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• W2775442832 abstract "•Openly shared, large non-human primate neuroimaging data resource•Multiple imaging modalities contributed from investigators around the world•Quality assessments of the dataset•Discussed pitfalls and challenges in analyzing the non-human primate MRI data Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience. Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMatE Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design, and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 25 independent data collections aggregated across 22 sites (total = 217 non-human primates). We also outline the unique pitfalls and challenges that should be considered in the analysis of non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets. Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience. Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMatE Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design, and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 25 independent data collections aggregated across 22 sites (total = 217 non-human primates). We also outline the unique pitfalls and challenges that should be considered in the analysis of non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets. Translational, comparative neuroscience research enables a bridging of knowledge gaps across species as well as invasive and noninvasive approaches. A growing body of research has documented the utility of magnetic resonance imaging (MRI) technologies to support in vivo examination of brain organization and function in non-human primates (Vanduffel et al., 2014Vanduffel W. Zhu Q. Orban G.A. Monkey cortex through fMRI glasses.Neuron. 2014; 83: 533-550Abstract Full Text Full Text PDF PubMed Scopus (82) Google Scholar, Rilling, 2014Rilling J.K. Comparative primate neuroimaging: insights into human brain evolution.Trends Cogn. Sci. 2014; 18: 46-55Abstract Full Text Full Text PDF PubMed Scopus (118) Google Scholar, Van Essen and Glasser, 2014Van Essen D.C. Glasser M.F. In vivo architectonics: a cortico-centric perspective.Neuroimage. 2014; 93: 157-164Crossref PubMed Scopus (44) Google Scholar, Zhang et al., 2013Zhang D. Guo L. Zhu D. Li K. Li L. Chen H. Zhao Q. Hu X. Liu T. Diffusion tensor imaging reveals evolution of primate brain architectures.Brain Struct. Funct. 2013; 218: 1429-1450Crossref PubMed Scopus (16) Google Scholar, Shmuel and Leopold, 2008Shmuel A. Leopold D.A. Neuronal correlates of spontaneous fluctuations in fMRI signals in monkey visual cortex: Implications for functional connectivity at rest.Hum. Brain Mapp. 2008; 29: 751-761Crossref PubMed Scopus (422) Google Scholar, Schwiedrzik et al., 2015Schwiedrzik C.M. Zarco W. Everling S. Freiwald W.A. Face patch resting state networks link face processing to social cognition.PLoS Biol. 2015; 13: e1002245Crossref PubMed Scopus (33) Google Scholar). Recent work has demonstrated the ability to recapitulate findings from gold-standard invasive methodologies (Ghahremani et al., 2017Ghahremani M. Hutchison R.M. Menon R.S. Everling S. Frontoparietal functional connectivity in the common marmoset.Cereb. Cortex. 2017; 27: 3890-3905PubMed Google Scholar, Donahue et al., 2016Donahue C.J. Sotiropoulos S.N. Jbabdi S. Hernandez-Fernandez M. Behrens T.E. Dyrby T.B. Coalson T. Kennedy H. Knoblauch K. Van Essen D.C. Glasser M.F. Using diffusion tractography to predict cortical connection strength and distance: a quantitative comparison with tracers in the monkey.J. Neurosci. 2016; 36: 6758-6770Crossref PubMed Scopus (206) Google Scholar, Grayson et al., 2016Grayson D.S. Bliss-Moreau E. Machado C.J. Bennett J. Shen K. Grant K.A. Fair D.A. Amaral D.G. The rhesus monkey connectome predicts disrupted functional networks resulting from pharmacogenetic inactivation of the amygdala.Neuron. 2016; 91: 453-466Abstract Full Text Full Text PDF PubMed Scopus (96) Google Scholar). This work also provides novel insights into the organizational principles of the non-human primate (NHP) connectome (Goulas et al., 2017Goulas A. Stiers P. Hutchison R.M. Everling S. Petrides M. Margulies D.S. Intrinsic functional architecture of the macaque dorsal and ventral lateral frontal cortex.J. Neurophysiol. 