our method is much faster and achieves better performance than FreeSurfer in population studies.

The clinical value of large neuroimaging data sets in Alzheimer's disease

Rapid advances in neuroimaging and cyberinfrastructure technologies have brought explosive growth in the Web-based warehousing, availability, and accessibility of imaging data on a variety of neurodegenerative and neuropsychiatric disorders and conditions. There has been a prolific development and emergence of complex computational infrastructures that serve as repositories of databases and provide critical functionalities such as sophisticated image analysis algorithm pipelines and powerful three-dimensional visualization and statistical tools. The statistical and operational advantages of collaborative, distributed team science in the form of multisite consortia push this approach in a diverse range of population-based investigations.

CoRPORATE: cortical reconstruction by pruning outliers with Reeb analysis and topology-preserving evolution

In this paper we propose a novel system for the accurate reconstruction of cortical surfaces from magnetic resonance images. At the core of our system is a novel framework for outlier detection and pruning by integrating intrinsic Reeb analysis of Laplace-Beltrami eigen-functions with topology-preserving evolution for localized filtering of outliers, which avoids unnecessary smoothing and shrinkage of cortical regions with high curvature. In our experiments, we compare our method with FreeSurfer and illustrate that our results can better capture cortical geometry in deep sulcal regions. To demonstrate the robustness of our method, we apply it to over 1300 scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We show that cross-sectional group differences and longitudinal changes can be detected successfully with our method.