Identifying brain hierarchical structures associated with Alzheimer's disease using a regularized regression method with tree predictors

Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheimer's disease outcomes. In this study, we propose an ℓ₁-type regularization for predictors that follow a hierarchical tree structure. Considering a tree as a directed acyclic graph, we interpret the model parameters from a path analysis perspective. Under this concept, the proposed penalty regulates the total effect of each predictor on the outcome. With regularity conditions, it is shown that under the proposed regularization, the estimator of the model coefficient is consistent in ℓ₂-norm and the model selection is also consistent. When applied to a brain sMRI dataset acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the proposed approach identifies brain regions where atrophy in these regions demonstrates the declination in memory. With regularization on the total effects, the findings suggest that the impact of atrophy on memory deficits is localized from small brain regions, but at various levels of brain segmentation. Data used in preparation of this paper were obtained from the ADNI database.     

Predicting Future Morphological Changes of Lesions from Radiotracer Uptake in 18F-FDG-PET Images

We introduce a novel computational framework to enable automated identification of texture and shape features of lesions on ¹⁸F-FDG-PET images through a graph-based image segmentation method. The proposed framework predicts future morphological changes of lesions with high accuracy. The presented methodology has several benefits over conventional qualitative and semi-quantitative methods, due to its fully quantitative nature and high accuracy in each step of (i) detection, (ii) segmentation, and (iii) feature extraction. To evaluate our proposed computational framework, thirty patients received 2 ¹⁸F-FDG-PET scans (60 scans total), at two different time points. Metastatic papillary renal cell carcinoma, cerebellar hemongioblastoma, non-small cell lung cancer, neurofibroma, lymphomatoid granulomatosis, lung neoplasm, neuroendocrine tumor, soft tissue thoracic mass, nonnecrotizing granulomatous inflammation, renal cell carcinoma with papillary and cystic features, diffuse large B-cell lymphoma, metastatic alveolar soft part sarcoma, and small cell lung cancer were included in this analysis. The radiotracer accumulation in patients'' scans was automatically detected and segmented by the proposed segmentation algorithm. Delineated regions were used to extract shape and textural features, with the proposed adaptive feature extraction framework, as well as standardized uptake values (SUV) of uptake regions, to conduct a broad quantitative analysis. Evaluation of segmentation results indicates that our proposed segmentation algorithm has a mean dice similarity coefficient of 85.75±1.75%. We found that 28 of 68 extracted imaging features were correlated well with SUV_max (p<0.05), and some of the textural features (such as entropy and maximum probability) were superior in predicting morphological changes of radiotracer uptake regions longitudinally, compared to single intensity feature such as SUV_max. We also found that integrating textural features with SUV measurements significantly improves the prediction accuracy of morphological changes (Spearman correlation coefficient = 0.8715, p<2e-16).     

CTANet: Confidence-Based Threshold Adaption Network for Semi-Supervised Segmentation of Uterine Regions from MR Images for HIFU Treatment

ObjectivesThe accurate preoperative segmentation of the uterus and uterine fibroids from magnetic resonance images (MRI) is an essential step for diagnosis and real-time ultrasound guidance during high-intensity focused ultrasound (HIFU) surgery. Conventional supervised methods are effective techniques for image segmentation. Recently, semi-supervised segmentation approaches have been reported in the literature. One popular technique for semi-supervised methods is to use pseudo-labels to artificially annotate unlabeled data. However, many existing pseudo-label generations rely on a fixed threshold used to generate a confidence map, regardless of the proportion of unlabeled and labeled data.Materials and MethodsTo address this issue, we propose a novel semi-supervised framework called Confidence-based Threshold Adaptation Network (CTANet) to improve the quality of pseudo-labels. Specifically, we propose an online pseudo-labels method to automatically adjust the threshold, producing high-confident unlabeled annotations and boosting segmentation accuracy. To further improve the network's generalization to fit the diversity of different patients, we design a novel mixup strategy by regularizing the network on each layer in the decoder part and introducing a consistency regularization loss between the outputs of two sub-networks in CTANet.ResultsWe compare our method with several state-of-the-art semi-supervised segmentation methods on the same uterine fibroids dataset containing 297 patients. The performance is evaluated by the Dice similarity coefficient, the precision, and the recall. The results show that our method outperforms other semi-supervised learning methods. Moreover, for the same training set, our method approaches the segmentation performance of a fully supervised U-Net (100% annotated data) but using 4 times less annotated data (25% annotated data, 75% unannotated data).ConclusionExperimental results are provided to illustrate the effectiveness of the proposed semi-supervised approach. The proposed method can contribute to multi-class segmentation of uterine regions from MRI for HIFU treatment.     

