Macrosight: A Novel Framework to Analyze the Shape and Movement of Interacting Macrophages Using Matlab®

This paper presents a novel software framework, called macrosight, which incorporates routines to detect, track, and analyze the shape and movement of objects, with special emphasis on macrophages. The key feature presented in macrosight consists of an algorithm to assess the changes of direction derived from cell–cell contact, where an interaction is assumed to occur. The main biological motivation is the determination of certain cell interactions influencing cell migration. Thus, the main objective of this work is to provide insights into the notion that interactions between cell structures cause a change in orientation. Macrosight analyzes the change of direction of cells before and after they come in contact with another cell. Interactions are determined when the cells overlap and form clumps of two or more cells. The framework integrates a segmentation technique capable of detecting overlapping cells and a tracking framework into a tool for the analysis of the trajectories of cells before and after they overlap. Preliminary results show promise into the analysis and the hypothesis proposed, and lays the groundwork for further developments. The extensive experimentation and data analysis show, with statistical significance, that under certain conditions, the movement changes before and after an interaction are different from movement in controlled cases.

Landmark Typology in Applied Morphometrics Studies: What's the Point?

Landmarks are the hallmark of biological shape analysis as discrete anatomical points of correspondence. Various systems have been developed for their classification. In the most widely used system, developed by Bookstein in the 1990s, landmarks are divided into three distinct types based on their anatomical locations and biological significance. As Bookstein and others have argued that different landmark types possess different qualities, e.g., that Type 3 landmarks contain deficient information about shape variation and are less reliably measured, researchers began using landmark types as justification for selecting or avoiding particular landmarks for measurement or analysis. Here, we demonstrate considerable variation in landmark classifications among 17 studies using geometric morphometrics (GM), due to disagreement in the application of both Bookstein's landmark typology and individual landmark definitions. A review of the literature furthermore shows little correlation between landmark type and measurement reproducibility, especially when factors such as differences in measurement tools (calipers, digitizer, or computer software) and data sources (dry crania, 3D models, or 2D images) are considered. Although landmark typology is valuable when teaching biological shape analysis, we find that employing it in research design introduces confusion without providing useful information. Instead, researchers should choose landmark configurations based on their ability to test specific research hypotheses, and research papers should include justifications of landmark choices along with landmark definitions, details on landmark collection methods, and appropriate interobserver and intraobserver analyses. Hence, while the landmarks themselves are crucial for GM, we argue that their typology is of little use in applied studies. Anat Rec, 302:1144-1153, 2019. © 2018 Wiley Periodicals, Inc.

Statistical Shape Analysis of Large Datasets Based on Diffeomorphic Iterative Centroids

In this paper, we propose an approach for template-based shape analysis of large datasets, using diffeomorphic centroids as atlas shapes. Diffeomorphic centroid methods fit in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework and use kernel metrics on currents to quantify surface dissimilarities. The statistical analysis is based on a Kernel Principal Component Analysis (Kernel PCA) performed on the set of initial momentum vectors which parametrize the deformations. We tested the approach on different datasets of hippocampal shapes extracted from brain magnetic resonance imaging (MRI), compared three different centroid methods and a variational template estimation. The largest dataset is composed of 1,000 surfaces, and we are able to analyse this dataset in 26 h using a diffeomorphic centroid. Our experiments demonstrate that computing diffeomorphic centroids in place of standard variational templates leads to similar shape analysis results and saves around 70% of computation time. Furthermore, the approach is able to adequately capture the variability of hippocampal shapes with a reasonable number of dimensions, and to predict anatomical features of the hippocampus, only present in 17% of the population, in healthy subjects.

