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Bayesian Brain

Yao-Tien Chen
The study proposes a novel approach for segmentation and visualization plus value-added surface area and volume measurements for brain medical image analysis. The proposed method contains edge detection and Bayesian based level set segmentation, surface and volume rendering, and surface area and volume measurements for 3D objects of interest (i.e., brain tumor, brain tissue, or whole brain). Two extensions based on edge detection and Bayesian level set are first used to segment 3D objects. Ray casting and a modified marching cubes algorithm are then adopted to facilitate volume and surface visualization of medical-image dataset...
February 17, 2017: Magnetic Resonance Imaging
Josephine Asafu-Adjei, G Tadesse Mahlet, Brent Coull, Raji Balasubramanian, Michael Lev, Lee Schwamm, Rebecca Betensky
Matched case-control designs are currently used in many biomedical applications. To ensure high efficiency and statistical power in identifying features that best discriminate cases from controls, it is important to account for the use of matched designs. However, in the setting of high dimensional data, few variable selection methods account for matching. Bayesian approaches to variable selection have several advantages, including the fact that such approaches visit a wider range of model subsets. In this paper, we propose a variable selection method to account for case-control matching in a Bayesian context and apply it using simulation studies, a matched brain imaging study conducted at Massachusetts General Hospital, and a matched cardiovascular biomarker study conducted by the High Risk Plaque Initiative...
January 31, 2017: International Journal of Biostatistics
Gonzalo López-Abente, Juan Locutura-Rupérez, Pablo Fernández-Navarro, Iván Martín-Méndez, Alejandro Bel-Lan, Olivier Núñez
The presence of toxic metals in soil per se, and in soil impacted by mining, industry, agriculture and urbanisation in particular, is a major concern for both human health and ecotoxicology. The dual aim of this study was: to ascertain whether topsoil composition could influence the spatial distribution of mortality due to different types of cancer and to identify possible errors committed by epidemiological studies which analyse soil composition data as a closed number system. We conducted an ecological cancer mortality study, covering 861,440 cancer deaths (27 cancer sites) in 7917 Spanish mainland towns, from 1999 to 2008...
February 2, 2017: Environmental Geochemistry and Health
Govinda R Poudel, Anjan Bhattarai, David L Dickinson, Sean P A Drummond
Many critical decisions require evaluation of accumulated previous information and/or newly acquired evidence. Although neural correlates of belief updating have been investigated, how these neural processes guide decisions involving Bayesian choice is less clear. Here, we used functional MRI to investigate neural activity during a Bayesian choice task involving two sources of information: base rate odds ('odds') and sample evidence ('evidence'). Thirty-seven healthy control individuals performed the Bayesian choice task in which they had to make probability judgements...
January 31, 2017: Neuroreport
Zhao-Hua Lu, Zakaria Khondker, Joseph G Ibrahim, Yue Wang, Hongtu Zhu
To perform a joint analysis of multivariate neuroimaging phenotypes and candidate genetic markers obtained from longitudinal studies, we develop a Bayesian longitudinal low-rank regression (L2R2) model. The L2R2 model integrates three key methodologies: a low-rank matrix for approximating the high-dimensional regression coefficient matrices corresponding to the genetic main effects and their interactions with time, penalized splines for characterizing the overall time effect, and a sparse factor analysis model coupled with random effects for capturing within-subject spatio-temporal correlations of longitudinal phenotypes...
January 29, 2017: NeuroImage
Richard H R Hahnloser, Gagan Narula
Motor systems are highly adaptive. Both birds and humans compensate for synthetically induced shifts in the pitch (fundamental frequency) of auditory feedback stemming from their vocalizations. Pitch-shift compensation is partial in the sense that large shifts lead to smaller relative compensatory adjustments of vocal pitch than small shifts. Also, compensation is larger in subjects with high motor variability. To formulate a mechanistic description of these findings, we adapt a Bayesian model of error relevance...
