September 2017
Volume 17, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2017
Oscillatory signatures of object recognition across cortical space and time.
Author Affiliations
  • Leila Reddy
    Université de Toulouse UPS Centre de Recherche Cerveau et Cognition 31052 Toulouse, France
    CNRS CerCo 31052 Toulouse France
  • Radoslaw Cichy
    Department of Education and Psychology, Free University Berlin, Berlin, Germany
  • Rufin VanRullen
    Université de Toulouse UPS Centre de Recherche Cerveau et Cognition 31052 Toulouse, France
    CNRS CerCo 31052 Toulouse France
Journal of Vision August 2017, Vol.17, 1346. doi:10.1167/17.10.1346
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      Leila Reddy, Radoslaw Cichy, Rufin VanRullen; Oscillatory signatures of object recognition across cortical space and time.. Journal of Vision 2017;17(10):1346. doi: 10.1167/17.10.1346.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Numerous theories propose a key role for brain oscillations in visual perception. Most of these postulate that sensory information is encoded in specific oscillatory components (e.g., power or phase) of specific frequency bands. These theories are often tested with whole-brain recording methods of low spatial resolution (EEG or MEG) or in depth recordings (LFP) that provide a local, incomplete view of the brain. Opportunities to bridge the gap between local neural populations and whole-brain signals are rare. Here, using representational similarity analysis (RSA) between separate MEG and fMRI datasets we systematically explored the correspondence between whole-brain oscillatory signals and local activity in specific brain regions (V1 and IT). Fifteen subjects were tested with MEG and fMRI while they viewed 92 different objects. Time-frequency (TF) analysis for each trial and MEG sensor was performed. Two oscillatory components (power and phase) were extracted at each TF coordinate. MEG representational dissimilarity matrices (RDMs) were computed for the two oscillatory measures at each TF coordinate, and fMRI-RDMs were computed for V1 and IT. Finally, RSA was performed between the MEG- and fMRI-RDMs. The RSA showed that at stimulus onset, most oscillatory signals correlated first with V1, and then with IT representations. However, later in the trial, different brain areas simultaneously carried stimulus information, but in different frequency bands (e.g., about 50ms prior to stimulus offset, high-beta 20-32Hz oscillations resembled V1 activity, while the theta 4-8Hz band resembled IT). Additionally, stimulus information in different oscillatory components at a specific frequency could simultaneously match representations from different brain regions (e.g., in the beta 13-20Hz band around 300ms, information in oscillatory power more resembled V1, while oscillatory phase information at the same time points more resembled IT). These results set the stage for a systematic understanding of the relation between whole-brain oscillatory signals and local neuronal activations.

Meeting abstract presented at VSS 2017

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