keyword
MENU ▼
Read by QxMD icon Read
search

Bayesian Brain

keyword
https://www.readbyqxmd.com/read/28814635/perception-of-the-dynamic-visual-vertical-during-sinusoidal-linear-motion
#1
Antonella Pomante, Luc P J Selen, W Pieter Medendorp
The vestibular system provides information for spatial orientation. However, this information is ambiguous: because the otoliths sense the gravito-inertial force, they cannot distinguish gravitational and inertial components. As a consequence, prolonged linear acceleration of the head can be interpreted as tilt, referred to as the somatogravic effect. Previous modeling work suggests that the brain disambiguates the otolith signal according to the rules of Bayesian inference, combining noisy canal cues with the a priori assumption that prolonged linear accelerations are unlikely...
August 16, 2017: Journal of Neurophysiology
https://www.readbyqxmd.com/read/28809668/tractography-based-score-for-learning-effective-connectivity-from-multimodal-imaging-data-using-dynamic-bayesian-networks
#2
Shilpa Dang, Santanu Chaudhury, Brejesh Lall, Prasun K Roy
OBJECTIVE: Effective connectivity (EC) is the methodology for determining functional-integration among the functionally-active segregated regions of the brain. By definition [1] EC is "the causal influence exerted by one neuronal group on another" which is constrained by anatomical connectivity (AC) (axonal connections). AC is necessary for EC but does not fully determine it, because synaptic communication occurs dynamically in a context-dependent fashion. Although there is a vast emerging evidence of structure-function relationship using multimodal imaging studies, till date only a few studies have done joint modelling of the two modalities: functional MRI (fMRI) and Diffusion Tensor Imaging (DTI)...
August 10, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28801251/modeling-correlated-noise-is-necessary-to-decode-uncertainty
#3
REVIEW
R S van Bergen, J F M Jehee
Brain decoding algorithms form an important part of the arsenal of analysis tools available to neuroscientists, allowing for a more detailed study of the kind of information represented in patterns of cortical activity. While most current decoding algorithms focus on estimating a single, most likely stimulus from the pattern of noisy fMRI responses, the presence of noise causes this estimate to be uncertain. This uncertainty in stimulus estimates is a potentially highly relevant aspect of cortical stimulus processing, and features prominently in Bayesian or probabilistic models of neural coding...
August 8, 2017: NeuroImage
https://www.readbyqxmd.com/read/28782681/a-bayesian-spatial-model-for-neuroimaging-data-based-on-biologically-informed-basis-functions
#4
Ismael Huertas, Marianne Oldehinkel, Erik S B van Oort, David Garcia-Solis, Pablo Mir, Christian F Beckmann, Andre F Marquand
The dominant approach to neuroimaging data analysis employs the voxel as the unit of computation. While convenient, voxels lack biological meaning and their size is arbitrarily determined by the resolution of the image. Here, we propose a multivariate spatial model in which neuroimaging data is characterized as a linearly weighted combination of multiscale basis functions which map onto underlying brain nuclei or networks or nuclei. In this model, the elementary building blocks are derived to reflect the functional anatomy of the brain during the resting state...
August 4, 2017: NeuroImage
https://www.readbyqxmd.com/read/28777721/infinite-von-mises-fisher-mixture-modeling-of-whole-brain-fmri-data
#5
Rasmus E Røge, Kristoffer H Madsen, Mikkel N Schmidt, Morten Mørup
Cluster analysis of functional magnetic resonance imaging (fMRI) data is often performed using gaussian mixture models, but when the time series are standardized such that the data reside on a hypersphere, this modeling assumption is questionable. The consequences of ignoring the underlying spherical manifold are rarely analyzed, in part due to the computational challenges imposed by directional statistics. In this letter, we discuss a Bayesian von Mises-Fisher (vMF) mixture model for data on the unit hypersphere and present an efficient inference procedure based on collapsed Markov chain Monte Carlo sampling...