2017: PloS One
Sofie S Meyer, Holly Rossiter, Matthew J Brookes, Mark W Woolrich, Sven Bestmann, Gareth R Barnes
Magnetoencephalography (MEG) enables non-invasive real time characterization of brain activity. However, convincing demonstrations of signal contributions from deeper sources such as the hippocampus remain controversial and are made difficult by its depth, structural complexity and proximity to neocortex. Here, we demonstrate a method for quantifying hippocampal engagement probabilistically using simulated hippocampal activity and realistic anatomical and electromagnetic source modelling. We construct two generative models, one which supports neuronal current flow on the cortical surface, and one which supports it on both the cortical and hippocampal surfaces...
January 25, 2017: NeuroImage
Matthieu Le, Herve Delingette, Jayashree Kalpathy-Cramer, Elizabeth Gerstner, Tracy Batchelor, Jan Unkelbach, Nicholas Ayache
The mathematical modeling of brain tumor growth has been the topic of numerous research studies. Most of this work focuses on the reaction-diffusion model, which suggests that the diffusion coefficient and the proliferation rate can be related to clinically relevant information. However, estimating the parameters of the reaction-diffusion model is difficult because of the lack of identifiability of the parameters, the uncertainty in the tumor segmentations, and the model approximation, which cannot perfectly capture the complex dynamics of the tumor evolution...
April 29, 2016: IEEE Transactions on Medical Imaging
Archana Venkataraman, Daniel Yang, Kevin Pelphrey, James Duncan
We propose a unified Bayesian framework to detect both hyper- and hypo-active communities within whole-brain fMRI data. Specifically, our model identifies dense subgraphs that exhibit population-level differences in functional synchrony between a control and clinical group. We derive a variational EM algorithm to solve for the latent posterior distributions and parameter estimates, which subsequently inform us about the afflicted network topology. We demonstrate that our method provides valuable insights into the neural mechanisms underlying social dysfunction in autism, as verified by the Neurosynth metaanalytic database...
March 2, 2016: IEEE Transactions on Medical Imaging
Zhixiang Lin, Tao Wang, Can Yang, Hongyu Zhao
In this article, we first propose a Bayesian neighborhood selection method to estimate Gaussian Graphical Models (GGMs). We show the graph selection consistency of this method in the sense that the posterior probability of the true model converges to one. When there are multiple groups of data available, instead of estimating the networks independently for each group, joint estimation of the networks may utilize the shared information among groups and lead to improved estimation for each individual network...
January 18, 2017: Biometrics
Christian Donner, Klaus Obermayer, Hideaki Shimazaki
The models in statistical physics such as an Ising model offer a convenient way to characterize stationary activity of neural populations. Such stationary activity of neurons may be expected for recordings from in vitro slices or anesthetized animals. However, modeling activity of cortical circuitries of awake animals has been more challenging because both spike-rates and interactions can change according to sensory stimulation, behavior, or an internal state of the brain. Previous approaches modeling the dynamics of neural interactions suffer from computational cost; therefore, its application was limited to only a dozen neurons...
January 2017: PLoS Computational Biology
Mauro Ursino, Cristiano Cuppini, Elisa Magosso
Recent theoretical and experimental studies suggest that in multisensory conditions, the brain performs a near-optimal Bayesian estimate of external events, giving more weight to the more reliable stimuli. However, the neural mechanisms responsible for this behavior, and its progressive maturation in a multisensory environment, are still insufficiently understood. The aim of this letter is to analyze this problem with a neural network model of audiovisual integration, based on probabilistic population coding-the idea that a population of neurons can encode probability functions to perform Bayesian inference...
