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Bayesian Brain

Robert T Keys, Anina N Rich, Regine Zopf
Tracking one's own body is essential for environmental interaction, and involves integrating multisensory cues with stored information about the body's typical features. Exactly how multisensory information is integrated in own-body perception is still unclear. For example, Ide and Hidaka (Exp Brain Res 228:43-50, 2013) found that participants made less precise visuo-tactile temporal order judgments (TOJ) when viewing hands in a plausible orientation (upright; typical for one's own hand) compared to an implausible orientation (rotated 180°)...
March 15, 2018: Experimental Brain Research. Experimentelle Hirnforschung. Expérimentation Cérébrale
Julia N Bailey, Laurence de Nijs, Dongsheng Bai, Toshimitsu Suzuki, Hiroyuki Miyamoto, Miyabi Tanaka, Christopher Patterson, Yu-Chen Lin, Marco T Medina, María E Alonso, José M Serratosa, Reyna M Durón, Viet H Nguyen, Jenny E Wight, Iris E Martínez-Juárez, Adriana Ochoa, Aurelio Jara-Prado, Laura Guilhoto, Yolly Molina, Elsa M Yacubian, Minerva López-Ruiz, Yushi Inoue, Sunao Kaneko, Shinichi Hirose, Makiko Osawa, Hirokazu Oguni, Shinji Fujimoto, Thierry M Grisar, John M Stern, Kazuhiro Yamakawa, Bernard Lakaye, Antonio V Delgado-Escueta
BACKGROUND: In juvenile myoclonic epilepsy, data are limited on the genetic basis of networks promoting convulsions with diffuse polyspikes on electroencephalography (EEG) and the subtle microscopic brain dysplasia called microdysgenesis. METHODS: Using Sanger sequencing, we sequenced the exomes of six members of a large family affected with juvenile myoclonic epilepsy and confirmed cosegregation in all 37 family members. We screened an additional 310 patients with this disorder for variants on DNA melting-curve analysis and targeted real-time DNA sequencing of the gene encoding intestinal-cell kinase ( ICK)...
March 15, 2018: New England Journal of Medicine
Thomas Parr, Geraint Rees, Karl J Friston
Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian frameworks. Bayesian approaches to understanding brain function formulate perception and action as inferential processes. These inferences combine 'prior' beliefs with a generative (predictive) model to explain the causes of sensations...
2018: Frontiers in Human Neuroscience
J Hadida, S N Sotiropoulos, R G Abeysuriya, M W Woolrich, S Jbabdi
The relationship between structure and function in the human brain is well established, but not yet well characterised. Large-scale biophysical models allow us to investigate this relationship, by leveraging structural information (e.g.derived from diffusion tractography) in order to couple dynamical models of local neuronal activity into networks of interacting regions distributed across the cortex. In practice however, these models are difficult to parametrise, and their simulation is often delicate and computationally expensive...
March 5, 2018: NeuroImage
Guillaume Sescousse, Romain Ligneul, Ruth J van Holst, Lieneke K Janssen, Femke de Boer, Marcel Janssen, Anne S Berry, William J Jagust, Roshan Cools
Dopamine is central to a number of cognitive functions and brain disorders. Given the cost of neurochemical imaging in humans, behavioral proxy measures of dopamine have gained in popularity in the past decade, such as spontaneous eye blink rate (sEBR). Increased sEBR is commonly associated with increased dopamine function based on pharmacological evidence and patient studies. Yet, this hypothesis has not been validated using in vivo measures of dopamine function in humans. In order to fill this gap, we measured sEBR and striatal dopamine synthesis capacity using [18 F]DOPA PET in 20 participants (9 healthy individuals and 11 pathological gamblers)...
March 7, 2018: European Journal of Neuroscience
Chia-Jung Chang, Mehrdad Jazayeri
To coordinate movements with events in a dynamic environment the brain has to anticipate when those events occur. A classic example is the estimation of time to contact ( TTC ), that is, when an object reaches a target. It is thought that TTC is estimated from kinematic variables. For example, a tennis player might use an estimate of distance ( d ) and speed ( v ) to estimate TTC ( TTC = d / v ). However, the tennis player may instead estimate TTC as twice the time it takes for the ball to move from the serve line to the net line...