August 4, 2017: Neural Computation
https://www.readbyqxmd.com/read/28774437/new-approach-to-detect-and-classify-stroke-in-skull-ct-images-via-analysis-of-brain-tissue-densities
#6
Pedro P Rebouças Filho, Róger Moura Sarmento, Gabriel Bandeira Holanda, Daniel de Alencar Lima
BACKGROUND AND OBJECTIVE: Cerebral vascular accident (CVA), also known as stroke, is an important health problem worldwide and it affects 16 million people worldwide every year. About 30% of those that have a stroke die and 40% remain with serious physical limitations. However, recovery in the damaged region is possible if treatment is performed immediately. In the case of a stroke, Computed Tomography (CT) is the most appropriate technique to confirm the occurrence and to investigate its extent and severity...
September 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28760786/how-are-age-related-differences-in-sleep-quality-associated-with-health-outcomes-an-epidemiological-investigation-in-a-uk-cohort-of-2406-adults
#7
Andrew Gadie, Meredith Shafto, Yue Leng, Rogier A Kievit
OBJECTIVES: To examine age-related differences in self-reported sleep quality and their associations with health outcomes across four domains: physical health, cognitive health, mental health and neural health. SETTING: Cambridge Centre for Ageing and Neuroscience (Cam-CAN) is a cohort study in East Anglia/England, which collected self-reported health and lifestyle questions as well as a range of objective measures from healthy adults. PARTICIPANTS: 2406 healthy adults (age 18-98) answered questions about their sleep quality (Pittsburgh Sleep Quality Index (PSQI)) and measures of physical, cognitive, mental and neural health...
July 31, 2017: BMJ Open
https://www.readbyqxmd.com/read/28748955/bayesian-association-scan-reveals-loci-associated-with-human-lifespan-and-linked-biomarkers
#8
Aaron F McDaid, Peter K Joshi, Eleonora Porcu, Andrea Komljenovic, Hao Li, Vincenzo Sorrentino, Maria Litovchenko, Roel P J Bevers, Sina Rüeger, Alexandre Reymond, Murielle Bochud, Bart Deplancke, Robert W Williams, Marc Robinson-Rechavi, Fred Paccaud, Valentin Rousson, Johan Auwerx, James F Wilson, Zoltán Kutalik
The enormous variation in human lifespan is in part due to a myriad of sequence variants, only a few of which have been revealed to date. Since many life-shortening events are related to diseases, we developed a Mendelian randomization-based method combining 58 disease-related GWA studies to derive longevity priors for all HapMap SNPs. A Bayesian association scan, informed by these priors, for parental age of death in the UK Biobank study (n=116,279) revealed 16 independent SNPs with significant Bayes factor at a 5% false discovery rate (FDR)...
July 27, 2017: Nature Communications
https://www.readbyqxmd.com/read/28740331/mixture-of-autoregressive-modeling-orders-and-its-implication-on-single-trial-eeg-classification
#9
Adham Atyabi, Frederick Shic, Adam Naples
Autoregressive (AR) models are of commonly utilized feature types in Electroencephalogram (EEG) studies due to offering better resolution, smoother spectra and being applicable to short segments of data. Identifying correct AR's modeling order is an open challenge. Lower model orders poorly represent the signal while higher orders increase noise. Conventional methods for estimating modeling order includes Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Final Prediction Error (FPE)...
December 15, 2016: Expert Systems with Applications
https://www.readbyqxmd.com/read/28733247/effective-connectivity-during-successful-and-unsuccessful-recollection-in-young-and-old-adults
#10
Selene Cansino, Patricia Trejo-Morales, Cinthya Estrada-Manilla, Erick Humberto Pasaye-Alcaraz, Erika Aguilar-Castañeda, Perla Salgado-Lujambio, Ana Luisa Sosa-Ortiz
Aging effects on regional brain activation have been studied extensively to explain the gradual recollection failure that occurs with advancing age. However, little is known about the consequence of aging on the interaction among brain regions that support recollection. The purpose of this study was to examine effective connectivity at encoding and retrieval during successful and unsuccessful recollection in young and old adults. In particular, we analyzed a recollection network that is characterized by its susceptibility to aging effects by middle age or later, which is comprised of the occipital cortex, hippocampus and orbitofrontal cortex...
