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https://www.readbyqxmd.com/read/28080966/active-interoceptive-inference-and-the-emotional-brain
#1
REVIEW
Anil K Seth, Karl J Friston
We review a recent shift in conceptions of interoception and its relationship to hierarchical inference in the brain. The notion of interoceptive inference means that bodily states are regulated by autonomic reflexes that are enslaved by descending predictions from deep generative models of our internal and external milieu. This re-conceptualization illuminates several issues in cognitive and clinical neuroscience with implications for experiences of selfhood and emotion. We first contextualize interoception in terms of active (Bayesian) inference in the brain, highlighting its enactivist (embodied) aspects...
November 19, 2016: Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
https://www.readbyqxmd.com/read/28054725/mapping-complementary-features-of-cross-species-structural-connectivity-to-construct-realistic-virtual-brains
#2
Gleb Bezgin, Ana Solodkin, Rembrandt Bakker, Petra Ritter, Anthony R McIntosh
Modern systems neuroscience increasingly leans on large-scale multi-lab neuroinformatics initiatives to provide necessary capacity for biologically realistic modeling of primate whole-brain activity. Here, we present a framework to assemble primate brain's biologically plausible anatomical backbone for such modeling initiatives. In this framework, structural connectivity is determined by adding complementary information from invasive macaque axonal tract tracing and non-invasive human diffusion tensor imaging...
January 5, 2017: Human Brain Mapping
https://www.readbyqxmd.com/read/28030543/human-inferences-about-sequences-a-minimal-transition-probability-model
#3
Florent Meyniel, Maxime Maheu, Stanislas Dehaene
The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. Here, we explore the hypothesis that the brain acts as a near-optimal inference device that constantly attempts to infer the time-varying matrix of transition probabilities between the stimuli it receives, even when those stimuli are in fact fully unpredictable...
December 2016: PLoS Computational Biology
https://www.readbyqxmd.com/read/28027842/editorial-to-the-special-issue-on-perspectives-on-human-probabilistic-inference-and-the-bayesian-brain
#4
EDITORIAL
Johan Kwisthout, William A Phillips, Anil K Seth, Iris van Rooij, Andy Clark
No abstract text is available yet for this article.
December 24, 2016: Brain and Cognition
https://www.readbyqxmd.com/read/28012854/discriminative-spatial-frequency-temporal-feature-extraction-and-classification-of-motor-imagery-eeg-an-sparse-regression-and-weighted-na%C3%A3-ve-bayesian-classifier-based-approach
#5
Minmin Miao, Hong Zeng, Aimin Wang, Changsen Zhao, Feixiang Liu
BACKGROUND: Common spatial pattern (CSP) is most widely used in motor imagery based brain-computer interface (BCI) systems. In conventional CSP algorithm, pairs of the eigenvectors corresponding to both extreme eigenvalues are selected to construct the optimal spatial filter. In addition, an appropriate selection of subject-specific time segments and frequency bands plays an important role in its successful application. NEW METHOD: This study proposes to optimize spatial-frequency-temporal patterns for discriminative feature extraction...
December 21, 2016: Journal of Neuroscience Methods
https://www.readbyqxmd.com/read/28007982/generalization-of-prior-information-for-rapid-bayesian-time-estimation
#6
Neil W Roach, Paul V McGraw, David J Whitaker, James Heron
To enable effective interaction with the environment, the brain combines noisy sensory information with expectations based on prior experience. There is ample evidence showing that humans can learn statistical regularities in sensory input and exploit this knowledge to improve perceptual decisions and actions. However, fundamental questions remain regarding how priors are learned and how they generalize to different sensory and behavioral contexts. In principle, maintaining a large set of highly specific priors may be inefficient and restrict the speed at which expectations can be formed and updated in response to changes in the environment...
December 22, 2016: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/27987383/model-based-inference-from-microvascular-measurements-combining-experimental-measurements-and-model-predictions-using-a-bayesian-probabilistic-approach
#7
Peter M Rasmussen, Amy F Smith, Sava Sakadžić, David A Boas, Axel R Pries, Timothy W Secomb, Leif Østergaard
In vivo imaging of the microcirculation and network-oriented modeling have emerged as powerful means of studying microvascular function and understanding its physiological significance. Network-oriented modeling may provide the means of summarizing vast amounts of data produced by high-throughput imaging techniques in terms of key, physiological indices. To estimate such indices with sufficient certainty, however, network-oriented analysis must be robust to the inevitable presence of uncertainty due to measurement errors as well as model errors METHODS: We propose the Bayesian probabilistic data analysis framework as a means of integrating experimental measurements and network model simulations into a combined and statistically coherent analysis...
December 17, 2016: Microcirculation: the Official Journal of the Microcirculatory Society, Inc
https://www.readbyqxmd.com/read/27986702/a-systems-science-approach-to-understanding-polytrauma-and-blast-related-injury-bayesian-network-model-of-data-from-a-survey-of-the-florida-national-guard
#8
Peter A Toyinbo, Rodney D Vanderploeg, Heather G Belanger, Andrea M Spehar, William A Lapcevic, Steven G Scott
We sought to further define the epidemiology of the complex, multiple injuries collectively known as polytrauma/blast-related injury (PT/BRI). Using a systems science approach, we performed Bayesian network modeling to find the most accurate representation of the complex system of PT/BRI and identify key variables for understanding the subsequent effects of blast exposure in a sample of Florida National Guard members (1,443 deployed to Operation Enduring Freedom/Operation Iraqi Freedom and 1,655 not deployed) who completed an online survey during the period from 2009 to 2010...
December 16, 2016: American Journal of Epidemiology
https://www.readbyqxmd.com/read/27983596/estrogenic-endocrine-disrupting-chemicals-influencing-nrf1-regulated-gene-networks-in-the-development-of-complex-human-brain-diseases
#9
REVIEW
Mark Preciados, Changwon Yoo, Deodutta Roy
During the development of an individual from a single cell to prenatal stages to adolescence to adulthood and through the complete life span, humans are exposed to countless environmental and stochastic factors, including estrogenic endocrine disrupting chemicals. Brain cells and neural circuits are likely to be influenced by estrogenic endocrine disruptors (EEDs) because they strongly dependent on estrogens. In this review, we discuss both environmental, epidemiological, and experimental evidence on brain health with exposure to oral contraceptives, hormonal therapy, and EEDs such as bisphenol-A (BPA), polychlorinated biphenyls (PCBs), phthalates, and metalloestrogens, such as, arsenic, cadmium, and manganese...
December 13, 2016: International Journal of Molecular Sciences
https://www.readbyqxmd.com/read/27979788/multivariate-dynamical-modelling-of-structural-change-during-development
#10
Gabriel Ziegler, Gerard R Ridgway, Sarah-Jayne Blakemore, John Ashburner, Will Penny
Here we introduce a multivariate framework for characterising longitudinal changes in structural MRI using dynamical systems. The general approach enables modelling changes of states in multiple imaging biomarkers typically observed during brain development, plasticity, ageing and degeneration, e.g. regional gray matter volume of multiple regions of interest (ROIs). Structural brain states follow intrinsic dynamics according to a linear system with additional inputs accounting for potential driving forces of brain development...
December 12, 2016: NeuroImage
https://www.readbyqxmd.com/read/27959921/temporal-dynamics-and-developmental-maturation-of-salience-default-and-central-executive-network-interactions-revealed-by-variational-bayes-hidden-markov-modeling
#11
Srikanth Ryali, Kaustubh Supekar, Tianwen Chen, John Kochalka, Weidong Cai, Jonathan Nicholas, Aarthi Padmanabhan, Vinod Menon
Little is currently known about dynamic brain networks involved in high-level cognition and their ontological basis. Here we develop a novel Variational Bayesian Hidden Markov Model (VB-HMM) to investigate dynamic temporal properties of interactions between salience (SN), default mode (DMN), and central executive (CEN) networks-three brain systems that play a critical role in human cognition. In contrast to conventional models, VB-HMM revealed multiple short-lived states characterized by rapid switching and transient connectivity between SN, CEN, and DMN...
