Prisma

Manufactured by Philips

Prisma is a compact and versatile laboratory equipment designed for precise light analysis and spectroscopy applications. It features a high-resolution monochromator that allows for accurate wavelength selection and measurement. The Prisma is a reliable and efficient tool for researchers and technicians working in various fields that require detailed optical analysis.

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Lab products found in correlation

Tim trio, by Philips (2 mentions) Firmm software, by Siemens (2 mentions) 750 3t mri, by GE Healthcare (2 mentions) Framewise integrated real time mri monitor firmm software, by Siemens (2 mentions) 32 channel head coil, by GE Healthcare (1 mentions) Skyra scanner, by Siemens (1 mentions) Tim trio scanner, by Siemens (1 mentions) 3t scanner, by GE Healthcare (1 mentions) 3t scanner, by Philips (1 mentions) Mr750, by Siemens (1 mentions) Prisma fit, by Philips (1 mentions)

23 protocols using prisma

ABCD MRI Quality Control Methods

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ABCD MRI methods and assessments have been optimized and harmonized across the 21 sites for 3 Tesla scanners (Siemens Prisma, General Electric 750, Philips) (Casey et al., 2018 (link); Hagler et al., 2018 ). Cortical surface reconstruction and subcortical segmentation was completed via FreeSurfer (version 5.3), including total gray and white matter as well as subcortical volumes, cortical thickness and cortical surface area estimates for cortical regions using the Desikan-Killiany Atlas (Dale, Fischl, & Sereno, 1999 (link); Hagler et al., 2018 ). At the central ABCD Data Analysis and Informatics Center (DAIC), T1-weighted structural images underwent quality control (QC) across five categories, both prior to and after post-processing to gauge the severity of motion, intensity inhomogeneity, white matter underestimation, pial overestimation, and magnetic susceptibility artifact (Hagler et al., 2018 ). Only image types passing QC for all categories were included in our analyses (SFig. 2). Subjects lost after MRI QA/QC (n=644) were significantly younger (age in months: 117.7 ± 7.3; p<0.0001) but did not significantly differ by sex (53.6% male; p=0.53) from the rest of the cohort.

Cserbik D., Chen J.C., McConnell R., Berhane K., Sowell E.R., Schwartz J., Hackman D.A., Kan E., Fan C.C, & Herting M.M. (2020). Fine particulate matter exposure during childhood relates to hemispheric-specific differences in brain structure. Environment international, 143, 105933.

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ABCD MRI Structural Imaging Protocol

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Details of the ABCD magnetic resonance imaging (MRI) protocol have been described previously [73 (link)]. Briefly, imaging data were collected on either a Siemens Prisma, Phillips, or GE 750 3T scanner using 32-channel head or 64-channel head/neck coils. T1-weighted scan parameters were as follows: Siemens - matrix size 256 × 256, 176 slices, FOV 256 × 256, resolution (mm) 1 × 1 × 1, TR 2500 ms, TE 2.88 ms, flip angle 8°, total scan time 7:12; Phillips - matrix size 256 × 256, 225 slices, FOV 256x240, resolution (mm) 1 × 1 × 1, TR 6.31 ms, TE 2.9 ms, flip angle 8°, total scan time 5:38; and GE - matrix size 256 × 256, 208 slices, FOV 256 × 256, resolution (mm) 1 × 1 × 1, TR 2500 ms, TE 2 ms, flip angle 8°, total scan time 6:09.
Tabulated data representing T1-weighted measurements of subcortical volume (VOL), cortical thickness (CT), and surface area (SA) as derived from the Desikan parcellation atlas [34 (link)] in FreeSurfer v5.3 (http://surfer.nmr.mgh.harvard.edu/) were downloaded from the NDA. ABCD-recommended imaging inclusion criteria were followed to ensure the inclusion of high-quality MRI scans, and this excluded participants with serious MR findings and participants whose T1-weighted images or FreeSurfer parcellations failed to pass ABCD DAIC quality control. Regional MRI metrics were averaged across hemispheres.

Hernandez L.M., Kim M., Zhang P., Bethlehem R.A., Hoftman G., Loughnan R., Smith D., Bookheimer S.Y., Fan C.C., Bearden C.E., Thompson W.K, & Gandal M.J. (2023). Multi-ancestry phenome-wide association of complement component 4 variation with psychiatric and brain phenotypes in youth. Genome Biology, 24, 42.

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Multimodal MRI Acquisition for ABCD Study

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ABCD images were acquired using Siemens Prisma, Philips, or GE 750 3 T scanners with a 32-channel head coil. Detailed acquisition parameters have been previously described in the literature (Casey et al., 2018 (link)). Scan sessions included a high-resolution T1-weighted scan, diffusion weighted images, T2-weighted spin echo images, resting-state fMRI, and task-based fMRI. Functional images were collected through 60 slices in the axial plane using echo-planar imaging sequence with the following parameters: TR = 800 ms, TE = 30 ms, flip angle = 52°, voxel size = 2.4 m㎥, multiband slice acceleration factor = 6.
Participants completed up to four runs of 5-minute resting-state fMRI scans. ABCD sites with Siemens scanners used Framewise Integrated Real-time MRI Monitoring (FIRMM; (Dosenbach et al., 2017 (link)), which monitors head motion in real-time and allows for the discontinuation of resting-state data collection after three runs if 12.5 min of low-motion data had been collected.