2017; 117: 1084-1099Crossref PubMed Scopus (18) Google Scholar, Hutchison and Everling, 2014Hutchison R.M. Everling S. Broad intrinsic functional connectivity boundaries of the macaque prefrontal cortex.Neuroimage. 2014; 88: 202-211Crossref PubMed Scopus (20) Google Scholar, Hutchison et al., 2011Hutchison R.M. Leung L.S. Mirsattari S.M. Gati J.S. Menon R.S. Everling S. Resting-state networks in the macaque at 7 T.Neuroimage. 2011; 56: 1546-1555Crossref PubMed Scopus (93) Google Scholar, Vincent et al., 2007Vincent J.L. Patel G.H. Fox M.D. Snyder A.Z. Baker J.T. Van Essen D.C. Zempel J.M. Snyder L.H. Corbetta M. Raichle M.E. Intrinsic functional architecture in the anaesthetized monkey brain.Nature. 2007; 447: 83-86Crossref PubMed Scopus (1394) Google Scholar) and cross-species comparative connectomics (Hutchison et al., 2012Hutchison R.M. Gallivan J.P. Culham J.C. Gati J.S. Menon R.S. Everling S. Functional connectivity of the frontal eye fields in humans and macaque monkeys investigated with resting-state fMRI.J. Neurophysiol. 2012; 107: 2463-2474Crossref PubMed Scopus (86) Google Scholar, Hutchison et al., 2015Hutchison R.M. Culham J.C. Flanagan J.R. Everling S. Gallivan J.P. Functional subdivisions of medial parieto-occipital cortex in humans and nonhuman primates using resting-state fMRI.Neuroimage. 2015; 116: 10-29Crossref PubMed Scopus (35) Google Scholar, Miranda-Dominguez et al., 2014Miranda-Dominguez O. Mills B.D. Grayson D. Woodall A. Grant K.A. Kroenke C.D. Fair D.A. Bridging the gap between the human and macaque connectome: a quantitative comparison of global interspecies structure-function relationships and network topology.J. Neurosci. 2014; 34: 5552-5563Crossref PubMed Scopus (100) Google Scholar, Mars et al., 2011Mars R.B. Jbabdi S. Sallet J. O’Reilly J.X. Croxson P.L. Olivier E. Noonan M.P. Bergmann C. Mitchell A.S. Baxter M.G. et al.Diffusion-weighted imaging tractography-based parcellation of the human parietal cortex and comparison with human and macaque resting-state functional connectivity.J. Neurosci. 2011; 31: 4087-4100Crossref PubMed Scopus (376) Google Scholar, Seidlitz et al., 2018aSeidlitz J. Váša F. Shinn M. Romero-Garcia R. Whitaker K.J. Vértes P.E. Wagstyl K. Kirkpatrick Reardon P. Clasen L. Liu S. et al.NSPN ConsortiumMorphometric similarity networks detect microscale cortical organization and predict inter-individual cognitive variation.Neuron. 2018; 97: 231-247.e7Abstract Full Text Full Text PDF PubMed Scopus (163) Google Scholar), which are possible only through in vivo studies. These advances are timely given the growing prominence of large-scale national and international initiatives focused on advancing our understanding of human brain organization and the ability to generate novel therapeutics for neurology and psychiatry (Bargmann and Newsome, 2014Bargmann C.I. Newsome W.T. The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) initiative and neurology.JAMA Neurol. 2014; 71: 675-676Crossref PubMed Scopus (43) Google Scholar). Despite the clear demonstrations of feasibility and utility, the field of non-human primate neuroimaging is still developing. Numerous unique challenges related to the acquisition and processing of non-human primate data are still being addressed (e.g., Seidlitz et al., 2018bSeidlitz J. Sponheim C. Glen D. Ye F.Q. Saleem K.S. Leopold D.A. Ungerleider L. Messinger A. A population MRI brain template and analysis tools for the macaque.Neuroimage. 2018; 170: 121-131Crossref PubMed Scopus (89) Google Scholar, Hutchison and Everling, 2012Hutchison R.M. Everling S. Monkey in the middle: why non-human primates are needed to bridge the gap in resting-state investigations.Front. Neuroanat. 