Joint level-set and spatio-temporal motion detection for cell segmentation

Background

Cell segmentation is a critical step for quantification and monitoring of cell cycle progression, cell migration, and growth control to investigate cellular immune response, embryonic development, tumorigenesis, and drug effects on live cells in time-lapse microscopy images.

Methods

In this study, we propose a joint spatio-temporal diffusion and region-based level-set optimization approach for moving cell segmentation. Moving regions are initially detected in each set of three consecutive sequence images by numerically solving a system of coupled spatio-temporal partial differential equations. In order to standardize intensities of each frame, we apply a histogram transformation approach to match the pixel intensities of each processed frame with an intensity distribution model learned from all frames of the sequence during the training stage. After the spatio-temporal diffusion stage is completed, we compute the edge map by nonparametric density estimation using Parzen kernels. This process is followed by watershed-based segmentation and moving cell detection. We use this result as an initial level-set function to evolve the cell boundaries, refine the delineation, and optimize the final segmentation result.

Results

We applied this method to several datasets of fluorescence microscopy images with varying levels of difficulty with respect to cell density, resolution, contrast, and signal-to-noise ratio. We compared the results with those produced by Chan and Vese segmentation, a temporally linked level-set technique, and nonlinear diffusion-based segmentation. We validated all segmentation techniques against reference masks provided by the international Cell Tracking Challenge consortium. The proposed approach delineated cells with an average Dice similarity coefficient of 89 % over a variety of simulated and real fluorescent image sequences. It yielded average improvements of 11 % in segmentation accuracy compared to both strictly spatial and temporally linked Chan-Vese techniques, and 4 % compared to the nonlinear spatio-temporal diffusion method.

Conclusions

Despite the wide variation in cell shape, density, mitotic events, and image quality among the datasets, our proposed method produced promising segmentation results. These results indicate the efficiency and robustness of this method especially for mitotic events and low SNR imaging, enabling the application of subsequent quantification tasks.

     

Comparison and testing of least-squares time domain inverse solutions in electrocardiography

The use of several mathematical methods for estimating epicardial ECG potentials from arrays of body surface potentials has been reported in the literature; most of these methods are based on least-squares reconstruction principles and operate in the time-space domain. In this paper we introduce a general Bayesian maximum a posteriori (MAP) framework for time domain inverse solutions in the presence of noise. The two most popular previously applied least-squares methods, constrained (regularized) least-squares and low-rank approximation through the singular value decomposition, are placed in this framework, each of them requiring the a priori knowledge of a ‘regularization parameter’, which defines the degree of smoothing to be applied to the inversion. Results of simulations using these two methods are presented; they compare the ability of each method to reconstruct epicardial potentials. We used the geometric configuration of the torso and internal organs of an individual subject as reconstructed from CT scans. The accuracy of each method at each epicardial location was tested as a function of measurement noise, the size and shape of the subarray of torso sensors, and the regularization parameter. We paid particular attention to an assessment of the potential of these methods for clinical use by testing the effect of using compact, small-size subarrays of torso potentials while maintaining a high degree of resolution on the epicardium.     

A fully-automatic caudate nucleus segmentation of brain MRI: Application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder

Background

Accurate automatic segmentation of the caudate nucleus in magnetic resonance images (MRI) of the brain is of great interest in the analysis of developmental disorders. Segmentation methods based on a single atlas or on multiple atlases have been shown to suitably localize caudate structure. However, the atlas prior information may not represent the structure of interest correctly. It may therefore be useful to introduce a more flexible technique for accurate segmentations.

