Visual gamma oscillations: The effects of stimulus type, visual field coverage and stimulus motion on MEG and EEG recordings S.D. Muthukumaraswamy ⁎, K.D. Singh CUBRIC, School of Psychology, Cardiff University, Cardiff, CF103AT, UK abstract article info Article history: Accepted 12 December 2012 Available online 27 December 2012 Keywords: Gamma oscillations Visual system MEG EEG Increases in the power of neural oscillations in the gamma (> 40 Hz) band are a key signature of information processing in cortical neuronal networks. However, non-invasive detection of these very small oscillations is difficult due to the presence of potential artefacts (both muscular and ocular) in the same frequency band and requires highly optimised paradigms. Numerous studies have shown that the properties of visual gamma-band responses to simple pattern stimuli are highly tuned to the stimuli parameters used. The aim of this work was to compare gamma oscillation response properties across some of the more commonly used stimulus configurations. To do this, MEG and EEG recordings were made during the presentation of eight different stimulus types in a 2 × 2 × 2 design. For the first stimulus factor, “Type”, the stimulus pattern was either an annulus grating or a square wave grating. For the second stimulus factor, “Field”, stimuli were presented in either four visual field quadrants simultaneously or only in the lower left quadrant. Finally, for the “Move” factor, stimuli either drifted at 1.33°s -1 or were stationary. For MEG gamma band responses, the following main effects were observed, a) gamma-band power was increased for annular stimuli compared to square wave stimuli, b) gamma-band power was increased for full field stimuli compared to sin- gle quadrant stimuli and c) gamma-band power was larger for drifting compared to stationary stimuli and were of significantly higher frequency. For the detectors used, the signal to noise ratio was substantially higher for MEG than EEG. The advantages and disadvantages of the different types of stimulus types are discussed. © 2012 Elsevier Inc. All rights reserved. 1. Introduction Increases in the power of neural oscillations in the gamma (>40 Hz) band are a key signature of information processing in cortical neuronal networks. Gamma frequency oscillations have been implicated in a diverse range of brain functions including, attention and memory (Jensen et al., 2007), visual perception (Melloni et al., 2007) including the perception of visual illusions (Parra et al., 2003), object perception (Tallon-Baudry and Bertrand, 1999) as well as motor control (Cheyne et al., 2008; Muthukumaraswamy, 2010). Regardless of the specific cortical function being probed, non-invasive detection of these very small oscillations is difficult due to the presence of potential artefacts (muscular and ocular), which overlap the same frequency range as the gamma oscillation (Whitham et al., 2007; Yuval-Greenberg et al., 2008). Therefore, artefact-free measurement of gamma band oscillations requires a combination of highly optimised paradigms coupled with careful data analysis procedures. Originally described in the cat visual cortex (Gray and Singer, 1989; Gray et al., 1989), gamma oscillations to simple geometric patterns, such as grating patches, have been reported many times in non-invasive MEG and EEG studies. These gamma oscillations have been found to be highly repeatable within participants (Muthukumaraswamy et al., 2010) and also appear to be highly heritable (van Pelt et al., 2012). Numerous animal and human studies have shown that the properties of visual gamma band response to simple pattern stimuli are strongly de- pendent on the specific properties of the stimuli used. For example, gamma oscillations show spatial frequency tuning characteristics with the optimal spatial frequency to elicit gamma oscillations being around three cycles per degree (Adjamian et al., 2004). Gamma oscillations are best elicited with high contrast stimuli (Hall et al., 2005; Henrie and Shapley, 2005) and increase in amplitude with stimulus size (Gieselmann and Thiele, 2008; Perry et al., 2013). Stimuli located in the non-foveal part of the visual field generate relatively small gamma (Swettenham et al., 2009) and square wave stimuli generate more activ- ity than sine waves (Muthukumaraswamy and Singh, 2009). While some experimenters (e.g. (Hoogenboom et al., 2010; Hoogenboom et al., 2006; Kahlbrock et al., 2012)) have used annular stimuli (concentric circles) others (e.g. (Adjamian et al., 2004; Muthukumaraswamy et al., 2010; Swettenham et al., 2009)) use 1-D gratings and it is unknown if there NeuroImage 69 (2013) 223–230 ⁎ Corresponding author at: CUBRIC, School of Psychology, Cardiff University, Park Place, Cardiff, UK. E-mail address: sdmuthu@cardiff.ac.uk (S.D. Muthukumaraswamy). 1053-8119/$ – see front matter © 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.neuroimage.2012.12.038 Contents lists available at SciVerse ScienceDirect NeuroImage journal homepage: www.elsevier.com/locate/ynimg