NeuroImage
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A fully automated brain tissue segmentation method is optimized and extended with white matter lesion segmentation. Cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM) are segmented by an atlas-based k-nearest neighbor classifier on multi-modal magnetic resonance imaging data. This classifier is trained by registering brain atlases to the subject. ⋯ No segmentation errors were found in 98% of the brain tissue segmentations and 97% of the WML segmentations. A quantitative evaluation using manual segmentations was performed on a subset of 6 subjects for CSF, GM and WM segmentation and an additional 14 for the WML segmentations. The results indicated that the automatic segmentation accuracy is close to the interobserver variability of manual segmentations.
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T2-weighted gradient echo (GE) images yield good contrast of iron-rich structures like the subthalamic nuclei due to microscopic susceptibility induced field gradients, providing landmarks for the exact placement of deep brain stimulation electrodes in Parkinson's disease treatment. An additional advantage is the low radio frequency (RF) exposure of GE sequences. However, T2-weighted images are also sensitive to macroscopic field inhomogeneities, resulting in signal losses, in particular in orbitofrontal and temporal brain areas, limiting anatomical information from these areas. ⋯ In a second step, intensity corrected images acquired at different echo times TE are combined using optimized weighting factors: in areas not affected by macroscopic field inhomogeneities, data acquired at long TE are weighted more strongly to achieve the contrast required. For large field gradients, data acquired at short TE are favored to avoid signal losses. When compared to the original data sets acquired at different TE and the respective intensity corrected data sets, the resulting combined data sets feature reduced signal losses in areas with major field gradients, while intensity profiles and a contrast-to-noise (CNR) analysis between subthalamic nucleus, red nucleus and the surrounding white matter demonstrate good contrast in deep brain areas.
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A visual task for semantic access involves a number of brain regions. However, previous studies either examined the role of each region separately using univariate approach, or analyzed a single brain network using covariance connectivity analysis. ⋯ Our results demonstrated that there were three task-related independent components (ICs), corresponding to various cognitive components involved in the visual task. Furthermore, ICA separation on the auditory task showed consistency of the results with our hypothesis, regardless of the input modalities.
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Randomized Controlled Trial
Expectancy and treatment interactions: a dissociation between acupuncture analgesia and expectancy evoked placebo analgesia.
Recent advances in placebo research have demonstrated the mind's power to alter physiology. In this study, we combined an expectancy manipulation model with both verum and sham acupuncture treatments to address: 1) how and to what extent treatment and expectancy effects - including both subjective pain intensity levels (pain sensory ratings) and objective physiological activations (fMRI) - interact; and 2) if the underlying mechanism of expectancy remains the same whether placebo treatment is given alone or in conjunction with active treatment. ⋯ We believe our study provides brain imaging evidence for the existence of different mechanisms underlying acupuncture analgesia and expectancy evoked placebo analgesia. Our results also suggest that the brain network involved in expectancy may vary under different treatment situations (verum and sham acupuncture treatment).