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Bayesian Brain

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https://www.readbyqxmd.com/read/29353280/microstructure-of-strategic-white-matter-tracts-and-cognition-in-memory-clinic-patients-with-vascular-brain-injury
#1
J Matthijs Biesbroek, Alexander Leemans, Hanna den Bakker, Marco Duering, Benno Gesierich, Huiberdina L Koek, Esther van den Berg, Albert Postma, Geert Jan Biessels
BACKGROUND: White matter injury is an important factor for cognitive impairment in memory clinic patients. We determined the added value of diffusion tensor imaging (DTI) of strategic white matter tracts in explaining variance in cognition in memory clinic patients with vascular brain injury. METHODS: We included 159 patients. Conventional MRI markers (white matter hyperintensity volume, lacunes, nonlacunar infarcts, brain atrophy, and microbleeds), and fractional anisotropy and mean diffusivity (MD) of the whole brain white matter and of 18 white matter tracts were related to cognition using linear regression and Bayesian network analysis...
January 19, 2018: Dementia and Geriatric Cognitive Disorders
https://www.readbyqxmd.com/read/29326067/transcutaneous-vagus-nerve-stimulation-tvns-enhances-divergent-thinking
#2
Lorenza S Colzato, Simone M Ritter, Laura Steenbergen
Creativity is one of the most important cognitive skills in our complex and fast-changing world. Previous correlative evidence showed that gamma-aminobutyric acid (GABA) is involved in divergent but not convergent thinking. In the current study, a placebo/sham-controlled, randomized between-group design was used to test a causal relation between vagus nerve and creativity. We employed transcutaneous vagus nerve stimulation (tVNS), a novel non-invasive brain stimulation technique to stimulate afferent fibers of the vagus nerve and speculated to increase GABA levels, in 80 healthy young volunteers...
January 8, 2018: Neuropsychologia
https://www.readbyqxmd.com/read/29324886/changes-in-network-connectivity-during-motor-imagery-and-execution
#3
Yun Kwan Kim, Eunhee Park, Ahee Lee, Chang-Hwan Im, Yun-Hee Kim
BACKGROUND: Recent studies of functional or effective connectivity in the brain have reported that motor-related brain regions were activated during motor execution and motor imagery, but the relationship between motor and cognitive areas has not yet been completely understood. The objectives of our study were to analyze the effective connectivity between motor and cognitive networks in order to define network dynamics during motor execution and motor imagery in healthy individuals. Second, we analyzed the differences in effective connectivity between correct and incorrect responses during motor execution and imagery using dynamic causal modeling (DCM) of electroencephalography (EEG) data...
2018: PloS One
https://www.readbyqxmd.com/read/29324402/c-fscv-compressive-fast-scan-cyclic-voltammetry-for-brain-dopamine-recording
#4
Hossein Zamani, Hamid Reza Bahrami, Preeti Chalwadi, Paul A Garris, Pedram Mohseni
This paper presents a novel compressive sensing framework for recording brain dopamine levels with fast-scan cyclic voltammetry (FSCV) at a carbon-fiber microelectrode. Termed compressive FSCV (C-FSCV), this approach compressively samples the measured total current in each FSCV scan and performs basic FSCV processing steps, e.g., background current averaging and subtraction, directly with compressed measurements. The resulting background-subtracted faradaic currents, which are shown to have a block-sparse representation in the discrete cosine transform domain, are next reconstructed from their compressively sampled counterparts with the block sparse Bayesian learning algorithm...
January 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/29324054/bayesian-diagnostics-of-hidden-markov-structural-equation-models-with-missing-data
#5
Jingheng Cai, Ming Ouyang, Kai Kang, Xinyuan Song
Cocaine is a type of drug that functions to increase the availability of the neurotransmitter dopamine in the brain. However, cocaine dependence or abuse is highly related to an increased risk of psychiatric disorders and deficits in cognitive performance, attention, and decision-making abilities. Given the chronic and persistent features of drug addiction, the progression of abstaining from cocaine often evolves across several states, such as addiction to, moderate dependence on, and swearing off cocaine. Hidden Markov models (HMMs) are well suited to the characterization of longitudinal data in terms of a set of unobservable states, and have increasingly been used to uncover the dynamic heterogeneity in progressive diseases or activities...
