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Bayesian Brain

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https://www.readbyqxmd.com/read/29765298/real-time-tracking-of-selective-auditory-attention-from-m-eeg-a-bayesian-filtering-approach
#1
Sina Miran, Sahar Akram, Alireza Sheikhattar, Jonathan Z Simon, Tao Zhang, Behtash Babadi
Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, an ability which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the attentional state of a listener in a competing-speaker environment from non-invasive neuroimaging recordings such as magnetoencephalography (MEG) and electroencephalography (EEG). To this end, most existing approaches compute correlation-based measures by either regressing the features of each speech stream to the M/EEG channels (the decoding approach) or vice versa (the encoding approach)...
2018: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/29762723/pyabc-distributed-likelihood-free-inference
#2
Emmanuel Klinger, Dennis Rickert, Jan Hasenauer
Summary: Likelihood-free methods are often required for inference in systems biology. While Approximate Bayesian Computation (ABC) provides a theoretical solution, its practical application has often been challenging due to its high computational demands. To scale likelihood-free inference to computationally demanding stochastic models we developed pyABC: a distributed and scalable ABC-Sequential Monte Carlo (ABC-SMC) framework. It implements a scalable, runtime-minimizing parallelization strategy for multi-core and distributed environments scaling to thousands of cores...
May 14, 2018: Bioinformatics
https://www.readbyqxmd.com/read/29760996/enhancement-of-morphological-and-vascular-features-in-oct-images-using-a-modified-bayesian-residual-transform
#3
Bingyao Tan, Alexander Wong, Kostadinka Bizheva
A novel image processing algorithm based on a modified Bayesian residual transform (MBRT) was developed for the enhancement of morphological and vascular features in optical coherence tomography (OCT) and OCT angiography (OCTA) images. The MBRT algorithm decomposes the original OCT image into multiple residual images, where each image presents information at a unique scale. Scale selective residual adaptation is used subsequently to enhance morphological features of interest, such as blood vessels and tissue layers, and to suppress irrelevant image features such as noise and motion artefacts...
May 1, 2018: Biomedical Optics Express
https://www.readbyqxmd.com/read/29757142/autistic-traits-but-not-schizotypy-predict-increased-weighting-of-sensory-information-in-bayesian-visual-integration
#4
Povilas Karvelis, Aaron R Seitz, Stephen M Lawrie, Peggy Seriès
Recent theories propose that schizophrenia/schizotypy and autistic spectrum disorder are related to impairments in Bayesian inference i.e. how the brain integrates sensory information (likelihoods) with prior knowledge. However existing accounts fail to clarify: i) how proposed theories differ in accounts of ASD vs. schizophrenia and ii) whether the impairments result from weaker priors or enhanced likelihoods. Here, we directly address these issues by characterizing how 91 healthy participants, scored for autistic and schizotypal traits, implicitly learned and combined priors with sensory information...
May 14, 2018: ELife
https://www.readbyqxmd.com/read/29746906/multi-subject-hierarchical-inverse-covariance-modelling-improves-estimation-of-functional-brain-networks
#5
Giles L Colclough, Mark W Woolrich, Samuel J Harrison, Pedro A Rojas López, Pedro A Valdes-Sosa, Stephen M Smith
A Bayesian model for sparse, hierarchical inverse covariance estimation is presented, and applied to multi-subject functional connectivity estimation in the human brain. It enables simultaneous inference of the strength of connectivity between brain regions at both subject and population level, and is applicable to fmri, meg and eeg data. Two versions of the model can encourage sparse connectivity, either using continuous priors to suppress irrelevant connections, or using an explicit description of the network structure to estimate the connection probability between each pair of regions...