2012; (Published online July 26, 2012)https://doi.org/10.3389/fnana.2012.00029Crossref PubMed Scopus (96) Google Scholar), and the potential for broad reaching cross-species studies remains unexploited. Perhaps most challenging is the limited availability of data. Here, we introduce the PRIMatE Data Exchange (PRIME-DE) to create an open science resource for the neuroimaging community that will facilitate the mapping of the non-human primate connectome. To accomplish this, we aggregate a combination of anatomical, functional, and diffusion MRI datasets from laboratories throughout the world and make these data available to the scientific community. It merits emphasis that PRIME-DE supports an ongoing process that will remain open to new contributions of data from macaques and other non-human primate species. At present, PRIME-DE contains 25 collections aggregated across 22 sites; to date, data from 217 primates are included (see Table 1 for information on each institution). Contributions will continue to be accepted and shared on a rolling basis.Table 1Experimental DesignInvestigatorsSpeciesaDetailed species information is available on the PRIME-DE site and in Navarrete et al., 2018SubjectsStateContrast AgentStructural T1Structural T2Resting State fMRINaturalistic Viewing fMRITask fMRIField mapDiffusion MRIAMUBelin, Brochier, SeinMM4AnesthetizedNo✔✔––––✔CaltechRajimehr, TsaoMM2AwakeYes–––96 min–––ECNU (C)Aihua ChenMM10AnesthetizedNo✔––––––ECNU (K)bECNU (K) provided magnetic resonance spectroscopyKwok, ZhouMM4AnesthetizedNo✔✔8 min–––✔Institute of Neuroscience (IoN)WangMM, MF8AnesthetizedNo✔–20–40 min––✔–Institut des Sciences Cognitives Marc JeannerodBen Hamed, HibaMM8Anesthetized/AwakeYes✔–✔–✔–✔Lyon Neuroscience Research CenterHadj-Bouziane, Meunier, GuedjMM1Anesthetized/AwakeYes/No✔✔13 min––––McGill UniversityMok, Rudko, ShmuelMM, MF3AnesthetizedNo✔✔–––––Mount Sinai (P)Croxson, FleysherMM, MF9AnesthetizedNo✔✔43 min––✔✔Mount Sinai (S)Croxson, Fleysher, Froudist-Walsh, Damatac, NagyMM5AnesthetizedNo✔✔––––✔NKISchroeder, MilhamMM2Anesthetized/AwakeYes/No✔76–155 min55–345 min–––NIMH (L)cThe usage agreement is DUA for those sites, CC-BY-NC-SA for all other sitesLeopold, RussMM3AwakeYes✔✔30–150 min170 min–––NIMH (M)cThe usage agreement is DUA for those sites, CC-BY-NC-SA for all other sites,dNIMH (M) provided cortical thickness and brain templateMessinger, Jung, Seidlitz, UngerleiderMM3Anesthetized/AwakeYes✔–10−15 min––––Netherlands Institute for Neuroscience (NIN)Klink, RoelfsemaMM2AnesthetizedNo✔✔9.7 min––––NeuroSpinJarraya, DehaeneMM3AnesthetizedYes/No✔–✔––––NewcastlePetkov, Nacef, Thiele, Poirier, Balezeau, Griffiths, Schmid, RiosMM14Anesthetized/AwakeNo✔✔21.6 min––––OHSUSullivan, FairMM2AnesthetizedYes/No✔✔480 min––––PrincetonKastner, PinskMM2Anesthetized✔✔–––✔✔RockefellerSchwiedrzik, Freiwald, ZarcoMM, MF6AnesthetizedYes✔–80 min––✔SBRIProcyk, Wilson, AmiezMM, MF22AnesthetizedNo✔✔✔––––UC DavisBaxter, Croxson, MorrisonMM19AnesthetizedNo✔✔13.5 min––✔✔Univ. of Minnesota (UMN)Yacoub, HarelM2Anesthetized–✔–27 min––✔✔Univ. of OxfordcThe usage agreement is DUA for those sites, CC-BY-NC-SA for all other sitesSallet, Mars, RushworthMM20AnesthetizedNo✔–53.43 min––––NIN Primate Brain Bank/Utrecht UniversityNavarrete, Blezer, Todorov, Lindenfors, Laland, ReaderMultiplea51Post-mortemYes/No✔––––––Univ. of Western Ontario (UWO)Everling, MenonMM12AnesthetizedNo✔✔60 min––––General information about PRIME-DE data collections contributed prior to the time of publication. For usage agreement, CC-BY-NC-SA: Creative Commons – Attribution-NonCommercial Share Alike, Standard INDI data sharing policy, prohibits use of the data for commercial purposes; DUA: Data Usage Agreement, users must complete a DUA prior to gaining access to the data. For species information, MM: Macaca mulatta; MF: Macaca fascicularis; M: Macaca.a Detailed species information is available on the PRIME-DE site and in Navarrete et al., 2018Navarrete A.F. Blezer E.L.A. Pagnotta M. de Viet E.S.M. Todorov O.S. Lindenfors P. Laland K.N. Reader S.M. Primate brain anatomy: New volumetric MRI measurements for neuroanatomical studies.Brain, Behavior and Evolution. 