Conclusions And Future Directions

In summary, the literature in the area of brain imaging of visceral perception published since 2002 clearly indicates significant progress in study design, methodology, and analyses techniques. While consensus is evolving in some areas (including the cognitive and emotional modulation of pain perception), considerable differences in reported results remain in other areas, from the comparison of brain responses to somatic and visceral pain stimuli to differences between control subjects and IBS patients to sex differences in brain activation. However, given the rapid advances that are being made in the such diverse fields as somatic pain modulation, emotion regulation, and imaging genomics, it is likely that the application of neuroimaging techniques to the study of brain-gut interactions in health and disease will lead to breakthroughs in the understanding of pathophysiology of chronic visceral pain conditions, including functional GI disorders and in the prediction of treatment responses in the near future.

Figure 1 summarizes an evolutionary process in brain imaging. The vast majority of studies described in this review have involved detecting and determining the extent of regional brain activation across levels of an independent variable such as stimulus intensity or group. This is essentially a univariate analysis in that each brain volume or a priori-chosen region is examined separately and the statistical threshold is adjusted for the large number of individual comparisons made. While important for generating hypotheses about what parts of the brain might be involved in visceral sensation and the response to these sensations, this descriptive approach to imaging clearly does not capture the critical interrelationships among structures that form the foundation of brain function. The brain operates as functional networks and activations in specific brain areas may have very different interpretations based on the coactivation of other regions that are connected via a network of inputs and projections (83). The first step toward understanding networks involves detection of regional

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