Rapuano K.M., Rosenberg M.D., Maza M.T., Dennis N.J., Dorji M., Greene A.S., Horien C., Scheinost D., Todd Constable R, & Casey B.J. (2020). Behavioral and brain signatures of substance use vulnerability in childhood. Developmental Cognitive Neuroscience, 46, 100878.

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MRI Acquisition Protocol for Neuroimaging

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MRI data were collected on a Siemens Prisma (Columbia University, MGH) or Philips Achieva (UC-Irvine) 3T platform. Sequences included T1-weighted scan (repetition time [TR]/echo time [TE]/inversion time [TI]: 2300/2.96/900 ms; voxel size: 1 × 1 × 1 mm³), T2-weighted fluid-attenuated inversion recovery (FLAIR; TR/TE/TI: 5000/386/1800 ms; voxel size: 0.4 × 0.4 × 0.9 mm³), and T2*-weighted gradient echo (GRE; TR/TE: 650/20 ms; voxel size: 0.8 × 0.8 × 4 mm³) or susceptibility weighted image (SWI; TR/TE: 27/20 ms; voxel size: 0.9 × 0.9 × 1.5 mm³).^{9 (link)}

Moni F., Petersen M.E., Zhang F., Lao P.J., Zimmerman M.E., Gu Y., Gutierrez J., Rizvi B., Laing K.K., Igwe K.C., Sathishkumar M., Keator D., Andrews H., Krinsky-McHale S., Head E., Lee J.H., Lai F., Yassa M.A., Rosas H.D., Silverman W., Lott I.T., Schupf N., O’Bryant S, & Brickman A.M. (2022). Probing the proteome to explore potential correlates of increased Alzheimer’s-related cerebrovascular disease in adults with Down syndrome. Alzheimer's & dementia : the journal of the Alzheimer's Association, 18(10), 1744-1753.

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Multimodal Neuroimaging Protocol for Resting-State Functional Connectivity

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Imaging data were collected on a 3T Siemens TIM Trio scanner at the MRN site and on a 3T Siemens Skyra scanner at the Nebraska site. A total of 650 volumes of multiband echo planar imaging blood-oxygen-level-dependent data (a length of 4 minutes and 59 seconds) were collected per resting state condition and per participant with repetition time (TR) of 0.46 s, time to echo of 29 ms, flip angle of 44 degrees, and a slice thickness of 3 mm with no gap. Rs-fMRI scans were acquired using a standard gradient-echo planar imaging paradigm; MRN site: field-of-view (FOV) of 246 × 246 mm (82 × 82 matrix), 56 sequential axial slices; Nebraska: FOV of 268 × 268 mm (82 × 82 matrix), 48 sequential axial slices. Eyes of the participants were monitored via an eye-tracker during the EO condition to ensure that participants were following the instructions. The order of the EO and EC scanning sessions were counter-balanced across participants at both sites.
The ABCD study were collected at 21 different sites, using Siemens Prisma, Phillips and GE 750 3T scanners with harmonized imaging parameters, in EO condition and passive viewing of a cross hair with a TR of 0.8 s. Please see (Casey et al., 2018 (link)) for the details of the ABCD study parameters.

Agcaoglu O., Wilson T.W., Wang Y.P., Stephen J.M., Fu Z, & Calhoun V.D. (2022). Altered Resting fMRI Spectral Power in Data-Driven Brain Networks During Development: A Longitudinal Study. Journal of neuroscience methods, 372, 109537.

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Structural Brain Imaging Data Processing

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Structural MRI (sMRI) data were collected by individual sites affiliated with the ABCD consortium on a Siemens Prisma, Philips, or GE 750 3T scanner (27 (link)). Morphometric measurements of CT, SA, and subcortical VOL were calculated in FreeSurfer (https://surfer.nmr.mgh.harvard.edu/) using the Desikan parcellation atlas (28 (link)). As recommended in the ABCD NDA 2.0.1 Release Notes for Imaging Instruments, exclusionary criteria included subjects with MRI findings that were considered for clinical referral (n = 402), subjects with poor-quality T1 images (as described above; n = 11), and subjects for whom the FreeSurfer parcellation performed poorly (n = 391). See Supplement 1 for additional information.

Hernandez L.M., Kim M., Hernandez C., Thompson W., Fan C.C., Galván A., Dapretto M., Bookheimer S.Y., Fuligni A, & Gandal M.J. (2022). Decoupling Sleep and Brain Size in Childhood: An Investigation of Genetic Covariation in the Adolescent Brain Cognitive Development Study. Biological Psychiatry Global Open Science, 3(1), 139-148.

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Harmonized Structural MRI Data Collection

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Structural MRI data were obtained at five different sites across Europe using Siemens 3 T (Tim-Trio and Prisma) or Philips 3 T (Achieva) scanners. T1-weighted scans were acquired using magnetization-prepared rapid acquisition gradient-echo sequences. The acquisition parameters were harmonized across sites under the supervision of an MR physicist and each site underwent site qualification procedures before starting data collection, which included scanning phantoms and checking for hardware problems (further details are given in the Supplementary Materials). Image quality was assessed by a trained radiographer immediately after each scan and, if necessary, the scan was repeated.

