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Our subjective sensory experiences are thought to be heavily shaped by interactions between expectations and incoming sensory information. However, the neural mechanisms supporting these interactions remain poorly understood. By using combined psychophysical and functional MRI techniques, brain activation related to the intensity of expected pain and experienced pain was characterized. As the magnitude of expected pain increased, activation increased in the thalamus, insula, prefrontal cortex, anterior cingulate cortex (ACC) and other brain regions. Pain-intensityrelated brain activation was identified in a widely distributed set of brain regions but overlapped partially with expectation-related activation in regions, including the anterior insula and ACC. When expected pain was manipulated, expectations of decreased pain powerfully reduced both the subjective experience of pain and activation of pain-related brain regions, such as the primary somatosensory cortex, insular cortex, and ACC. These results confirm that a mental representation of an impending sensory event can significantly shape neural processes that underlie the formulation of the actual sensory experience and provide insight as to how positive expectations diminish the severity of chronic disease states.functional MRI ͉ mental imagery ͉ placebo ͉ psychophysical T he experience of a sensory event is highly subjective and can vary substantially from one individual to the next (1). Much of this individual variation may result from the manner in which past experience and future predictions about a stimulus are used to interpret afferent information. Consistent pairing of environmental cues with sensory events provides a learned historical context that is critically important for the prediction and processing of future sensations (2, 3). However, expectations that are inconsistent with sensory information can dramatically alter the sensory experience. In the case of pain, positive expectations can powerfully reduce the subjective experience of pain evoked by a consistently noxious stimulus, whereas negative expectations may result in the amplification of pain (4-7). Furthermore, expectations in which there is a high degree of certainty as to the outcome may activate descending control systems to diminish pain, whereas expectations associated with uncertain outcomes may amplify pain (8).The prefrontal cortex (PFC), anterior insula, and anterior cingulate cortex (ACC) are activated during the anticipation of pain, but their exact role in pain expectation remains poorly delineated (9-12). Moreover, the neural mechanisms by which conscious predictions about the magnitude of pain influence the experience of pain remain poorly understood and largely unexploited in the treatment of pain. At the most fundamental level, expectation-induced modulation of pain must necessarily engage three neural processes. First, an active mental representation of an impending event must be formulated by incorporating past information with the present context and future implica...

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Small-world networks, according to Watts and Strogatz, are a class of networks that are ''highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.'' These characteristics result in networks with unique properties of regional specialization with efficient information transfer. Social networks are intuitive examples of this organization, in which cliques or clusters of friends being interconnected but each person is really only five or six people away from anyone else. Although this qualitative definition has prevailed in network science theory, in application, the standard quantitative application is to compare path length (a surrogate measure of distributed processing) and clustering (a surrogate measure of regional specialization) to an equivalent random network. It is demonstrated here that comparing network clustering to that of a random network can result in aberrant findings and that networks once thought to exhibit small-world properties may not. We propose a new small-world metric, x (omega), which compares network clustering to an equivalent lattice network and path length to a random network, as Watts and Strogatz originally described. Example networks are presented that would be interpreted as small-world when clustering is compared to a random network but are not small-world according to x. These findings have important implications in network science because small-world networks have unique topological properties, and it is critical to accurately distinguish them from networks without simultaneous high clustering and short path length.