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Functional magnetic resonance imaging has been widely used to study brain activation induced either by specific sensory stimulation or motor or cognitive task performance. We demonstrate that functional magnetic resonance imaging can provide information of brain regions involved in opioid-induced central nervous system effects. The reproducibility of the responses in the predefined regions of interest was confirmed by repeated boluses of ultra-short acting mu-opioid receptor agonist remifentanil and saline. We report spatially and temporally detailed information after remifentanil administration. Areas rich in mu-opioid receptors showed strong activations, whereas primary somatosensory cortex that has the lowest density of mu-opioid receptors showed negligible activation. The cingulate, orbitofrontal, posterior parietal and insular cortices, and amygdala showed activation, which was temporally closely related to most subjective sensations that were strongest at 80 to 90 s after drug administration. These areas belong to a circuitry that modulates the affective experience of sensory stimuli.

Results from several recent studies suggest that neuronal processing of sound content and its spatial location may be dissociated. The use of modern neuroimaging techniques has allowed for the determination that different brain structures may be specifically activated during working memory processing of pitch and location of sound. The time course of these task-related differences, however, remains uncertain. In the present study, we performed simultaneous whole-head electroencephalogram and magnetoencephalogram recordings, using a new behavioral paradigm, to investigate the dynamics of differences between "what" and "where" evoked responses in the auditory system as a function of memory load. In the location task the latency of the N1m was shorter and its generator was situated more inferiorly than in the pitch task. Working memory processing of the tonal frequency enhanced the amplitude of the N2 component, as well as the negative-going deflection at a latency around 400 ms. A memory-load-dependent task-related difference was found in the positive slow wave which was higher during the location than pitch task at the low load. Late slow waves were affected by memory load but not type of task. These results suggest that separate neuronal networks are involved in the attribute-specific analysis of auditory stimuli and their encoding into working memory, whereas the maintenance of auditory information is accomplished by a common, nonspecific neuronal network.

We studied the effect of use of contextual information on the reproducibility of the results in analysis of fMRI data. We used data from a repeated simple motor fMRI experiment. In the first approach, statistical parametric maps were computed from a spatially unsmoothed data and thresholded using a Bonferroni corrected threshold. In the second approach, the maps were computed from a spatially unsmoothed data but were segmented into nonactive and active regions using a spatial contextual clustering method. In the third approach, the statistical parametric maps were computed from spatially smoothed data and thresholded, using, optionally, a spatial extent threshold. The variation in the classification was largest in the Bonferroni thresholded statistical parametric maps. There were no significant differences in variation between statistical parametric maps generated with all the other methods. In addition to reproducibility, the detection rates of weak simulated activations in the presence of measured scanner and physiological noise were investigated. Contextual clustering method was the most sensitive method, while the least sensitive method was the Bonferroni corrected thresholding. Using simulated data, we demonstrated that the contextual clustering method preserves the shapes of activation regions better than the method using spatial smoothing of the data.

Gaussian random field (GRF)-based methods are commonly used for statistical inference and to control the family-wise error rate (FWE) in neuroimaging. They require that the error fields are reasonable lattice approximations to an underlying continuous multivariate Gaussian random field and have differentiable and invertible spatial autocorrelation function. Permutation test estimates the distribution of the test statistic from the data and adjusts automatically for the FWE. Here we present a new analysis procedure, the cluster mass permutation test with contextual enhancement (CMPCE), and compare it to GRF. In CMPCE, the data are first pre-whitened to remove temporal autocorrelations. The FWE rates, the cluster detection probability and delineation accuracy of CMPCE and GRF were compared using measured null data and null data containing simulated activations. We also applied both methods to an fMRI experiment where tactile somatosensory stimulation into the right hand was used. When analyzing the FWE using null data, both CMPCE and GRF gave significantly higher FWEs (CMPCE up to 0.12, GRF up to 0.18) than the nominal significance level 0.05, indicating that the pre-whitening, motion correction or high-pass filtering partially failed. In the simulated activation data, CMPCE gave less falsely classified voxels for the same cluster detection probability level than GRF. The maximal cluster detection probability was on the other hand higher in the GRF-based method. Both methods gave qualitatively similar results in the tactile fMRI data. CMPCE seems to be a promising fMRI analysis method, especially if high delineation accuracy is required.

Movement in the visual field is an important part of perceptual information. Previous imaging studies have identified a number of cortical areas sensitive to visual motion [1,2], including what is considered to be the human homologue of area MT/V5 [3,4]. Our study sought to reproduce and expand upon a previous functional magnetic resonance imaging (fMRI) investigation [4] and to examine the timing of activity in the network of regions participating in the processing of visual motion information with magnetoencephalography (MEG).