Method

We present Cau-dateCut: a new fully-automatic method of segmenting the caudate nucleus in MRI. CaudateCut combines an atlas-based segmentation strategy with the Graph Cut energy-minimization framework. We adapt the Graph Cut model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus, by defining new energy function data and boundary potentials. In particular, we exploit information concerning the intensity and geometry, and we add supervised energies based on contextual brain structures. Furthermore, we reinforce boundary detection using a new multi-scale edgeness measure.

Results

We apply the novel CaudateCut method to the segmentation of the caudate nucleus to a new set of 39 pediatric attention-deficit/hyperactivity disorder (ADHD) patients and 40 control children, as well as to a public database of 18 subjects. We evaluate the quality of the segmentation using several volumetric and voxel by voxel measures. Our results show improved performance in terms of segmentation compared to state-of-the-art approaches, obtaining a mean overlap of 80.75%. Moreover, we present a quantitative volumetric analysis of caudate abnormalities in pediatric ADHD, the results of which show strong correlation with expert manual analysis.

Conclusion

CaudateCut generates segmentation results that are comparable to gold-standard segmentations and which are reliable in the analysis of differentiating neuroanatomical abnormalities between healthy controls and pediatric ADHD.     

Respiratory cycle related EEG changes: Modified respiratory cycle segmentation

Respiratory cycle related EEG change (RCREC) is characterized by significant relative EEG power changes within different stages of respiration during sleep. RCREC has been demonstrated to predict sleepiness in patients with obstructive sleep apnoea and is hypothesized to represent microarousals. As such RCREC may provide a sensitive marker of respiratory arousals. A key step in quantification of RCREC is respiratory signal segmentation which is conventionally based on local maxima and minima of the nasal flow signal. We have investigated an alternative respiratory cycle segmentation method based on inspiratory/expiratory transitions. Sixty two healthy paediatric participants were recruited through staff of local universities in Bolivia. Subjects underwent attended polysomnography on a single night (Compumedics PS2 system). Studies were sleep staged according to standard criteria. C3/A2 EEG channel and time-locked nasal flow (thermistor) were used in RCREC quantification. Forty Seven subjects aged 7–17 (11.4 ± 3) years (24M:23F) were found to have usable polysomnographs for the purpose of RCREC calculation. Respiratory cycles were segmented using both the conventional and novel (transition) methods and differences in RCREC derived from the two methods were compared in each frequency band. Significance of transition RCREC as measured by Fisher's F value through analysis of variance (ANOVA) was found to be significantly higher than the conventional RCREC in all frequency bands (P < 0.05) but beta. This increase in statistical significance of RCREC as demonstrated with the novel transition segmentation approach suggests better alignment of the respiratory cycle segments with the underlying physiology driving RCREC.     

VolPy: Automated and scalable analysis pipelines for voltage imaging datasets

Voltage imaging enables monitoring neural activity at sub-millisecond and sub-cellular scale, unlocking the study of subthreshold activity, synchrony, and network dynamics with unprecedented spatio-temporal resolution. However, high data rates (>800MB/s) and low signal-to-noise ratios create bottlenecks for analyzing such datasets. Here we present VolPy, an automated and scalable pipeline to pre-process voltage imaging datasets. VolPy features motion correction, memory mapping, automated segmentation, denoising and spike extraction, all built on a highly parallelizable, modular, and extensible framework optimized for memory and speed. To aid automated segmentation, we introduce a corpus of 24 manually annotated datasets from different preparations, brain areas and voltage indicators. We benchmark VolPy against ground truth segmentation, simulations and electrophysiology recordings, and we compare its performance with existing algorithms in detecting spikes. Our results indicate that VolPy’s performance in spike extraction and scalability are state-of-the-art.     

