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Quantitative susceptibility mapping (QSM) and R2* relaxation rate mapping have demonstrated increased iron deposition in the substantia nigra of patients with idiopathic Parkinson's disease (PD). However, the findings in other subcortical deep gray matter nuclei are converse and the sensitivity of QSM and R2* for morphological changes and their relation to clinical measures of disease severity has so far been investigated only sparsely. The local ethics committee approved this study and all subjects gave written informed consent. 66 patients with idiopathic Parkinson's disease and 58 control subjects underwent quantitative MRI at 3T. Susceptibility and R2* maps were reconstructed from a spoiled multi-echo 3D gradient echo sequence. Mean susceptibilities and R2* rates were measured in subcortical deep gray matter nuclei and compared between patients with PD and controls as well as related to clinical variables. Compared to control subjects, patients with PD had increased R2* values in the substantia nigra. QSM also showed higher susceptibilities in patients with PD in substantia nigra, in the nucleus ruber, thalamus, and globus pallidus. Magnetic susceptibility of several of these structures was correlated with the levodopa-equivalent daily dose (LEDD) and clinical markers of motor and non-motor disease severity (total MDS-UPDRS, MDS-UPDRS-I and II). Disease severity as assessed by the Hoehn & Yahr scale was correlated with magnetic susceptibility in the substantia nigra. The established finding of higher R2* rates in the substantia nigra was extended by QSM showing superior sensitivity for PD-related tissue changes in nigrostriatal dopaminergic pathways. QSM additionally reflected the levodopa-dosage and disease severity. These results suggest a more widespread pathologic involvement and QSM as a novel means for its investigation, more sensitive than current MRI techniques.

•Depression is linked to salience, executive, and sensorimotor networks.•Little is known about deep brain nuclei involvement in depression.•Deep brain nuclei covariance with sensorimotor areas differs in depression.•Results may implicate sensorimotor processing and biases observed in depression.•Further work examining morphometric covariance is encouraged. Depression impacts nearly 3% of the global adult population. Symptomatology is likely related to regions encompassing frontoparietal, somatosensory, and salience networks. Questions regarding deep brain nuclei (DBN), including the substantia nigra (STN), subthalamic nucleus (STN), and red nucleus (RN) remain unanswered. Using an existing structural neuroimaging dataset including 86 individuals (Baranger et al., 2021; nDEP = 39), frequentist and Bayesian logistic regressions assessed whether DBN volumes predict diagnosis, then structural covariance analyses in FreeSurfer tested diagnostic differences in deep brain volume and cortical morphometry covariance. Exploratory correlations tested relationships between implicated cortical regions and Hamilton Depression Rating Scale (HAM-D) scores. Group differences emerged in deep brain/cortical covariance. Right RN volume covaried with left parietal operculum volume and central sulcus thickness, while left RN and right STN volumes covaried with right occipital pole volume. Positive relationships were observed within the unaffected group and negative relationships among those with depression. These cortical areas did not correlate with HAM-D scores. Simple DBN volumes did not predict diagnostic group. Structural codependence between DBN and cortical regions may be important in depression, potentially for sensorimotor features. Future work should focus on causal mechanisms of DBN involvement with sensory integration.

The substantia nigra (SN), the subthalamic nucleus (STN), and the red nucleus (RN) are midbrain structures of ample interest in many neuroimaging studies, which may benefit from the availability of automated segmentation methods. The high iron content of these structures awards them high contrast in quantitative susceptibility mapping (QSM) images. We present a novel segmentation method that leverages the information of these images to produce automated segmentations of the SN, STN, and RN. The algorithm builds a map of spatial priors for the structures by non-linearly registering a set of manually-traced training labels to the midbrain. The priors are used to inform a Gaussian mixture model of the image intensities, with smoothness constraints imposed to ensure anatomical plausibility. The method was validated on manual segmentations from a sample of 40 healthy younger and older subjects. Average Dice scores were 0.81 (0.05) for the SN, 0.66 (0.14) for the STN and 0.88 (0.04) for the RN in the left hemisphere, and similar values were obtained for the right hemisphere. In all structures, volumes of manual and automatically obtained segmentations were significantly correlated. The algorithm showed lower accuracy on R2* and T2-weighted Fluid Attenuated Inversion Recovery (FLAIR) images, which are also sensitive to iron content. To illustrate an application of the method, we show that the automated segmentations were comparable to the manual ones regarding detection of age-related differences to putative iron content.

