Meta-analysis of functional neuroimaging data using Bayesian nonparametric binary regression

In this work we perform a meta-analysis of neuroimaging data, consisting of locations of peak activations identified in 162 separate studies on emotion. Neuroimaging meta-analyses are typically performed using kernel-based methods. However, these methods require the width of the kernel to be set a priori and to be constant across the brain. To address these issues, we propose a fully Bayesian nonparametric binary regression method to perform neuroimaging meta-analyses. In our method, each location (or voxel) has a probability of being a peak activation, and the corresponding probability function is based on a spatially adaptive Gaussian Markov random field (GMRF). We also include parameters in the model to robustify the procedure against miscoding of the voxel response. Posterior inference is implemented using efficient MCMC algorithms extended from those introduced in Holmes and Held [Bayesian Anal. 1 (2006) 145--168]. Our method allows the probability function to be locally adaptive with respect to the covariates, that is, to be smooth in one region of the covariate space and wiggly or even discontinuous in another. Posterior miscoding probabilities for each of the identified voxels can also be obtained, identifying voxels that may have been falsely classified as being activated. Simulation studies and application to the emotion neuroimaging data indicate that our method is superior to standard kernel-based methods.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS523 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Spatial Gaussian Markov Random Fields: Modelling, Applications and Efficient Computations

A powerful modelling tool for spatial data is the framework of Gaussian Markov random fields (GMRFs), which are discrete domain Gaussian random fields equipped with a Markov property. GMRFs allow us to combine the analytical results for the Gaussian distribution as well as Markov properties, thus allow for the development of computationally efficient algorithms. Here we briefly review popular spatial GMRFs, show how to construct them, and outline their recent developments and possible future work

A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis

Cortical surface functional magnetic resonance imaging (cs-fMRI) has recently grown in popularity versus traditional volumetric fMRI. In addition to offering better whole-brain visualization, dimension reduction, removal of extraneous tissue types, and improved alignment of cortical areas across subjects, it is also more compatible with common assumptions of Bayesian spatial models. However, as no spatial Bayesian model has been proposed for cs-fMRI data, most analyses continue to employ the classical general linear model (GLM), a “massive univariate” approach. Here, we propose a spatial Bayesian GLM for cs-fMRI, which employs a class of sophisticated spatial processes to model latent activation fields. We make several advances compared with existing spatial Bayesian models for volumetric fMRI. First, we use integrated nested Laplacian approximations, a highly accurate and efficient Bayesian computation technique, rather than variational Bayes. To identify regions of activation, we utilize an excursions set method based on the joint posterior distribution of the latent fields, rather than the marginal distribution at each location. Finally, we propose the first multi-subject spatial Bayesian modeling approach, which addresses a major gap in the existing literature. The methods are very computationally advantageous and are validated through simulation studies and two task fMRI studies from the Human Connectome Project. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement

The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since July 2014. This paper describes the second data release from this phase, and the fourteenth from SDSS overall (making this, Data Release Fourteen or DR14). This release makes public data taken by SDSS-IV in its first two years of operation (July 2014-2016). Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey (eBOSS); the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data driven machine learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS website (www.sdss.org) has been updated for this release, and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020, and will be followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14 happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov 2017 (this is the "post-print" and "post-proofs" version; minor corrections only from v1, and most of errors found in proofs corrected

Non-coding RNAs in pancreatic ductal adenocarcinoma: New approaches for better diagnosis and therapy

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive malignancies with a 5-year survival rate less than 8%, which has remained unchanged over the last 50 years. Early detection is particularly difficult due to the lack of disease-specific symptoms and a reliable biomarker. Multimodality treatment including chemotherapy, radiotherapy (used sparingly) and surgery has become the standard of care for patients with PDAC. Carbohydrate antigen 19–9 (CA 19–9) is the most common diagnostic biomarker; however, it is not specific enough especially for asymptomatic patients. Non-coding RNAs are often deregulated in human malignancies and shown to be involved in cancer-related mechanisms such as cell growth, differentiation, and cell death. Several micro, long non-coding and circular RNAs have been reported to date which are involved in PDAC. Aim of this review is to discuss the roles and functions of non-coding RNAs in diagnosis and treatments of PDAC