March 5, 2018: Proceedings of the National Academy of Sciences of the United States of America
Jaime S Ide, Sanja Nedic, Kin F Wong, Shmuel L Strey, Elizabeth A Lawson, Bradford C Dickerson, Lawrence L Wald, Giancarlo La Camera, L R Mujica-Parodi
Oxytocin (OT) is an endogenous neuropeptide that, while originally thought to promote trust, has more recently been found to be context-dependent. Here we extend experimental paradigms previously restricted to de novo decision-to-trust, to a more realistic environment in which social relationships evolve in response to iterative feedback over twenty interactions. In a randomized, double blind, placebo-controlled within-subject/crossover experiment of human adult males, we investigated the effects of a single dose of intranasal OT (40 IU) on Bayesian expectation updating and reinforcement learning within a social context, with associated brain circuit dynamics...
February 24, 2018: NeuroImage
Jenessa Lancaster, Romy Lorenz, Rob Leech, James H Cole
Neuroimaging-based age prediction using machine learning is proposed as a biomarker of brain aging, relating to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalization to a common template and resampling to a common voxel size, followed by spatial smoothing...
2018: Frontiers in Aging Neuroscience
Azam Korhani Kangi, Abbas Bahrampour
Introduction and purpose: In recent years the use of neural networks without any premises for investigation of prognosis in analyzing survival data has increased. Artificial neural networks (ANN) use small processors with a continuous network to solve problems inspired by the human brain. Bayesian neural networks (BNN) constitute a neural-based approach to modeling and non-linearization of complex issues using special algorithms and statistical methods. Gastric cancer incidence is the first and third ranking for men and women in Iran, respectively...
February 26, 2018: Asian Pacific Journal of Cancer Prevention: APJCP
James E Clark, Stuart Watson, Karl J Friston
The neurobiological understanding of mood, and by extension mood disorders, remains elusive despite decades of research implicating several neuromodulator systems. This review considers a new approach based on existing theories of functional brain organisation. The free energy principle (a.k.a. active inference), and its instantiation in the Bayesian brain, offers a complete and simple formulation of mood. It has been proposed that emotions reflect the precision of - or certainty about - the predicted sensorimotor/interoceptive consequences of action...
February 26, 2018: Psychological Medicine
M A R Anjum, Pawel A Dmochowski, Paul D Teal
Fast, accurate and automatic extraction of parameters of nuclear magnetic resonance Free Induction Decay (FID) signal for chemical spectroscopy is a challenging problem. Recently, the Steiglitz-McBride Algorithm (SMA) has been shown to exhibit superior performance in terms of speed, accuracy and automation when applied to the extraction of T2 relaxation parameters for myelin water imaging of brain. Applying it to FID data reveals that it falls short of the second objective, the accuracy. Especially, it struggles with the issue of missed spectral peaks when applied to chemical samples with relatively dense frequency spectra...
February 23, 2018: Magnetic Resonance in Chemistry: MRC
Maria A Bobes, Agustin Lage-Castellanos, Ela I Olivares, Jhoanna Perez Hidalgo-Gato, Jaime Iglesias, Ana Maria Castro-Laguardia, Pedro Valdes-Sosa
Event related potentials (ERPs) provide precise temporal information about cognitive processing, but with poor spatial resolution, while functional magnetic resonance imaging (fMRI) reliably identifies brain areas involved, but with poor temporal resolution. Here we use fMRI to guide source localization of the ERPs at different times for studying the temporal dynamics of the neural system for recognizing familiar faces. fMRI activation areas were defined in a previous experiment applying the same paradigm used for ERPs...
February 20, 2018: Brain Topography
Jean-Didier Lemaréchal, Nathalie George, Olivier David
Dynamic causal modeling (DCM) is a methodological approach to study effective connectivity among brain regions. Based on a set of observations and a biophysical model of brain interactions, DCM uses a Bayesian framework to estimate the posterior distribution of the free parameters of the model (e.g. modulation of connectivity) and infer architectural properties of the most plausible model (i.e. model selection). When modeling electrophysiological event-related responses, the estimation of the model relies on the integration of the system of delay differential equations (DDEs) that describe the dynamics of the system...
