Poster #: 12
Topic: Attention and distraction
Friday, Sep 11, 2015
Effects of deviant predictability on MMN-like MEG responses in oddball paradigms
1Special Lab Non-invasive Brain Imaging, Leibniz Institute for Neurobiology Magdeburg, Magdeburg, Germany
2Department Systems Physiology of Learning, Leibniz Institute for Neurobiology Magdeburg, Magdeburg, Germany
The auditory mismatch negativity (MMN) is commonly the result of a mathematical operation performed off-line on sound-evoked responses in oddball paradigms measured with MEG or EEG, namely the subtraction of trial-averaged responses to frequent “standard” sounds from those to rare “deviant” sounds. We studied the effects of attention and/or stimulus predictability on MMN-like responses. In the repeated-tone-sequence paradigm, subjects listened to 100 identical tone sequences, each consisting of 27 standards and three deviants, with the deviants occurring at the same positions in all sequences. In the random-tone-sequence paradigm, the 300 deviants occurred pseudo-randomly across the 100 sequences. Responses were analyzed over a 400-ms time window commencing with stimulus onset. Evaluation of single subject data reveals that, in the random-tone-sequence paradigm, differences between waveforms elicited by standards and deviants were subtle, whereas in the repeated-tone-sequence paradigm, they were pronounced beyond the M100 peak in most subjects, supporting the notion of an important role of attention and stimulus predictability in the generation of the waveform differences.
Computation and comparison of grand mean waveforms across the two experimental conditions requires stabilizing and equalizing the variance of the waveforms. Application of the asinh-transform to MEG waveforms leads to the same homogeneous variance in all conditions and results in normally distributed data, thus, allowing regressions and parametric tests for statistical analyses and comparisons of data from different experimental conditions. Hence, we will also discuss the impact of the asinh-transform on the (statistical) analysis of our data. (Supported by DFG KO1713/10-1).