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Dynamics and Variability of Brain Activation: Searching for Neural Correlates of Skill Acquisition

A functional brain image is taken in response to a specific task, and the result is a snapshot of activation of one region related to another across some fixed window of time. This practical limitation is a problem when trying to capture the dynamics of thought for it treats information processing as if it were a state rather than a cascade of events. The brain imagery technology involved is of course truly amazing and we try to build up a dynamic model from static pictures. But forming a dynamic model from snapshots is very difficult, and therefore part of the challenge in the coming decades will be to harness a dynamic imaging system. While we have had dynamic brain models for centuries—from the medieval Cell Doctrine to Wernicke’s language model to current models of visual processing—their dynamics have not been empirically verifiable. EEG and ERP methods are fast enough to capture information processing, although current theory suggests that at least 128 electrode sites are needed for accurate localization (Srinivasan, Tucker, & Murias, 1998; Gevins, 1995). Metabolic systems such as PET and fMRI localize well but work too slowly, considering that the hemodynamic response is on the order of several seconds and any moving-window method necessarily blurs the neuronal response (Pollman, Wiggins, et al., 1998). Magnetoencephalographic and ERP–fMRI coregistration techniques hold much promise. Let us assume that the strengths of the electrical activation and hemodynamic response paradigms will be merged in this next century and made practical for many scientists. What could it be applied to? The first obvious application is a chronometric display of information processing. However, I would like to address a less well-discussed aspect, inherent to dynamic activation—variability itself.