Visual representations are dominated by intrinsic fluctuations correlated between areas

Intrinsic cortical dynamics are thought to underlie trial-to-trial variability of visually evoked responses in animal models. Understanding their function in the context of sensory processing and representation is a major current challenge. Here we report that intrinsic cortical dynamics strongly affect the representational geometry of a brain region, as reflected in response-pattern dissimilarities, and exaggerate the similarity of representations between brain regions. We characterized the representations in several human visual areas by representational dissimilarity matrices (RDMs) constructed from fMRI response-patterns for natural image stimuli. The RDMs of different visual areas were highly similar when the response-patterns were estimated on the basis of the same trials (sharing intrinsic cortical dynamics), and quite distinct when patterns were estimated on the basis of separate trials (sharing only the stimulus-driven component). We show that the greater similarity of the representational geometries can be explained by coherent fluctuations of regional-mean activation within visual cortex, reflecting intrinsic dynamics. Using separate trials to study stimulus-driven representations revealed clearer distinctions between the representational geometries: a Gabor wavelet pyramid model explained representational geometry in visual areas V1–3 and a categorical animate–inanimate model in the object-responsive lateral occipital cortex.

[1]  D. Heeger,et al.  Activity in primary visual cortex predicts performance in a visual detection task , 2000, Nature Neuroscience.

[2]  J. Movshon,et al.  Nature and interaction of signals from the receptive field center and surround in macaque V1 neurons. , 2002, Journal of neurophysiology.

[3]  N. Kriegeskorte,et al.  Author ' s personal copy Representational geometry : integrating cognition , computation , and the brain , 2013 .

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

[5]  Jonathan D. Cohen,et al.  Reproducibility Distinguishes Conscious from Nonconscious Neural Representations , 2010, Science.

[6]  Jeffrey M. Zacks,et al.  Coherent spontaneous activity accounts for trial-to-trial variability in human evoked brain responses , 2006, Nature Neuroscience.

[7]  Nicole C. Rust,et al.  Do We Know What the Early Visual System Does? , 2005, The Journal of Neuroscience.

[8]  M. Fox,et al.  Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging , 2007, Nature Reviews Neuroscience.

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

[10]  A. Grinvald,et al.  Dynamics of Ongoing Activity: Explanation of the Large Variability in Evoked Cortical Responses , 1996, Science.

[11]  Eero P. Simoncelli,et al.  A functional and perceptual signature of the second visual area in primates , 2013, Nature Neuroscience.

[12]  Yehezkel Yeshurun,et al.  Widespread functional connectivity and fMRI fluctuations in human visual cortex in the absence of visual stimulation , 2006, NeuroImage.

[13]  Nikolaus Kriegeskorte,et al.  Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation , 2014, PLoS Comput. Biol..

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

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

[16]  Andrew P. Yonelinas,et al.  Functional Connectivity Relationships Predict Similarities in Task Activation and Pattern Information during Associative Memory Encoding , 2014, Journal of Cognitive Neuroscience.

[17]  M. Carandini,et al.  Adaptation maintains population homeostasis in primary visual cortex , 2013, Nature Neuroscience.

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

[19]  I. Fried,et al.  Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex , 2008, Nature Neuroscience.

[20]  Christian Wallraven,et al.  Neuronal Correlates of Spontaneous Fluctuations in fMRI Signals in Monkey Visual Cortex: Implications for Functional Connectivity at Rest , 2008 .

[21]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[22]  Robert O. Duncan,et al.  Cortical Magnification within Human Primary Visual Cortex Correlates with Acuity Thresholds , 2003, Neuron.

[23]  József Fiser,et al.  Coding of Natural Scenes in Primary Visual Cortex , 2003, Neuron.

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

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

[26]  P. Lennie,et al.  Rapid adaptation in visual cortex to the structure of images. , 1999, Science.

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

[28]  J. Gallant,et al.  Cortical representation of animate and inanimate objects in complex natural scenes , 2012, Journal of Physiology-Paris.

[29]  Keiji Tanaka,et al.  Object category structure in response patterns of neuronal population in monkey inferior temporal cortex. , 2007, Journal of neurophysiology.

[30]  Jonathan Winawer,et al.  GLMdenoise: a fast, automated technique for denoising task-based fMRI data , 2013, Front. Neurosci..

[31]  M. Weliky,et al.  Small modulation of ongoing cortical dynamics by sensory input during natural vision , 2004, Nature.

[32]  Li Su,et al.  A Toolbox for Representational Similarity Analysis , 2014, PLoS Comput. Biol..

[33]  Jonathan Winawer,et al.  A Two-Stage Cascade Model of BOLD Responses in Human Visual Cortex , 2013, PLoS Comput. Biol..

[34]  A. Villringer,et al.  How Ongoing Neuronal Oscillations Account for Evoked fMRI Variability , 2011, The Journal of Neuroscience.

[35]  J. S. Guntupalli,et al.  The Representation of Biological Classes in the Human Brain , 2012, The Journal of Neuroscience.