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

The appearance and the movements of immune cells are driven by their environment. As a reaction to a pathogen invasion, the immune cells are recruited to the site of inflammation and are activated to prevent a further spreading of the invasion. This is also reflected by changes in the behavior and the morphological appearance of the immune cells. In cancerous tissue, similar morphokinetic changes have been observed in the behavior of microglial cells: intra-tumoral microglia have less complex 3-dimensional shapes, having less-branched cellular processes, and move more rapidly than those in healthy tissue. The examination of such morphokinetic properties requires complex 3D microscopy techniques, which can be extremely challenging when executed longitudinally. Therefore, the recording of a static 3D shape of a cell is much simpler, because this does not require intravital measurements and can be performed on excised tissue as well. However, it is essential to possess analysis tools that allow the fast and precise description of the 3D shapes and allows the diagnostic classification of healthy and pathogenic tissue samples based solely on static, shape-related information. Here, we present a toolkit that analyzes the discrete Fourier components of the outline of a set of 2D projections of the 3D cell surfaces via Self-Organizing Maps. The application of artificial intelligence methods allows our framework to learn about various cell shapes as it is applied to more and more tissue samples, whilst the workflow remains simple.

Optic nerve head three-dimensional shape analysis

We present a method for optic nerve head (ONH) 3-D shape analysis from retinal optical coherence tomography (OCT). The possibility to noninvasively acquire in vivo high-resolution 3-D volumes of the ONH using spectral domain OCT drives the need to develop tools that quantify the shape of this structure and extract information for clinical applications. The presented method automatically generates a 3-D ONH model and then allows the computation of several 3-D parameters describing the ONH. The method starts with a high-resolution OCT volume scan as input. From this scan, the model-defining inner limiting membrane (ILM) as inner surface and the retinal pigment epithelium as outer surface are segmented, and the Bruch's membrane opening (BMO) as the model origin is detected. Based on the generated ONH model by triangulated 3-D surface reconstruction, different parameters (areas, volumes, annular surface ring, minimum distances) of different ONH regions can then be computed. Additionally, the bending energy (roughness) in the BMO region on the ILM surface and 3-D BMO-MRW surface area are computed. We show that our method is reliable and robust across a large variety of ONH topologies (specific to this structure) and present a first clinical application.

Development of an in situ procedure to evaluate the reticulo-rumen morphology of sheep selected for divergent methane emissions

Published studies have shown that methane yield (g CH4/kg dry matter) from sheep is positively correlated with the size (volume and surface area) of the reticulo-rumen (RR) and the weight of its contents. However, the relationship between CH4 yield and RR shape has not been investigated. In this work, shape analysis has been performed on a data set of computerised tomography (CT) scans of the RR from sheep having high and low CH4 yields (n=20 and n=17, respectively). The three-dimensional geometries of the RRs were reconstructed from segmented scan data and split into three anatomical regions. An iterative fitting technique combining radial basis functions and principal component (PC) fitting was used to create a set of consistent landmarks which were then used as variables in a PC analysis to identify shape variation within the data. Significant size differences were detected for regions corresponding to the dorsal and ventral compartments between sheep with high and low CH4 yields. When the analysis was repeated after scaling the geometries to remove the effect of size, there was no significant shape variation correlating with CH4 yield. The results have demonstrated the feasibility of CT-based computational shape determination for studying the morphological characteristics of the RR and indicate that size, but not shape correlates with CH4 yield in sheep.

Global Hippocampal Volume Reductions and Local CA1 Shape Deformations in Amyotrophic Lateral Sclerosis

There is increasing evidence for hippocampal involvement in Amyotrophic Lateral Sclerosis (ALS). Recent neuroimaging studies have been focused on disease-related hippocampal volume alterations while changes in hippocampal shape have been investigated less frequently. Here, we aimed to characterize the patterns of hippocampal degeneration using both an automatic and manual volumetric and surface-based approach in a group of 31 patients with ALS and 29 healthy controls. Irrespective of the segmentation type, left, and right hippocampal volumes were significantly reduced in ALS compared to controls. Local shape alterations were identified in the hippocampal head region of patients with ALS that corresponds to the cornu ammonis field 1 (CA1), a region known to be involved in novelty detection, memory processing, and integration of hippocampal input and output information. The results suggest a global hippocampal volume loss in ALS that is complemented by local shape deformations in a highly interconnected region within the hippocampus.

