Dataset Information

A probabilistic atlas of the basal ganglia using 7 T MRI.

ABSTRACT: A common localization procedure in functional imaging studies includes the overlay of statistical parametric functional magnetic resonance imaging (fMRI) maps or coordinates with neuroanatomical atlases in standard space, e.g., MNI-space. This procedure allows the identification of specific brain regions. Most standard MRI software packages include a wide range of atlases but have a poor coverage of the subcortex. We estimated that approximately 7% of the known subcortical structures are mapped in standard MRI-compatible atlases [1]. Here we provide a data description of a subcortical probabilistic atlas based on ultra-high resolution in-vivo anatomical imaging using 7 T (T) MRI. The atlas includes six subcortical nuclei: the striatum (STR), the globus pallidus internal and external segment (GPi/e), the subthalamic nucleus (STN), the substantia nigra (SN), and the red nucleus (RN). These probabilistic atlases are shared on freely available platforms such as NITRC and NeuroVault and are published in NeuroImage "Quantifying inter-individual anatomical variability in the subcortex using 7 T structural MRI" [2].

SUBMITTER: Keuken MC

PROVIDER: S-EPMC4543077 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A probabilistic atlas of the basal ganglia using 7 T MRI.

Keuken Max C MC Forstmann Birte U BU

Data in brief 20150731

A common localization procedure in functional imaging studies includes the overlay of statistical parametric functional magnetic resonance imaging (fMRI) maps or coordinates with neuroanatomical atlases in standard space, e.g., MNI-space. This procedure allows the identification of specific brain regions. Most standard MRI software packages include a wide range of atlases but have a poor coverage of the subcortex. We estimated that approximately 7% of the known subcortical structures are mapped ...[more]

PMID: 26322322

Similar Datasets

Project description: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.

Project description:Basal ganglia circuits are affected in neurological disorders such as Parkinson's disease (PD), essential tremor, dystonia and Tourette syndrome. Understanding the structural and functional connectivity of these circuits is critical for elucidating the mechanisms of the movement and neuropsychiatric disorders, and is vital for developing new therapeutic strategies such as deep brain stimulation (DBS). Knowledge about the connectivity of the human basal ganglia and thalamus has rapidly evolved over recent years through non-invasive imaging techniques, but has remained incomplete because of insufficient resolution and sensitivity of these techniques. Here, we present an imaging and computational protocol designed to generate a comprehensive in vivo and subject-specific, three-dimensional model of the structure and connections of the human basal ganglia. High-resolution structural and functional magnetic resonance images were acquired with a 7-Tesla magnet. Capitalizing on the enhanced signal-to-noise ratio (SNR) and enriched contrast obtained at high-field MRI, detailed structural and connectivity representations of the human basal ganglia and thalamus were achieved. This unique combination of multiple imaging modalities enabled the in-vivo visualization of the individual human basal ganglia and thalamic nuclei, the reconstruction of seven white-matter pathways and their connectivity probability that, to date, have only been reported in animal studies, histologically, or group-averaged MRI population studies. Also described are subject-specific parcellations of the basal ganglia and thalamus into sub-territories based on their distinct connectivity patterns. These anatomical connectivity findings are supported by functional connectivity data derived from resting-state functional MRI (R-fMRI). This work demonstrates new capabilities for studying basal ganglia circuitry, and opens new avenues of investigation into the movement and neuropsychiatric disorders, in individual human subjects.

Project description:Background: Despite the popularity of functional connectivity analyses and the well-known topology of several intrinsic cortical networks, relatively little is known about the white matter regions (i.e., structural connectivity) underlying these networks. In the current study, we have therefore performed fMRI-guided diffusion tensor imaging (DTI) tractography to create probabilistic white matter atlases for eight previously identified functional brain networks, including the Auditory, Basal Ganglia, Language, Precuneus, Sensorimotor, Primary Visual, Higher Visual and Visuospatial Networks. Methods: Whole-brain diffusion imaging data were acquired from a cohort of 32 healthy volunteers, and were warped to the ICBM template using a two-stage, high-dimensional, non-linear spatial normalization procedure. Deterministic tractography, with fractional anisotropy (FA) ?0.15 and deviation angle <50°, was then performed using the Fiber Association by Continuous Tracking (FACT) algorithm, and a multi-ROI approach to identify tracts of interest. Regions-of-interest (ROIs) for each of the eight networks were taken from a pre-existing atlas of functionally defined regions to explore all ROI-to-ROI connections within each network, and all resulting streamlines were saved as binary masks to create probabilistic atlases (across participants) for tracts between each ROI-to-ROI pair. Results: The resulting functionally-defined white matter atlases (i.e., for each tract and each network as a whole) were saved as NIFTI images in stereotaxic ICBM coordinates, and have been added to the UManitoba-JHU Functionally-Defined Human White Matter Atlas (http://www.nitrc.org/projects/uofm_jhu_atlas/). Conclusion: To the best of our knowledge, this work represents the first attempt to comprehensively identify and map white matter connectomes for the Auditory, Basal Ganglia, Language, Precuneus, Sensorimotor, Primary Visual, Higher Visual and Visuospatial Networks. Therefore, the resulting probabilistic atlases represent a unique tool for future neuroimaging studies wishing to ascribe voxel-wise or ROI-based changes (i.e., in DTI or other quantitative white matter imaging signals) to these functional brain networks.

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.

Dataset Information

A probabilistic atlas of the basal ganglia using 7 T MRI.

Publications

A probabilistic atlas of the basal ganglia using 7 T MRI.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets