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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.

Small-world networks are a class of networks that exhibit efficient long-distance communication and tightly interconnected local neighborhoods. In recent years, functional and structural brain networks have been examined using network theory-based methods, and consistently shown to have small-world properties. Moreover, some voxel-based brain networks exhibited properties of scale-free networks, a class of networks with mega-hubs. However, there are considerable inconsistencies across studies in the methods used and the results observed, particularly between region-based and voxel-based brain networks. We constructed functional brain networks at multiple resolutions using the same resting-state fMRI data, and compared various network metrics, degree distribution, and localization of nodes of interest. It was found that the networks with higher resolutions exhibited the properties of small-world networks more prominently. It was also found that voxel-based networks were more robust against network fragmentation compared to region-based networks. Although the degree distributions of all networks followed an exponentially truncated power law rather than true power law, the higher the resolution, the closer the distribution was to a power law. The voxel-based analyses also enhanced visualization of the results in the 3D brain space. It was found that nodes with high connectivity tended have high efficiency, a co-localization of properties that was not as consistently observed in the region-based networks. Our results demonstrate benefits of constructing the brain network at the finest scale the experiment will permit.

In recent years multiple brain MR imaging modalities have emerged; however, analysis methodologies have mainly remained modality specific. In addition, when comparing across imaging modalities, most researchers have been forced to rely on simple region-of-interest type analyses, which do not allow the voxel-by-voxel comparisons necessary to answer more sophisticated neuroscience questions. To overcome these limitations, we developed a toolbox for multimodal image analysis called biological parametric mapping (BPM), based on a voxel-wise use of the general linear model. The BPM toolbox incorporates information obtained from other modalities as regressors in a voxel-wise analysis, thereby permitting investigation of more sophisticated hypotheses. The BPM toolbox has been developed in MATLAB with a user friendly interface for performing analyses, including voxel-wise multimodal correlation, ANCOVA, and multiple regression. It has a high degree of integration with the SPM (statistical parametric mapping) software relying on it for visualization and statistical inference. Furthermore, statistical inference for a correlation field, rather than a widely-used T-field, has been implemented in the correlation analysis for more accurate results. An example with in-vivo data is presented demonstrating the potential of the BPM methodology as a tool for multimodal image analysis.

Visual and auditory cortices traditionally have been considered to be "modality-specific." Thus, their activity has been thought to be unchanged by information in other sensory modalities. However, using functional magnetic resonance imaging (fMRI), the present experiments revealed that ongoing activity in the visual cortex could be modulated by auditory information and ongoing activity in the auditory cortex could be modulated by visual information. In both cases, this cross-modal modulation of activity took the form of deactivation. Yet, the deactivation response was not evident in either cortical area during the paired presentation of visual and auditory stimuli. These data suggest that cross-modal inhibitory processes operate within traditional modality-specific cortices and that these processes can be switched on or off in different circumstances.