Quantitative Tumor Segmentation for Evaluation of Extent of Glioblastoma Resection to Facilitate Multisite Clinical Trials

Standard-of-care therapy for glioblastomas, the most common and aggressive primary adult brain neoplasm, is maximal safe resection, followed by radiation and chemotherapy. Because maximizing resection may be beneficial for these patients, improving tumor extent of resection (EOR) with methods such as intraoperative 5-aminolevulinic acid fluorescence-guided surgery (FGS) is currently under evaluation. However, it is difficult to reproducibly judge EOR in these studies due to the lack of reliable tumor segmentation methods, especially for postoperative magnetic resonance imaging (MRI) scans. Therefore, a reliable, easily distributable segmentation method is needed to permit valid comparison, especially across multiple sites. We report a segmentation method that combines versatile region-of-interest blob generation with automated clustering methods. We applied this to glioblastoma cases undergoing FGS and matched controls to illustrate the method's reliability and accuracy. Agreement and interrater variability between segmentations were assessed using the concordance correlation coefficient, and spatial accuracy was determined using the Dice similarity index and mean Euclidean distance. Fuzzy C-means clustering with three classes was the best performing method, generating volumes with high agreement with manual contouring and high interrater agreement preoperatively and postoperatively. The proposed segmentation method allows tumor volume measurements of contrast-enhanced T₁-weighted images in the unbiased, reproducible fashion necessary for quantifying EOR in multicenter trials.     

Trajectory Data Analyses for Pedestrian Space-time Activity Study

It is well recognized that human movement in the spatial and temporal dimensions has direct influence on disease transmission^1-3. An infectious disease typically spreads via contact between infected and susceptible individuals in their overlapped activity spaces. Therefore, daily mobility-activity information can be used as an indicator to measure exposures to risk factors of infection. However, a major difficulty and thus the reason for paucity of studies of infectious disease transmission at the micro scale arise from the lack of detailed individual mobility data. Previously in transportation and tourism research detailed space-time activity data often relied on the time-space diary technique, which requires subjects to actively record their activities in time and space. This is highly demanding for the participants and collaboration from the participants greatly affects the quality of data⁴.Modern technologies such as GPS and mobile communications have made possible the automatic collection of trajectory data. The data collected, however, is not ideal for modeling human space-time activities, limited by the accuracies of existing devices. There is also no readily available tool for efficient processing of the data for human behavior study. We present here a suite of methods and an integrated ArcGIS desktop-based visual interface for the pre-processing and spatiotemporal analyses of trajectory data. We provide examples of how such processing may be used to model human space-time activities, especially with error-rich pedestrian trajectory data, that could be useful in public health studies such as infectious disease transmission modeling.The procedure presented includes pre-processing, trajectory segmentation, activity space characterization, density estimation and visualization, and a few other exploratory analysis methods. Pre-processing is the cleaning of noisy raw trajectory data. We introduce an interactive visual pre-processing interface as well as an automatic module. Trajectory segmentation⁵ involves the identification of indoor and outdoor parts from pre-processed space-time tracks. Again, both interactive visual segmentation and automatic segmentation are supported. Segmented space-time tracks are then analyzed to derive characteristics of one''s activity space such as activity radius etc. Density estimation and visualization are used to examine large amount of trajectory data to model hot spots and interactions. We demonstrate both density surface mapping⁶ and density volume rendering⁷. We also include a couple of other exploratory data analyses (EDA) and visualizations tools, such as Google Earth animation support and connection analysis. The suite of analytical as well as visual methods presented in this paper may be applied to any trajectory data for space-time activity studies.     

PombeX: Robust Cell Segmentation for Fission Yeast Transillumination Images

Schizosaccharomyces pombe shares many genes and proteins with humans and is a good model for chromosome behavior and DNA dynamics, which can be analyzed by visualizing the behavior of fluorescently tagged proteins in vivo. Performing a genome-wide screen for changes in such proteins requires developing methods that automate analysis of a large amount of images, the first step of which requires robust segmentation of the cell. We developed a segmentation system, PombeX, that can segment cells from transmitted illumination images with focus gradient and varying contrast. Corrections for focus gradient are applied to the image to aid in accurate detection of cell membrane and cytoplasm pixels, which is used to generate initial contours for cells. Gradient vector flow snake evolution is used to obtain the final cell contours. Finally, a machine learning-based validation of cell contours removes most incorrect or spurious contours. Quantitative evaluations show overall good segmentation performance on a large set of images, regardless of differences in image quality, lighting condition, focus condition and phenotypic profile. Comparisons with recent related methods for yeast cells show that PombeX outperforms current methods, both in terms of segmentation accuracy and computational speed.     

