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Multiparametric MRI for Characterization of the Basal Ganglia and the Midbrain.

ABSTRACT: 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.

SUBMITTER: Schneider TM

PROVIDER: S-EPMC8255625 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The basal ganglia are important for movement and reinforcement learning. Using mice of either sex, we found that the main basal ganglia GABAergic output in the midbrain, the substantia nigra pars reticulata (SNr), shows movement-related neural activity during the expression of a negatively reinforced signaled locomotor action known as signaled active avoidance; this action involves mice moving away during a warning signal to avoid a threat. In particular, many SNr neurons deactivate during active avoidance responses. However, whether SNr deactivation has an essential role driving or regulating active avoidance responses is unknown. We found that optogenetic excitation of SNr or striatal GABAergic fibers that project to an area in the pedunculopontine tegmentum (PPT) within the midbrain locomotor region abolishes signaled active avoidance responses, while optogenetic inhibition of SNr cells (mimicking the SNr deactivation observed during an active avoidance behavior) serves as an effective conditioned stimulus signal to drive avoidance responses by disinhibiting PPT neurons. However, preclusion of SNr deactivation, or direct inhibition of SNr fibers in the PPT, does not impair the expression of signaled active avoidance, indicating that SNr output does not drive the expression of a signaled locomotor action mediated by the midbrain. Consistent with a permissive regulatory role, SNr output provides information about the state of the ongoing action to downstream structures that mediate the action.SIGNIFICANCE STATEMENT During signaled active avoidance behavior, subjects move away to avoid a threat when directed by an innocuous sensory stimulus. Excitation of GABAergic cells in the substantia nigra pars reticulata (SNr), the main output of the basal ganglia, blocks signaled active avoidance, while inhibition of SNr cells is an effective stimulus to drive active avoidance. Interestingly, many SNr cells inhibit their firing during active avoidance responses, suggesting that SNr inhibition could be driving avoidance responses by disinhibiting downstream areas. However, interfering with the modulation of SNr cells does not impair the behavior. Thus, SNr may regulate the active avoidance movement in downstream areas that mediate the behavior, but does not drive it.

Project description:Background and purposeThe hypervascular nature of parathyroid adenomas can be explored by proper dynamic imaging to narrow the target lesions for surgical exploration. The purpose of this study was to establish MR perfusion characteristics of parathyroid adenomas to differentiate them from their mimics, such as subjacent thyroid tissue and cervical lymph nodes.Materials and methodsPreoperative high-spatial and -temporal resolution dynamic 4D contrast-enhanced MR imaging in 30 patients with surgically proved parathyroid adenomas was evaluated retrospectively. Using coregistered images, we placed ROIs over the parathyroid adenoma, thyroid gland, and a cervical lymph node (jugulodigastric) to obtain peak enhancement, time-to-peak, wash-in, and washout in each patient. Data were analyzed by logistic regression and analysis of variance. Receiver operating characteristic analysis was performed to determine the optimal parameters for determination of parathyroid adenomas versus thyroid tissue and cervical lymph nodes.ResultsParathyroid adenomas showed significantly (P < .05) faster time-to-peak, higher wash-in, and higher washout compared with cervical lymph nodes and significantly (P < .05) higher peak enhancement, faster time-to-peak, higher wash-in, and higher washout compared with thyroid tissue. Logistic regression analysis indicated significant contribution from time-to-peak (P = .02), wash-in (P = .03), and washout (P = .008) for differentiation of parathyroid adenomas from thyroid and cervical lymph nodes. Using receiver operating characteristic analysis, we obtained the best diagnostic accuracy from a combination of time-to-peak/wash-in/washout in the differentiation of parathyroid adenomas versus lymph nodes (area under the curve, 0.96; sensitivity/specificity, 88%/90%) and in distinguishing parathyroid adenomas versus thyroid tissue (area under the curve, 0.96; sensitivity/specificity, 91%/95%).ConclusionsDynamic 4D contrast-enhanced MR imaging can be used to exploit the hypervascular nature of parathyroid adenomas. Multiparametric MR perfusion can distinguish parathyroid adenomas from subjacent thyroid tissue or lymph nodes with diagnostic accuracies of 96%.

Project description:Cerebral perforating artery flow velocity and pulsatility can be measured using 7 tesla (T) MRI. Enabling these flow metrics on more widely available 3T systems would make them more employable. It is currently unknown whether these measurements can be performed at 3T MRI due to the lower signal-to-noise ratio (SNR). Therefore, the aim of this study is to investigate if flow velocity and pulsatility in the perforating arteries of the basal ganglia (BG) can be measured at 3T MRI and assess the agreement with 7T MRI measurements as reference. Twenty-nine subjects were included, of which 14 patients with aortic coarctation [median age 29 years (21-72)] and 15 controls [median age 27 years (22-64)]. Using a cardiac-gated 2D phase-contrast MRI sequence BG perforating arteries were imaged at 3T and 7T MRI and perforating artery density (N density , #/cm2), flow velocity (V mean , cm/s) and pulsatility index (PI) were determined. Agreement between scanner modalities was assessed using correlation and difference plots with linear regression. A p-value ≤ 0.05 indicated statistical significance. It was shown that perforating artery flow velocity and pulsatility can be measured at 3T MRI (N density = 0.21 ± 0.11; V mean = 6.04 ± 1.27; PI = 0.49 ± 0.19), although values differed from 7T MRI measurements (N density = 0.95 ± 0.21; V mean = 3.89 ± 0.56; PI = 0.28 ± 0.08). The number of detected arteries was lower at 3T (5 ± 3) than 7T MRI (24 ± 6), indicating that 3T MRI is on average a factor 4.8 less sensitive to detect cerebral perforating arteries. Comparison with 7T MRI as reference showed some agreement in N density , but little to no agreement for V mean and PI. Equalizing the modalities' sensitivity by comparing the detected arteries on 7T MRI with the highest velocity with all vessels detected on 3T MRI, showed some improvement in agreement for PI, but not for V mean . This study shows that it is possible to measure cerebral perforating artery flow velocity and pulsatility at 3T MRI, although an approximately fivefold sample size is needed at 3T relative to 7T MRI for a given effect size, and the measurements should be performed with equal scanner field strength and protocol.

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Multiparametric MRI for Characterization of the Basal Ganglia and the Midbrain.

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