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Bayesian Brain

S Azadeh, B P Hobbs, L Ma, D A Nielsen, F G Moeller, V Baladandayuthapani
Advances in neuromedicine have emerged from endeavors to elucidate the distinct genetic factors that influence the changes in brain structure that underlie various neurological conditions. We present a framework for examining the extent to which genetic factors impact imaging phenotypes described by voxel-wise measurements organized into collections of functionally relevant regions of interest (ROIs) that span the entire brain. Statistically, the integration of neuroimaging and genetic data is challenging. Because genetic variants are expected to impact different regions of the brain, an appropriate method of inference must simultaneously account for spatial dependence and model uncertainty...
April 2016: Proceedings of the IEEE International Symposium on Biomedical Imaging: from Nano to Macro
Stewart Shipp
Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backward in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many 'illusory' instances of perception where what is seen (heard, etc...
2016: Frontiers in Psychology
Tian Dai, Ying Guo
Network-oriented analysis of functional magnetic resonance imaging (fMRI), especially resting-state fMRI, has revealed important association between abnormal connectivity and brain disorders such as schizophrenia, major depression and Alzheimer's disease. Imaging-based brain connectivity measures have become a useful tool for investigating the pathophysiology, progression and treatment response of psychiatric disorders and neurodegenerative diseases. Recent studies have started to explore the possibility of using functional neuroimaging to help predict disease progression and guide treatment selection for individual patients...
November 30, 2016: NeuroImage
Mikkel Bo Hansen, Anna Tietze, Jayashree Kalpathy-Cramer, Elizabeth R Gerstner, Tracy T Batchelor, Leif Østergaard, Kim Mouridsen
PURPOSE: To present and quantify the performance of a method to compute tissue hemodynamic parameters from dynamic susceptibility contrast (DSC) MRI data in brain tissue with possible nonintact blood-brain barrier. THEORY AND MATERIALS AND METHODS: We propose a Bayesian scheme to obtain perfusion metrics, including capillary transit-time heterogeneity (CTH), from DSC-MRI data in the presence of contrast agent extravasation. Initial performance assessment is performed through simulations...
November 30, 2016: Journal of Magnetic Resonance Imaging: JMRI
Klaas E Stephan, Zina M Manjaly, Christoph D Mathys, Lilian A E Weber, Saee Paliwal, Tim Gard, Marc Tittgemeyer, Stephen M Fleming, Helene Haker, Anil K Seth, Frederike H Petzschner
This paper outlines a hierarchical Bayesian framework for interoception, homeostatic/allostatic control, and meta-cognition that connects fatigue and depression to the experience of chronic dyshomeostasis. Specifically, viewing interoception as the inversion of a generative model of viscerosensory inputs allows for a formal definition of dyshomeostasis (as chronically enhanced surprise about bodily signals, or, equivalently, low evidence for the brain's model of bodily states) and allostasis (as a change in prior beliefs or predictions which define setpoints for homeostatic reflex arcs)...
2016: Frontiers in Human Neuroscience
James A Grand
Stereotype threat describes a situation in which individuals are faced with the risk of upholding a negative stereotype about their subgroup based on their actions. Empirical work in this area has primarily examined the impact of negative stereotypes on performance for threatened individuals. However, this body of research seldom acknowledges that performance is a function of learning-which may also be impaired by pervasive group stereotypes. This study presents evidence from a 3-day self-guided training program demonstrating that stereotype threat impairs acquisition of cognitive learning outcomes for females facing a negative group stereotype...
November 28, 2016: Journal of Applied Psychology
Per Sidén, Anders Eklund, David Bolin, Mattias Villani
Spatial whole-brain Bayesian modeling of task-related functional magnetic resonance imaging (fMRI) is a great computational challenge. Most of the currently proposed methods therefore do inference in subregions of the brain separately or do approximate inference without comparison to the true posterior distribution. A popular such method, which is now the standard method for Bayesian single subject analysis in the SPM software, is introduced in Penny et al. (2005b). The method processes the data slice-by-slice and uses an approximate variational Bayes (VB) estimation algorithm that enforces posterior independence between activity coefficients in different voxels...