January 11, 2018: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/29259537/a-hierarchical-bayesian-model-for-the-identification-of-pet-markers-associated-to-the-prediction-of-surgical-outcome-after-anterior-temporal-lobe-resection
#6
Sharon Chiang, Michele Guindani, Hsiang J Yeh, Sandra Dewar, Zulfi Haneef, John M Stern, Marina Vannucci
We develop an integrative Bayesian predictive modeling framework that identifies individual pathological brain states based on the selection of fluoro-deoxyglucose positron emission tomography (PET) imaging biomarkers and evaluates the association of those states with a clinical outcome. We consider data from a study on temporal lobe epilepsy (TLE) patients who subsequently underwent anterior temporal lobe resection. Our modeling framework looks at the observed profiles of regional glucose metabolism in PET as the phenotypic manifestation of a latent individual pathologic state, which is assumed to vary across the population...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/29248498/towards-a-neuro-computational-account-of-prism-adaptation
#7
Pierre Petitet, Jill X O'Reilly, Jacinta O'Shea
Prism adaptation has a long history as an experimental paradigm used to investigate the functional and neural processes that underlie sensorimotor control. In the neuropsychology literature, prism adaptation behaviour is typically explained by reference to a traditional cognitive psychology framework that distinguishes putative functions, such as 'strategic control' versus 'spatial realignment'. This theoretical framework lacks conceptual clarity, quantitative precision and explanatory power. Here, we advocate for an alternative computational framework that offers several advantages: 1) an algorithmic explanatory account of the computations and operations that drive behaviour; 2) expressed in quantitative mathematical terms; 3) embedded within a principled theoretical framework (Bayesian decision theory, state-space modelling); 4) that offers a means to generate and test quantitative behavioural predictions...
December 14, 2017: Neuropsychologia
https://www.readbyqxmd.com/read/29232684/beyond-autism-introducing-the-dialectical-misattunement-hypothesis-and-a-bayesian-account-of-intersubjectivity
#8
Dimitris Bolis, Joshua Balsters, Nicole Wenderoth, Cristina Becchio, Leonhard Schilbach
Drawing on sociocultural theories and Bayesian accounts of brain function, in this article we construe psychiatric conditions as disorders of social interaction to fully account for their complexity and dynamicity across levels of description and temporal scales. After an introduction of the theoretical underpinnings of our integrative approach, we take autism spectrum conditions (ASC) as a paradigm example and discuss how neurocognitive hypotheses can be translated into a Bayesian formulation, i.e., in terms of predictive processing and active inference...
December 13, 2017: Psychopathology
https://www.readbyqxmd.com/read/29228054/brain-to-brain-hyperclassification-reveals-action-specific-motor-mapping-of-observed-actions-in-humans
#9
Dmitry Smirnov, Fanny Lachat, Tomi Peltola, Juha M Lahnakoski, Olli-Pekka Koistinen, Enrico Glerean, Aki Vehtari, Riitta Hari, Mikko Sams, Lauri Nummenmaa
Seeing an action may activate the corresponding action motor code in the observer. It remains unresolved whether seeing and performing an action activates similar action-specific motor codes in the observer and the actor. We used novel hyperclassification approach to reveal shared brain activation signatures of action execution and observation in interacting human subjects. In the first experiment, two "actors" performed four types of hand actions while their haemodynamic brain activations were measured with 3-T functional magnetic resonance imaging (fMRI)...
2017: PloS One
https://www.readbyqxmd.com/read/29214978/characterization-of-dynamic-changes-of-current-source-localization-based-on-spatiotemporal-fmri-constrained-eeg-source-imaging
#10
Thinh Nguyen, Thomas Potter, Robert Grossman, Yingchun Zhang
OBJECTIVE: Neuroimaging has been employed as a promising approach to advance our understanding of brain networks in both basic and clinical neuroscience. Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) represent two neuroimaging modalities with complementary features; EEG has high temporal resolution and low spatial resolution while fMRI has high spatial resolution and low temporal resolution. Multimodal EEG inverse methods have attempted to capitalize on these properties but have been subjected to localization error...
December 7, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/29185805/an-optimized-method-for-bayesian-connectivity-change-point-model
#11
Xiuchun Xiao, Bing Liu, Jing Zhang, Xueli Xiao, Yi Pan
The brain undergoes functional dynamic changes at all times. Investigating functional dynamics has been recently verified to be helpful for detecting psychological conditions and powerful for analyzing disease-related abnormalities of the brain. This article aims to detect functional dynamics. Specifically, we focus on how to effectively distinguish corresponding functional connectivity and change points from functional magnetic resonance imaging (fMRI) data. By combining Bayesian connectivity change point model (BCCPM), a modified genetic algorithm (GA) is presented to optimize the evolutionary procedure toward the most probable distributions of real change points in fMRI...
November 29, 2017: Journal of Computational Biology: a Journal of Computational Molecular Cell Biology
https://www.readbyqxmd.com/read/29169647/an-eeg-based-functional-connectivity-measure-for-automatic-detection-of-alcohol-use-disorder
#12
Wajid Mumtaz, Mohamad Naufal B Mohamad Saad, Nidal Kamel, Syed Saad Azhar Ali, Aamir Saeed Malik
BACKGROUND: The abnormal alcohol consumption could cause toxicity and could alter the human brain's structure and function, termed as alcohol used disorder (AUD). Unfortunately, the conventional screening methods for AUD patients are subjective and manual. Hence, to perform automatic screening of AUD patients, objective methods are needed. The electroencephalographic (EEG) data have been utilized to study the differences of brain signals between alcoholics and healthy controls that could further developed as an automatic screening tool for alcoholics...
November 20, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/29118202/bayesian-optimal-adaptation-explains-age-related-human-sensorimotor-changes
#13
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
#14
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
#15
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
#16
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
#17
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
#18
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
#19
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
#20
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
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