Comparative effects of intragastric and intraduodenal administration of quinine on the plasma glucose response to a mixed-nutrient drink in healthy men: relations with glucoregulatory hormones and gastric emptying

BACKGROUND: In preclinical studies, bitter compounds, including quinine, stimulate secretion of glucoregulatory hormones [e.g., glucagon-like peptide-1 (GLP-1)] and slow gastric emptying, both key determinants of postprandial glycemia. A greater density of bitter-taste receptors has been reported in the duodenum than the stomach. Thus, intraduodenal (ID) delivery may be more effective in stimulating GI functions to lower postprandial glucose. OBJECTIVE: We compared effects of intragastric (IG) and ID quinine [as quinine hydrochloride (QHCl)] administration on the plasma glucose response to a mixed-nutrient drink and relations with gastric emptying, plasma C-peptide (reflecting insulin secretion), and GLP-1. METHODS: Fourteen healthy men [mean ± SD age: 25 ± 3 y; BMI (in kg/m2): 22.5 ± 0.5] received, on 4 separate occasions, in double-blind, randomly assigned order, 600 mg QHCl or control, IG or ID, 60 min (IG conditions) or 30 min (IG conditions) before a mixed-nutrient drink. Plasma glucose (primary outcome) and hormones were measured before, and for 2 h following, the drink. Gastric emptying of the drink was measured using a 13C-acetate breath test. Data were analyzed using repeated-measures 2-way ANOVAs (factors: treatment and route of administration) to evaluate effects of QHCl alone and 3-way ANOVAs (factors: treatment, route-of-administration, and time) for responses to the drink. RESULTS: After QHCl alone, there were effects of treatment, but not route of administration, on C-peptide, GLP-1, and glucose (P < 0.05); QHCl stimulated C-peptide and GLP-1 and lowered glucose concentrations (IG control: 4.5 ± 0.1; IG-QHCl: 3.9 ± 0.1; ID-control: 4.6 ± 0.1; ID-QHCl: 4.2 ± 0.1 mmol/L) compared with control. Postdrink, there were treatment × time interactions for glucose, C-peptide, and gastric emptying, and a treatment effect for GLP-1 (all P < 0.05), but no route-of-administration effects. QHCl stimulated C-peptide and GLP-1, slowed gastric emptying, and reduced glucose (IG control: 7.2 ± 0.3; IG-QHCl: 6.2 ± 0.3; ID-control: 7.2 ± 0.3; ID-QHCl: 6.4 ± 0.4 mmol/L)  compared with control. CONCLUSIONS: In healthy men, IG and ID quinine administration similarly lowered plasma glucose, increased plasma insulin and GLP-1, and slowed gastric emptying. These findings have potential implications for lowering blood glucose in type 2 diabetes. This study was registered as a clinical trial with the Australian New Zealand Clinical Trials at www.anzctr.org.au as ACTRN12619001269123.Braden D Rose, Vida Bitarafan, Peyman Rezaie, Penelope C E Fitzgerald, Michael Horowitz, Christine Feinle-Bisse

Treating Pediatric Neuromuscular Disorders: The future is now

Pediatric neuromuscular diseases encompass all disorders with onset in childhood and where the primary area of pathology is in the peripheral nervous system. These conditions are largely genetic in etiology, and only those with a genetic underpinning will be presented in this review. This includes disorders of the anterior horn cell (e.g., spinal muscular atrophy), peripheral nerve (e.g., Charcot-Marie-Tooth disease), the neuromuscular junction (e.g., congenital myasthenic syndrome), and the muscle (myopathies and muscular dystrophies). Historically, pediatric neuromuscular disorders have uniformly been considered to be without treatment possibilities and to have dire prognoses. This perception has gradually changed, starting in part with the discovery and widespread application of corticosteroids for Duchenne muscular dystrophy. At present, several exciting therapeutic avenues are under investigation for a range of conditions, offering the potential for significant improvements in patient morbidities and mortality and, in some cases, curative intervention. In this review, we will present the current state of treatment for the most common pediatric neuromuscular conditions, and detail the treatment strategies with the greatest potential for helping with these devastating diseases