December 2016: PLoS Computational Biology
https://www.readbyqxmd.com/read/27935937/let-s-not-waste-time-using-temporal-information-in-clustered-activity-estimation-with-spatial-adjacency-restrictions-caesar-for-parcellating-fmri-data
#12
Ronald J Janssen, Pasi Jylänki, Marcel A J van Gerven
We have proposed a Bayesian approach for functional parcellation of whole-brain FMRI measurements which we call Clustered Activity Estimation with Spatial Adjacency Restrictions (CAESAR). We use distance-dependent Chinese restaurant processes (dd-CRPs) to define a flexible prior which partitions the voxel measurements into clusters whose number and shapes are unknown a priori. With dd-CRPs we can conveniently implement spatial constraints to ensure that our parcellations remain spatially contiguous and thereby physiologically meaningful...
2016: PloS One
https://www.readbyqxmd.com/read/27932074/distributed-neural-signatures-of-natural-audiovisual-speech-and-music-in-the-human-auditory-cortex
#13
Juha Salmi, Olli-Pekka Koistinen, Enrico Glerean, Pasi Jylänki, Aki Vehtari, Iiro P Jääskeläinen, Sasu Mäkelä, Lauri Nummenmaa, Katarina Nummi-Kuisma, Ilari Nummi, Mikko Sams
During a conversation or when listening to music, auditory and visual information are combined automatically into audiovisual objects. However, it is still poorly understood how specific type of visual information shapes neural processing of sounds in lifelike stimulus environments. Here we applied multi-voxel pattern analysis to investigate how naturally matching visual input modulates supratemporal cortex activity during processing of naturalistic acoustic speech, singing and instrumental music. Bayesian logistic regression classifiers with sparsity-promoting priors were trained to predict whether the stimulus was audiovisual or auditory, and whether it contained piano playing, speech, or singing...
December 5, 2016: NeuroImage
https://www.readbyqxmd.com/read/27930904/computations-underlying-social-hierarchy-learning-distinct-neural-mechanisms-for-updating-and-representing-self-relevant-information
#14
Dharshan Kumaran, Andrea Banino, Charles Blundell, Demis Hassabis, Peter Dayan
Knowledge about social hierarchies organizes human behavior, yet we understand little about the underlying computations. Here we show that a Bayesian inference scheme, which tracks the power of individuals, better captures behavioral and neural data compared with a reinforcement learning model inspired by rating systems used in games such as chess. We provide evidence that the medial prefrontal cortex (MPFC) selectively mediates the updating of knowledge about one's own hierarchy, as opposed to that of another individual, a process that underpinned successful performance and involved functional interactions with the amygdala and hippocampus...
December 7, 2016: Neuron
https://www.readbyqxmd.com/read/27917260/integrative-bayesian-analysis-of-neuroimaging-genetic-data-through-hierarchical-dimension-reduction
#15
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
https://www.readbyqxmd.com/read/27917138/neural-elements-for-predictive-coding
#16
REVIEW
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
https://www.readbyqxmd.com/read/27915121/predicting-individual-brain-functional-connectivity-using-a-bayesian-hierarchical-model
#17
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
https://www.readbyqxmd.com/read/27902858/reliable-estimation-of-microvascular-flow-patterns-in-patients-with-disrupted-blood-brain-barrier-using-dynamic-susceptibility-contrast-mri
#18
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
https://www.readbyqxmd.com/read/27895566/allostatic-self-efficacy-a-metacognitive-theory-of-dyshomeostasis-induced-fatigue-and-depression
#19
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
https://www.readbyqxmd.com/read/27893260/brain-drain-an-examination-of-stereotype-threat-effects-during-training-on-knowledge-acquisition-and-organizational-effectiveness
#20
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
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