Cornwell H., Toschi N., Hamilton-Giachritsis C., Staginnus M., Smaragdi A., Gonzalez-Madruga K., Mackes N., Rogers J., Martinelli A., Kohls G., Raschle N.M., Konrad K., Stadler C., Freitag C.M., De Brito S.A, & Fairchild G. (2024). Identifying cortical structure markers of resilience to adversity in young people using surface-based morphometry. Social Cognitive and Affective Neuroscience, 19(1), nsae006.

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Standardized Multi-Site MRI Acquisition

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Each site followed a site qualification procedure before starting data collection to ensure comparability of MRI data acquisition. Images were acquired using either a Siemens Trio (Frankfurt and Southampton), Siemens Prisma (Aachen and Basel) or Philips scanner (Birmingham) – all at 3 Tesla. Structural T1-weighted magnetization prepared rapid gradient echo (MPRAGE) images were acquired for each participant which included 192 slices, field of view 256 mm, voxel size 1 × 1 × 1 mm, repetition time 1900 ms, echo time 2.42 (Aachen and Basel), 2.74 (Frankfurt), 3.7 (Birmingham), or 4.1 ms (Southampton), flip angle 9 degrees.

Cubillo A., Tkalcec A., Oldenhof H., Unternaehrer E., Raschle N., Kohls G., Nauta-Jansen L., Hervas A., Fernandez-Rivas A., Konrad K., Popma A., Freitag C., de Brito S., Fairchild G, & Stadler C. (2023). Linking heart rate variability to psychological health and brain structure in adolescents with and without conduct disorder. Frontiers in Psychiatry, 14, 1101064.

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Resting-state fMRI acquisition protocol

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Imaging was performed at 21 sites in the United States, harmonized across Siemens Prisma, Philips and GE 3T scanners. Details on image acquisition can be found in ref.^{23 (link)}. Twenty minutes (4 × 5 min runs) of eyes-open resting-state blood oxygenation level dependent (BOLD) data were acquired to ensure at least 8 min of low-motion data. All resting-state scans fMRI scans used a gradient-echo echo planar imaging (EPI) sequence (repetition time = 800 ms, echo time = 30 ms, flip angle = 90°, voxel size = 2.4 mm³, 60 slices). Head motion was monitored using framewise integrated real-time MRI monitoring (FIRMM) software at many of the Siemens sites^{35 (link)}.

Marek S., Tervo-Clemmens B., Calabro F.J., Montez D.F., Kay B.P., Hatoum A.S., Donohue M.R., Foran W., Miller R.L., Hendrickson T.J., Malone S.M., Kandala S., Feczko E., Miranda-Dominguez O., Graham A.M., Earl E.A., Perrone A.J., Cordova M., Doyle O., Moore L.A., Conan G.M., Uriarte J., Snider K., Lynch B.J., Wilgenbusch J.C., Pengo T., Tam A., Chen J., Newbold D.J., Zheng A., Seider N.A., Van A.N., Metoki A., Chauvin R.J., Laumann T.O., Greene D.J., Petersen S.E., Garavan H., Thompson W.K., Nichols T.E., Yeo B.T., Barch D.M., Luna B., Fair D.A, & Dosenbach N.U. (2022). Reproducible brain-wide association studies require thousands of individuals. Nature, 603(7902), 654-660.

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ABCD Neuroimaging Protocol Harmonization

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Extensive details of the ABCD neuroimaging acquisition sequences and processing streams have been presented elsewhere (Casey et al. 2018 (link); Hagler et al. 2019 (link)). Our analyses focused on metrics extracted from the Desikan-Killiany (DK) and Destrieux Atlases implemented in Freesurfer, with evidence that identification of the features on which these atlases are based works well down to age 4 (Ghosh et al. 2010 (link)). The ABCD imaging protocol was harmonized across data collection sites for three 3T scanning systems (Siemens Prisma, Philips, General Electric 750), all of which used standard adult-size multi-channel head coils and multiband echo planar imaging (EPI) acquisitions. The diffusion MRI acquisition had high resolution (1.7 mm isotropic voxels) and utilized multiband EPI (Hagler et al. 2019 (link)). The scanning sequences that yield structural data (Casey et al. 2018 (link)) include a localizer, T-1 weighted scan, diffusion tensor imaging (DTI), and T-2 weighted scans. Real-time motion detection and correction during acquisition are implemented by customized hardware and software. Imaging parameters were harmonized as much as possible between scanner manufacturers.

Maes H.H., Lapato D.M., Schmitt J.E., Luciana M., Banich M.T., Bjork J.M., Hewitt J.K., Madden P.A., Heath A.C., Barch D.M., Thompson W.K., Iacono W.G, & Neale M.C. (2022). Genetic and Environmental Variation in Continuous Phenotypes in the ABCD Study®. Behavior Genetics, 53(1), 1-24.

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