ERP): Exploring the Time-Course of Visual Emotion Processing Using Topographic and Principal Component Analyses
Total Page：16
File Type：pdf, Size：1020Kb
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by RERO DOC Digital Library Brain Topogr (2008) 20:265–277 DOI 10.1007/s10548-008-0053-6 ORIGINAL PAPER Beyond Conventional Event-related Brain Potential (ERP): Exploring the Time-course of Visual Emotion Processing Using Topographic and Principal Component Analyses Gilles Pourtois Æ Sylvain Delplanque Æ Christoph Michel Æ Patrik Vuilleumier Accepted: 4 February 2008 / Published online: 13 March 2008 Ó Springer Science+Business Media, LLC 2008 Abstract Recent technological advances with the scalp and qualitative changes in the electric field configuration EEG methodology allow researchers to record electric recorded at the scalp level, which are not apparent when fields generated in the human brain using a large number of using conventional ERP analyses. Additional information electrodes or sensors (e.g. 64–256) distributed over the gained from these approaches include the identification of a head surface (multi-channel recording). As a consequence, sequence of successive processing stages that may not fully such high-density ERP mapping yields fairly dense ERP be reflected in ERP waveforms only, and the segregation of data sets that are often hard to analyze comprehensively or multiple or partly overlapping neural events that may be to relate straightforwardly to specific cognitive or emo- blended within a single ERP waveform. These findings tional processes, because of the richness of the recorded highlight the added value of such alternative analyses when signal in both the temporal (millisecond time-resolution) exploring the electrophysiological manifestations of com- and spatial (multidimensional topographic information) plex and distributed mental functions, as for instance domains. Principal component analyses (PCA) and topo- during emotion processing. graphic analyses (combined with distributed source localization algorithms) have been developed and suc- Keywords Scalp EEG Á High-density ERP mapping Á cessfully used to deal with this complexity, now offering Temporal PCA Á Spatial PCA Á Topography Á Field powerful alternative strategies for data-driven analyses in strength Á Dissimilarity Á Clustering Á Emotion perception Á complement to more traditional ERP analyses based on Vision Á Early perceptual effect Á P3a Á P3b. waveforms and peak measures. In this paper, we first briefly review the basic principles of these approaches, and then describe recent ERP studies that illustrate how they Limits of Conventional ERP Data Analysis can inform about the precise spatio-temporal dynamic of emotion processing. These studies show that the perception Given its millisecond time-resolution and direct relation- of emotional visual stimuli may produce both quantitative ship to neuronal activity (i.e., post-synaptic dendritic potentials of a large number of neurons activated synchro- nously and arranged in a geometrical configuration such as G. Pourtois Á P. Vuilleumier Laboratory for Behavioral Neurology & Imaging of Cognition, to yield a dipolar field), scalp electro-encephalogram (EEG) Department of Neuroscience & Clinic of Neurology, University is a highly valuable time-resolved brain-imaging technique of Geneva, Geneva, Switzerland (see [1, 2]). Event-related brain potentials (ERPs) are computed from the EEG by using, in the vast majority of G. Pourtois (&) Á S. Delplanque Á P. Vuilleumier Swiss Center for Affective Sciences, University of Geneva, cases, the averaging of data as a signal extraction technique 7 rue des Battoirs, 1205 Geneva, Switzerland (see [3, 4] for different techniques, including frequency and e-mail: [email protected] single trials analyses). EEG epochs are time-locked to the same event class (either a stimulus or a response), and then C. Michel Functional Brain Mapping Laboratory, Department of averaged to yield a waveform carrying a mean amplitude Neurology, University Hospital, Geneva, Switzerland value at each time-point, whose successive negative and 123 266 Brain Topogr (2008) 20:265–277 positive deflections over time are thought to reflect specific radically change (and sometimes even cross the zero stages of sensory, cognitive, or decision-related processes baseline and switch polarity) as a function of the position [5]. of the reference electrode. Changing the reference also According to published guidelines ([6], p. 141), ‘‘the changes statistical outcomes. By contrast, analysis methods simplest approach is to consider the ERP waveform as a set that consider the spatial distribution of the ERP, such as of waves, to pick the peaks (and troughs) of these waves, microstate segmentation [8, 13] and (spatial) PCA [14], are and to measure the amplitude