Nectar yeasts in Delphinium nuttallianum (Ranunculaceae) and their effects on nectar quality

Microorganisms colonize the nectar of many angiosperms. Variable diversity and spatio-temporal dynamics of nectar-inhabiting microorganisms (e.g., yeasts) may drive variation in nectar sugar composition and subsequent plant–pollinator interactions. We assessed yeast frequency of occurrence and density in the nectar of the perennial herb, Delphinium nuttallianum, across multiple spatio-temporal scales, including flower lifetime and sex-phase transition, flowering season, populations, and years. We tested the hypothesis that pollinators vector yeasts by comparing densities between virgin flowers and those open to visitation. Finally, we identified yeasts using molecular methods and tested for an association between yeast density and nectar composition using ultra-performance liquid chromatography. Yeasts were frequent colonists of Delphinium nectar, occurring in all populations and years sampled. Yeast frequency of occurrence and density varied across most spatio-temporal scales examined. Pollinators were vectors of yeast: virgin flowers remained yeast-free, while those open to visitation became inoculated. Nectar samples were species-poor, with a majority colonized by Metschnikowia reukaufii. Finally, increasing yeast density was correlated with a decrease in sucrose and an increase in monosaccharides. Our results document that yeasts form species-poor communities in populations of this hermaphroditic perennial, in addition to highlighting their spatio-temporal dynamics and effects on nectar quality. Spatio-temporal variation in frequency of occurrence, density, and changes in nectar may have important implications for the nature and strength of interactions between Delphinium and its pollinators.     

Rotation gene set testing for longitudinal expression data

Gene set analysis methods are popular tools for identifying differentially expressed gene sets in microarray data. Most existing methods use a permutation test to assess significance for each gene set. The permutation test's assumption of exchangeable samples is often not satisfied for time‐series data and complex experimental designs, and in addition it requires a certain number of samples to compute p‐values accurately. The method presented here uses a rotation test rather than a permutation test to assess significance. The rotation test can compute accurate p‐values also for very small sample sizes. The method can handle complex designs and is particularly suited for longitudinal microarray data where the samples may have complex correlation structures. Dependencies between genes, modeled with the use of gene networks, are incorporated in the estimation of correlations between samples. In addition, the method can test for both gene sets that are differentially expressed and gene sets that show strong time trends. We show on simulated longitudinal data that the ability to identify important gene sets may be improved by taking the correlation structure between samples into account. Applied to real data, the method identifies both gene sets with constant expression and gene sets with strong time trends.     

A Stochastic Model for Detecting Overlapping and Hierarchical Community Structure

Community detection is a fundamental problem in the analysis of complex networks. Recently, many researchers have concentrated on the detection of overlapping communities, where a vertex may belong to more than one community. However, most current methods require the number (or the size) of the communities as a priori information, which is usually unavailable in real-world networks. Thus, a practical algorithm should not only find the overlapping community structure, but also automatically determine the number of communities. Furthermore, it is preferable if this method is able to reveal the hierarchical structure of networks as well. In this work, we firstly propose a generative model that employs a nonnegative matrix factorization (NMF) formulization with a l_2,1 norm regularization term, balanced by a resolution parameter. The NMF has the nature that provides overlapping community structure by assigning soft membership variables to each vertex; the l_2,1 regularization term is a technique of group sparsity which can automatically determine the number of communities by penalizing too many nonempty communities; and hence the resolution parameter enables us to explore the hierarchical structure of networks. Thereafter, we derive the multiplicative update rule to learn the model parameters, and offer the proof of its correctness. Finally, we test our approach on a variety of synthetic and real-world networks, and compare it with some state-of-the-art algorithms. The results validate the superior performance of our new method.     