Offspring of older parents are smaller-but no less bilaterally symmetrical-than offspring of younger parents in the aquatic plant Lemna turionifera

Offspring quality decreases with parental age in many taxa, with offspring of older parents exhibiting reduced life span, reproductive capacity, and fitness, compared to offspring of younger parents. These "parental age effects," whose consequences arise in the next generation, can be considered as manifestations of parental senescence, in addition to the more familiar age-related declines in parent-generation survival and reproduction. Parental age effects are important because they may have feedback effects on the evolution of demographic trajectories and longevity. In addition to altering the timing of offspring life-history milestones, parental age effects can also have a negative impact on offspring size, with offspring of older parents being smaller than offspring of younger parents. Here, we consider the effects of advancing parental age on a different aspect of offspring morphology, body symmetry. In this study, we followed all 403 offspring of 30 parents of a bilaterally symmetrical, clonally reproducing aquatic plant species, Lemna turionifera, to test the hypothesis that successive offspring become less symmetrical as their parent ages, using the "Continuous Symmetry Measure" as an index. Although successive offspring of aging parents older than one week became smaller and smaller, we found scant evidence for any reduction in bilateral symmetry.

Hyperplanar Morphological Clustering of a Hippocampus by Using Volumetric Computerized Tomography in Early Alzheimer's Disease

Background: On diagnosing Alzheimer’s disease (AD), most existing imaging-based schemes have relied on analyzing the hippocampus and its peripheral structures. Recent studies have confirmed that volumetric variations are one of the primary indicators in differentiating symptomatic AD from healthy aging. In this study, we focused on deriving discriminative shape-based parameters that could effectively identify early AD from volumetric computerized tomography (VCT) delineation, which was previously almost intangible. Methods: Participants were 63 volunteers of Thai nationality, whose ages were between 40 and 90 years old. Thirty subjects (age 68.51 ± 5.5) were diagnosed with early AD, by using Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV) criteria and the National Institute of Neurological and Communicative Disorders and the Stroke and the Alzheimer’s disease and Related Disorders Association (NINCDS-ADRDA) criteria, while the remaining 33 were in the healthy control group (age 67.93 ± 5.5). The structural imaging study was conducted by using VCT. Three uninformed readers were asked to draw left and right hippocampal outlines on a coronal section. The resultant shapes were aligned and then analyzed with statistical shape analysis to obtain the first few dominant variational parameters, residing in hyperplanes. A supervised machine learning, i.e., support vector machine (SVM) was then employed to elucidate the proposed scheme. Results: Provided trivial delineations, relatively as low as 5 to 7 implicit model parameters could be extracted and used as discriminants. Clinical verification showed that the model could differentiate early AD from aging, with high sensitivity, specificity, accuracy and F-measure of 0.970, 0.968, 0.983 and 0.983, respectively, with no apparent effect of left-right asymmetry. Thanks to a less laborious task required, yet high discriminating capability, the proposed scheme is expected to be applicable in a typical clinical setting, equipped with only a moderate-specs VCT.

Development and Assessment of Fully Automated and Globally Transitive Geometric Morphometric Methods, With Application to a Biological Comparative Dataset With High Interspecific Variation

