STGP: Spatio-temporal Gaussian process models for longitudinal neuroimaging data

Longitudinal neuroimaging data plays an important role in mapping the neural developmental profile of major neuropsychiatric and neurodegenerative disorders and normal brain. The development of such developmental maps is critical for the prevention, diagnosis, and treatment of many brain-related diseases. The aim of this paper is to develop a spatio-temporal Gaussian process (STGP) framework to accurately delineate the developmental trajectories of brain structure and function, while achieving better prediction by explicitly incorporating the spatial and temporal features of longitudinal neuroimaging data. Our STGP integrates a functional principal component model (FPCA) and a partition parametric space-time covariance model to capture the medium-to-large and small-to-medium spatio-temporal dependence structures, respectively. We develop a three-stage efficient estimation procedure as well as a predictive method based on a kriging technique. Two key novelties of STGP are that it can efficiently use a small number of parameters to capture complex non-stationary and non-separable spatio-temporal dependence structures and that it can accurately predict spatio-temporal changes. We illustrate STGP using simulated data sets and two real data analyses including longitudinal positron emission tomography data from the Alzheimers Disease Neuroimaging Initiative (ADNI) and longitudinal lateral ventricle surface data from a longitudinal study of early brain development

Diseased Region Detection of Longitudinal Knee Magnetic Resonance Imaging Data

Magnetic resonance imaging (MRI) has become an important imaging technique for quantifying the spatial location and magnitude/direction of longitudinal cartilage morphology changes in patients with osteoarthritis (OA). Although several analytical methods, such as subregion-based analysis, have been developed to refine and improve quantitative cartilage analyses, they can be suboptimal due to two major issues: the lack of spatial correspondence across subjects and time and the spatial heterogeneity of cartilage progression across subjects. The aim of this paper is to present a statistical method for longitudinal cartilage quantification in OA patients, while addressing these two issues. The 3D knee image data is preprocessed to establish spatial correspondence across subjects and/or time. Then, a Gaussian hidden Markov model (GHMM) is proposed to deal with the spatial heterogeneity of cartilage progression across both time and OA subjects. To estimate unknown parameters in GHMM, we employ a pseudo-likelihood function and optimize it by using an expectation-maximization (EM) algorithm. The proposed model can effectively detect diseased regions in each OA subject and present a localized analysis of longitudinal cartilage thickness within each latent subpopulation. Our GHMM integrates the strengths of two standard statistical methods including the local subregion-based analysis and the ordered value approach. We use simulation studies and the Pfizer longitudinal knee MRI dataset to evaluate the finite sample performance of GHMM in the quantification of longitudinal cartilage morphology changes. Our results indicate that GHMM significantly outperforms several standard analytical methods

Brain immune interactions and air pollution: macrophage inhibitory factor (MIF), prion cellular protein (PrPC), Interleukin-6 (IL-6), interleukin 1 receptor antagonist (IL-1Ra), and interleukin-2 (IL-2) in cerebrospinal fluid and MIF in serum differentiate urban children exposed to severe vs. low air pollution

Mexico City Metropolitan Area children chronically exposed to high concentrations of air pollutants exhibit an early brain imbalance in genes involved in oxidative stress, inflammation, innate and adaptive immune responses along with accumulation of misfolded proteins observed in the early stages of Alzheimer and Parkinson's diseases. A complex modulation of serum cytokines and chemokines influences children's brain structural and gray/white matter volumetric responses to air pollution. The search for biomarkers associating systemic and CNS inflammation to brain growth and cognitive deficits in the short term and neurodegeneration in the long-term is our principal aim. We explored and compared a profile of cytokines, chemokines (Multiplexing LASER Bead Technology) and Cellular prion protein (PrP(C)) in normal cerebro-spinal-fluid (CSF) of urban children with high vs. low air pollution exposures. PrP(C) and macrophage inhibitory factor (MIF) were also measured in serum. Samples from 139 children ages 11.91 ± 4.2 years were measured. Highly exposed children exhibited significant increases in CSF MIF (p = 0.002), IL6 (p = 0.006), IL1ra (p = 0.014), IL-2 (p = 0.04), and PrP(C) (p = 0.039) vs. controls. MIF serum concentrations were higher in exposed children (p = 0.009). Our results suggest CSF as a MIF, IL6, IL1Ra, IL-2, and PrP(C) compartment that can possibly differentiate air pollution exposures in children. MIF, a key neuro-immune mediator, is a potential biomarker bridge to identify children with CNS inflammation. Fine tuning of immune-to-brain communication is crucial to neural networks appropriate functioning, thus the short and long term effects of systemic inflammation and dysregulated neural immune responses are of deep concern for millions of exposed children. Defining the linkage and the health consequences of the brain / immune system interactions in the developing brain chronically exposed to air pollutants ought to be of pressing importance for public health

