September 2017
Volume 17, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2017
A highly effective approach in fMRI brain mapping of visual categorization
Author Affiliations
  • Xiaoqing Gao
    Psychological Sciences Research Institute, Institute of Neuroscience, University of Louvain, Belgium
  • Francesco Gentile
    Psychological Sciences Research Institute, Institute of Neuroscience, University of Louvain, Belgium
    Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, The Netherlands, Maastricht Brain Imaging Center (M-BIC), Maastricht University, The Netherlands
  • Bruno Rossion
    Psychological Sciences Research Institute, Institute of Neuroscience, University of Louvain, Belgium
    Neurology Unit, Centre Hospitalier Regional Universitaire (CHRU) de Nancy, F-54000 Nancy, France
Journal of Vision August 2017, Vol.17, 1260. doi:10.1167/17.10.1260
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      Xiaoqing Gao, Francesco Gentile, Bruno Rossion; A highly effective approach in fMRI brain mapping of visual categorization. Journal of Vision 2017;17(10):1260. doi: 10.1167/17.10.1260.

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

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Abstract

Despite measuring neural activity indirectly, functional magnetic resonance imaging (fMRI) has become the most important tool in mapping the human visual system. However, current fMRI measurements may suffer from low signal-to-noise ratio (SNR) in detecting high-level neural responses in individual brains, leading to low test-retest reliability in spatial activation maps. Here we developed a novel fMRI approach to map visual categorization with fMRI. As in EEG frequency-tagging (Rossion et al., 2015), we presented a large variety of natural images at a fast rate (6Hz) throughout the entire experiment to stimulate the visual areas continuously. By introducing transient switches to a target category (faces) at a slow fixed frequency (1/54 stimuli, i.e., 0.111 Hz), we obtained a periodic differential neural response that directly reflects category selectivity. A model-free Fast Fourier Transform (FFT) of hemodynamic activity in this paradigm achieved a two-fold increase in sensitivity (peak SNR) in comparison to a conventional block design, allowing us to map comprehensive extended face-selective areas including the anterior temporal lobe (ATL) in individual brains. Using diverse natural images, we created contrasts at both lower-level visual properties and higher-level category properties in successive images. While the lower-level contrasts were at random time points, the category contrast would only happen at a fixed frequency by design. Therefore, we effectively eliminated the influence of low-level visual cues and increased the specificity of category-selective response. As a result of high sensitivity and specificity, we achieved high test-retest reliability, which reached the highest values (80-90%) ever reported in this area of research. The power of a model-free fast periodic visual stimulation (FPVS) approach with a slow temporal resolution method opens a real avenue for understanding brain mapping of visual categorization.

Meeting abstract presented at VSS 2017

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