One particular area of interest to developmental researchers using scalp elec- trophysiology is the opportunity presented by recent developments in analy- sis techniques for EEG and ERP data. Johnson et al. (2001) discuss examples of such approaches to analyzing EEG and ERP data from infants, with a particular emphasis on high-density (64-channel) recordings. They outline two specific analysis techniques, independent component analysis (ICA), and source localization, both of which we will consider in more detail below.
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142 Peter J. Marshall and Nathan A. Fox
ICA attempts to separate the EEG into mutually independent scalp maps, or components. The electrical activity in the EEG and the ERP signal arises from several sources, such as separate neural clusters, and includes artifact such as that generated by eye movement. Each source projects a unique electrophysiological pattern onto the scalp, with the EEG being the result of many of these patterns being superimposed on the scalp. ICA aims to identify components both temporally and spatially by examining covariation of the signals from different electrodes across time (Makeig et al.,1997). Two important clarifications are that ICA does not model the location of dipole generators within the head, and that the networks creating such independent components may be distributed brain networks rather than discrete regions of the cortex. ICA is one of a family of multivariate techniques that have been used for analyzing EEG and ERP datasets. For example, principal components analysis (PCA) separates up to second-order statistics, whereas ICA separates data using higher-order statistics. PCA has been employed in developmental ERP studies (Molfese et al.,2001), but use of ICA with infants and children is still in its very beginning stages. One methodological consideration of ICA is that electrode arrays with fewer than 32 electrodes are usually considered too sparse for practical use of ICA with EEG or ERP data.
The second technique described by Johnson et al. (2001) is that of source localization, which aims to identify the location of dipoles that are responsi- ble for the generation of ERP components or of the EEG signal in a specific frequency band. Source localization can potentially be combined with the use of ICA, for example, a temporally stable ERP component can be derived from ICA and then the brain source of this component modeled using source localization. One approach to source localization is Brain Electrical Source Analysis (Scherg & Berg,1996; Scherg & Picton,1991), which has been used to a limited extent in developmental work, including the application to audi- tory ERP components in infants (Dehaene-Lambertz & Baillet,1998). Other tomographical approaches include Low-Resolution Electrical Tomographic Analysis, or LORETA (Pascual-Marqui, Esslen, Kochi, & Lehmann,2002), which has been also only utilized in a very small number of developmental studies. For example, a recent paper employed LORETA to localize the mis- match negativity component in the auditory ERP to a source generator in the temporal lobe for both children and adults (Maurer, Bucher, Brem, & Brandeis,2003). Although dense electrode arrays (64, 128, or 256 sites) are usually considered to be optimal or even essential for the application of source localization to EEG and ERP date, LORETA has been successfully used with relatively sparse electrode arrays in adults (e.g., 28 sites, Pizzagalli et al., 2001).
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There are a number of major caveats about applying localization tech- niques to developmental EEG or ERP data. One fundamental limitation of scalp EEG measurement is that even with a very large number of electrodes, a specific patterning of the scalp EEG signal could be explained by many differ- ent distributions of generators within the brain. This example is commonly referred to as the “inverse problem.” Methods such as BESA and LORETA employ sophisticated algorithms to take a “best guess” at underlying dipoles, but the precision of localization does not rival other techniques, such as fMRI. There are also many rather complex assumptions (e.g., about the physical parameters of the head) used in source localization that need to be clearly understood by the developmental researcher, who must also be aware that such assumptions may not necessarily translate well from adult work (where source localization techniques were developed) to data from infants and chil- dren. In addition, source localization may be supplementary to the questions of interest, which may involve simply asking whether different experimental conditions or groups of individuals differ with respect to EEG power spectral amplitude over specific scalp locations, or with respect to ERP amplitudes or latencies. Conventional EEG and ERP analyses in social and emotional development typically consider data from a relatively small number of elec- trode sites, and examine individual differences in EEG spectral power or amplitude/latency of ERP components to different trial types. It may not be the task of researchers in social and emotional development to localize spe- cific electrophysiological phenomena in the cortex. Instead, these researchers may draw on related work in the cognitive neurosciences that uses multiple methodologies (e.g., fMRI, MEG) to localize specific electrophysiological and behavioral phenomena in the brain. Using this knowledge, developmen- tal researchers can focus their questions more clearly by the consideration of known cognitive correlates and neurophysiological underpinnings of the measure that they are studying. That said, there is a good deal of work to be done on both sides of this integration, including the application of advanced analysis techniques and multi-method approaches to electrophysiological measures in early development.