Use of pHluorin to Assess the Dynamics of Axon Guidance Receptors in Cell Culture and in the Chick Embryo

During development,axon guidance receptors play a crucial role in regulating axons sensitivity to both attractive and repulsive cues. Indeed, activation of the guidance receptors is the first step of the signaling mechanisms allowing axon tips, the growth cones, to respond to the ligands. As such, the modulation of their availability at the cell surface is one of the mechanisms that participate in setting the growth cone sensitivity. We describe here a method to precisely visualize the spatio-temporal cell surface dynamics of an axon guidance receptor both in vitro and in vivo in the developing chick spinal cord. We took advantage of the pH-dependent fluorescence property of a green fluorescent protein (GFP) variant to specifically detect the fraction of the axon guidance receptor that is addressed to the plasma membrane. We first describe the in vitro validation of such pH-dependent constructs and we further detail their use in vivo, in the chick spinal chord, to assess the spatio-temporal dynamics of the axon guidance receptor of interest.     

Dynamics of Sylvatic Chagas Disease Vectors in Coastal Ecuador Is Driven by Changes in Land Cover

Background

Chagas disease is a serious public health problem in Latin America where about ten million individuals show Trypanosoma cruzi infection. Despite significant success in controlling domiciliated triatomines, sylvatic populations frequently infest houses after insecticide treatment which hampers long term control prospects in vast geographical areas where vectorial transmission is endemic. As a key issue, the spatio-temporal dynamics of sylvatic populations is likely influenced by landscape yet evidence showing this effect is rare. The aim of this work is to examine the role of land cover changes in sylvatic triatomine ecology, based on an exhaustive field survey of pathogens, vectors, hosts, and microhabitat characteristics'' dynamics.

Methodology and Principal Findings

The study was performed in agricultural landscapes of coastal Ecuador as a study model. Over one year, a spatially-randomized sampling design (490 collection points) allowed quantifying triatomine densities in natural, cultivated and domestic habitats. We also assessed infection of the bugs with trypanosomes, documented their microhabitats and potential hosts, and recorded changes in landscape characteristics. In total we collected 886 individuals, mainly represented by nymphal stages of one triatomine species Rhodnius ecuadoriensis. As main results, we found that 1) sylvatic triatomines had very high T. cruzi infection rates (71%) and 2) densities of T. cruzi-infected sylvatic triatomines varied predictably over time due to changes in land cover and occurrence of associated rodent hosts.

Conclusion

We propose a framework for identifying the factors affecting the yearly distribution of sylvatic T. cruzi vectors. Beyond providing key basic information for the control of human habitat colonization by sylvatic vector populations, our framework highlights the importance of both environmental and sociological factors in shaping the spatio-temporal population dynamics of triatomines. A better understanding of the dynamics of such socio-ecological systems is a crucial, yet poorly considered, issue for the long-term control of Chagas disease.     

High Temporal Resolution Parametric MRI Monitoring of the Initial Ischemia/Reperfusion Phase in Experimental Acute Kidney Injury

Ischemia/reperfusion (I/R) injury, a consequence of kidney hypoperfusion or temporary interruption of blood flow is a common cause of acute kidney injury (AKI). There is an unmet need to better understand the mechanisms operative during the initial phase of ischemic AKI. Non-invasive in vivo parametric magnetic resonance imaging (MRI) may elucidate spatio-temporal pathophysiological changes in the kidney by monitoring the MR relaxation parameters T₂* and T₂, which are known to be sensitive to blood oxygenation. The aim of our study was to establish the technical feasibility of fast continuous T₂*/T₂ mapping throughout renal I/R. MRI was combined with a remotely controlled I/R model and a segmentation model based semi-automated quantitative analysis. This technique enabled the detailed assessment of in vivo changes in all kidney regions during ischemia and early reperfusion. Significant changes in T₂* and T₂ were observed shortly after induction of renal ischemia and during the initial reperfusion phase. Our study demonstrated for the first time that continuous and high temporal resolution parametric MRI is feasible for in-vivo monitoring and characterization of I/R induced AKI in rats. This technique may help in the identification of the timeline of key events responsible for development of renal damage in hypoperfusion-induced AKI.