Automated geometric morphometric methods are promising tools for shape analysis in comparative biology, improving researchers' abilities to quantify variation extensively (by permitting more specimens to be analyzed) and intensively (by characterizing shapes with greater fidelity). Although use of these methods has increased, published automated methods have some notable limitations: pairwise correspondences are frequently inaccurate and pairwise mappings are not globally consistent (i.e., they lack transitivity across the full sample). Here, we reassess the accuracy of published automated methods-cPDist (Boyer et al. Proc Nat Acad Sci 108 () 18221-18226) and auto3Dgm (Boyer et al.: Anat Rec 298 () 249-276)-and evaluate several modifications to these methods. We show that a substantial percentage of alignments and pairwise maps between specimens of dissimilar geometries were inaccurate in the study of Boyer et al. (Proc Nat Acad Sci 108 () 18221-18226), despite a taxonomically partitioned variance structure of continuous Procrustes distances. We show these inaccuracies are remedied using a globally informed methodology within a collection of shapes, rather than relying on pairwise comparisons (c.f. Boyer et al.: Anat Rec 298 () 249-276). Unfortunately, while global information generally enhances maps between dissimilar objects, it can degrade the quality of correspondences between similar objects due to the accumulation of numerical error. We explore a number of approaches to mitigate this degradation, quantify their performance, and compare the generated pairwise maps (and the shape space characterized by these maps) to a "ground truth" obtained from landmarks manually collected by geometric morphometricians. Novel methods both improve the quality of the pairwise correspondences relative to cPDist and achieve a taxonomic distinctiveness comparable to auto3Dgm. Anat Rec, 301:636-658, 2018. © 2017 Wiley Periodicals, Inc.

Predicting auditory feedback control of speech production from subregional shape of subcortical structures

Although a growing body of research has focused on the cortical sensorimotor mechanisms that support auditory feedback control of speech production, much less is known about the subcortical contributions to this control process. This study examined whether subregional anatomy of subcortical structures assessed by statistical shape analysis is associated with vocal compensations and cortical event-related potentials in response to pitch feedback errors. The results revealed significant negative correlations between the magnitudes of vocal compensations and subregional shape of the right thalamus, between the latencies of vocal compensations and subregional shape of the left caudate and pallidum, and between the latencies of cortical N1 responses and subregional shape of the left putamen. These associations indicate that smaller local volumes of the basal ganglia and thalamus are predictive of slower and larger neurobehavioral responses to vocal pitch errors. Furthermore, increased local volumes of the left hippocampus and right amygdala were predictive of larger vocal compensations, suggesting that there is an interplay between the memory-related subcortical structures and auditory-vocal integration. These results, for the first time, provide evidence for differential associations of subregional morphology of the basal ganglia, thalamus, hippocampus, and amygdala with neurobehavioral processing of vocal pitch errors, suggesting that subregional shape measures of subcortical structures can predict behavioral outcome of auditory-vocal integration and associated neural features. Hum Brain Mapp 39:459-471, 2018. © 2017 Wiley Periodicals, Inc.

Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging

As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.

In vivo placental MRI shape and textural features predict fetal growth restriction and postnatal outcome

PURPOSE

To investigate the ability of three-dimensional (3D) MRI placental shape and textural features to predict fetal growth restriction (FGR) and birth weight (BW) for both healthy and FGR fetuses.

MATERIALS AND METHODS

We recruited two groups of pregnant volunteers between 18 and 39 weeks of gestation; 46 healthy subjects and 34 FGR. Both groups underwent fetal MR imaging on a 1.5 Tesla GE scanner using an eight-channel receiver coil. We acquired T2-weighted images on either the coronal or the axial plane to obtain MR volumes with a slice thickness of either 4 or 8 mm covering the full placenta. Placental shape features (volume, thickness, elongation) were combined with textural features; first order textural features (mean, variance, kurtosis, and skewness of placental gray levels), as well as, textural features computed on the gray level co-occurrence and run-length matrices characterizing placental homogeneity, symmetry, and coarseness. The features were used in two machine learning frameworks to predict FGR and BW.

RESULTS

The proposed machine-learning based method using shape and textural features identified FGR pregnancies with 86% accuracy, 77% precision and 86% recall. BW estimations were 0.3 ± 13.4% (mean percentage error ± standard error) for healthy fetuses and -2.6 ± 15.9% for FGR.

CONCLUSION

The proposed FGR identification and BW estimation methods using in utero placental shape and textural features computed on 3D MR images demonstrated high accuracy in our healthy and high-risk cohorts. Future studies to assess the evolution of each feature with regard to placental development are currently underway.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:449-458.

