NeuroImage
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Previous human imaging studies have revealed a network of brain regions involved in the processing of allodynic pain; this includes prefrontal areas, insula, cingulate cortex, primary and secondary somatosensory cortices and parietal association areas. In this study, the neural correlates of the perceived intensity of allodynic pain in neuropathic pain patients were investigated. In eight patients, dynamic mechanical allodynia was provoked and brain responses recorded using functional magnetic resonance imaging (fMRI). ⋯ This matches the representation of other clinical pain syndromes, confirmed by a literature review. In contrast, experimental pain in healthy volunteers resides predominantly in the cAI, as shown by the same literature review. Taken together, our data and the literature review suggest a functional segregation of anterior insular cortex.
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An understanding of the relationship between changes in neural activity and the accompanying hemodynamic response is crucial for accurate interpretation of functional brain imaging data and in particular the blood oxygen level-dependent (BOLD) fMRI signal. Much physiological research investigating this topic uses anesthetized animal preparations, and yet, the effects of anesthesia upon the neural and hemodynamic responses measured in such studies are not well understood. In this study, we electrically stimulated the whisker pad of both awake and urethane anesthetized rats at frequencies of 1-40 Hz. ⋯ Additionally, the temporal structure of the hemodynamic response function was different in awake compared to anesthetized animals. The responses in each case were well approximated by gamma variates, but these were different in terms of mean latency (approximately 2 s awake; 4 s anesthetized) and width (approximately 0.6 s awake; 2.5 s anesthetized). These findings have important implications for research into the intrinsic signals that underpin BOLD fMRI and for biophysical models of cortical hemodynamics and neural-hemodynamic coupling.
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Magnetic resonance imaging (MRI) is of great utility in diagnosis and monitoring of multiple sclerosis (MS). Axonal loss is considered the main cause of accumulating irreversible disability. MRI using ultrasmall-super-paramagnetic-iron-oxide (USPIO) nanoparticles is a new technique to disclose in vivo central nervous system (CNS) inflammatory lesions infiltrated by macrophages in experimental autoimmune encephalomyelitis (EAE). ⋯ Although animals did not present any clinically significant differences during the first attack, USPIO+ rats presented significantly more important tissue alterations at the first attack (onset and initiated recovery phase) and, at the second attack, more severe clinical disease with axonal loss compared to USPIO- rats. MRI lesion load and volume at the first attack correlate significantly with inflammation, macrophage recruitment, demyelination, acute axonal damage and, at the second attack, extent of axonal loss. This new MRI application of in vivo monitoring of macrophage infiltration provides a new platform to investigate the severity of inflammatory demyelinating CNS diseases.
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The widely used technique of functional magnetic resonance imaging (fMRI) based on the blood oxygenation level-dependent (BOLD) effect is a tool for the investigation of changes in local brain activity upon stimulation. The principle of measurement is based on the assumption that there is a strong coupling between changes in neural activity, metabolism, vascular response and oxygen extraction in the area under investigation. As fMRI is on the way to become a routine tool in clinical examinations, we wanted to investigate whether, generally and under a variety of conditions, there is a strong link between the BOLD signal and neural activity. ⋯ Thus, we observed an uncoupling between electrical, i.e., neural activity and the BOLD signal. According to our results, the absence of BOLD signal changes did not permit the conclusion that there was no neural activity in the area under investigation. Our findings are especially relevant for the clinical application of fMRI in patients suffering from cerebrovascular and other brain diseases.
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Intensity inhomogeneity in MR images is an undesired phenomenon, which often hampers different steps of quantitative image analysis such as segmentation or registration. In this paper, we propose a novel fully automated method for retrospective correction of intensity inhomogeneity. The basic assumption is that inhomogeneity correction could be improved by integrating spatial and intensity information from multiple MR channels, i.e., T1, T2, and PD weighted images. ⋯ In the second step, intensity correction forces that tend to minimize joint entropy of multispectral image are estimated for all image voxels. Third, independent inhomogeneity correction fields are obtained for each channel by regularization and normalization of voxel forces, and last, corresponding partial inhomogeneity corrections are performed separately for each channel. The method was quantitatively evaluated on simulated and real MR brain images and compared to three other methods.