Dataset Information

Multiscale pattern analysis of orientation-selective activity in the primary visual cortex.

ABSTRACT: Although orientation columns are less than a millimeter in width, recent neuroimaging studies indicate that viewed orientations can be decoded from cortical activity patterns sampled at relatively coarse resolutions of several millimeters. One proposal is that these differential signals arise from random spatial irregularities in the columnar map. However, direct support for this hypothesis has yet to be obtained. Here, we used high-field, high-resolution functional magnetic resonance imaging (fMRI) and multivariate pattern analysis to determine the spatial scales at which orientation-selective information can be found in the primary visual cortex (V1) of cats and humans. We applied a multiscale pattern analysis approach in which fine- and coarse-scale signals were first removed by ideal spatial lowpass and highpass filters, and the residual activity patterns then analyzed by linear classifiers. Cat visual cortex, imaged at 0.3125 mm resolution, showed a strong orientation signal at the scale of individual columns. Nonetheless, reliable orientation bias could still be found at spatial scales of several millimeters. In the human visual cortex, imaged at 1 mm resolution, a majority of orientation information was found on scales of millimeters, with small contributions from global spatial biases exceeding approximately 1 cm. Our high-resolution imaging results demonstrate a reliable millimeters-scale orientation signal, likely emerging from irregular spatial arrangements of orientation columns and their supporting vasculature. fMRI pattern analysis methods are thus likely to be sensitive to signals originating from other irregular columnar structures elsewhere in the brain.

SUBMITTER: Swisher JD

PROVIDER: S-EPMC2823298 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature

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Publications

Multiscale pattern analysis of orientation-selective activity in the primary visual cortex.

Swisher Jascha D JD Gatenby J Christopher JC Gore John C JC Wolfe Benjamin A BA Moon Chan-Hong CH Kim Seong-Gi SG Tong Frank F

The Journal of neuroscience : the official journal of the Society for Neuroscience 20100101 1

Although orientation columns are less than a millimeter in width, recent neuroimaging studies indicate that viewed orientations can be decoded from cortical activity patterns sampled at relatively coarse resolutions of several millimeters. One proposal is that these differential signals arise from random spatial irregularities in the columnar map. However, direct support for this hypothesis has yet to be obtained. Here, we used high-field, high-resolution functional magnetic resonance imaging (f ...[more]

PMID: 20053913

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Project description:Neurons in sensory areas of neocortex exhibit responses tuned to specific features of the environment. In visual cortex, information about features such as edges or textures with particular orientations must be integrated to recognize a visual scene or object. Connectivity studies in rodent cortex have revealed that neurons make specific connections within sub-networks sharing common input tuning. In principle, this sub-network architecture enables local cortical circuits to integrate sensory information. However, whether feature integration indeed occurs locally in rodent primary sensory areas has not been examined directly. We studied local integration of sensory features in primary visual cortex (V1) of the mouse by presenting drifting grating and plaid stimuli, while recording the activity of neuronal populations with two-photon calcium imaging. Using a Bayesian model-based analysis framework, we classified single-cell responses as being selective for either individual grating components or for moving plaid patterns. Rather than relying on trial-averaged responses, our model-based framework takes into account single-trial responses and can easily be extended to consider any number of arbitrary predictive models. Our analysis method was able to successfully classify significantly more responses than traditional partial correlation (PC) analysis, and provides a rigorous statistical framework to rank any number of models and reject poorly performing models. We also found a large proportion of cells that respond strongly to only one stimulus class. In addition, a quarter of selectively responding neurons had more complex responses that could not be explained by any simple integration model. Our results show that a broad range of pattern integration processes already take place at the level of V1. This diversity of integration is consistent with processing of visual inputs by local sub-networks within V1 that are tuned to combinations of sensory features.

Dataset Information

Multiscale pattern analysis of orientation-selective activity in the primary visual cortex.

Publications

Multiscale pattern analysis of orientation-selective activity in the primary visual cortex.

Similar Datasets

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