DATA-DRIVEN CLUSTER SELECTION FOR SUBCORTICAL SHAPE AND CORTICAL THICKNESS PREDICTS RECOVERY FROM DEPRESSIVE SYMPTOMS

Patients with major depressive disorder (MDD) who do not achieve full symptomatic recovery after antidepressant treatment have a higher risk of relapse. Compared to pharmacotherapies, electroconvulsive therapy (ECT) more rapidly produces a greater extent of response in severely depressed patients. However, prediction of which candidates are most likely to improve after ECT remains challenging. Using structural MRI data from 42 ECT patients scanned prior to ECT treatment, we developed a random forest classifier based on data-driven shape cluster selection and cortical thickness features to predict remission. Right hemisphere hippocampal shape and right inferior temporal cortical thickness was most predictive of remission, with the predicted probability of recovery decreasing when these regions were thicker prior to treatment. Remission was predicted with an average 73% balanced accuracy. Classification of MRI data may help identify treatment-responsive patients and aid in clinical decision-making. Our results show promise for the development of personalized treatment strategies.

An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT

PURPOSE

Distinguishing between benign granulmoas and adenocarcinomas is confounded by their similar visual appearance on routine CT scans. Unfortunately, owing to the inability to discriminate these lesions radigraphically, many patients with benign granulomas are subjected to unnecessary surgical wedge resections and biopsies for pathologic confirmation of cancer presence or absence. This suggests the need for improved computerized characterization of these nodules in order to distinguish between these two classes of lesions on CT scans. While there has been substantial interest in the use of textural analysis for radiomic characterization of lung nodules, relatively less work has been done in shape based characterization of lung nodules, particularly with respect to granulmoas and adenocarcinomas. The primary goal of this study is to evaluate the role of 3D shape features for discrimination of benign granulomas from malignant adenocarcinomas on lung CT images. Towards this end we present an integrated framework for segmentation, feature characterization and classification of these nodules on CT.

METHODS

The nodule segmentation method starts with separation of lung regions from the surrounding lung anatomy. Next, the lung CT scans are projected into and represented in a three dimensional spectral embedding (SE) space, allowing for better determination of the boundaries of the nodule. This then enables the application of a gradient vector flow active contour (SEGvAC) model for nodule boundary extraction. A set of 24 shape features from both 2D slices and 3D surface of the segmented nodules are extracted, including features pertaining to the angularity, spiculation, elongation and nodule compactness. A feature selection scheme, PCA-VIP, is employed to identify the most discriminating set of features to distinguish granulmoas from adenocarcinomas within a learning set of 82 patients. The features thus identified were then combined with a support vector machine classifier and independently validated on a distinct test set comprising 67 patients. The performance of the classifier for both of the training and validation cohorts was evaluated by the area under receiver characteristic curve (ROC).

RESULTS

We used 82 and 67 studies from two different institutions respectively for training and independent validation of the model and the shape features. The Dice coefficient between automatically segmented nodules by SEGvAC and the manual delineations by expert radiologists (readers) was 0.84± 0.04 whereas inter-reader segmentation agreement was 0.79± 0.12. We also identified a set of consistent features (Roughness, Convexity and Spherecity) that were found to be strongly correlated across both manual and automated nodule segmentations (R > 0.80, p < 0.0001) and capture the marginal smoothness and 3D compactness of the nodules. On the independent validation set of 67 studies our classifier yielded a ROC AUC of 0.72 and 0.64 for manually- and automatically segmented nodules respectively. On a subset of 20 studies, the AUCs for the two expert radiologists and 1 pulmonologist were found to be 0.82, 0.68 and 0.58 respectively.

CONCLUSIONS

The major finding of this study was that certain shape features appear to differentially express between granulomas and adenocarcinomas and thus computer extracted shape cues could be used to distinguish these radiographically similar pathologies.