November 19, 2016: NeuroImage
Sharon Chiang, Michele Guindani, Hsiang J Yeh, Zulfi Haneef, John M Stern, Marina Vannucci
In this article a multi-subject vector autoregressive (VAR) modeling approach was proposed for inference on effective connectivity based on resting-state functional MRI data. Their framework uses a Bayesian variable selection approach to allow for simultaneous inference on effective connectivity at both the subject- and group-level. Furthermore, it accounts for multi-modal data by integrating structural imaging information into the prior model, encouraging effective connectivity between structurally connected regions...
November 16, 2016: Human Brain Mapping
Jianping Hu, Sien Hu, Julianna R Maisano, Herta H Chao, Sheng Zhang, Chiang-Shan R Li
Proactive control allows us to maneuver a changing environment and individuals are distinct in how they anticipate and approach such changes. Here, we examined how individual differences in personality traits influence cerebral responses to conflict anticipation, a critical process of proactive control. We explored this issue in an fMRI study of the stop signal task, in which the probability of stop signal - p(Stop) - was computed trial by trial with a Bayesian model. Higher p(Stop) is associated with prolonged go trial reaction time, indicating conflict anticipation and proactive control of motor response...
2016: Frontiers in Human Neuroscience
Bart B G T Alberts, Anouk J de Brouwer, Luc P J Selen, W Pieter Medendorp
Panoramic visual cues, as generated by the objects in the environment, provide the brain with important information about gravity direction. To derive an optimal, i.e., Bayesian, estimate of gravity direction, the brain must combine panoramic information with gravity information detected by the vestibular system. Here, we examined the individual sensory contributions to this estimate psychometrically. We asked human subjects to judge the orientation (clockwise or counterclockwise relative to gravity) of a briefly flashed luminous rod, presented within an oriented square frame (rod-in-frame)...
September 2016: ENeuro
Frank de Vocht
BACKGROUND: Mobile phone use has been increasing rapidly in the past decades and, in parallel, so has the annual incidence of certain types of brain cancers. However, it remains unclear whether this correlation is coincidental or whether use of mobile phones may cause the development, promotion or progression of specific cancers. The 1985-2014 incidence of selected brain cancer subtypes in England were analyzed and compared to counterfactual 'synthetic control' timeseries. METHODS: Annual 1985-2014 incidence of malignant glioma, glioblastoma multiforme, and malignant neoplasms of the temporal and parietal lobes in England were modelled based on population-level covariates using Bayesian structural time series models assuming 5,10 and 15year minimal latency periods...
November 8, 2016: Environment International
Nicholas J Hughes, Geoffrey J Goodhill
A striking example of brain organisation is the stereotyped arrangement of cell preferences in the visual cortex for edges of particular orientations in the visual image. These "orientation preference maps" appear to have remarkably consistent statistical properties across many species. However fine scale analysis of these properties requires the accurate reconstruction of maps from imaging data which is highly noisy. A new approach for solving this reconstruction problem is to use Bayesian Gaussian process methods, which produce more accurate results than classical techniques...
November 2, 2016: IEEE Transactions on Pattern Analysis and Machine Intelligence
David J Unsworth, Jane L Mathias
Alcohol and substance (drugs and/or alcohol) abuse are major risk factors for traumatic brain injury (TBI); however, it remains unclear whether outcomes differ for those with and without a history of preinjury abuse. A meta-analysis was performed to examine this issue. The PubMed, Embase, and PsycINFO databases were searched for research that compared the neuroradiological, cognitive, or psychological outcomes of adults with and without a documented history of alcohol and/or substance abuse who sustained nonpenetrating TBIs...
