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mri scanner  (Siemens AG)

 
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    Siemens AG mri scanner
    Mri Scanner, supplied by Siemens AG, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/fmri+data+preprocessing/mri+scanner/pmc06867376-132-13-12
    Average 90 stars, based on 1 article reviews
    mri scanner - by Bioz Stars, 2026-10
    90/100 stars

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    Related Articles

    other:

    Article Title: Measurement of cyclosporin induced changes in P-glycoprotein function at the human blood-brain barrier using [ 18 F]MC225 and PET.
    Article Snippet: MRI scans were performed using a 3.0 T Siemens Magnetom Prisma (Siemens AG, Erlangen, Germany) with a 64-channel head coil and a voxel size of 1.5 × 1.5 × 3.0 mm.

    Article Title: Structural-functional brain network coupling during cognitive demand reveals intelligence-relevant communication strategies.
    Article Snippet: Neuroimaging data acquisition and preprocessing MRI scans were obtained on a Siemens Skyra 3T scanner equipped with a 32-channel head coil.

    Article Title: Dissociable effects of psilocybin and escitalopram for depression on processing of musical surprises.
    Article Snippet: Participants were asked to complete a continuous music rating task immediately after the scan (see Supplementary Methods). fMRI data were acquired using a Siemens TIM Trio 3 Tesla MRI scanner (Siemens, Erlangen), equipped with a 32-channel phased-array head coil.


    Article Title: Neural basis of the association between future time perspective and ADHD characteristics: functional connectivity between Left inferior parietal lobule and mPFC.
    Article Snippet: ADHD is a neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity, leading to ongoing challenges in academic, career, and social adaptation.. Previous research indicated that individuals with higher levels of future time perspective (FTP) tend to exhibit lower ADHD traits.. However, little is known about the neural substrates between the relationship of FTP and ADHD traits.

    Article Title: Intraepineurial Fat Fraction: A Novel MR Neurography-Based Biomarker in Transthyretin Amyloidosis Polyneuropathy.
    Article Snippet: MRI was performed at 1.5 T (Avanto, Siemens Heathineers).

    Article Title: MR Imaging Features of Juvenile Pilocytic Astrocytoma in the Suprasellar Region: A Study on 11 Patients.
    Article Snippet: Eleven consecutive children with pathologically confirmed suprasellar PAs in our hospital from May 2015 to November 2021 were enrolled in this study.. The clinical data and preoperative MR images were retrospectively reviewed.. MRI included T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), fluid-attenuated inversion recovery (FLAIR), and postcontrast T1WI.

    Magnetic Resonance Imaging:

    Article Title: Multiparametric quantitative MRI uncovers putamen microstructural changes in Parkinson's disease.
    Article Snippet: .. Although our data were acquired on a specific MRI scanner (Siemens) and field strength (3 T), prior study demonstrated that similar gradient patterns were reproducible across three independent qMRI datasets from healthy subjects scanned on different MRI systems (Siemens, Philips, GE) and field strengths (3 T and 7 T). ..



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    Figure 1. The schematic illustration of the main method. (A) The procedures to obtain multiscale FCNs. (i) the <t>fMRI</t> <t>data</t> from one individual is inputted and preprocessed. (ii) The application of the Schaefer’s multiscale atlases to the fMRI data. The black border lines indicate boundaries of ROIs and the colors encode the resting-state network (RSN) to which the ROI belongs. The RSNs include DMN, frontoparietal network (FP), limbic network (LIM), salience network (SAL), attention network (ATT), somatomotor network (SM), and visual network (VIS). (iii) The extraction of ROI-averaged signals. (iv) The construction of multiscale FCNs from individual fMRI data. (B) The architecture for multiscale atlas-based GCN (MAGCN). The multiscale FCNs are extracted via a series of GCNs connected by the APs. The nodal features h are integrated with skip connections and concatenations, based on which individualized diagnosis is generated.
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    Figure 1. The schematic illustration of the main method. (A) The procedures to obtain multiscale FCNs. (i) the <t>fMRI</t> <t>data</t> from one individual is inputted and preprocessed. (ii) The application of the Schaefer’s multiscale atlases to the fMRI data. The black border lines indicate boundaries of ROIs and the colors encode the resting-state network (RSN) to which the ROI belongs. The RSNs include DMN, frontoparietal network (FP), limbic network (LIM), salience network (SAL), attention network (ATT), somatomotor network (SM), and visual network (VIS). (iii) The extraction of ROI-averaged signals. (iv) The construction of multiscale FCNs from individual fMRI data. (B) The architecture for multiscale atlas-based GCN (MAGCN). The multiscale FCNs are extracted via a series of GCNs connected by the APs. The nodal features h are integrated with skip connections and concatenations, based on which individualized diagnosis is generated.
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    Figure 1. The schematic illustration of the main method. (A) The procedures to obtain multiscale FCNs. (i) the fMRI data from one individual is inputted and preprocessed. (ii) The application of the Schaefer’s multiscale atlases to the fMRI data. The black border lines indicate boundaries of ROIs and the colors encode the resting-state network (RSN) to which the ROI belongs. The RSNs include DMN, frontoparietal network (FP), limbic network (LIM), salience network (SAL), attention network (ATT), somatomotor network (SM), and visual network (VIS). (iii) The extraction of ROI-averaged signals. (iv) The construction of multiscale FCNs from individual fMRI data. (B) The architecture for multiscale atlas-based GCN (MAGCN). The multiscale FCNs are extracted via a series of GCNs connected by the APs. The nodal features h are integrated with skip connections and concatenations, based on which individualized diagnosis is generated.

    Journal: Cerebral cortex (New York, N.Y. : 1991)

    Article Title: Multiscale functional connectome abnormality predicts cognitive outcomes in subcortical ischemic vascular disease.

    doi: 10.1093/cercor/bhab507

    Figure Lengend Snippet: Figure 1. The schematic illustration of the main method. (A) The procedures to obtain multiscale FCNs. (i) the fMRI data from one individual is inputted and preprocessed. (ii) The application of the Schaefer’s multiscale atlases to the fMRI data. The black border lines indicate boundaries of ROIs and the colors encode the resting-state network (RSN) to which the ROI belongs. The RSNs include DMN, frontoparietal network (FP), limbic network (LIM), salience network (SAL), attention network (ATT), somatomotor network (SM), and visual network (VIS). (iii) The extraction of ROI-averaged signals. (iv) The construction of multiscale FCNs from individual fMRI data. (B) The architecture for multiscale atlas-based GCN (MAGCN). The multiscale FCNs are extracted via a series of GCNs connected by the APs. The nodal features h are integrated with skip connections and concatenations, based on which individualized diagnosis is generated.

    Article Snippet: The sagittal T1-weighted images covering the whole brain were acquired by a 3Dfast spoiled gradient recalled echo sequence: TR = 5.6 ms, TE = 1.8 ms, matrix = 256 × 256, inversion time = 450 ms, flip angle = 15◦, slice thickness/gap = 1/0 mm, number of slices = 156, gap = 0, and FOV = 256 × 256 mm2. fMRI Data Preprocessing We adopt the standardized pipeline from the public available toolbox Data Processing Assistant for Resting-State fMRI (Yan and Zang 2010) in Matlab (Mathworks.

    Techniques: Extraction, Biomarker Discovery, Generated