May 7, 2018: NeuroImage
https://www.readbyqxmd.com/read/29731954/msiq-joint-modeling-of-multiple-rna-seq-samples-for-accurate-isoform-quantification
#6
Wei Vivian Li, Anqi Zhao, Shihua Zhang, Jingyi Jessica Li
Next-generation RNA sequencing (RNA-seq) technology has been widely used to assess full-length RNA isoform abundance in a high-throughput manner. RNA-seq data offer insight into gene expression levels and transcriptome structures, enabling us to better understand the regulation of gene expression and fundamental biological processes. Accurate isoform quantification from RNA-seq data is challenging due to the information loss in sequencing experiments. A recent accumulation of multiple RNA-seq data sets from the same tissue or cell type provides new opportunities to improve the accuracy of isoform quantification...
March 2018: Annals of Applied Statistics
https://www.readbyqxmd.com/read/29713112/bayesian-modeling-of-nmr-data-quantifying-longitudinal-relaxation-in-vivo-and-in-vitro-with-a-tissue-water-relaxation-mimic-crosslinked-bovine-serum-albumin
#7
Kelsey Meinerz, Scott C Beeman, Chong Duan, G Larry Bretthorst, Joel R Garbow, Joseph J H Ackerman
Recently, a number of MRI protocols have been reported that seek to exploit the effect of dissolved oxygen (O2 , paramagnetic) on the longitudinal 1 H relaxation of tissue water, thus providing image contrast related to tissue oxygen content. However, tissue water relaxation is dependent on a number of mechanisms, and this raises the issue of how best to model the relaxation data. This problem, the model selection problem, occurs in many branches of science and is optimally addressed by Bayesian probability theory...
January 2018: Applied Magnetic Resonance
https://www.readbyqxmd.com/read/29712780/the-neural-correlates-of-hierarchical-predictions-for-perceptual-decisions
#8
Veith Weilnhammer, Heiner Stuke, Philipp Sterzer, Katharina Schmack
Sensory information is inherently noisy, sparse and ambiguous. In contrast, visual experience is usually clear, detailed and stable. Bayesian theories of perception resolve this discrepancy by assuming that prior knowledge about the causes underlying sensory stimulation actively shapes perceptual decisions. To this end, the central nervous system is believed to entertain a generative model aligned to dynamic changes in the hierarchical states of our volatile sensory environment. Here, we used model-based fMRI to study the neural correlates of the dynamic updating of hierarchically structured predictions in male and female human observers...
April 30, 2018: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/29706587/frontostriatal-dysfunction-during-decision-making-in-attention-deficit-hyperactivity-disorder-and-obsessive-compulsive-disorder
#9
Luke J Norman, Christina O Carlisi, Anastasia Christakou, Clodagh M Murphy, Kaylita Chantiluke, Vincent Giampietro, Andrew Simmons, Michael Brammer, David Mataix-Cols, Katya Rubia
BACKGROUND: The aim of the current paper is to provide the first comparison of computational mechanisms and neurofunctional substrates in adolescents with attention-deficit/hyperactivity disorder (ADHD) and adolescents with obsessive-compulsive disorder (OCD) during decision making under ambiguity. METHODS: Sixteen boys with ADHD, 20 boys with OCD, and 20 matched control subjects (12-18 years of age) completed a functional magnetic resonance imaging version of the Iowa Gambling Task...
March 24, 2018: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
https://www.readbyqxmd.com/read/29705690/transferring-and-generalizing-deep-learning-based-neural-encoding-models-across-subjects
#10
Haiguang Wen, Junxing Shi, Wei Chen, Zhongming Liu
Recent studies have shown the value of using deep learning models for mapping and characterizing how the brain represents and organizes information for natural vision. However, modeling the relationship between deep learning models and the brain (or encoding models), requires measuring cortical responses to large and diverse sets of natural visual stimuli from single subjects. This requirement limits prior studies to few subjects, making it difficult to generalize findings across subjects or for a population...
