Mapping the human brainstem: Brain nuclei and fiber tracts at 3 T and 7 T

The Solitary Nucleus Connectivity to Key Autonomic Regions in Humans MRI and Literature based Considerations

The nucleus tractus solitarius (NTS), is a key brainstem structure relaying interoceptive peripheral information to the interrelated brain centers for eliciting rapid autonomic responses and for shaping longer-term neuroendocrine and motor patterns. Structural and functional NTS' connectivity has been extensively investigated in laboratory animals. But there is limited information about NTS' connectome in humans. Using MRI, we examined diffusion and resting state data from 20 healthy participants in the Human Connectome Project. The regions within the brainstem (n=8), subcortical (n=6), cerebellar (n=2) and cortical (n=5) parts of the brain were selected via a systematic review of the literature and their white matter NTS connections were evaluated via probabilistic tractography along with functional and directional (i.e., Granger-causality) analyses. The underlying study confirms previous results from animal models and provides novel aspects on NTS integration in humans. Two key findings can be summarized: (i) the NTS predominantly processes afferent input and (ii) a lateralization towards a predominantly left-sided NTS processing. Our results lay the foundations for future investigations into the NTS' tripartite role comprised of interoreceptors' input integration, the resultant neurochemical outflow and cognitive/affective processing. The implications of these data add to the understanding of NTS' role in specific aspects of autonomic functions.

7 Tesla and Beyond: Advanced Methods and Clinical Applications in Magnetic Resonance Imaging

ABSTRACT

Ultrahigh magnetic fields offer significantly higher signal-to-noise ratio, and several magnetic resonance applications additionally benefit from a higher contrast-to-noise ratio, with static magnetic field strengths of B0 ≥ 7 T currently being referred to as ultrahigh fields (UHFs). The advantages of UHF can be used to resolve structures more precisely or to visualize physiological/pathophysiological effects that would be difficult or even impossible to detect at lower field strengths. However, with these advantages also come challenges, such as inhomogeneities applying standard radiofrequency excitation techniques, higher energy deposition in the human body, and enhanced B0 field inhomogeneities. The advantages but also the challenges of UHF as well as promising advanced methodological developments and clinical applications that particularly benefit from UHF are discussed in this review article.

Multiparametric MRI for Characterization of the Basal Ganglia and the Midbrain

Objectives To characterize subcortical nuclei by multi-parametric quantitative magnetic resonance imaging.

Materials and Methods: The following quantitative multiparametric MR data of five healthy volunteers were acquired on a 7T MRI system: 3D gradient echo (GRE) data for the calculation of quantitative susceptibility maps (QSM), GRE sequences with and without off-resonant magnetic transfer pulse for magnetization transfer ratio (MTR) calculation, a magnetization−prepared 2 rapid acquisition gradient echo sequence for T₁ mapping, and (after a coil change) a density-adapted 3D radial pulse sequence for ²³Na imaging. First, all data were co-registered to the GRE data, volumes of interest (VOIs) for 21 subcortical structures were drawn manually for each volunteer, and a combined voxel-wise analysis of the four MR contrasts (QSM, MTR, T₁, ²³Na) in each structure was conducted to assess the quantitative, MR value-based differentiability of structures. Second, a machine learning algorithm based on random forests was trained to automatically classify the groups of multi-parametric voxel values from each VOI according to their association to one of the 21 subcortical structures.

Results The analysis of the integrated multimodal visualization of quantitative MR values in each structure yielded a successful classification among nuclei of the ascending reticular activation system (ARAS), the limbic system and the extrapyramidal system, while classification among (epi-)thalamic nuclei was less successful. The machine learning-based approach facilitated quantitative MR value-based structure classification especially in the group of extrapyramidal nuclei and reached an overall accuracy of 85% regarding all selected nuclei.

Conclusion Multimodal quantitative MR enabled excellent differentiation of a wide spectrum of subcortical nuclei with reasonable accuracy and may thus enable sensitive detection of disease and nucleus-specific MR-based contrast alterations in the future.

Toward quantitative neuroimaging biomarkers for Friedreich's ataxia at 7 Tesla: Susceptibility mapping, diffusion imaging, R2 and R1 relaxometry

Friedreich's ataxia (FRDA) is a rare genetic disorder leading to degenerative processes. So far, no effective treatment has been found. Therefore, it is important to assist the development of medication with imaging biomarkers reflecting disease status and progress. Ten FRDA patients (mean age 37 ± 14 years; four female) and 10 age- and sex-matched controls were included. Acquisition of magnetic resonance imaging (MRI) data for quantitative susceptibility mapping, R₁ , R₂ relaxometry and diffusion imaging was performed at 7 Tesla. Results of volume of interest (VOI)-based analyses of the quantitative data were compared with a voxel-based morphometry (VBM) evaluation. Differences between patients and controls were assessed using the analysis of covariance (ANCOVA; p < 0.01) with age and sex as covariates, effect size of group differences, and correlations with disease characteristics with Spearman correlation coefficient. For the VBM analysis, a statistical threshold of 0.001 for uncorrected and 0.05 for corrected p-values was used. Statistically significant differences between FRDA patients and controls were found in five out of twelve investigated structures, and statistically significant correlations with disease characteristics were revealed. Moreover, VBM revealed significant white matter atrophy within regions of the brainstem, and the cerebellum. These regions overlapped partially with brain regions for which significant differences between healthy controls and patients were found in the VOI-based quantitative MRI evaluation. It was shown that two independent analyses provided overlapping results. Moreover, positive results on correlations with disease characteristics were found, indicating that these quantitative MRI parameters could provide more detailed information and assist the search for effective treatments.

Mapping internal brainstem structures using MP2RAGE derived T1 weighted and T1 relaxation images at 3 and 7 T

The brainstem is a site of early pathology in several neurodegenerative diseases. The overall goal of this project was (a) To develop a method to segment internal brainstem structures from MP2RAGE derived images. (b) To compare the segmentations at 3 and 7 T. (c) To investigate age effects on intensities and segmentations. MP2RAGE derived T1 weighted images (UNI) and T1 relaxation maps (T1map) were obtained from two public data sets (LEMON: 50 3 T data sets, ATAG: 46 7 T data sets). The UNI and T1map images were rescaled using a linear scaling procedure and a ratio (RATIO) image calculated. The brainstem was extracted and k‐mean clustering used to identify six intensity clusters from the UNI, T1map and RATIO at 3 and 7 T. Nonlinear diffeomorphic mapping was used to warp the six intensity clusters in subject space into a common space to generate probabilistic group averages/priors that were used to inform the final probabilistic segmentations at the single subject level for each field strength. The six clusters corresponded to six brainstem tissue types (three gray matter clusters and two white matter clusters and one csf/tissue boundary cluster). The quantitative comparison of the 3 and 7 T probabilistic averages showed subtle differences that affected the localization of age‐associated brainstem volume losses. The segmentation approach presented here identified the same brainstem gray and white matter structures at both field strengths. Further studies are necessary to investigate how resolution and field strength contribute to the subtle differences observed at the two field strengths.