Brainstem nuclei (Bn) in humans play a crucial role in vital functions, such as arousal, autonomic homeostasis, sensory and motor relay, nociception, sleep, and cranial nerve function, and they have been implicated in a vast array of brain pathologies. However, an in vivo delineation of most human Bn has been elusive because of limited sensitivity and contrast for detecting these small regions using standard neuroimaging methods. To precisely identify several human Bn in vivo, we employed a 7 Tesla scanner equipped with multi-channel receive-coil array, which provided high magnetic resonance imaging sensitivity, and a multi-contrast (diffusion fractional anisotropy and T2-weighted) echo-planar-imaging approach, which provided complementary contrasts for Bn anatomy with matched geometric distortions and resolution. Through a combined examination of 1.3 mm 3 multi-contrast anatomical images acquired in healthy human adults, we semi-automatically generated in vivo probabilistic Bn labels of the ascending arousal (median and dorsal raphe), autonomic (raphe magnus, periaqueductal gray), and motor (inferior olivary nuclei, two subregions of the substantia nigra compatible with pars compacta and pars reticulata, two subregions of the red nucleus, and, in the diencephalon, two subregions of the subthalamic nucleus) systems. These labels constitute a first step toward the development of an in vivo neuroimaging template of Bn in standard space to facilitate future clinical and research investigations of human brainstem function and pathology. Proof-of-concept clinical use of this template is demonstrated in a minimally conscious patient with traumatic brainstem hemorrhages precisely localized to the raphe Bn involved in arousal.
This project aims to characterize the impact of underlying noise distributions on diffusion-weighted imaging. The noise floor is a well-known problem for traditional magnitude-based diffusion-weighted MRI (dMRI) data, leading to biased diffusion model fits and inaccurate signal averaging. Here, we introduce a total-variation-based algorithm to eliminate shot-to-shot phase variations of complex-valued diffusion data with the intention to extract real-valued dMRI datasets. The obtained real-valued diffusion data are no longer superimposed by a noise floor but instead by a zero-mean Gaussian noise distribution, yielding dMRI data without signal bias. We acquired high-resolution dMRI data with strong diffusion weighting and, thus, low signal-to-noise ratio. Both the extracted real-valued and traditional magnitude data were compared regarding signal averaging, diffusion model fitting and accuracy in resolving crossing fibers. Our results clearly indicate that real-valued diffusion data enables idealized conditions for signal averaging. Furthermore, the proposed method enables unbiased use of widely employed linear least squares estimators for model fitting and demonstrates an increased sensitivity to detect secondary fiber directions with reduced angular error. The use of phase-corrected, real-valued data for dMRI will therefore help to clear the way for more detailed and accurate studies of white matter microstructure and structural connectivity on a fine scale.
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