The estimation of skeletal maturity of patients with cleft lip and palate using statistical shape analysis: a preliminary study

OBJECTIVES

To propose a skeletal maturity assessment method by developing a statistical regression analysis model through the integration of lateral and axial images of the cervical vertebrae of patients with cleft lip and palate obtained through CBCT.

METHODS

49 patients with cleft lip and palate (28 females, 21 males; age range, 4-16 years) underwent CBCT examination, and the hand-wrist radiographic data were selected. With coordinates of landmarks from lateral and axial images of the cervical vertebrae, Procrustes analysis and principal component (PC) analysis yielded PC scores of each cervical vertebra, with the centroid size as the size factor. The meaningful PC scores from these were used for multiple regression models.

RESULTS

When both axial and lateral cervical vertebrae were used together, there was a 6.7% increase in the Sempé maturation level explanatory power for skeletal maturation estimation in females and an 11.4% increase in males compared with that when only the chronological age was used.

CONCLUSIONS

This study improved the estimating regression models using statistical shape analysis with lateral and axial cervical vertebral shapes. The obtained models had improved explanatory power for skeletal maturity estimation than previous studies with healthy people.

SHAPE BASED IMAGE RECONSTRUCTION USING LINEARIZED DEFORMATIONS

We introduce a reconstruction framework that can account for shape related a priori information in ill-posed linear inverse problems in imaging. It is a variational scheme that uses a shape functional defined using deformable templates machinery from shape theory. As proof of concept, we apply the proposed shape based reconstruction to 2D tomography with very sparse measurements, and demonstrate strong empirical results.

A Segmentation Editing Framework Based on Shape Change Statistics

Segmentation is a key task in medical image analysis because its accuracy significantly affects successive steps. Automatic segmentation methods often produce inadequate segmentations, which require the user to manually edit the produced segmentation slice by slice. Because editing is time-consuming, an editing tool that enables the user to produce accurate segmentations by only drawing a sparse set of contours would be needed. This paper describes such a framework as applied to a single object. Constrained by the additional information enabled by the manually segmented contours, the proposed framework utilizes object shape statistics to transform the failed automatic segmentation to a more accurate version. Instead of modeling the object shape, the proposed framework utilizes shape change statistics that were generated to capture the object deformation from the failed automatic segmentation to its corresponding correct segmentation. An optimization procedure was used to minimize an energy function that consists of two terms, an external contour match term and an internal shape change regularity term. The high accuracy of the proposed segmentation editing approach was confirmed by testing it on a simulated data set based on 10 in-vivo infant magnetic resonance brain data sets using four similarity metrics. Segmentation results indicated that our method can provide efficient and adequately accurate segmentations (Dice segmentation accuracy increase of 10%), with very sparse contours (only 10%), which is promising in greatly decreasing the work expected from the user.

Sexual Dimorphism of the First Rib: A Comparative Approach Using Metric and Geometric Morphometric Analyses

This research investigated the sexual dimorphism of the first human rib using geometric morphometric and metric approaches on a sample of 285 specimens containing European Americans and African Americans from the Hamann-Todd collection. Metric measurements were investigated for sexual dimorphism and ancestral differences using univariate statistics. Four type II landmarks and 40 sliding semi-landmarks were placed outlining the dorsal and ventral curvatures of the ribs. Landmark data were processed using Generalized Procrustes Analyses with Procrustes distance sliding, and the subsequent coordinates were investigated for sexual dimorphism and ancestral differences using Procrustes ANOVAs. Both geometric morphometric and metric data were analyzed using cross-validated discriminant function analyses to test the hypothesis that variables from both approaches can be combined to increase sex classification rate. European Americans had sex correctly classified as high as 88.05% and African Americans as high as 70.86% using a combination of metric and geometric morphometric variables.