FVGWAS: Fast voxelwise genome wide association analysis of large-scale imaging genetic data

More and more large-scale imaging genetic studies are being widely conducted to collect a rich set of imaging, genetic, and clinical data to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. Several major big-data challenges arise from testing genome-wide (NC > 12 million known variants) associations with signals at millions of locations (NV ~ 106) in the brain from thousands of subjects (n ~ 103). The aim of this paper is to develop a Fast Voxelwise Genome Wide Association analysiS (FVGWAS) framework to e ciently carry out whole-genome analyses of whole-brain data. FVGWAS consists of three components including a heteroscedastic linear model, a global sure independence screening (G-SIS) procedure, and a detection procedure based on wild bootstrap methods. Specifically, for standard linear association, the computational complexity is O(nNV NC) for voxelwise genome wide association analysis (VGWAS) method compared with O((NC + NV)n2) for FVGWAS. Simulation studies show that FVGWAS is an effcient method of searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. Finally, we have successfully applied FVGWAS to a large-scale imaging genetic data analysis of ADNI data with 708 subjects, 193,275 voxels in RAVENS maps, and 501,584 SNPs, and the total processing time was 203,645 seconds for a single CPU. Our FVG-WAS may be a valuable statistical toolbox for large-scale imaging genetic analysis as the field is rapidly advancing with ultra-high-resolution imaging and whole-genome sequencing

3D tract-specific local and global analysis of white matter integrity in Alzheimer's disease: White Matter Integrity in Alzheimer's Disease

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive decline in memory and other aspects of cognitive function. Diffusion‐weighted imaging (DWI) offers a non‐invasive approach to delineate the effects of AD on white matter (WM) integrity. Previous studies calculated either some summary statistics over regions of interest (ROI analysis) or some statistics along mean skeleton lines (Tract Based Spatial Statistic [TBSS]), so they cannot quantify subtle local WM alterations along major tracts. Here, a comprehensive WM analysis framework to map disease effects on 3D tracts both locally and globally, based on a study of 200 subjects: 49 healthy elderly normal controls, 110 with mild cognitive impairment, and 41 AD patients has been presented. 18 major WM tracts were extracted with our automated clustering algorithm—autoMATE (automated Multi‐Atlas Tract Extraction); we then extracted multiple DWI‐derived parameters of WM integrity along the WM tracts across all subjects. A novel statistical functional analysis method—FADTTS (Functional Analysis for Diffusion Tensor Tract Statistics) was applied to quantify degenerative patterns along WM tracts across different stages of AD. Gradually increasing WM alterations were found in all tracts in successive stages of AD. Among all 18 WM tracts, the fornix was most adversely affected. Among all the parameters, mean diffusivity (MD) was the most sensitive to WM alterations in AD. This study provides a systematic workflow to examine WM integrity across automatically computed 3D tracts in AD and may be useful in studying other neurological and psychiatric disorders. Hum Brain Mapp 38:1191–1207, 2017. © 2016 Wiley Periodicals, Inc

Anti-MUC1 Monoclonal Antibody (C595) and Docetaxel Markedly Reduce Tumor Burden and Ascites, and Prolong Survival in an in vivo Ovarian Cancer Model