July 19, 2017: Neuropsychologia
https://www.readbyqxmd.com/read/28727850/hybrid-cubature-kalman-filtering-for-identifying-nonlinear-models-from-sampled-recording-estimation-of-neuronal-dynamics
#11
Mahmoud K Madi, Fadi N Karameh
Kalman filtering methods have long been regarded as efficient adaptive Bayesian techniques for estimating hidden states in models of linear dynamical systems under Gaussian uncertainty. Recent advents of the Cubature Kalman filter (CKF) have extended this efficient estimation property to nonlinear systems, and also to hybrid nonlinear problems where by the processes are continuous and the observations are discrete (continuous-discrete CD-CKF). Employing CKF techniques, therefore, carries high promise for modeling many biological phenomena where the underlying processes exhibit inherently nonlinear, continuous, and noisy dynamics and the associated measurements are uncertain and time-sampled...
2017: PloS One
https://www.readbyqxmd.com/read/28714853/unveiling-the-development-of-intracranial-injury-using-dynamic-brain-eit-an-evaluation-of-current-reconstruction-algorithms
#12
Haoting Li, Rongqing Chen, Canhua Xu, Benyuan Liu, Mengxing Tang, Lin Yang, Xiuzhen Dong, Feng Fu
Dynamic brain EIT is a promising technique for continuous monitoring the development of cerebral injury. While there are many reconstruction algorithms available to brain EIT, there is still a lack of study to compare their performance in the context of dynamic brain monitoring. To address the problem, we develop a framework for evaluating different current algorithms with their ability to correctly identify small intracranial conductivity changes. Firstly, a simulation 3D head phantom with realistic layered structure and impedance distribution were developed...
July 17, 2017: Physiological Measurement
https://www.readbyqxmd.com/read/28702345/hierarchical-disruption-in-the-bayesian-brain-focal-epilepsy-and-brain-networks
#13
Amir Omidvarnia, Mangor Pedersen, Richard E Rosch, Karl J Friston, Graeme D Jackson
In this opinion paper, we describe a combined view of functional and effective brain connectivity along with the free-energy principle for investigating persistent disruptions in brain networks of patients with focal epilepsy. These changes are likely reflected in effective connectivity along the cortical hierarchy and construct the basis of increased local functional connectivity in focal epilepsy. We propose a testable framework based on dynamic causal modelling and functional connectivity analysis with the capacity of explaining commonly observed connectivity changes during interictal periods...
2017: NeuroImage: Clinical
https://www.readbyqxmd.com/read/28688579/replicable-and-coupled-changes-in-innate-and-adaptive-immune-gene-expression-in-two-case-control-studies-of-blood-microarrays-in-major-depressive-disorder
#14
Gwenaël G R Leday, Petra E Vértes, Sylvia Richardson, Jonathan R Greene, Tim Regan, Shahid Khan, Robbie Henderson, Tom C Freeman, Carmine M Pariante, Neil A Harrison, V Hugh Perry, Wayne C Drevets, Gayle M Wittenberg, Edward T Bullmore
BACKGROUND: Peripheral inflammation is often associated with major depressive disorder (MDD), and immunological biomarkers of depression remain a focus of investigation. METHODS: We used microarray data on whole blood from two independent case-control studies of MDD: the GlaxoSmithKline-High-Throughput Disease-specific target Identification Program [GSK-HiTDiP] study (113 patients and 57 healthy control subjects) and the Janssen-Brain Resource Company study (94 patients and 100 control subjects)...
July 6, 2017: Biological Psychiatry
https://www.readbyqxmd.com/read/28663729/estimating-the-information-extracted-by-a-single-spiking-neuron-from-a-continuous-input-time-series
#15
Fleur Zeldenrust, Sicco de Knecht, Wytse J Wadman, Sophie Denève, Boris Gutkin
Understanding the relation between (sensory) stimuli and the activity of neurons (i.e., "the neural code") lies at heart of understanding the computational properties of the brain. However, quantifying the information between a stimulus and a spike train has proven to be challenging. We propose a new (in vitro) method to measure how much information a single neuron transfers from the input it receives to its output spike train. The input is generated by an artificial neural network that responds to a randomly appearing and disappearing "sensory stimulus": the hidden state...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28663726/neural-cross-frequency-coupling-functions
#16
Tomislav Stankovski, Valentina Ticcinelli, Peter V E McClintock, Aneta Stefanovska
Although neural interactions are usually characterized only by their coupling strength and directionality, there is often a need to go beyond this by establishing the functional mechanisms of the interaction. We introduce the use of dynamical Bayesian inference for estimation of the coupling functions of neural oscillations in the presence of noise. By grouping the partial functional contributions, the coupling is decomposed into its functional components and its most important characteristics-strength and form-are quantified...
2017: Frontiers in Systems Neuroscience
https://www.readbyqxmd.com/read/28658252/integration-of-individual-and-social-information-for-decision-making-in-groups-of-different-sizes
#17
Seongmin A Park, Sidney Goïame, David A O'Connor, Jean-Claude Dreher
When making judgments in a group, individuals often revise their initial beliefs about the best judgment to make given what others believe. Despite the ubiquity of this phenomenon, we know little about how the brain updates beliefs when integrating personal judgments (individual information) with those of others (social information). Here, we investigated the neurocomputational mechanisms of how we adapt our judgments to those made by groups of different sizes, in the context of jury decisions for a criminal...
June 2017: PLoS Biology
https://www.readbyqxmd.com/read/28649314/hierarchical-cortical-transcriptome-disorganization-in-autism
#18
Michael V Lombardo, Eric Courchesne, Nathan E Lewis, Tiziano Pramparo
BACKGROUND: Autism spectrum disorders (ASD) are etiologically heterogeneous and complex. Functional genomics work has begun to identify a diverse array of dysregulated transcriptomic programs (e.g., synaptic, immune, cell cycle, DNA damage, WNT signaling, cortical patterning and differentiation) potentially involved in ASD brain abnormalities during childhood and adulthood. However, it remains unclear whether such diverse dysregulated pathways are independent of each other or instead reflect coordinated hierarchical systems-level pathology...
2017: Molecular Autism
https://www.readbyqxmd.com/read/28647485/dynamic-network-model-with-continuous-valued-nodes-for-longitudinal-brain-morphometry
#19
Rong Chen, Yuanjie Zheng, Erika Nixon, Edward H Herskovits
Longitudinal brain morphometry probes time-related brain morphometric patterns. We propose a method called dynamic network modeling with continuous valued nodes to generate a dynamic brain network from continuous valued longitudinal morphometric data. The mathematical framework of this method is based on state-space modeling. We use a bootstrap-enhanced least absolute shrinkage operator to solve the network-structure generation problem. In contrast to discrete dynamic Bayesian network modeling, the proposed method enables network generation directly from continuous valued high-dimensional short sequence data, being free from any discretization process...
June 21, 2017: NeuroImage
https://www.readbyqxmd.com/read/28630937/high-precision-neural-decoding-of-complex-movement-trajectories-using-recursive-bayesian-estimation-with-dynamic-movement-primitives
#20
Guy Hotson, Ryan J Smith, Adam G Rouse, Marc H Schieber, Nitish V Thakor, Brock A Wester
Brain-machine interfaces (BMIs) are a rapidly progressing technology with the potential to restore function to victims of severe paralysis via neural control of robotic systems. Great strides have been made in directly mapping a user's cortical activity to control of the individual degrees of freedom of robotic end-effectors. While BMIs have yet to achieve the level of reliability desired for widespread clinical use, environmental sensors (e.g. RGB-D cameras for object detection) and prior knowledge of common movement trajectories hold great potential for improving system performance...
July 2016: IEEE Robotics and Automation Letters
keyword
keyword
57181
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"