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Bayesian Brain

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https://www.readbyqxmd.com/read/29118202/bayesian-optimal-adaptation-explains-age-related-human-sensorimotor-changes
#1
Faisal Karmali, Gregory T Whitman, Richard F Lewis
The brain uses information from different sensory systems to guide motor behavior, and aging is associated with a simultaneous decline in the quality of sensory information provided to the brain and a deterioration in motor control. Correlations between age-dependent decline in sensory anatomical structures and behavior have been demonstrated, and it has recently been suggested that a Bayesian framework could explain these relationships. Here we show that age-dependent changes in a human sensorimotor reflex, the vestibulo-ocular reflex, are explained by a Bayesian optimal adaptation in the brain occurring in response to death of motion-sensing hair cells...
November 8, 2017: Journal of Neurophysiology
https://www.readbyqxmd.com/read/29104148/laminar-fmri-and-computational-theories-of-brain-function
#2
REVIEW
K E Stephan, F H Petzschner, L Kasper, J Bayer, K V Wellstein, G Stefanics, K P Pruessmann, J Heinzle
Recently developed methods for functional MRI at the resolution of cortical layers (laminar fMRI) offer a novel window into neurophysiological mechanisms of cortical activity. Beyond physiology, laminar fMRI also offers an unprecedented opportunity to test influential theories of brain function. Specifically, hierarchical Bayesian theories of brain function, such as predictive coding, assign specific computational roles to different cortical layers. Combined with computational models, laminar fMRI offers a unique opportunity to test these proposals noninvasively in humans...
November 2, 2017: NeuroImage
https://www.readbyqxmd.com/read/29100938/generative-diffeomorphic-modelling-of-large-mri-data-sets-for-probabilistic-template-construction
#3
Claudia Blaiotta, Patrick Freund, M Jorge Cardoso, John Ashburner
In this paper we present a hierarchical generative model of medical image data, which can capture simultaneously the variability of both signal intensity and anatomical shapes across large populations. Such a model has a direct application for learning average-shaped probabilistic tissue templates in a fully automated manner. While in principle the generality of the proposed Bayesian approach makes it suitable to address a wide range of medical image computing problems, our work focuses primarily on neuroimaging applications...
October 31, 2017: NeuroImage
https://www.readbyqxmd.com/read/29093658/on-the-complexity-of-human-neuroanatomy-at-the-millimeter-morphome-scale-developing-codes-and-characterizing-entropy-indexed-to-spatial-scale
#4
Daniel J Tward, Michael I Miller
In this work we devise a strategy for discrete coding of anatomical form as described by a Bayesian prior model, quantifying the entropy of this representation as a function of code rate (number of bits), and its relationship geometric accuracy at clinically relevant scales. We study the shape of subcortical gray matter structures in the human brain through diffeomorphic transformations that relate them to a template, using data from the Alzheimer's Disease Neuroimaging Initiative to train a multivariate Gaussian prior model...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/29074891/stochastic-spin-orbit-torque-devices-as-elements-for-bayesian-inference
#5
Yong Shim, Shuhan Chen, Abhronil Sengupta, Kaushik Roy
Probabilistic inference from real-time input data is becoming increasingly popular and may be one of the potential pathways at enabling cognitive intelligence. As a matter of fact, preliminary research has revealed that stochastic functionalities also underlie the spiking behavior of neurons in cortical microcircuits of the human brain. In tune with such observations, neuromorphic and other unconventional computing platforms have recently started adopting the usage of computational units that generate outputs probabilistically, depending on the magnitude of the input stimulus...
October 26, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29073108/visual-perception-as-retrospective-bayesian-decoding-from-high-to-low-level-features
#6
Stephanie Ding, Christopher J Cueva, Misha Tsodyks, Ning Qian
When a stimulus is presented, its encoding is known to progress from low- to high-level features. How these features are decoded to produce perception is less clear, and most models assume that decoding follows the same low- to high-level hierarchy of encoding. There are also theories arguing for global precedence, reversed hierarchy, or bidirectional processing, but they are descriptive without quantitative comparison with human perception. Moreover, observers often inspect different parts of a scene sequentially to form overall perception, suggesting that perceptual decoding requires working memory, yet few models consider how working-memory properties may affect decoding hierarchy...