February 17, 2018: NeuroImage
Elliot G Neal, Stephanie Maciver, Fernando L Vale
BACKGROUND: Despite rigorous preoperative evaluation, epilepsy surgery achieves seizure freedom in only two-thirds of cases. Current preoperative evaluation does not include a detailed network analysis despite the association of network-level changes with epilepsy. OBJECTIVE: We sought to create a software algorithm to map individualized epilepsy networks by combining noninvasive electroencephalography (EEG) source localization and nonconcurrent resting state functional magnetic resonance imaging (rsfMRI)...
February 17, 2018: Epilepsy & Behavior: E&B
Tiago P Peixoto
We present a Bayesian formulation of weighted stochastic block models that can be used to infer the large-scale modular structure of weighted networks, including their hierarchical organization. Our method is nonparametric, and thus does not require the prior knowledge of the number of groups or other dimensions of the model, which are instead inferred from data. We give a comprehensive treatment of different kinds of edge weights (i.e., continuous or discrete, signed or unsigned, bounded or unbounded), as well as arbitrary weight transformations, and describe an unsupervised model selection approach to choose the best network description...
January 2018: Physical Review. E
Jeremy R Manning, Xia Zhu, Theodore L Willke, Rajesh Ranganath, Kimberly Stachenfeld, Uri Hasson, David M Blei, Kenneth A Norman
Recent research shows that the covariance structure of functional magnetic resonance imaging (fMRI) data - commonly described as functional connectivity - can change as a function of the participant's cognitive state (for review see (Turk-Browne, 2013)). Here we present a Bayesian hierarchical matrix factorization model, termed hierarchical topographic factor analysis (HTFA), for efficiently discovering full-brain networks in large multi-subject neuroimaging datasets. HTFA approximates each subject's network by first re-representing each brain image in terms of the activities of a set of localized nodes, and then computing the covariance of the activity time series of these nodes...
February 12, 2018: NeuroImage
Charles L Nunn, David R Samson
OBJECTIVES: Primates vary in their sleep durations and, remarkably, humans sleep the least per 24-hr period of the 30 primates that have been studied. Using phylogenetic methods that quantitatively situate human phenotypes within a broader primate comparative context, we investigated the evolution of human sleep architecture, focusing on: total sleep duration, rapid eye movement (REM) sleep duration, non-rapid eye movement (NREM) sleep duration, and proportion of sleep in REM. MATERIALS AND METHODS: We used two different Bayesian methods: phylogenetic prediction based on phylogenetic generalized least squares and a multistate Onrstein-Uhlenbeck (OU) evolutionary model of random drift and stabilizing selection...
February 14, 2018: American Journal of Physical Anthropology
Christina Artemenko, Andra Coldea, Mojtaba Soltanlou, Thomas Dresler, Hans-Christoph Nuerk, Ann-Christine Ehlis
In our daily lives, we are constantly exposed to numbers and letters. However, it is still under debate how letters and numbers are processed in the brain, while information on this topic would allow for a more comprehensive understanding of, for example, known influences of language on numerical cognition or neural circuits shared by numerical cognition and language processing. Some findings provide evidence for a double dissociation between numbers and letters, with numbers being represented in the right and letters in the left hemisphere, while the opposing view suggests a shared neural network...
February 14, 2018: Experimental Brain Research. Experimentelle Hirnforschung. Expérimentation Cérébrale
Julia Neitzel, Rachel Nuttall, Christian Sorg
Previous animal research suggests that the spread of pathological agents in Alzheimer's disease (AD) follows the direction of signaling pathways. Specifically, tau pathology has been suggested to propagate in an infection-like mode along axons, from transentorhinal cortices to medial temporal lobe cortices and consequently to other cortical regions, while amyloid-beta (Aβ) pathology seems to spread in an activity-dependent manner among and from isocortical regions into limbic and then subcortical regions. These directed connectivity-based spread models, however, have not been tested directly in AD patients due to the lack of an in vivo method to identify directed connectivity in humans...
2018: Frontiers in Neurology
Anna Pidnebesna, David Tomeček, Jaroslav Hlinka
BACKGROUND AND OBJECTIVE: Precise estimation of neuronal activity from neuroimaging data is one of the central challenges of the application of noninvasive neuroimaging methods. One of the widely used methods for studying brain activity is functional magnetic resonance imaging, which is a neuroimaging procedure that measures brain activity based on the blood oxygenation level dependent effect. The blood oxygenation level dependent signal can be modeled as a linear convolution of a hemodynamic response function with an input signal corresponding to the neuronal activity...
March 2018: Computer Methods and Programs in Biomedicine
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