Information Decoding from fMRI Images

Conventional analysis of functional magnetic resonance imaging (fMRI) time series is based on univariate statistical analysis. In this approach, a spatially invariant model of the expected blood oxygenation level-dependent (BOLD) response is fitted independently at each voxel’s time course, and the differences between estimated activation levels during two or more experimental conditions are tested. Together with methods for mitigating the problem of performing a large number of tests, this massively univariate analysis produces statistical maps of response differences, highlighting brain locations that are “selective” or “specialized” for a certain stimulus dimension, that is, voxels or regions of interest (ROI) that respond more vigorously to a sensory, motor, or cognitive stimulus compared to one or more appropriate control conditions. This approach is not appropriate when the relevant question is what is the “information” content of a certain network of brain regions rather than which is its “activation” level.

[1]  Essa Yacoub,et al.  Mechanisms underlying decoding at 7 T: Ocular dominance columns, broad structures, and macroscopic blood vessels in V1 convey information on the stimulated eye , 2010, NeuroImage.

[2]  Essa Yacoub,et al.  Spatial organization of frequency preference and selectivity in the human inferior colliculus , 2012, Nature Communications.

[3]  Rainer Goebel,et al.  Multimodal imaging: an evaluation of univariate and multivariate methods for simultaneous EEG/fMRI. , 2010, Magnetic resonance imaging.

[4]  Rainer Goebel,et al.  Predicting subject-driven actions and sensory experience in a virtual world with Relevance Vector Machine Regression of fMRI data , 2011, NeuroImage.

[5]  Tom M. Mitchell,et al.  Machine learning classifiers and fMRI: A tutorial overview , 2009, NeuroImage.

[6]  Rainer Goebel,et al.  Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns , 2008, NeuroImage.

[7]  Essa Yacoub,et al.  Representation of pitch chroma by multi-peak spectral tuning in human auditory cortex , 2015, NeuroImage.

[8]  Rainer Goebel,et al.  "Who" Is Saying "What"? Brain-Based Decoding of Human Voice and Speech , 2008, Science.

[9]  Polina Golland,et al.  Detecting stable distributed patterns of brain activation using Gini contrast , 2011, NeuroImage.

[10]  Rainer Goebel,et al.  Information-based functional brain mapping. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Nikolaus Kriegeskorte,et al.  Analyzing for information, not activation, to exploit high-resolution fMRI , 2007, NeuroImage.

[12]  Yoram Singer,et al.  Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers , 2000, J. Mach. Learn. Res..

[13]  F. Tong,et al.  Decoding the visual and subjective contents of the human brain , 2005, Nature Neuroscience.

[14]  Karl J. Friston,et al.  Dynamic discrimination analysis: A spatial–temporal SVM , 2007, NeuroImage.

[15]  N. Kanwisher,et al.  Multivariate Patterns in Object-Selective Cortex Dissociate Perceptual and Physical Shape Similarity , 2008, PLoS biology.

[16]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[17]  Lars Kai Hansen,et al.  Visualization of nonlinear kernel models in neuroimaging by sensitivity maps , 2011, NeuroImage.

[18]  Janaina Mourão Miranda,et al.  Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data , 2005, NeuroImage.

[19]  Johan Wessberg,et al.  A Monte Carlo method for locally multivariate brain mapping , 2011, NeuroImage.

[20]  David D. Cox,et al.  Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex , 2003, NeuroImage.

[21]  S Makeig,et al.  Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.

[22]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[23]  Johan Wessberg,et al.  An Evolutionary Approach to the Identification of Informative Voxel Clusters for Brain State Discrimination , 2008, IEEE Journal of Selected Topics in Signal Processing.

[24]  A. Ishai,et al.  Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.

[25]  Giancarlo Valente,et al.  Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning. , 2008, Magnetic resonance imaging.

[26]  Tom M. Mitchell,et al.  Learning to Decode Cognitive States from Brain Images , 2004, Machine Learning.

[27]  N. Kriegeskorte,et al.  Revealing representational content with pattern-information fMRI--an introductory guide. , 2009, Social cognitive and affective neuroscience.

[28]  Mitsuo Kawato,et al.  Sparse linear regression for reconstructing muscle activity from human cortical fMRI , 2008, NeuroImage.

[29]  Janaina Mourão Miranda,et al.  Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes , 2010, NeuroImage.

[30]  Alexander Borst,et al.  How does Nature Program Neuron Types? , 2008, Front. Neurosci..

[31]  Essa Yacoub,et al.  Modeling and analysis of mechanisms underlying fMRI-based decoding of information conveyed in cortical columns , 2011, NeuroImage.

[32]  Juan José Rodríguez Diez,et al.  Random Subspace Ensembles for fMRI Classification , 2010, IEEE Transactions on Medical Imaging.

[33]  G. Rees,et al.  Predicting the orientation of invisible stimuli from activity in human primary visual cortex , 2005, Nature Neuroscience.

[34]  C. Furmanski,et al.  An oblique effect in human primary visual cortex , 2000, Nature Neuroscience.

