September 2011
Volume 11, Issue 11
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2011
De Bruijn cycles for neural decoding
Author Affiliations
  • Marcelo G. Mattar
    Department of Neurology, University of Pennsylvania
  • Lucía Magis-Weinberg
    School of Medicine, National Autonomous University of Mexico
  • Geoffrey K. Aguirre
    Department of Neurology, University of Pennsylvania
Journal of Vision September 2011, Vol.11, 848. doi:10.1167/11.11.848
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      Marcelo G. Mattar, Lucía Magis-Weinberg, Geoffrey K. Aguirre; De Bruijn cycles for neural decoding. Journal of Vision 2011;11(11):848. doi: 10.1167/11.11.848.

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

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Abstract

Stimulus ordering is critical in studies of “carry-over effects”: the impact of stimulus history and context upon neural response. All studies of neural adaptation are measures of carry-over effects, as are studies of anticipation, priming, contrast, and temporal non-linearity. These effects are measured efficiently and without bias only in the setting of counter-balanced stimulus sequences.

We introduce de Bruijn cycles, a class of combinatorial objects, as the ideal source of pseudo-random stimulus sequences with arbitrary levels of counter-balance. Neuro-vascular imaging studies (such as BOLD fMRI) have an additional requirement imposed by the filtering and noise properties of the method: only some temporal frequencies of neural modulation are detectable. Extant methods of generating counter-balanced stimulus sequences yield neural modulations that are weakly (or not at all) detected by BOLD fMRI. We have developed a novel “path-guided” approach for the generation of de Bruijn cycles which produces sequences with markedly improved detection power for neuro-vascular imaging techniques. These sequences may be used to study stimulus context and history effects in a manner not previously possible.

We will describe a method for creation of these sequences and provide several worked examples of possible BOLD fMRI experiments. One example study is of neural adaptation to transitions between 16 stimuli drawn from a two-dimensional stimulus space. We derive an order of stimulus presentation that provides a 2-fold improvement of statistical power with second-level counterbalance, and a 10-fold improvement with third-level counterbalance. We will demonstrate the applicability of these types of designs to a broad array of neuroscience experiments, including those that make use of multi-voxel pattern analysis.

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