and latency at these deflec- reference-independent. This is because the configuration of tions’’. These peak amplitude measurements are not the scalp topography is independent of the specific refer- representing absolute values of electric brain activity, but ence electrode [13, 15]. When calculating the voltage are obtained either relative to a pre-stimulus baseline distribution (using interpolation methods such as spherical (baseline to peak analysis) or sometimes to an immediately splines; see [16]), the resulting equipotential lines preceding or following peak (peak-to-peak analysis). (reflecting subtle borders and changes in the distribution of ‘‘Relevant’’ electrophysiological events are therefore the electric field over the scalp surface) remain exactly the selected a priori by searching for electrodes with potential same, and unlike conventional ERPs, the electric ‘‘land- peaks that can be either negative or positive deflections scape’’ remains unaffected by changes in the recording depending on the actual configuration of the underlying montage (see [11] for a recent demonstration). For tech- generators. Although this ‘‘simple’’ ERP analysis method nically oriented considerations related to the inverse has proven its immense powerfulness to shed light on the solution problem itself (e.g., the violation of the quasi- time-course of various cognitive and emotional processes stationary state assumption), the average reference of the in the human brain (see [5, 7] for recent reviews), the surface potential is usually calculated and used for sub- experimenter using a conventional ERP technique has to sequent data analysis looking at the spatial distribution of adhere to a number of prerequisites and be aware of some the ERPs [8, 9, 11, 17]. of the limitations bound to this specific data analysis. Hence, to circumvent some of the difficulties associated Among them, a key assumption underlying the ERP with the conventional ERP analysis method [6] but also to analysis method is that potentially interesting aspects of deal more effectively with the increasing complexity of the cortical brain processes are primarily reflected in these current ERP data sets nowadays routinely obtained with maxima (peaks) but, by extension, not discernible when the multiple channels [18], modern data-driven analyses (such amplitude is low or close to zero [6]. However, this con- as microstate segmentation and PCA) have been developed jecture is not verified by neurophysiological data as low and used to study the spatio-temporal dynamics of various EEG/ERP signal amplitude does not mean absence of domains of human cognition [19–21] and emotion [22, 23]. important neuronal events [2, 3]. In addition, difficulties Microstate segmentation is also sometimes called topo- may arise because the latency of a peak may vary some- graphic pattern analysis [9]. In both cases, it refers to a what across different electrodes, a limitation that becomes whole set of ERP data analyses (allowing to test for and more obvious when increasing the number of channels. tease apart differences in strength, topography, latency and This concern has led some researchers to identify peaks component sequence), as we introduce and illustrate in the using a measurement of Global Field Power (GFP), which next sections. is defined as the spatial root mean squared across all Importantly, microstate segmentation and PCA provide electrodes and which is reference-independent ([8], see the clear advantage of minimizing the amount of user- Fig. 2 of [9]). GFP has the clear advantage of providing a dependent biases and a priories (e.g., assuming that rele- global and spatially unbiased measure of the electric field vant aspects of cognitive or emotional processes would strength at the scalp, which is related to the amount of mainly be reflected in peaks, see [6]). Both microstate synchronously active neurons in the brain [10]. The GFP segmentation and PCA can give new insights on the time- measure is therefore a general estimate of the electric course and structure of brain activity associated with spe- signal amplitude at each time point despite slight variations cific cognitive or emotional events, without the need to of individual peak latencies across different electrode restrict a priori the ERP analysis to a few time points only positions [11]. (e.g., where the amplitude is visibly high) and/or to a few Another problem associated with the conventional ERP electrode positions only [14, 19], as in conventional ERP method concerns the location of the reference electrode analysis [6]. (the so-called ‘‘reference-problem’’ in ERP literature; see Below, we will first shortly present the basic principles [11, 12], see Fig. 1 of [9]). Waveform analyses (and of the microstate segmentation [8, 9, 19, 24, 25], whose amplitude measurements