2018; 91: 1-9Crossref PubMed Scopus (15) Google Scholarb ECNU (K) provided magnetic resonance spectroscopyc The usage agreement is DUA for those sites, CC-BY-NC-SA for all other sitesd NIMH (M) provided cortical thickness and brain template Open table in a new tab General information about PRIME-DE data collections contributed prior to the time of publication. For usage agreement, CC-BY-NC-SA: Creative Commons – Attribution-NonCommercial Share Alike, Standard INDI data sharing policy, prohibits use of the data for commercial purposes; DUA: Data Usage Agreement, users must complete a DUA prior to gaining access to the data. For species information, MM: Macaca mulatta; MF: Macaca fascicularis; M: Macaca. To promote usage of a standardized data format, we organized all data using the Brain Imaging Data Structure (BIDS) format (Gorgolewski et al., 2017Gorgolewski K.J. Alfaro-Almagro F. Auer T. Bellec P. Capotă M. Chakravarty M.M. Churchill N.W. Cohen A.L. Craddock R.C. Devenyi G.A. et al.BIDS apps: improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods.PLoS Comput. Biol. 2017; 13: e1005209Crossref PubMed Scopus (135) Google Scholar). All PRIME-DE datasets can be accessed through the PRIME-DE site (http://fcon_1000.projects.nitrc.org/indi/indiPRIME.html). Prior to downloading the data, users are required to establish a user account on NITRC and register with the International Neuroimaging Data-sharing Initiative (INDI; anticipated time: <1 min). With one exception, for each of the PRIME-DE collections, at least one structural MRI (sMRI) is available for each unique ID number (see Table 1). Eighteen of the collections contain at least one corresponding resting-state functional MRI (R-fMRI) dataset, and three of the collections contain naturalistic viewing fMRI (NV-fMRI). In addition, one collection from the National Institutes of Mental Health (NIMH (M)) also provided cortical thickness data and R-fMRI data aligned to an anatomical template. Corresponding diffusion MRI (dMRI) datasets are available for nine collections. Field map images for fMRI correction are available for six collections. Consistent with its popularity in the imaging community and prior usage in INDI efforts, the NIFTI file format was selected for storage of the PRIME-DE MRI datasets. Table 2 lists the specific MRI scanners and head coils utilized for each collection. Specific MRI sequence parameters for the various data collections are summarized in Tables S1, S2, S3, and S4 and detailed on the PRIME-DE website. Across collections, R-fMRI acquisition durations varied from 8 to 155 min per subject. In two collections, subjects were in an awake state. In five collections, subjects were scanned both awake and under anesthesia. One collection scanned 51 post-mortem specimens. In the remaining 17 collections, subjects were scanned under anesthesia. For the three collections with NV-FMRI, acquisition durations varied from 55 to 375 min. See Figures 3 and 4 for example structural and functional images from the different sites aligned in a common space.Table 2Scanner InformationSiteManufacturerModelField Strength (T)Head coil # channelsAMUSiemensPrisma3Body transmit array, 11 cm loop receiving coilCaltechSiemensTim Trio38ECNU (C)SiemensTim Trio3–ECNU (K)SiemensTim Trio31-channel surface coilInstitute of Neuroscience (IoN)SiemensTim Trio38-channel phased-array transceiver coilsInstitut des Sciences Cognitives Marc JeannerodSiemensSonata/Prisma1.5/38-channel custom head coils/association of independent circular coilsLyon Neuroscience Research CenterSiemensSonata/Prisma1.5/3Custom-made 10 cm loop receiving coil 2 × L11 and 1 × L7 Siemens loop-receiving coilMcGill UniversitySiemensTim Trio3Custom-made 8-channel phased-array receive coilMount Sinai (P)PhilipsAchieva3Single loop receive coil (T1 and T2) 4-channel phased-array receive, transmit through body coil (resting state and diffusion)Mount Sinai (S)SiemensSkyra38-channel phased-array receive with a single loop transmitNKISiemensTim Trio3Custom-made 8-channel phased-array receive coil (KU Leuven) with a custom 16-channel pre-amplifier (MRcoils)NIMH (L)BrukerBiospecVertical4.78NIMH (M)BrukerBiospecVertical4.71–4Netherlands Institute for Neuroscience (NIN)PhilipsIngenia3Custom-made 8-channel phased-array receive coil (KU Leuven) with a custom 16-channel pre-amplifier (MRcoils).NeuroSpinSiemensTim Trio/PrismaFit31chTxRxcoil/1Tx-8RxchcoilNewcastleBrukerVertical Bruker4.74–8OHSUSiemensTim Trio3Knee coil 15 channelPrincetonSiemensPrisma VE11C3Siemens Loop Coil, Large (11 cm)RockefellerSiemensTIM Trio + AC88 gradient38-channel phased-array receive with a single-loop transmitSBRISiemensSonata/Prisma1.5/3Custom made 10 cm loop receiving coil 2 × L11 and 1 × L7 Siemens loop receiving coilUC DavisSiemensSkyra34Univ. of Minnesota (UMN)SiemensSyngoB17716-channel transmit/receive + 6 receive onlyUniv. of Oxford––3A four-channel phased-array coilNIN Primate Brain Bank/Utrecht UniversityVarian/SiemensSmall-bore scanner/Magnetom trio9.4/3–Univ. of Western Ontario (UWO)SiemensMagnetom7Custom-made 24-channel phased-array receive coil with an 8-channel transmit coilInformation on scanner and head coil for PRIME-DE data collections contributed prior to the time of publication. Note that scanner information from University of Oxford is not reported due to an agreement made previously with the scanner manufacturer. For scan sequences, see also Tables S1, S2, S3, and S4. Open table in a new tab Information on scanner and head coil for PRIME-DE data collections contributed prior to the time of publication. Note that scanner information from University of Oxford is not reported due to an agreement made previously with the scanner manufacturer. For scan sequences, see also Tables S1, S2, S3, and S4. Contributors to PRIME-DE will be able to set the sharing policy for their data in accord with their preferences and institutional requirements. For each sample, the contributor will set the sharing permissions for their data using one or more the following three policies:(1)Creative Commons – Attribution-Non-Commercial Share Alike (CC-BY-NC-SA) (https://creativecommons.org/licenses/by-nc-sa/4.0/). Standard INDI data sharing policy. Prohibits use of the data for commercial purposes.(2)Creative Commons – Attribution (CC-BY) (https://creativecommons.org/licenses/by/4.0/). Least restrictive data sharing policy.(3)Custom Data Usage Agreement. Users must complete a data usage agreement (DUA) prior to gaining access to the data. Contributors can customize the agreement as they see fit, including determining whether or not signatures from authorized institutional official are required prior to executing the DUA. Note: this option was created to facilitate potential contributors whose institution requires completion of a formal interinstitutional agreement in order to share non-human primate data. Of note, one lesson learned from the human neuroimaging literature is that such agreements are not dissuasive, as is evidenced by the success of the Human Connectome Project (Van Essen et al., 2013Van Essen D.C. Smith S.M. Barch D.M. Behrens T.E.J. Yacoub E. Ugurbil K. WU-Minn HCP ConsortiumThe WU-Minn Human Connectome Project: an overview.Neuroimage. 2013; 80: 62-79Crossref PubMed Scopus (2672) Google Scholar) and the NKI-Rockland Sample (Nooner et al., 2012Nooner K.B. Colcombe S.J. Tobe R.H. Mennes M. Benedict M.M. Moreno A.L. Panek L.J. Brown S. Zavitz S.T. Li Q. et al.The NKI-Rockland sample: a model for accelerating the pace of discovery science in psychiatry.Front. Neurosci. 2012; 6: 152Crossref PubMed Scopus (460) Google Scholar). Consistent with the established policy of INDI, all data contributed to PRIME-DE was made available to users regardless of data quality; users should check data quality before inclusion in their analyses. The rationale of this decision has been the lack of consensus on optimal quality criteria in regards to specific measures or their combinations and cutoffs—a reality that is even more pronounced in non-human primate imaging given the variation in data quality and characteristics across scan protocols. Of note, a benefit of sharing data with differing levels of quality data is also important for those working to develop methods for evaluating, and at times overcoming, such variations. Following the tradition of recent INDI data-sharing consortia, a collection of automated, reference-free quality assurance measures, known as the Preprocessed Connectome Project Quality Assurance Protocol (PCP-QAP; Shehzad et al., 2015Shehzad Z. Giavasis S. Li Q. Benhajali Y. Yan C. Yang Z. Milham M. Bellec P. Craddock C. The Preprocessed Connectomes Project Quality Assessment Protocol - a resource for measuring the quality of MRI data.Front. Neurosci. 