Otolith morphology varies between populations, sexes and male alternative reproductive tactics in a vocal toadfish Porichthys notatus

In this study, the morphology of sagittal otoliths of the plainfin midshipman fish Porichthys notatus was compared between populations, sexes and male alternative reproductive phenotypes (known as 'type I males or guarders' and 'type II males or sneakers'). Sagitta size increased with P. notatus size and changes in shape were also detected with increasing body size. Porichthys notatus sagittae begin as simple rounded structures, but then elongate as they grow and take on a more triangular and complex shape with several prominent notches and indentations along the dorsal and caudal edges. Moreover, the sagittae of the two geographically and genetically distinct populations of P. notatus (northern and southern) differed in shape. Porichthys notatus from the north possessed taller sagittae with deeper caudal indentations compared to P. notatus from the south. Sagitta shape also differed between females and males of the conventional guarder tactic. Furthermore, guarder males had smaller sagittae for their body size than did sneaker males or females. These differences in sagittal otolith morphology are discussed in relation to ecological and life history differences between the sexes and male tactics of this species. This is the first study to investigate teleost otolith morphology from the perspective of alternative reproductive tactics.

Morphometry analysis of basal ganglia structures in children with obstructive sleep apnea

BACKGROUND

Obstructive sleep apnea (OSA) affects both adults and children, likely mediated by the deficits of various brain regions. The association between structural alterations in the brain and OSA syndrome have been reported in adult patients, but the corresponding evidence for OSA children is still limited.

OBJECTIVE

The proposed study aimed to investigate the structural alterations in the brain of children with OSA, with focus on basal ganglia structures.

METHODS

We recruited 25 OSA children (aged 10.3±1.5 years) and 30 healthy children (aged 10.1±1.8 years) with T1-weighted brain MRI and performed automatic segmentation of their brains. The shape alterations of the basal ganglia structures for OSA syndrome was determined by comparison of the OSA group and control group with surface-based shape analysis.

RESULTS

Differences in the morphometry of the left thalamus and the left pallidum were found between the OSA group and control group. Compared to the control group, the OSA group presented significant atrophy in the ventral posterior nucleus and the medial dorsal nucleus of the left thalamus, while regional dilation was found in both the internal and external segments of the left pallidum.

CONCLUSION

These findings identified the association between the structural deficits of the thalamus and OSA syndrome in children, which was consistent with the existing findings in OSA adults. In addition, the present study provided new insights to the distinctive pattern of structural changes of the pallidum in pediatric OSA when compared to adult OSA.

Diffusive Motion of Linear Microgel Assemblies in Solution

Due to the ability of microgels to rapidly contract and expand in response to external stimuli, assemblies of interconnected microgels are promising for actuation applications, e.g., as contracting fibers for artificial muscles. Among the properties determining the suitability of microgel assemblies for actuation are mechanical parameters such as bending stiffness and mobility. Here, we study the properties of linear, one-dimensional chains of poly(N-vinylcaprolactam) microgels dispersed in water. They were fabricated by utilizing wrinkled surfaces as templates and UV-cross-linking the microgels. We image the shapes of the chains on surfaces and in solution using atomic force microscopy (AFM) and fluorescence microscopy, respectively. In solution, the chains are observed to execute translational and rotational diffusive motions. Evaluation of the motions yields translational and rotational diffusion coefficients and, from the translational diffusion coefficient, the chain mobility. The microgel chains show no perceptible bending, which yields a lower limit on their bending stiffness.

