NeuroImage
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Recent data from [(11)C]carfentanil ligand-PET indicate that in the human brain, the availability of mu-opioid (MOP) receptor binding sites is affected by the Val(108/158)Met polymorphism of the catechol-O-methyltransferase (COMT) gene. This prompted us to validate the impact of COMT Val(108/158)Met on MOP receptors in human post-mortem brain. [(3)H]DAMGO receptor autoradiography was performed in frontal cortex, basal ganglia, thalamus and cerebellum (8 Met/Met, 6 Met/Val, 3 Val/Val). With respect to genotype, numbers of MOP binding sites in COMT Met(108/158) homozygous and Val(108/158)Met heterozygous cases were higher than in Val(108/158) homozygous. ⋯ In the striatum, mRNA levels were significantly higher in COMT Met(108/158) homozygous cases indicating that MOP binding sites and enkephalin are regulated in parallel. Moreover, the transcript was not detectable in the thalamus. Thus, mechanisms other than an enkephalin-dependent receptor turnover must be responsible for COMT-related differences in MOP binding site availability in the human brain.
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Although the neural underpinnings of music cognition have been widely studied in the last 5 years, relatively little is known about the neuroscience underlying emotional reactions that music induces in listeners. Many people spend a significant amount of time listening to music, and its emotional power is assumed but not well understood. Here, we use functional and effective connectivity analyses to show for the first time that listening to music strongly modulates activity in a network of mesolimbic structures involved in reward processing including the nucleus accumbens (NAc) and the ventral tegmental area (VTA), as well as the hypothalamus and insula, which are thought to be involved in regulating autonomic and physiological responses to rewarding and emotional stimuli. ⋯ Responses in the NAc and the hypothalamus were also strongly correlated across subjects, suggesting a mechanism by which listening to pleasant music evokes physiological reactions. Effective connectivity confirmed these findings, and showed significant VTA-mediated interaction of the NAc with the hypothalamus, insula, and orbitofrontal cortex. The enhanced functional and effective connectivity between brain regions mediating reward, autonomic, and cognitive processing provides insight into understanding why listening to music is one of the most rewarding and pleasurable human experiences.
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Clinical Trial
Spatial registration of multichannel multi-subject fNIRS data to MNI space without MRI.
The registration of functional brain data to the common brain space offers great advantages for inter-modal data integration and sharing. However, this is difficult to achieve in functional near-infrared spectroscopy (fNIRS) because fNIRS data are primary obtained from the head surface and lack structural information of the measured brain. Therefore, in our previous articles, we presented a method for probabilistic registration of fNIRS data to the standard Montreal Neurological Institute (MNI) template through international 10-20 system without using the subject's magnetic resonance image (MRI). ⋯ This means that, if the spatial registration error is within an acceptable tolerance limit, it is possible to perform multi-subject fNIRS analysis to make inference at the population level and to provide information on positional variability in the population, even when subjects' MRIs are not available. In essence, the current method enables the multi-subject fNIRS data to be presented in the MNI space with clear description of associated positional variability. Such data presentation on a common platform, will not only strengthen the validity of the population analysis of fNIRS studies, but will also facilitate both intra- and inter-modal data sharing among the neuroimaging community.
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The dependency of word processing on spare attentional resources has been debated for several decades. Recent research in the study of selective attention has emphasized the role of task load in determining the fate of ignored information. In parallel to behavioral evidence, neuroimaging data show that the activation generated by unattended stimuli is eliminated in task-relevant brain regions during high attentional load tasks. ⋯ As compared to nonwords, word stimuli drove responses in left frontal, left temporal and parietal areas when letters were attended. However, although the behavioral measures suggested that ignored words were not analyzed when drawings were attended, a comparison of ignored words to ignored nonwords indicated the involvement of several regions including left insula, right cerebellum and bilateral pulvinar. Interestingly, word-specific activations found when attended and ignored words were compared showed no anatomical overlap, suggesting a change in processing pathways for attended and ignored words presented in a high attentional load task.
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This study assesses the performance of public-domain automated methodologies for MRI-based segmentation of the hippocampus in elderly subjects with Alzheimer's disease (AD) and mild cognitive impairment (MCI). Structural MR images of 54 age- and gender-matched healthy elderly individuals, subjects with probable AD, and subjects with MCI were collected at the University of Pittsburgh Alzheimer's Disease Research Center. Hippocampi in subject images were automatically segmented by using AIR, SPM, FLIRT, and the fully deformable method of Chen to align the images to the Harvard atlas, MNI atlas, and randomly selected, manually labeled subject images ("cohort atlases"). ⋯ Side of the brain, presence of AD, choice of reference image, and manual tracing protocol were also significant factors contributing to automated segmentation performance. Fully automated techniques can be competitive with human raters on this difficult segmentation task, but a rigorous statistical analysis shows that a variety of methodological factors must be carefully considered to insure that automated methods perform well in practice. The use of fully deformable registration methods, cohort atlases, and user-defined manual tracings are recommended for highest performance in fully automated hippocampus segmentation.