MUC1 is associated with cellular transformation and tumorigenicity and is considered as an important tumor-associated antigen (TAA) for cancer therapy. We previously reported that anti-MUC1 monoclonal antibody C595 (MAb C595) plus docetaxel (DTX) increased efficacy of DTX alone and caused cultured human epithelial ovarian cancer (EOC) cells to undergo apoptosis. To further study the mechanisms of this combination-mediated apoptosis, we investigated the effectiveness of this combination therapy in vivo in an intraperitoneal (i.p.) EOC mouse model. OVCAR-3 cells were implanted intraperitoneally in female athymic nude mice and allowed to grow tumor and ascites. Mice were then treated with single MAb C595, DTX, combination test (MAb C595 and DTX), combination control (negative MAb IgG3 and DTX) or vehicle control i.p for 3 weeks. Treated mice were killed 4 weeks post-treatment. Ascites volume, tumor weight, CA125 levels from ascites and survival of animals were assessed. The expression of MUC1, CD31, Ki-67, TUNEL and apoptotic proteins in tumor xenografts was evaluated by immunohistochemistry. MAb C595 alone inhibited i.p. tumor growth and ascites production in a dose-dependent manner but did not obviously prevent tumor development. However, combination test significantly reduced ascites volume, tumor growth and metastases, CA125 levels in ascites and improved survival of treated mice compared with single agent-treated mice, combination control or vehicle control-treated mice (P<0.05). The data was in a good agreement with that from cultured cells in vitro. The mechanisms behind the observed effects could be through targeting MUC1 antigens, inhibition of tumor angiogenesis, and induction of apoptosis. Our results suggest that this combination approach can effectively reduce tumor burden and ascites, prolong survival of animals through induction of tumor apoptosis and necrosis, and may provide a potential therapy for advanced metastatic EOC

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges

Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the “HPV” challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the “local recurrence” challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings

A Functional Varying-Coefficient Single Index Model for Functional Response Data

10.1080/01621459.2016.1195742Journal of the American Statistical Association1125191169-118

Hsa-miR-105-1 Regulates Cisplatin-Resistance in Ovarian Carcinoma Cells by Targeting ANXA9

Purpose. Cisplatin is one of the most effective drugs for treating ovarian carcinoma (OC), which is among the most lethal types of carcinoma. However, the chemoresistance to cisplatin that develops over time leads to a poor clinical outcome for many OC patients. Therefore, it is necessary to clearly understand the molecular mechanisms of chemoresistance. In this study, we examined how Hsa-miR-105-1 functions in cisplatin-resistant OC cells. Methods. The levels of Hsa-miR-105-1 expression in cisplatin-sensitive and resistant OC cell lines were detected by qRT-PCR. The target gene of Hsa-miR-105-1 was predicted by using the TargetScan and Starbase databases and verified by the double luciferase reporter gene assay. The target gene of Hsa-miR-105-1 was identified as ANXA9, and ANXA9 expression was evaluated by qRT-PCR, western blotting, and immunofluorescence. To validate the function of Hsa-miR-105-1 in OC cells, we silenced or overexpressed Hsa-miR-105-1 in cisplatin-sensitive or resistant OC cell lines, respectively. Furthermore, the expression levels of several apoptosis-related proteins, including P53, P21, E2F1, Bcl-2, Bax, and caspase-3, were examined by western blot analysis. Results. The levels of Hsa-miR-105-1 expression were abnormally downregulated in cisplatin-resistant OC cells, while ANXA9 expression was significantly upregulated in those cells. Treatment with an Hsa-miR-105-1 inhibitor promoted the expression of ANXA9 mRNA and protein, enhanced the resistance to cisplatin, and attenuated the cell apoptosis induced by cisplatin in cisplatin-sensitive OC cells. Moreover, treatment with Hsa-miR-105-1 mimics inhibited ANXA9 expression, which further increased the levels of P53, P21, and Bax expression and decreased the levels of E2F1 and Bcl-2 expression, finally resulting in an increased sensitivity to cisplatin in cisplatin-resistant OC cells. Conclusion. We found that a downregulation of Hsa-miR-105-1 expression enhanced cisplatin-resistance, while an upregulation of Hsa-miR-105-1 restored the sensitivity of OC cells to cisplatin. The Hsa-miR-105-1/ANXA9 axis plays an important role in the cisplatin-resistance of OC cells