Volumetric and shape analysis of the thalamus and striatum in amnestic mild cognitive impairment

Alterations in brain structures, including progressive neurodegeneration, are a hallmark in patients with Alzheimer's disease (AD). However, pathological mechanisms, such as the accumulation of amyloid and the proliferation of tau, are thought to begin years, even decades, before the initial clinical manifestations of AD. In this study, we compare the brain anatomy of amnestic mild cognitive impairment patients (aMCI, n = 16) to healthy subjects (CS, n = 22) using cortical thickness, subcortical volume, and shape analysis, which we believe to be complimentary to volumetric measures. We were able to replicate "classical" cortical thickness alterations in aMCI in the hippocampus, amygdala, putamen, insula, and inferior temporal regions. Additionally, aMCI showed significant thalamic and striatal shape differences. We observed higher global amyloid deposition in aMCI, a significant correlation between striatal displacement and global amyloid, and an inverse correlation between executive function and right-hemispheric thalamic displacement. In contrast, no volumetric differences were detected in thalamic, striatal, and hippocampal regions. Our results provide new evidence for early subcortical neuroanatomical changes in patients with aMCI, which are linked to cognitive abilities and amyloid deposition. Hence, shape analysis may aid in the identification of structural biomarkers for identifying individuals at highest risk of conversion to AD.

Shape Analysis of DNA-Au Hybrid Particles by Analytical Ultracentrifugation

Current developments in nanotechnology have increased the demand for nanocrystal assemblies with well-defined shapes and tunable sizes. DNA is a particularly well-suited building block in nanoscale assemblies because of its scalable sizes, conformational variability, and convenient self-assembly capabilities via base pairing. In hybrid materials, gold nanoparticles (AuNPs) can be assembled into nanoparticle structures with programmable interparticle distances by applying appropriate DNA sequences. However, the development of stoichiometrically defined DNA/NP structures is still challenging since product mixtures are frequently obtained and their purification and characterization is the rate-limiting step in the development of DNA-NP hybrid assemblies. Improvements in nanostructure fractionation and characterization techniques offer great potential for nanotechnology applications in general. This study reports the application of analytical ultracentrifugation (AUC) for the characterization of anisotropic DNA-linked metal-crystal assemblies. On the basis of transmission electron microscopy data and the DNA primary sequence, hydrodynamic bead models are set up for the interpretation of the measured frictional ratios and sedimentation coefficients. We demonstrate that the presence of single DNA strands on particle surfaces as well as the shape factors of multiparticle structures in mixtures can be quantitatively described by AUC. This study will significantly broaden the possibilities to analyze mixtures of shape-anisotropic nanoparticle assemblies. By establishing insights into the analysis of nanostructure mixtures based on fundamental principles of sedimentation, a wide range of potential applications in basic research and industry become accessible.

Atlas-Based Ventricular Shape Analysis for Understanding Congenital Heart Disease

Congenital heart disease is associated with abnormal ventricular shape that can affect wall mechanics and may be predictive of long-term adverse outcomes. Atlas-based parametric shape analysis was used to analyze ventricular geometries of eight adolescent or adult single-ventricle CHD patients with tricuspid atresia and Fontans. These patients were compared with an "atlas" of non-congenital asymptomatic volunteers, resulting in a set of z-scores which quantify deviations from the control population distribution on a patient-by-patient basis. We examined the potential of these scores to: (1) quantify abnormalities of ventricular geometry in single ventricle physiologies relative to the normal population; (2) comprehensively quantify wall motion in CHD patients; and (3) identify possible relationships between ventricular shape and wall motion that may reflect underlying functional defects or remodeling in CHD patients. CHD ventricular geometries at end-diastole and end-systole were individually compared with statistical shape properties of an asymptomatic population from the Cardiac Atlas Project. Shape analysis-derived model properties, and myocardial wall motions between end-diastole and end-systole, were compared with physician observations of clinical functional parameters. Relationships between altered shape and altered function were evaluated via correlations between atlas-based shape and wall motion scores. Atlas-based shape analysis identified a diverse set of specific quantifiable abnormalities in ventricular geometry or myocardial wall motion in all subjects. Moreover, this initial cohort displayed significant relationships between specific shape abnormalities such as increased ventricular sphericity and functional defects in myocardial deformation, such as decreased long-axis wall motion. These findings suggest that atlas-based ventricular shape analysis may be a useful new tool in the management of patients with CHD who are at risk of impaired ventricular wall mechanics and chamber remodeling.