Reconstructing visual experiences from brain activity evoked by natural movies
Shinji Nishimoto,An T. Vu,Thomas Naselaris,Yuval Benjamini,Bin Yu,Jack L. Gallant,Jack L. Gallant +6 more
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TLDR
A new motion-energy encoding model is presented that largely overcomes the limitation of blood oxygen level-dependent signals measured via fMRI and demonstrates that dynamic brain activity measured under naturalistic conditions can be decoded using current fMRI technology.About:
This article is published in Current Biology.The article was published on 2011-10-11 and is currently open access. It has received 864 citations till now. The article focuses on the topics: Brain mapping & Visual cortex.read more
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Natural speech reveals the semantic maps that tile human cerebral cortex
Alexander G. Huth,Wendy A. de Heer,Thomas L. Griffiths,Thomas L. Griffiths,Frédéric E. Theunissen,Frédéric E. Theunissen,Jack L. Gallant,Jack L. Gallant +7 more
TL;DR: This study systematically map semantic selectivity across the cortex using voxel-wise modelling of functional MRI data collected while subjects listened to hours of narrative stories, and uses a novel generative model to create a detailed semantic atlas.
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A Continuous Semantic Space Describes the Representation of Thousands of Object and Action Categories across the Human Brain
TL;DR: This work used fMRI to measure human brain activity evoked by natural movies and voxelwise models to examine the cortical representation of 1,705 object and action categories to find a semantic space mapped smoothly across the cortical surface.
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Building better biomarkers: brain models in translational neuroimaging
TL;DR: The state of translational neuroimaging is reviewed, an approach to developing brain signatures that can be shared, tested in multiple contexts and applied in clinical settings is outlined and a program of broad exploration followed by increasingly rigorous assessment of generalizability is outlined.
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Characterizing the dynamics of mental representations: the temporal generalization method
TL;DR: By testing at which moment a specific mental content becomes decodable in brain activity, this work can characterize the time course of cognitive codes and provide a novel way to understand how mental representations are manipulated and transformed.
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Decoding Neural Representational Spaces Using Multivariate Pattern Analysis
TL;DR: This article reviews the development of methods for decoding human neural activity, such as multivariate pattern classification, representational similarity analysis, hyperalignment, and stimulus-model-based encoding and decoding, into a common framework organized around the concept of high-dimensional representational spaces.
References
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TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Book
An introduction to the bootstrap
Bradley Efron,Robert Tibshirani +1 more
TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.
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A 2dvEv- bit distributed algorithm for the directed Euler trail problem
Wen-Huei Chen,Chuan Yi Tang +1 more
TL;DR: The algorithm can be used as a building block for solving other distributed graph problems, and can be slightly modified to run on a strongly-connected diagraph for generating the existent Euler trail or to report that no Euler trails exist.
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An Introduction to the Bootstrap.
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Dynamic causal modelling.
TL;DR: As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling, but unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.