[35]  J. Gallant,et al.  Identifying natural images from human brain activity , 2008, Nature.

[36]  Nikolaus Kriegeskorte,et al.  Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .

[37]  Justin L. Gardner,et al.  Is cortical vasculature functionally organized? , 2010, NeuroImage.

[38]  Johan Wessberg,et al.  Somatotopic Organization of Gentle Touch Processing in the Posterior Insular Cortex , 2009, The Journal of Neuroscience.

[39]  Rainer Goebel,et al.  Predicting EEG single trial responses with simultaneous fMRI and Relevance Vector Machine regression , 2011, NeuroImage.

[40]  Elia Formisano,et al.  Processing of Natural Sounds in Human Auditory Cortex: Tonotopy, Spectral Tuning, and Relation to Voice Sensitivity , 2012, The Journal of Neuroscience.

[41]  Patrick J. F. Groenen,et al.  Modern multidimensional scaling: Theory and applications, 2nd ed. , 2005 .

[42]  Hans P. Op de Beeck,et al.  Probing the mysterious underpinnings of multi-voxel fMRI analyses , 2010, NeuroImage.

[43]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[44]  Kaustubh Supekar,et al.  Sparse logistic regression for whole-brain classification of fMRI data , 2010, NeuroImage.

[45]  Masa-aki Sato,et al.  Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns , 2008, NeuroImage.

[46]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[47]  Sean M. Polyn,et al.  Beyond mind-reading: multi-voxel pattern analysis of fMRI data , 2006, Trends in Cognitive Sciences.

[48]  R. Goebel,et al.  Processing of Natural Sounds: Characterization of Multipeak Spectral Tuning in Human Auditory Cortex , 2013, The Journal of Neuroscience.

[49]  Nikolaus Kriegeskorte,et al.  How does an fMRI voxel sample the neuronal activity pattern: Compact-kernel or complex spatiotemporal filter? , 2010, NeuroImage.

[50]  Tom Heskes,et al.  Efficient Bayesian multivariate fMRI analysis using a sparsifying spatio-temporal prior , 2010, NeuroImage.

[51]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[52]  Masa-aki Sato,et al.  Visual Image Reconstruction from Human Brain Activity using a Combination of Multiscale Local Image Decoders , 2008, Neuron.

[53]  Manel Martínez-Ramón,et al.  fMRI pattern classification using neuroanatomically constrained boosting , 2006, NeuroImage.

[54]  Tom Michael Mitchell,et al.  Predicting Human Brain Activity Associated with the Meanings of Nouns , 2008, Science.

[55]  Alice J. O'Toole,et al.  Theoretical, Statistical, and Practical Perspectives on Pattern-based Classification Approaches to the Analysis of Functional Neuroimaging Data , 2007, Journal of Cognitive Neuroscience.

[56]  Nikolaus Kriegeskorte,et al.  Comparison of multivariate classifiers and response normalizations for pattern-information fMRI , 2010, NeuroImage.

[57]  John Ashburner,et al.  Kernel regression for fMRI pattern prediction , 2011, NeuroImage.

[58]  Ryan J. Prenger,et al.  Bayesian Reconstruction of Natural Images from Human Brain Activity , 2009, Neuron.

[59]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[60]  Rainer Goebel,et al.  Dissecting cognitive stages with time-resolved fMRI data: a comparison of fuzzy clustering and independent component analysis. , 2007, Magnetic resonance imaging.

[61]  Michael E. Tipping.  Sparse Bayesian Learning and the Relevance Vector Machine , 2001, J. Mach. Learn. Res..

[62]  Wim Vanduffel,et al.  The Radial Bias: A Different Slant on Visual Orientation Sensitivity in Human and Nonhuman Primates , 2006, Neuron.

[63]  Noël Staeren,et al.  Sound Categories Are Represented as Distributed Patterns in the Human Auditory Cortex , 2009, Current Biology.

[64]  Francisco Pereira,et al.  Information mapping with pattern classifiers: A comparative study , 2011, NeuroImage.

[65]  A. Ravishankar Rao,et al.  Prediction and interpretation of distributed neural activity with sparse models , 2009, NeuroImage.

[66]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[67]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[68]  Hans P. Op de Beeck,et al.  Against hyperacuity in brain reading: Spatial smoothing does not hurt multivariate fMRI analyses? , 2010, NeuroImage.

[69]  Yaroslav O. Halchenko,et al.  Brain Reading Using Full Brain Support Vector Machines for Object Recognition: There Is No Face Identification Area , 2008, Neural Computation.

[70]  Yasuhito Sawahata,et al.  Spatial smoothing hurts localization but not information: Pitfalls for brain mappers , 2010, NeuroImage.

[71]  Michael S Beauchamp,et al.  Distributed Representation of Single Touches in Somatosensory and Visual Cortex , 2009, NeuroImage.

[72]  Keiji Tanaka,et al.  Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey , 2008, Neuron.

[73]  Janaina Mourão Miranda,et al.  The impact of temporal compression and space selection on SVM analysis of single-subject and multi-subject fMRI data , 2006, NeuroImage.

[74]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[75]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .