The folding fingerprint of visual cortex reveals the timing of human V1 and V2

Primate neocortex contains over 30 visual areas. Recent techniques such as functional magnetic resonance imaging (fMRI) have successfully identified many of these areas in the human brain, but have been of limited value for revealing the temporal dynamics between visual areas. The electroencephalogram (EEG) provides information with high temporal precision, but has had limited success separating out the signals from individual neighboring cortical areas. Consequently, controversies exist over the temporal dynamics across cortical areas. In order to address this problem we developed a new method to identify the sources of the EEG. An individual's unique cortical pattern of sulci and gyri along with a visual area's functional retinotopic layout provides a folding fingerprint that predicts specific scalp topographies for stimuli presented in different parts of the visual field. Using this folding fingerprint with a 96 or 192 location stimulus severely constrains the solution space making it relatively easy to extract the temporal response of multiple visual areas to multiple stimulus locations. The large number of stimuli also provides a means to validate the waveforms by comparing across stimulus sets, an important feature not present in most EEG source identification procedures. Using this method our data reveal that both V1 and V2 waveforms have similar onset latencies, and their temporal dynamics provide new information regarding the response latencies of these areas in humans. Our method enables the previously unattainable separation of EEG responses from neighboring brain areas. While we applied the method to the first two cortical visual areas, V1 and V2, this method is also applicable to somatosensory areas that have defined mappings. This method provides a means to study the rapid information flow in the human brain to reveal top-down and bottom-up cognitive processes.

[1]  B. Lutkenhoner.  Dipole separability in a neuromagnetic source analysis , 1998, IEEE Transactions on Biomedical Engineering.

[2]  Don L. Jewett,et al.  Insidious errors in dipole parameters due to shell model misspecification using multiple time-points , 2005, Brain Topography.

[3]  Chantal Delon-Martin,et al.  Sequence of pattern onset responses in the human visual areas: an fMRI constrained VEP source analysis , 2004, NeuroImage.

[4]  Michael Scherg,et al.  Functional imaging and localization of electromagnetic brain activity , 2005, Brain Topography.

[5]  Thom Carney,et al.  Localizing Sites of Activation in Primary Visual Cortex Using Visual-Evoked Potentials and Functional Magnetic Resonance Imaging , 2006, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[6]  Thom Carney,et al.  The usefulness of the Laplacian in principal component analysis and dipole source localization , 2005, Brain Topography.

[7]  F. Di Russo,et al.  Electrophysiological analysis of cortical mechanisms of selective attention to high and low spatial frequencies , 2001, Clinical Neurophysiology.

[8]  Guillermo Sapiro,et al.  Creating connected representations of cortical gray matter for functional MRI visualization , 1997, IEEE Transactions on Medical Imaging.

[9]  G. Glover,et al.  Retinotopic organization in human visual cortex and the spatial precision of functional MRI. , 1997, Cerebral cortex.

[10]  Erich E. Sutter,et al.  The Fast m-Transform: A Fast Computation of Cross-Correlations with Binary m-Sequences , 1991, SIAM J. Comput..

[11]  E. Halgren,et al.  Source estimates for MEG/EEG visual evoked responses constrained by multiple, retinotopically‐mapped stimulus locations , 2009, Human brain mapping.

[12]  Justin M. Ales,et al.  Methods for quantifying intra- and inter-subject variability of evoked potential data applied to the multifocal visual evoked potential , 2007, Journal of Neuroscience Methods.

[13]  C. Schroeder,et al.  Intermodal selective attention in monkeys. I: distribution and timing of effects across visual areas. , 2000, Cerebral cortex.

[14]  B. Wandell,et al.  Topographic Organization of Human Visual Areas in the Absence of Input from Primary Cortex , 1999, The Journal of Neuroscience.

[15]  E. DeYoe,et al.  Functional magnetic resonance imaging (FMRI) of the human brain , 1994, Journal of Neuroscience Methods.

[16]  R. Tibshirani,et al.  An introduction to the bootstrap , 1993 .

[17]  Thom Carney,et al.  Using multi-stimulus VEP source localization to obtain a retinotopic map of human primary visual cortex , 1999, Clinical Neurophysiology.

[18]  J. Horton,et al.  The representation of the visual field in human striate cortex. A revision of the classic Holmes map. , 1991, Archives of ophthalmology.

[19]  B. Fischer,et al.  Visual field representations and locations of visual areas V1/2/3 in human visual cortex. , 2003, Journal of vision.

[20]  J. Fermaglich.  Electric Fields of the Brain: The Neurophysics of EEG , 1982 .

[21]  S. Hillyard,et al.  Identification of early visual evoked potential generators by retinotopic and topographic analyses , 1994 .

[22]  R. Ilmoniemi,et al.  Interpreting magnetic fields of the brain: minimum norm estimates , 2006, Medical and Biological Engineering and Computing.

[23]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[24]  A. Dale,et al.  High‐resolution intersubject averaging and a coordinate system for the cortical surface , 1999, Human brain mapping.

[25]  Karl J. Friston,et al.  Anatomically Informed Basis Functions for EEG Source Localization: Combining Functional and Anatomical Constraints , 2002, NeuroImage.

[26]  Don M. Tucker,et al.  The spatial resolution of scalp EEG , 2001, Neurocomputing.

[27]  S. Hillyard,et al.  Cortical sources of the early components of the visual evoked potential , 2002, Human brain mapping.

[28]  J J Riera,et al.  Evaluation of inverse methods and head models for EEG source localization using a human skull phantom , 2001, Physics in medicine and biology.

[29]  Anders M. Dale,et al.  Dynamic Statistical Parametric Neurotechnique Mapping: Combining fMRI and MEG for High-Resolution Imaging of Cortical Activity , 2000 .

[30]  E. Halgren,et al.  Dynamic Statistical Parametric Mapping Combining fMRI and MEG for High-Resolution Imaging of Cortical Activity , 2000, Neuron.

[31]  Alex R. Wade,et al.  Two-dimensional mapping of the central and parafoveal visual field to human visual cortex. , 2007, Journal of neurophysiology.

[32]  Peter Dayan,et al.  Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .

[33]  M. E. Spencer,et al.  Error bounds for EEG and MEG dipole source localization. , 1993, Electroencephalography and clinical neurophysiology.

[34]  R. Leahy,et al.  EEG and MEG: forward solutions for inverse methods , 1999, IEEE Transactions on Biomedical Engineering.

[35]  Stanley A. Klein,et al.  The folding fingerprint of visual cortex reveals the timing of human V1 and V2 , 2007 .

[36]  Xian Zhang,et al.  A principal component analysis of multifocal pattern reversal VEP. , 2004, Journal of vision.

[37]  E. Sutter,et al.  M and P Components of the VEP and their Visual Field Distribution , 1997, Vision Research.

[38]  Roger B. H. Tootell,et al.  The advantage of combining MEG and EEG: Comparison to fMRI in focally stimulated visual cortex , 2007, NeuroImage.

[39]  A. Leventhal,et al.  Signal timing across the macaque visual system. , 1998, Journal of neurophysiology.

[40]  Z. Zhang,et al.  A fast method to compute surface potentials generated by dipoles within multilayer anisotropic spheres. , 1995, Physics in medicine and biology.

[41]  Justin M. Ales,et al.  Advances in multifocal methods for imaging human brain activity , 2006, Electronic Imaging.

[42]  Fetsje Bijma,et al.  In vivo measurement of the brain and skull resistivities using an EIT-based method and realistic models for the head , 2003, IEEE Transactions on Biomedical Engineering.