April 26, 2018: NeuroImage
https://www.readbyqxmd.com/read/29675463/caregiver-burden-and-caregiver-appraisal-of-psychiatric-symptoms-are-not-modulated-by-subthalamic-deep-brain-stimulation-for-parkinson-s-disease
#11
Philip E Mosley, Michael Breakspear, Terry Coyne, Peter Silburn, David Smith
Subthalamic deep brain stimulation is an advanced therapy that typically improves quality of life for persons with Parkinson's disease (PD). However, the effect on caregiver burden is unclear. We recruited 64 persons with PD and their caregivers from a movement disorders clinic during the assessment of eligibility for subthalamic DBS. We used clinician-, patient- and caregiver-rated instruments to follow the patient-caregiver dyad from pre- to postoperative status, sampling repeatedly in the postoperative period to ascertain fluctuations in phenotypic variables...
2018: NPJ Parkinson's Disease
https://www.readbyqxmd.com/read/29664887/%C3%AE-aminobutyric-acid-type-a-receptor-potentiation-inhibits-learning-in-a-computational-network-model
#12
Kingsley P Storer, George N Reeke
BACKGROUND: Propofol produces memory impairment at concentrations well below those abolishing consciousness. Episodic memory, mediated by the hippocampus, is most sensitive. Two potentially overlapping scenarios may explain how γ-aminobutyric acid receptor type A (GABAA) potentiation by propofol disrupts episodic memory-the first mediated by shifting the balance from excitation to inhibition while the second involves disruption of rhythmic oscillations. We use a hippocampal network model to explore these scenarios...
April 17, 2018: Anesthesiology
https://www.readbyqxmd.com/read/29662345/stochastic-isotropic-hyperelastic-materials-constitutive-calibration-and-model-selection
#13
L Angela Mihai, Thomas E Woolley, Alain Goriely
Biological and synthetic materials often exhibit intrinsic variability in their elastic responses under large strains, owing to microstructural inhomogeneity or when elastic data are extracted from viscoelastic mechanical tests. For these materials, although hyperelastic models calibrated to mean data are useful, stochastic representations accounting also for data dispersion carry extra information about the variability of material properties found in practical applications. We combine finite elasticity and information theories to construct homogeneous isotropic hyperelastic models with random field parameters calibrated to discrete mean values and standard deviations of either the stress-strain function or the nonlinear shear modulus, which is a function of the deformation, estimated from experimental tests...
March 2018: Proceedings. Mathematical, Physical, and Engineering Sciences
https://www.readbyqxmd.com/read/29655936/dynamic-causal-modelling-on-infant-fnirs-data-a-validation-study-on-a-simultaneously-recorded-fnirs-fmri-dataset
#14
Chiara Bulgarelli, Anna Blasi, Simon Arridge, Samuel Powell, Carina C J M de Klerk, Victoria Southgate, Sabrina Brigadoi, William Penny, Sungho Tak, Antonia Hamilton
Tracking the connectivity of the developing brain from infancy through childhood is an area of increasing research interest, and fNIRS provides an ideal method for studying the infant brain as it is compact, safe and robust to motion. However, data analysis methods for fNIRS are still underdeveloped compared to those available for fMRI. Dynamic causal modelling (DCM) is an advanced connectivity technique developed for fMRI data, that aims to estimate the coupling between brain regions and how this might be modulated by changes in experimental conditions...
April 12, 2018: NeuroImage
https://www.readbyqxmd.com/read/29628877/source-reconstruction-of-brain-potentials-using-bayesian-model-averaging-to-analyze-face-intra-domain-vs-face-occupation-cross-domain-processing
#15
Ela I Olivares, Agustín Lage-Castellanos, María A Bobes, Jaime Iglesias
We investigated the neural correlates of the access to and retrieval of face structure information in contrast to those concerning the access to and retrieval of person-related verbal information, triggered by faces. We experimentally induced stimulus familiarity via a systematic learning procedure including faces with and without associated verbal information. Then, we recorded event-related potentials (ERPs) in both intra-domain (face-feature) and cross-domain (face-occupation) matching tasks while N400-like responses were elicited by incorrect eyes-eyebrows completions and occupations, respectively...
2018: Frontiers in Integrative Neuroscience
https://www.readbyqxmd.com/read/29621266/intrinsic-and-extrinsic-motivators-of-attachment-under-active-inference
#16
David Cittern, Tobias Nolte, Karl Friston, Abbas Edalat
This paper addresses the formation of infant attachment types within the context of active inference: a holistic account of action, perception and learning in the brain. We show how the organised forms of attachment (secure, avoidant and ambivalent) might arise in (Bayesian) infants. Specifically, we show that these distinct forms of attachment emerge from a minimisation of free energy-over interoceptive states relating to internal stress levels-when seeking proximity to caregivers who have a varying impact on these interoceptive states...
2018: PloS One
https://www.readbyqxmd.com/read/29614077/a-probabilistic-distributed-recursive-mechanism-for-decision-making-in-the-brain
#17
Javier A Caballero, Mark D Humphries, Kevin N Gurney
Decision formation recruits many brain regions, but the procedure they jointly execute is unknown. Here we characterize its essential composition, using as a framework a novel recursive Bayesian algorithm that makes decisions based on spike-trains with the statistics of those in sensory cortex (MT). Using it to simulate the random-dot-motion task, we demonstrate it quantitatively replicates the choice behaviour of monkeys, whilst predicting losses of otherwise usable information from MT. Its architecture maps to the recurrent cortico-basal-ganglia-thalamo-cortical loops, whose components are all implicated in decision-making...
April 2018: PLoS Computational Biology
https://www.readbyqxmd.com/read/29614016/objecthood-agency-and-mutualism-in-valenced-farm-animal-environments
#18
REVIEW
Ian G Colditz
Genetic selection of farm animals for productivity, and intensification of farming practices have yielded substantial improvements in efficiency; however, the capacity of animals to cope with environmental challenges has diminished. Understanding how the animal and environment interact is central to efforts to improve the health, fitness, and welfare of animals through breeding and management strategies. The review examines aspects of the environment that are sensed by the animal. The predictive brain model of sensory perception and motor action (the Bayesian brain model) and its recent extension to account for anticipatory, predictive control of physiological activities is described...
April 3, 2018: Animals: An Open Access Journal From MDPI
https://www.readbyqxmd.com/read/29610105/cortical-thinning-and-cognitive-impairment-in-parkinson-s-disease-without-dementia
#19
Lijun Zhang, Ming Wang, Nicholas W Sterling, Eun-Young Lee, Paul J Eslinger, Daymond Wagner, Guangwei Du, Mechelle M Lewis, Young Truong, F DuBois Bowman, Xuemei Huang
Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized clinically by motor dysfunction (bradykinesia, rigidity, tremor, and postural instability), and pathologically by the loss of dopaminergic neurons in the substantia nigra of the basal ganglia. Growing literature supports that cognitive deficits may also be present in PD, even in non-demented patients. Gray matter (GM) atrophy has been reported in PD and may be related to cognitive decline. This study investigated cortical thickness in non-demented PD subjects and elucidated its relationship to cognitive impairment using high-resolution T1-weighted brain MRI and comprehensive cognitive function scores from 71 non-demented PD and 48 control subjects matched for age, gender, and education...
March 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/29610102/bayesian-multiresolution-variable-selection-for-ultra-high-dimensional-neuroimaging-data
#20
Yize Zhao, Jian Kang, Qi Long
Ultra-high dimensional variable selection has become increasingly important in analysis of neuroimaging data. For example, in the Autism Brain Imaging Data Exchange (ABIDE) study, neuroscientists are interested in identifying important biomarkers for early detection of the autism spectrum disorder (ASD) using high resolution brain images that include hundreds of thousands voxels. However, most existing methods are not feasible for solving this problem due to their extensive computational costs. In this work, we propose a novel multiresolution variable selection procedure under a Bayesian probit regression framework...
March 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
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