January 17, 2017: Neural Computation
Zizhen Yao, John K Mich, Sherman Ku, Vilas Menon, Anne-Rachel Krostag, Refugio A Martinez, Leon Furchtgott, Heather Mulholland, Susan Bort, Margaret A Fuqua, Ben W Gregor, Rebecca D Hodge, Anu Jayabalu, Ryan C May, Samuel Melton, Angelique M Nelson, N Kiet Ngo, Nadiya V Shapovalova, Soraya I Shehata, Michael W Smith, Leah J Tait, Carol L Thompson, Elliot R Thomsen, Chaoyang Ye, Ian A Glass, Ajamete Kaykas, Shuyuan Yao, John W Phillips, Joshua S Grimley, Boaz P Levi, Yanling Wang, Sharad Ramanathan
During human brain development, multiple signaling pathways generate diverse cell types with varied regional identities. Here, we integrate single-cell RNA sequencing and clonal analyses to reveal lineage trees and molecular signals underlying early forebrain and mid/hindbrain cell differentiation from human embryonic stem cells (hESCs). Clustering single-cell transcriptomic data identified 41 distinct populations of progenitor, neuronal, and non-neural cells across our differentiation time course. Comparisons with primary mouse and human gene expression data demonstrated rostral and caudal progenitor and neuronal identities from early brain development...
January 5, 2017: Cell Stem Cell
Dogu Baran Aydogan, Yonggang Shi
While tractography is widely used in brain imaging research, its quantitative validation is highly difficult. Many fiber systems, however, have well-known topographic organization which can even be quantitatively mapped such as the retinotopy of visual pathway. Motivated by this previously untapped anatomical knowledge, we develop a novel tractography method that preserves both topographic and geometric regularity of fiber systems. For topographic preservation, we propose a novel likelihood function that tests the match between parallel curves and fiber orientation distributions...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Anil K Seth, Karl J Friston
We review a recent shift in conceptions of interoception and its relationship to hierarchical inference in the brain. The notion of interoceptive inference means that bodily states are regulated by autonomic reflexes that are enslaved by descending predictions from deep generative models of our internal and external milieu. This re-conceptualization illuminates several issues in cognitive and clinical neuroscience with implications for experiences of selfhood and emotion. We first contextualize interoception in terms of active (Bayesian) inference in the brain, highlighting its enactivist (embodied) aspects...
November 19, 2016: Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
Gleb Bezgin, Ana Solodkin, Rembrandt Bakker, Petra Ritter, Anthony R McIntosh
Modern systems neuroscience increasingly leans on large-scale multi-lab neuroinformatics initiatives to provide necessary capacity for biologically realistic modeling of primate whole-brain activity. Here, we present a framework to assemble primate brain's biologically plausible anatomical backbone for such modeling initiatives. In this framework, structural connectivity is determined by adding complementary information from invasive macaque axonal tract tracing and non-invasive human diffusion tensor imaging...
January 5, 2017: Human Brain Mapping
Florent Meyniel, Maxime Maheu, Stanislas Dehaene
The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. Here, we explore the hypothesis that the brain acts as a near-optimal inference device that constantly attempts to infer the time-varying matrix of transition probabilities between the stimuli it receives, even when those stimuli are in fact fully unpredictable...
December 2016: PLoS Computational Biology
Johan Kwisthout, William A Phillips, Anil K Seth, Iris van Rooij, Andy Clark
No abstract text is available yet for this article.
December 24, 2016: Brain and Cognition
Minmin Miao, Hong Zeng, Aimin Wang, Changsen Zhao, Feixiang Liu
BACKGROUND: Common spatial pattern (CSP) is most widely used in motor imagery based brain-computer interface (BCI) systems. In conventional CSP algorithm, pairs of the eigenvectors corresponding to both extreme eigenvalues are selected to construct the optimal spatial filter. In addition, an appropriate selection of subject-specific time segments and frequency bands plays an important role in its successful application. NEW METHOD: This study proposes to optimize spatial-frequency-temporal patterns for discriminative feature extraction...
December 21, 2016: Journal of Neuroscience Methods
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