The red nucleus, a large brainstem structure, coordinates limb movement for locomotion in quadrupedal animals. In humans, its pattern of anatomical connectivity differs from that of quadrupeds, suggesting a different purpose. Here, we apply our most advanced resting-state functional connectivity based precision functional mapping in highly sampled individuals ( n  = 5), resting-state functional connectivity in large group-averaged datasets (combined n  ~ 45,000), and task based analysis of reward, motor, and action related contrasts from group-averaged datasets ( n  > 1000) and meta-analyses ( n  > 14,000 studies) to precisely examine red nucleus function. Notably, red nucleus functional connectivity with motor-effector networks (somatomotor hand, foot, and mouth) is minimal. Instead, connectivity is strongest to the action-mode and salience networks, which are important for action/cognitive control and reward/motivated behavior. Consistent with this, the red nucleus responds to motor planning more than to actual movement, while also responding to rewards. Our results suggest the human red nucleus implements goal-directed behavior by integrating behavioral valence and action plans instead of serving a pure motor-effector function. The authors find the human red nucleus is functionally connected with action and motivated behavior networks, instead of motor-effector networks. They argue the red nucleus implements goal-directed behavior, integrating behavioral valence and action plans.

The aging brain undergoes several anatomical changes that can be measured with Magnetic Resonance Imaging (MRI). Early studies using lower field strengths have assessed changes in tissue properties mainly qualitatively, using T 1 - or T 2 ∗ - weighted images to provide image contrast. With the development of higher field strengths (7 T and above) and more advanced MRI contrasts, quantitative measures can be acquired even of small subcortical structures. This study investigates volumetric, spatial, and quantitative MRI parameter changes associated with healthy aging in a range of subcortical nuclei, including the basal ganglia, red nucleus, and the periaqueductal grey. The results show that aging has a heterogenous effects across regions. Across the subcortical areas an increase of T 1 values is observed, most likely indicating a loss of myelin. Only for a number of areas, a decrease of T 2 ∗ and increase of QSM is found, indicating an increase of iron. Aging also results in a location shift for a number of structures indicating the need for visualization of the anatomy of individual brains.

•7 Tesla submillimeter whole-brain dataset.•105 intrinsically aligned quantitative contrasts from a single scan acquisition.•Probabilistic atlases of the basal ganglia based on >1000 manual delineations.•Data freely available for further analyses. Normative databases allow testing of novel hypotheses without the costly collection of magnetic resonance imaging (MRI) data. Here we present the Amsterdam Ultra-high field adult lifespan database (AHEAD). The AHEAD consists of 105 7 Tesla (T) whole-brain structural MRI scans tailored specifically to imaging of the human subcortex, including both male and female participants and covering the entire adult life span (18–80 yrs). We used these data to create probability maps for the subthalamic nucleus, substantia nigra, internal and external segment of the globus pallidus, and the red nucleus. Data was acquired at a submillimeter resolution using a multi-echo (ME) extension of the second gradient-echo image of the MP2RAGE sequence (MP2RAGEME) sequence, resulting in complete anatomical alignment of quantitative, R1-maps, R2*-maps, T1-maps, T1-weighted images, T2*-maps, and quantitative susceptibility mapping (QSM). Quantitative MRI maps, and derived probability maps of basal ganglia structures are freely available for further analyses.

The Red Nucleus (RN) is a large nucleus located in the ventral midbrain: it is subdivided into a small caudal magnocellular part (mRN) and a large rostral parvocellular part (pRN). These distinct structural regions are part of functionally different networks and show distinctive connectivity features: the mRN is connected to the interposed nucleus, whilst the pRN is mainly connected to dentate nucleus, cortex and inferior olivary complex. Despite functional neuroimaging studies suggest RN involvement in complex motor and higher order functions, the pRN and mRN cannot be distinguished using conventional MRI. Herein, we employ high-quality structural and diffusion MRI data of 100 individuals from the Human Connectome Project repository and constrained spherical deconvolution tractography to perform connectivity-based segmentation of the human RN. In particular, we tracked connections of RN with the inferior olivary complex, the interposed nucleus, the dentate nucleus and the cerebral cortex. We found that the RN can be subdivided according to its connectivity into two clusters: a large ventrolateral one, mainly connected with the cerebral cortex and the inferior olivary complex, and a smaller dorsomedial one, mainly connected with the interposed nucleus. This structural topography strongly reflects the connectivity patterns of pRN and mRN respectively. Structural connectivity-based segmentation could represent a useful tool for the identification of distinct subregions of the human red nucleus on 3T MRI thus allowing a better evaluation of this subcortical structure in healthy and pathological conditions.