2015; (Published online August 5, 2015)https://doi.org/10.3389/conf.fnins.2015.91.00047/event_abstractCrossref Google Scholar), is being made available with the PRIME-DE datasets. These measures focus on structural and temporal (when appropriate) aspects of the datasets. Table 3 provides a brief description of the measures included, and Figures 1 and 2 depict a subset of QAP results (Magnotta et al., 2006Magnotta V.A. Friedman L. FIRST BIRNMeasurement of signal-to-noise and contrast-to-noise in the fBIRN multicenter imaging study.J. Digit. Imaging. 2006; 19: 140-147Crossref PubMed Scopus (105) Google Scholar, Mortamet et al., 2009Mortamet B. Bernstein M.A. Jack Jr., C.R. Gunter J.L. Ward C. Britson P.J. Meuli R. Thiran J.-P. Krueger G. Alzheimer’s Disease Neuroimaging InitiativeAutomatic quality assessment in structural brain magnetic resonance imaging.Magn. Reson. Med. 2009; 62: 365-372Crossref PubMed Scopus (107) Google Scholar, Giannelli et al., 2010Giannelli M. Diciotti S. Tessa C. Mascalchi M. Characterization of Nyquist ghost in EPI-fMRI acquisition sequences implemented on two clinical 1.5 T MR scanner systems: effect of readout bandwidth and echo spacing.J. Appl. Clin. Med. Phys. 2010; 11: 3237Crossref PubMed Scopus (20) Google Scholar, Jenkinson et al., 2002Jenkinson M. Bannister P. Brady M. Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images.Neuroimage. 2002; 17: 825-841Crossref PubMed Google Scholar, Friedman et al., 2006Friedman L. Glover G.H. Krenz D. Magnotta V. FIRST BIRNReducing inter-scanner variability of activation in a multicenter fMRI study: role of smoothness equalization.Neuroimage. 2006; 32: 1656-1668Crossref PubMed Scopus (123) Google Scholar, Nichols, 2012Nichols T.E. Standardizing DVARS, 28/10/12. Neuroimaging Statistics Tips & Tools, 2012Google Scholar). As would be expected, measures of head motion are notably smaller for sites using anesthetized scan sessions than for awake (NIMH (L), NIMH (M), NKI, Newcastle, Lyon Neuroscience Research Center). Importantly, the measures provided are not intended to be definitive for the field or all encompassing; rather, they are included to spur interest in the potential utility and further development of automated measures.Table 3Description of PCP QAP MeasuresSpatial MetricsDescriptionReferencesContrast-to-noise ratio (CNR) (sMRI only)MGM intensity—MWM intensity/SDair intensity. Larger values reflect a better distinction between WM and GM.Magnotta et al., 2006Magnotta V.A. Friedman L. FIRST BIRNMeasurement of signal-to-noise and contrast-to-noise in the fBIRN multicenter imaging study.J. Digit. Imaging. 2006; 19: 140-147Crossref PubMed Scopus (105) Google ScholarArtifactual voxel detection (Qi1) (sMRI only)Voxels with intensity corrupted by artifacts/voxels in the background. Larger values reflect more artifacts which likely due to motion or image instability.Mortamet et al., 2009Mortamet B. Bernstein M.A. Jack Jr., C.R. Gunter J.L. Ward C. Britson P.J. Meuli R. Thiran J.-P. Krueger G. Alzheimer’s Disease Neuroimaging InitiativeAutomatic quality assessment in structural brain magnetic resonance imaging.Magn. Reson. Med. 2009; 62: 365-372Crossref PubMed Scopus (107) Google ScholarSmoothness of Voxels (FWHM)aFor R-fMRI data, these metrics are computed on mean functional dataFull width at half maximum of the spatial distribution of the image intensity values. Larger values reflect more spatial smoothing perhaps due to motion or technical differences.Friedman et al., 2006Friedman L. Glover G.H. Krenz D. Magnotta V. FIRST BIRNReducing inter-scanner variability of activation in a multicenter fMRI study: role of smoothness equalization.Neuroimage. 