November 10, 2016: Journal of Clinical and Experimental Neuropsychology
Jane Nikles, Geoffrey Mitchell, Lynne McKinlay, Mary-Clare Waugh, Adrienne Epps, Sue-Ann Carmont, Philip J Schluter, Owen Lloyd, M Hugh Senior
BACKGROUND: There is controversy about whether central nervous system stimulant (CNS) medication is an effective method of treating acquired attention deficits in children with acquired brain injury (ABI). OBJECTIVE: The primary objective was to determine the effectiveness of stimulants on attention, concentration and executive function in children with ABI. METHODS: Randomised, double-blind, placebo-controlled, multi-centre n-of-1 trials of stimulants assessing effect on attention, concentration and executive function in 53 children and adolescents with ABI who were outpatients of three tertiary hospitals in Australia...
October 31, 2016: NeuroRehabilitation
Alfonso Delgado-Bonal, Javier Martín-Torres
It is commonly accepted that the evolution of the human eye has been driven by the maximum intensity of the radiation emitted by the Sun. However, the interpretation of the surrounding environment is constrained not only by the amount of energy received but also by the information content of the radiation. Information is related to entropy rather than energy. The human brain follows Bayesian statistical inference for the interpretation of visual space. The maximization of information occurs in the process of maximizing the entropy...
November 3, 2016: Scientific Reports
Shuihua Wang, Ming Yang, Sidan Du, Jiquan Yang, Bin Liu, Juan M Gorriz, Javier Ramírez, Ti-Fei Yuan, Yudong Zhang
Highlights We develop computer-aided diagnosis system for unilateral hearing loss detection in structural magnetic resonance imaging.Wavelet entropy is introduced to extract image global features from brain images. Directed acyclic graph is employed to endow support vector machine an ability to handle multi-class problems.The developed computer-aided diagnosis system achieves an overall accuracy of 95.1% for this three-class problem of differentiating left-sided and right-sided hearing loss from healthy controls...
2016: Frontiers in Computational Neuroscience
Dror Dotan, Stanislas Dehaene
The number-to-position task, in which children and adults are asked to place numbers on a spatial number line, has become a classic measure of number comprehension. We present a detailed experimental and theoretical dissection of the processing stages that underlie this task. We used a continuous finger-tracking technique, which provides detailed information about the time course of processing stages. When adults map the position of 2-digit numbers onto a line, their final mapping is essentially linear, but intermediate finger location show a transient logarithmic mapping...
November 2016: Psychological Review
Pierpaolo Croce, Alessio Basti, Laura Marzetti, Filippo Zappasodi, Cosimo Del Gratta
OBJECTIVE: Due to the complementary nature of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), and given the possibility of simultaneous acquisition, the joint data analysis can afford a better understanding of the underlying neural activity estimation. In this simulation study we want to show the benefit of the joint EEG-fMRI neural activity estimation in a Bayesian framework. APPROACH: We built a dynamic Bayesian framework in order to perform joint EEG-fMRI neural activity time course estimation...
December 2016: Journal of Neural Engineering
Dean R Freestone, Kelvin J Layton, Levin Kuhlmann, Mark J Cook
Data-driven model-based analysis of electrophysiological data is an emerging technique for understanding the mechanisms of seizures. Model-based analysis enables tracking of hidden brain states that are represented by the dynamics of neural mass models. Neural mass models describe the mean firing rates and mean membrane potentials of populations of neurons. Various neural mass models exist with different levels of complexity and realism. An ideal data-driven model-based analysis framework will incorporate the most realistic model possible, enabling accurate imaging of the physiological variables...
June 9, 2016: International Journal of Neural Systems
Sara T Baker, Alan M Leslie, C R Gallistel, Bruce M Hood
Although learning and development reflect changes situated in an individual brain, most discussions of behavioral change are based on the evidence of group averages. Our reliance on group-averaged data creates a dilemma. On the one hand, we need to use traditional inferential statistics. On the other hand, group averages are highly ambiguous when we need to understand change in the individual; the average pattern of change may characterize all, some, or none of the individuals in the group. Here we present a new method for statistically characterizing developmental change in each individual child we study...
October 20, 2016: Cognitive Psychology
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