Seeing patterns through the hemodynamic veil — The future of pattern-information fMRI

Pattern-information fMRI (pi-fMRI) has become a popular method in neuroscience. The technique is motivated by the idea that spatial patterns of fMRI activity reflect the neuronal population codes of perception, cognition, and action. In this commentary, we discuss three fundamental outstanding questions: (1) What is the relationship between neuronal patterns and fMRI patterns? (2) Does pattern-information fMRI benefit from hyperacuity, enabling the investigation of columnar-level neuronal information, even at low resolution? (3) Do high-resolution and high-field fMRI increase sensitivity to pattern information? The empirical answers will enable us to optimize pi-fMRI data acquisition and to understand the ultimate potential and appropriate interpretation of pi-fMRI results. Furthermore, considering the relationship between neuronal activity and fMRI at the level of spatiotemporal patterns provides a novel and important perspective on the basis of the fMRI signal.

[1]  E. Formisano,et al.  Auditory Cortex Encodes the Perceptual Interpretation of Ambiguous Sound , 2011, The Journal of Neuroscience.

[2]  Lawrence L. Wald,et al.  Laminar analysis of 7T BOLD using an imposed spatial activation pattern in human V1 , 2010, NeuroImage.

[3]  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.

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

[5]  Lawrence L. Wald,et al.  Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters , 2005, NeuroImage.

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

[7]  W. Edelstein,et al.  The intrinsic signal‐to‐noise ratio in NMR imaging , 1986, Magnetic resonance in medicine.

[8]  Nikolaus Kriegeskorte,et al.  Relating Population-Code Representations between Man, Monkey, and Computational Models , 2009, Front. Neurosci..

[9]  Lawrence L. Wald,et al.  Physiological noise and signal-to-noise ratio in fMRI with multi-channel array coils , 2011, NeuroImage.

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

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

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

[13]  Essa Yacoub,et al.  Robust detection of ocular dominance columns in humans using Hahn Spin Echo BOLD functional MRI at 7 Tesla , 2007, NeuroImage.

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

[15]  J. Mumford,et al.  Greater Neural Pattern Similarity Across Repetitions Is Associated with Better Memory , 2010, Science.

[16]  K. Uğurbil,et al.  Ultrahigh field magnetic resonance imaging and spectroscopy. , 2003, Magnetic resonance imaging.

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

[18]  Marlene Behrmann,et al.  Unraveling the distributed neural code of facial identity through spatiotemporal pattern analysis , 2011, Proceedings of the National Academy of Sciences.

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

[20]  Robert Turner,et al.  How Much Cortex Can a Vein Drain? Downstream Dilution of Activation-Related Cerebral Blood Oxygenation Changes , 2002, NeuroImage.

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

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

[23]  Geoffrey M Boynton,et al.  Imaging orientation selectivity: decoding conscious perception in V1 , 2005, Nature Neuroscience.

[24]  Kamil Ugurbil,et al.  An integrative model for neuronal activity-induced signal changes for gradient and spin echo functional imaging , 2009, NeuroImage.

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

[26]  Jeremy Freeman,et al.  Orientation Decoding Depends on Maps, Not Columns , 2011, The Journal of Neuroscience.

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

[28]  Nikolaus Kriegeskorte,et al.  Pattern-information analysis: From stimulus decoding to computational-model testing , 2011, NeuroImage.

[29]  Janaina Mourão Miranda,et al.  Unsupervised analysis of fMRI data using kernel canonical correlation , 2007, NeuroImage.

[30]  Essa Yacoub,et al.  High-field fMRI unveils orientation columns in humans , 2008, Proceedings of the National Academy of Sciences.

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

[32]  Colin W. G. Clifford,et al.  Discrimination of the local orientation structure of spiral Glass patterns early in human visual cortex , 2009, NeuroImage.

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

[34]  Gabriel Kreiman,et al.  Visual population codes : toward a common multivariate framework for cell recording and functional imaging , 2012 .

[35]  Essa Yacoub,et al.  Spatio-temporal point-spread function of fMRI signal in human gray matter at 7 Tesla , 2007, NeuroImage.

[36]  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.

[37]  R. Goebel,et al.  7T vs. 4T: RF power, homogeneity, and signal‐to‐noise comparison in head images , 2001, Magnetic resonance in medicine.

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

[39]  Stephen D. Mayhew,et al.  Article Learning Shapes the Representation of Behavioral Choice in the Human Brain , 2022 .

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

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

[42]  Kevin Murphy,et al.  Mapping the MRI voxel volume in which thermal noise matches physiological noise—Implications for fMRI , 2007, NeuroImage.

[43]  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.

[44]  F. Tong,et al.  Decoding reveals the contents of visual working memory in early visual areas , 2009, Nature.

[45]  Vaidehi S. Natu,et al.  Category-Specific Cortical Activity Precedes Retrieval During Memory Search , 2005, Science.

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

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

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

[49]  Essa Yacoub,et al.  Signal and noise characteristics of Hahn SE and GE BOLD fMRI at 7 T in humans , 2005, NeuroImage.

[50]  R. Goebel,et al.  Individual faces elicit distinct response patterns in human anterior temporal cortex , 2007, Proceedings of the National Academy of Sciences.

[51]  N. Logothetis,et al.  Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.

[52]  Jascha D. Swisher,et al.  Multiscale Pattern Analysis of Orientation-Selective Activity in the Primary Visual Cortex , 2010, The Journal of Neuroscience.

[53]  Karl J. Friston,et al.  Bayesian decoding of brain images , 2008, NeuroImage.

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

[55]  A. Shmuel,et al.  Imaging brain function in humans at 7 Tesla , 2001, Magnetic resonance in medicine.

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

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

[58]  D. Heeger,et al.  Linear Systems Analysis of Functional Magnetic Resonance Imaging in Human V1 , 1996, The Journal of Neuroscience.

[59]  T. Ethofer,et al.  Decoding of emotional information in voice-sensitive cortices , 2009, NeuroImage.

[60]  K. Uğurbil,et al.  Spin‐echo fMRI in humans using high spatial resolutions and high magnetic fields , 2003, Magnetic resonance in medicine.

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

[62]  Brian A. Wandell,et al.  Population receptive field estimates in human visual cortex , 2008, NeuroImage.

[63]  G. Glover,et al.  Physiological noise in oxygenation‐sensitive magnetic resonance imaging , 2001, Magnetic resonance in medicine.

[64]  T. Carlson,et al.  Patterns of Activity in the Categorical Representations of Objects , 2003 .

[65]  Karl J. Friston,et al.  Analysis of fMRI Time-Series Revisited , 1995, NeuroImage.

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