2006; 32: 1656-1668Crossref PubMed Scopus (123) Google ScholarSignal-to-noise ratio (SNR)MGM intensity/SDair intensity. Larger values reflect less noise.Magnotta et al., 2006Magnotta V.A. Friedman L. FIRST BIRNMeasurement of signal-to-noise and contrast-to-noise in the fBIRN multicenter imaging study.J. Digit. Imaging. 2006; 19: 140-147Crossref PubMed Scopus (105) Google ScholarTemporal Metrics (fMRI and DTI only)DescriptionReferencesGhost-to-Signal Ratio (GSR)aFor R-fMRI data, these metrics are computed on mean functional dataM signal in the “ghost” image divided by the M signal within the brain. Larger values reflect more ghosting likely due to physiological noise, motion, or technical issues.Giannelli et al., 2010Giannelli M. Diciotti S. Tessa C. Mascalchi M. Characterization of Nyquist ghost in EPI-fMRI acquisition sequences implemented on two clinical 1.5 T MR scanner systems: effect of readout bandwidth and echo spacing.J. Appl. Clin. Med. Phys. 2010; 11: 3237Crossref PubMed Scopus (20) Google ScholarMean frame-wise displacement- Jenkinson (meanFD)bFor R-fMRI, these metrics are computed on time series data. M, mean; GM, gray matter; WM, white matter; SD, standard deviationSum absolute displacement changes in the x, y, and z directions and rotational changes around them. Rotational changes are given distance values based on changes across the surface of a 50 mm radius sphere. Larger values reflect more movement.Jenkinson et al., 2002Jenkinson M. Bannister P. Brady M. Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images.Neuroimage. 2002; 17: 825-841Crossref PubMed Google ScholarStandardized DVARSbFor R-fMRI, these metrics are computed on time series data. M, mean; GM, gray matter; WM, white matter; SD, standard deviationSpatial SD of the data temporal derivative normalized by the temporal SD and autocorrelation. Larger values reflect larger frame-to-frame differences in signal intensity due to head motion or scanner instability.Nichols, 2012Nichols T.E. Standardizing DVARS, 28/10/12. Neuroimaging Statistics Tips & Tools, 2012Google ScholarGlobal Correlation (GCORR)bFor R-fMRI, these metrics are computed on time series data. M, mean; GM, gray matter; WM, white matter; SD, standard deviationM correlation of all combinations of voxels in a time series. Illustrates differences between data due to motion/physiological noise. Larger values reflect a greater degree of spatial correlation between slices, which may be due to head motion or “signal leakage” in simultaneous multi-slice acquisitions.–Here, we provide a brief description of the Preprocessed Connectome Project Quality Assessment Protocol. These measures have been computed for all structural MRI (sMRI) and resting-state functional MRI (R-fMRI) datasets in PRIME-DE. The table was adopted from Di Martino et al., 2017Di Martino A. O’Connor D. Chen B. Alaerts K. Anderson J.S. Assaf M. Balsters J.H. Baxter L. Beggiato A. Bernaerts S. et al.Enhancing studies of the connectome in autism using the autism brain imaging data exchange II.Sci. Data. 2017; (Published online March 14, 2017)https://doi.org/10.1038/sdata.2017.10Crossref PubMed Scopus (265) Google Scholar.a For R-fMRI data, these metrics are computed on mean functional datab For R-fMRI, these metrics are computed on time series data. M, mean; GM, gray matter; WM, white matter; SD, standard deviation Open table in a new tab Figure 2Spatial and Temporal Quality Metrics for Functional MRI DatasetsShow full captionSpatial quality metrics include: ghost-to-single ratio (GSR), smoothness of voxels indexed as full width at half maximum (FWHM), and signal-to-noise ratio (SNR). Temporal metrics are mean frame-wise displacement (Mean FD), standardized DVARS, global correlation (GCORR), and temporal signal-to-noise ratio (tSNR). See Table 3 for details on this and the other quality metrics released. The colored scatt" @default.
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