October 24, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/29060718/spatially-regularized-multifractal-analysis-for-fmri-data
#7
Philippe Ciuciu, Herwig Wendt, Sebastien Combrexelle, Patrice Abry
Scale-free dynamics is nowadays a massively used paradigm to model infraslow macroscopic brain activity. Multifractal analysis is becoming the standard tool to characterize scale-free dynamics. It is commonly used on various modalities of neuroimaging data to evaluate whether arrhythmic fluctuations in ongoing or evoked brain activity are related to pathologies (Alzheimer, epilepsy) or task performance. The success of multifractal analysis in neurosciences remains however so far contrasted: While it lead to relevant findings on M/EEG data, less clear impact was shown when applied to fMRI data...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29060522/context-aware-recursive-bayesian-graph-traversal-in-bcis
#8
Seyed Sadegh Mohseni Salehi, Mohammad Moghadamfalahi, Hooman Nezamfar, Marzieh Haghighi, Deniz Erdogmus
Noninvasive brain computer interfaces (BCI), and more specifically Electroencephalography (EEG) based systems for intent detection need to compensate for the low signal to noise ratio of EEG signals. In many applications, the temporal dependency information from consecutive decisions and contextual data can be used to provide a prior probability for the upcoming decision. In this study we proposed two probabilistic graphical models (PGMs), using context information and previously observed EEG evidences to estimate a probability distribution over the decision space in graph based decision-making mechanism...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29060296/design-of-deep-convolutional-networks-for-prediction-of-image-rapid-serial-visual-presentation-events
#9
Zijing Mao, Wan Xiang Yao, Yufe Huang
We report in this paper an investigation of convolutional neural network (CNN) models for target prediction in time-locked image rapid serial visual presentation (RSVP) experiment. We investigated CNN models with 11 different designs of convolution filters in capturing spatial and temporal correlations in EEG data. We showed that for both within-subject and cross-subject predictions, the CNN models outperform the state-of-the-art algorithms: Bayesian linear discriminant analysis (BLDA) and xDAWN spatial filtering and achieved >6% improvement...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29059185/decoding-brain-activity-using-a-large-scale-probabilistic-functional-anatomical-atlas-of-human-cognition
#10
Timothy N Rubin, Oluwasanmi Koyejo, Krzysztof J Gorgolewski, Michael N Jones, Russell A Poldrack, Tal Yarkoni
A central goal of cognitive neuroscience is to decode human brain activity-that is, to infer mental processes from observed patterns of whole-brain activation. Previous decoding efforts have focused on classifying brain activity into a small set of discrete cognitive states. To attain maximal utility, a decoding framework must be open-ended, systematic, and context-sensitive-that is, capable of interpreting numerous brain states, presented in arbitrary combinations, in light of prior information. Here we take steps towards this objective by introducing a probabilistic decoding framework based on a novel topic model-Generalized Correspondence Latent Dirichlet Allocation-that learns latent topics from a database of over 11,000 published fMRI studies...
October 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/29046631/development-of-a-bayesian-estimator-for-audio-visual-integration-a-neurocomputational-study
#11
Mauro Ursino, Andrea Crisafulli, Giuseppe di Pellegrino, Elisa Magosso, Cristiano Cuppini
The brain integrates information from different sensory modalities to generate a coherent and accurate percept of external events. Several experimental studies suggest that this integration follows the principle of Bayesian estimate. However, the neural mechanisms responsible for this behavior, and its development in a multisensory environment, are still insufficiently understood. We recently presented a neural network model of audio-visual integration (Neural Computation, 2017) to investigate how a Bayesian estimator can spontaneously develop from the statistics of external stimuli...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/29046111/eeg-classification-with-a-sequential-decision-making-method-in-motor-imagery-bci
#12
Rong Liu, Yongxuan Wang, Geoffrey I Newman, Nitish V Thakor, Sarah Ying
To develop subject-specific classifier to recognize mental states fast and reliably is an important issue in brain-computer interfaces (BCI), particularly in practical real-time applications such as wheelchair or neuroprosthetic control. In this paper, a sequential decision-making strategy is explored in conjunction with an optimal wavelet analysis for EEG classification. The subject-specific wavelet parameters based on a grid-search method were first developed to determine evidence accumulative curve for the sequential classifier...
September 7, 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/29034263/modeling-causal-relationships-among-brain-areas-in-the-mesocorticolimbic-system-during-resting-state-in-cocaine-users-utilizing-a-graph-theoretic-approach
#13
Suchismita Ray, Bharat B Biswal, Ashley Aya, Suril Gohel, Aradhana Srinagesh, Catherine Hanson, Stephen J Hanson
OBJECTIVE: While effective connectivity (EC, causal interaction) between brain areas has been investigated in chronic users of cocaine as they view cocaine pictures cues, no study has examined EC while they take part in a resting-state scan. This resting-state fMRI study aims to investigate the causal interaction among brain areas in the mesocorticolimbic system (MCLS), which is involved in reward and motivation, in cocaine users (vs. controls). METHOD: Twenty cocaine users and 17 healthy controls finished a structural and a resting-state scan...
August 2017: Journal of Alcoholism and Drug Dependence
https://www.readbyqxmd.com/read/29034059/lcn-a-random-graph-mixture-model-for-community-detection-in-functional-brain-networks
#14
Christopher Bryant, Hongtu Zhu, Mihye Ahn, Joseph Ibrahim
The aim of this article is to develop a Bayesian random graph mixture model (RGMM) to detect the latent class network (LCN) structure of brain connectivity networks and estimate the parameters governing this structure. The use of conjugate priors for unknown parameters leads to efficient estimation, and a well-known nonidentifiability issue is avoided by a particular parameterization of the stochastic block model (SBM). Posterior computation proceeds via an efficient Markov Chain Monte Carlo algorithm. Simulations demonstrate that LCN outperforms several other competing methods for community detection in weighted networks, and we apply our RGMM to estimate the latent community structures in the functional resting brain networks of 185 subjects from the ADHD-200 sample...
2017: Statistics and its Interface
https://www.readbyqxmd.com/read/29022581/the-impact-of-rare-variation-on-gene-expression-across-tissues
#15
Xin Li, Yungil Kim, Emily K Tsang, Joe R Davis, Farhan N Damani, Colby Chiang, Gaelen T Hess, Zachary Zappala, Benjamin J Strober, Alexandra J Scott, Amy Li, Andrea Ganna, Michael C Bassik, Jason D Merker, Ira M Hall, Alexis Battle, Stephen B Montgomery
Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge...
October 11, 2017: Nature
https://www.readbyqxmd.com/read/29016865/inability-of-positive-phase-ii-clinical-trials-of-investigational-treatments-to-subsequently-predict-positive-phase-iii-clinical-trials-in-glioblastoma
#16
Jacob J Mandel, Shlomit Yust-Katz, Akash J Patel, David Cachia, Diane Liu, Minjeong Park, Ying Yuan, Thomas A Kent, John F de Groot
Background: Glioblastoma is the most common primary malignant brain tumor in adults, but effective therapies are lacking. With the scarcity of positive phase III trials, which are increasing in cost, we examined the ability of positive phase II trials to predict statistically significant improvement in clinical outcomes of phase III trials. Methods: A PubMed search was conducted to identify phase III clinical trials performed in the past 25 years for patients with newly diagnosed or recurrent glioblastoma...
July 31, 2017: Neuro-oncology
https://www.readbyqxmd.com/read/28942084/with-or-without-you-predictive-coding-and-bayesian-inference-in-the-brain
#17
REVIEW
Laurence Aitchison, Máté Lengyel
Two theoretical ideas have emerged recently with the ambition to provide a unifying functional explanation of neural population coding and dynamics: predictive coding and Bayesian inference. Here, we describe the two theories and their combination into a single framework: Bayesian predictive coding. We clarify how the two theories can be distinguished, despite sharing core computational concepts and addressing an overlapping set of empirical phenomena. We argue that predictive coding is an algorithmic/representational motif that can serve several different computational goals of which Bayesian inference is but one...
October 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28936158/bayesian-tractography-using-geometric-shape-priors
#18
Xiaoming Dong, Zhengwu Zhang, Anuj Srivastava
The problem of estimating neuronal fiber tracts connecting different brain regions is important for various types of brain studies, including understanding brain functionality and diagnosing cognitive impairments. The popular techniques for tractography are mostly sequential-tracts are grown sequentially following principal directions of local water diffusion profiles. Despite several advancements on this basic idea, the solutions easily get stuck in local solutions, and can't incorporate global shape information...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28915367/breathlessness-and-the-body-neuroimaging-clues-for-the-inferential-leap
#19
Olivia K Faull, Anja Hayen, Kyle T S Pattinson
Breathlessness debilitates millions of people with chronic illness. Mismatch between breathlessness severity and objective disease markers is common and poorly understood. Traditionally, sensory perception was conceptualised as a stimulus-response relationship, although this cannot explain how conditioned symptoms may occur in the absence of physiological signals from the lungs or airways. A Bayesian model is now proposed, in which the brain generates sensations based on expectations learnt from past experiences (priors), which are then checked against incoming afferent signals...
October 2017: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
https://www.readbyqxmd.com/read/28912425/predictive-models-of-minimal-hepatic-encephalopathy-for-cirrhotic-patients-based-on-large-scale-brain-intrinsic-connectivity-networks
#20
Yun Jiao, Xun-Heng Wang, Rong Chen, Tian-Yu Tang, Xi-Qi Zhu, Gao-Jun Teng
We aimed to find the most representative connectivity patterns for minimal hepatic encephalopathy (MHE) using large-scale intrinsic connectivity networks (ICNs) and machine learning methods. Resting-state fMRI was administered to 33 cirrhotic patients with MHE and 43 cirrhotic patients without MHE (NMHE). The connectivity maps of 20 ICNs for each participant were obtained by dual regression. A Bayesian machine learning technique, called Graphical Model-based Multivariate Analysis, was applied to determine ICN regions that characterized group differences...
September 14, 2017: Scientific Reports
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