The dentato-rubro-thalamic tract (DRTT) regulates motor control, connecting the cerebellum to the thalamus. This tract is modulated by deep-brain stimulation in the surgical treatment of medically refractory tremor, especially in essential tremor, where high-frequency stimulation of the thalamus can improve symptoms. The DRTT is classically described as a decussating pathway, ascending to the contralateral thalamus. However, the existence of a nondecussating (i.e. ipsilateral) DRTT in humans was recently demonstrated, and these tracts are arranged in distinct regions of the superior cerebellar peduncle. We hypothesized that the ipsilateral DRTT is connected to specific thalamic nuclei and therefore may have unique functional relevance. The goals of this study were to confirm the presence of the decussating and nondecussating DRTT pathways, identify thalamic termination zones of each tract, and compare whether structural connectivity findings agree with functional connectivity. Diffusion-weighted imaging was used to perform probabilistic tractography of the decussating and nondecussating DRTT in young healthy subjects from the Human Connectome Project (n = 91) scanned using multi-shell diffusion-weighted imaging (270 directions; TR/TE = 5500/89 ms; spatial resolution = 1.25 mm isotropic). To define thalamic anatomical landmarks, a segmentation procedure based on the Morel Atlas was employed, and DRTT targeting was quantified based on the proportion of streamlines arriving at each nucleus. In parallel, functional connectivity analysis was performed using resting-state functional MRI (TR/TE = 720/33 ms; spatial resolution = 2 mm isotropic). It was found that the decussating and nondecussating DRTTs have significantly different thalamic endpoints, with the former preferentially targeting relatively anterior and lateral thalamic nuclei, and the latter connected to more posterior and medial nuclei (p < 0.001). Functional and structural connectivity measures were found to be significantly correlated (r = 0.45, p = 0.031). These findings provide new insight into pathways through which unilateral cerebellum can exert bilateral influence on movement and raise questions about the functional implications of ipsilateral cerebellar efferents. •The dentato-rubro-thalamic tract is a cerebellar white matter efferent.•An ipsilateral (nondecussating) component of the tract was recently identified.•Diffusion tractography and functional MRI measured tract-thalamus connectivity.•Ipsilateral tract connects to more medial/posterior thalamus vs. contralateral.•Structural and functional connectivity measures are significantly associated.

Background Our recent studies have identified that the red nucleus (RN) dual-directionally modulates the development and maintenance of mononeuropathic pain through secreting proinflammatory and anti-inflammatory cytokines. Here, we further explored the action of red nucleus IL-33 in the early development of mononeuropathic pain. Methods In this study, male rats with spared nerve injury (SNI) were used as mononeuropathic pain model. Immunohistochemistry, Western blotting, and behavioral testing were used to assess the expressions, cellular distributions, and actions of red nucleus IL-33 and its related downstream signaling molecules. Results IL-33 and its receptor ST2 were constitutively expressed in the RN in naive rats. After SNI, both IL-33 and ST2 were upregulated significantly at 3 days and peaked at 1 week post-injury, especially in RN neurons, oligodendrocytes, and microglia. Blockade of red nucleus IL-33 with anti-IL-33 neutralizing antibody attenuated SNI-induced mononeuropathic pain, while intrarubral administration of exogenous IL-33 evoked mechanical hypersensitivity in naive rats. Red nucleus IL-33 generated an algesic effect in the early development of SNI-induced mononeuropathic pain through activating NF-κB, ERK, p38 MAPK, and JAK2/STAT3, suppression of NF-κB, ERK, p38 MAPK, and JAK2/STAT3 with corresponding inhibitors markedly attenuated SNI-induced mononeuropathic pain or IL-33-evoked mechanical hypersensitivity in naive rats. Red nucleus IL-33 contributed to SNI-induced mononeuropathic pain by stimulating TNF-α expression, which could be abolished by administration of inhibitors against ERK, p38 MAPK, and JAK2/STAT3, but not NF-κB. Conclusions These results suggest that red nucleus IL-33 facilitates the early development of mononeuropathic pain through activating NF-κB, ERK, p38 MAPK, and JAK2/STAT3. IL-33 mediates algesic effect partly by inducing TNF-α through activating ERK, p38 MAPK and JAK2/STAT3.

The advent of susceptibility‐sensitive MRI techniques, such as susceptibility weighted imaging (SWI), has enabled accurate in vivo visualization and quantification of iron deposition within the human brain. Although previous approaches have been introduced to segment iron‐rich brain regions, such as the substantia nigra, subthalamic nucleus, red nucleus, and dentate nucleus, these methods are largely unavailable and manual annotation remains the most used approach to label these regions. Furthermore, given their recent success in outperforming other segmentation approaches, convolutional neural networks (CNN) promise better performances. The aim of this study was thus to evaluate state‐of‐the‐art CNN architectures for the labeling of deep brain nuclei from SW images. We implemented five CNN architectures and considered ensembles of these models. Furthermore, a multi‐atlas segmentation model was included to provide a comparison not based on CNN. We evaluated two prediction strategies: individual prediction, where a model is trained independently for each region, and combined prediction, which simultaneously predicts multiple closely located regions. In the training dataset, all models performed with high accuracy with Dice coefficients ranging from 0.80 to 0.95. The regional SWI intensities and volumes from the models' labels were strongly correlated with those obtained from manual labels. Performances were reduced on the external dataset, but were higher or comparable to the intrarater reliability and most models achieved significantly better results compared to multi‐atlas segmentation. CNNs can accurately capture the individual variability of deep brain nuclei and represent a highly useful tool for their segmentation from SW images. In this study, we evaluated a range of convolutional neural network architectures in the task of segmenting deep brain nuclei from susceptibility weighted images. In the training dataset, all models performed with high accuracy with Dice coefficients ranging from 0.80 to 0.95. Performances were reduced on the external dataset, but were higher or comparable to the intrarater reliability and most models achieved significantly better results compared to multi‐atlas segmentation.