Scans were collected using a custom-made Siemens Connectom Skyra scanner with a 32-channel head coil. T1- and T2-weighted anatomical scans were acquired at 0.7 mm isotropic resolution (37 (link)). Structural images were “minimally preprocessed” by HCP investigators through standardized pipelines (43 (link)). Images were corrected for gradient non-linearity-induced distortions, readout distortions, and intensity inhomogeneities, and then aligned to the MNI atlas. Then, images were processed through a customized version of Freesurfer. We used the volume values for all subcortical regions (averaged across the left and right regions, where possible) in the Desikan-Killany parcellation (44 (link)), which resulted in the analysis of 10 regions: Amygdala, Hippocampus, Putamen, Caudate, Nucleus Accumbens, Thalamus, Pallidum, Brainstem, Cerebellar Cortex, and Cerebellar White Matter.
Connectom skyra
Manufactured by Siemens
Sourced in Germany
The Connectom Skyra is a high-performance magnetic resonance imaging (MRI) system designed for advanced neuroimaging research. It features a powerful superconducting magnet and state-of-the-art gradient and radio frequency (RF) systems, enabling high-resolution imaging and comprehensive data acquisition capabilities.
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Lab products found in correlation
7 protocols using connectom skyra
1
Subcortical Brain Volume Analysis
2
Diffusion MRI of Human Connectome
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We drew from the preprocessed, publicly released HCP dMRI data (73 (link)). We randomly chose 50 unrelated subjects (age range, 22 to 35 years). Briefly, the HCP data were acquired using a bespoke 3T Connectom Skyra (Siemens, Erlangen) with a monopolar diffusion-weighted (Stejskal-Tanner) spin-echo echo planar imaging (EPI) sequence, an isotropic spatial resolution of 1.25 mm, three shells (b values = 1000, 2000, and 3000 s/mm2), and 90 unique diffusion directions per shell plus 6 b = 0 s/mm2 volumes, acquired twice with opposing phase encoding polarities. Protocol details are summarized in table S2. Nonlinear transformations to the MNI152 standard space were obtained using T1-weighted images with FSL’s FNIRT (74 ). The distortion-corrected dMRI data were separately linearly aligned to the T1-weighted space, and the concatenation of the diffusion-to-T1 and T1-to-MNI transforms allowed diffusion-to-MNI warp fields to be obtained.
3
Multimodal Brain Imaging with Diffusion Scans
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Scans were collected using a custom-made Siemens Connectom Skyra scanner with a 32-channel head coil. T1- and T2-weighted anatomical scans were acquired at 0.7 mm isotropic resolution. Diffusion-weighted data were collected over six runs with three different shells of b = 1000, 2000, and 3000 s/mm2 (including 90 diffusion weighting directions and six b = 0 acquisitions interspersed throughout each run) [33 (link)].
4
Multimodal MRI Acquisition for the HCP Dataset
5
High-Resolution Brain Imaging Protocol
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The HCP structural data were acquired using a customized 3-T Siemens Connectom Skyra scanner with a standard Siemens 32-channel radio frequency (RF)–receive head coil. For each participant, at least one three-dimensional (3D) T1w MPRAGE image and one 3D T2w SPACE image were collected at 0.7-mm isotropic resolution.
6
Advanced Diffusion-Weighted MRI Imaging Protocol
7
Comprehensive Diffusion MRI Acquisition and Preprocessing
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Diffusion MRI data were obtained from the Human Connectome Project (Van Essen et al, 2013) . Full acquisition protocol details are described in (Sotiropoulos et al., 2013) . Briefly, a monopolar Stejskal-Tanner echo planar imaging sequence was used in a 3T Siemens Connectom Skyra to acquire data at (1.25 mm) 3 isotropic resolution. Diffusion-sensitization was applied with 3 b values (b=1000, 2000 and 3000 s/mm 2 ) and along 90 directions per b-shell. Two repeats were obtained with blip-reversed phase encoding. The minimally processed data were used (Glasser et al, 2013) , where susceptibilityinduced distortions, eddy currents and subject motion were all corrected simultaneously using a non-parametric framework (Andersson and Sotiropoulos, 2016) based on Gaussian processes (Andersson and Sotiropoulos, 2015) .
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