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A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol

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Abstract

Large-scale consortium efforts such as Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and other collaborative efforts show that combining statistical data from multiple independent studies can boost statistical power and achieve more accurate estimates of effect sizes, contributing to more reliable and reproducible research. A meta- analysis would pool effects from studies conducted in a similar manner, yet to date, no such harmonized protocol exists for resting state fMRI (rsfMRI) data. Here, we propose an initial pipeline for multi-site rsfMRI analysis to allow research groups around the world to analyze scans in a harmonized way, and to perform coordinated statistical tests. The challenge lies in the fact that resting state fMRI measurements collected by researchers over the last decade vary widely, with variable quality and differing spatial or temporal signal-to-noise ratio (tSNR). An effective harmonization must provide optimal measures for all quality data. Here we used rsfMRI data from twenty-two independent studies with approximately fifty corresponding T1-weighted and rsfMRI datasets each, to (A) review and aggregate the state of existing rsfMRI data, (B) demonstrate utility of principal component analysis (PCA)-based denoising and (C) develop a deformable ENIGMA EPI template based on the representative anatomy that incorporates spatial distortion patterns from various protocols and populations.

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References

  • Adams, H. H., Hibar, D. P., Chouraki, V., Stein, J. L., Nyquist, P. A., Renteria, M. E., et al. (2016). Novel genetic loci underlying human intracranial volume identified through genome-wide association. Nature Neuroscience, 19(12), 1569–1582. https://doi.org/10.1038/nn.4398.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Avants, B. B., Epstein, C. L., Grossman, M., & Gee, J. C. (2008). Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12(1), 26–41.

    Article  CAS  PubMed  Google Scholar 

  • Avants, B. B., Yushkevich, P., Pluta, J., Minkoff, D., Korczykowski, M., Detre, J., & Gee, J. C. (2010). The optimal template effect in hippocampus studies of diseased populations. Neuroimage, 49(3), 2457–2466.

    Article  PubMed  Google Scholar 

  • Avants, B. B., Johnson, H. J., & Tustison, N. J. (2015). Neuroinformatics and the the insight ToolKit. Frontiers in Neuroinformatics, 9, 5. https://doi.org/10.3389/fninf.2015.00005.

    Article  PubMed  PubMed Central  Google Scholar 

  • Bajaj, S., Butler, A. J., Drake, D., & Dhamala, M. (2015a). Brain effective connectivity during motor-imagery and execution following stroke and rehabilitation. Neuroimage Clinical, 8, 572–582. https://doi.org/10.1016/j.nicl.2015.06.006.

    Article  PubMed  PubMed Central  Google Scholar 

  • Bajaj, S., Butler, A. J., Drake, D., & Dhamala, M. (2015b). Functional organization and restoration of the brain motor-execution network after stroke and rehabilitation. Frontiers in Human Neuroscience, 9, 173. https://doi.org/10.3389/fnhum.2015.00173.

    Article  PubMed  PubMed Central  Google Scholar 

  • Bajaj, S., Adhikari, B. M., Friston, K. J., & Dhamala, M. (2016). Bridging the gap: dynamic causal modeling and granger causality analysis of resting state functional magnetic resonance imaging. Brain Connectivity. https://doi.org/10.1089/brain.2016.0422.

  • Baloch, S., & Davatzikos, C. (2009). Morphological appearance manifolds in computational anatomy: groupwise registration and morphological analysis. Neuroimage, 45(S1), S73–S85.

    Article  PubMed  Google Scholar 

  • Bearden, C. E., van Erp, T. G. M., Dutton, R. A., Tran, H., Zimmermann, L., Sun, D., et al. (2007). Mapping cortical thickness in children with 22q11.2 deletions. Cerebral Cortex, 17, 1889–1898.

    Article  PubMed  Google Scholar 

  • Beckmann, C. F., & Smith, S. M. (2004). Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Transactions on Medical Imaging, 23(2), 137–152.

    Article  PubMed  Google Scholar 

  • Beg, M. F., & Khan, A. (2007). Symmetric data attachment terms for large deformation image registration. IEEE Transactions on Medical Imaging, 26, 1179–1189.

    Article  PubMed  Google Scholar 

  • Bis, J. C., DeCarli, C., Smith, A. V., van der Lijn, F., Crivello, F., Fornage, M., Debette, S., Shulman, J. M., Schmidt, H., Srikanth, V., Schuur, M., Yu, L., Choi, S. H., Sigurdsson, S., Verhaaren, B. F., DeStefano, A., Lambert, J. C., Jack CR Jr, Struchalin, M., Stankovich, J., Ibrahim-Verbaas, C. A., Fleischman, D., Zijdenbos, A., den Heijer, T., Mazoyer, B., Coker, L. H., Enzinger, C., Danoy, P., Amin, N., Arfanakis, K., van Buchem, M., de Bruijn, R. F., Beiser, A., Dufouil, C., Huang, J., Cavalieri, M., Thomson, R., Niessen, W. J., Chibnik, L. B., Gislason, G. K., Hofman, A., Pikula, A., Amouyel, P., Freeman, K. B., Phan, T. G., Oostra, B. A., Stein, J. L., Medland, S. E., Vasquez, A. A., Hibar, D. P., Wright, M. J., Franke, B., Martin, N. G., Thompson, P. M., Enhancing Neuro Imaging Genetics through Meta-Analysis Consortium, Nalls, M. A., Uitterlinden, A. G., Au, R., Elbaz, A., Beare, R. J., van Swieten, J., Lopez, O. L., Harris, T. B., Chouraki, V., Breteler, M. M., de Jager, P. L., Becker, J. T., Vernooij, M. W., Knopman, D., Fazekas, F., Wolf, P. A., van der Lugt, A., Gudnason, V., Longstreth WT Jr, Brown, M. A., Bennett, D. A., van Duijn, C., Mosley, T. H., Schmidt, R., Tzourio, C., Launer, L. J., Ikram, M. A., Seshadri, S., & Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium. (2012). Common variants at 12q14 and 12q24 are associated with hippocampal volume. Nature Genetics, 44(5), 545–551. https://doi.org/10.1038/ng.2237.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Biswal, B., Yetkin, F. Z., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine, 34, 537–541.

    Article  CAS  Google Scholar 

  • Bodurka, J., Ye, F., Petridou, N., Murphy, K., & Bandettini, P. A. (2007). Mapping the MRI voxel volume in which thermal noise matches physiological noise-implications for fMRI. Neuroimage, 34(2), 542–549.

    Article  CAS  PubMed  Google Scholar 

  • Boedhoe, P. S., Schmaal, L., Abe, Y., Ameis, S. H., Arnold, P. D., Batistuzzo, M. C., et al. (2016). Distinct subcortical volume alterations in pediatric and adult OCD: a worldwide meta- and mega-analysis. American Journal of Psychiatry. https://doi.org/10.1176/appi.ajp.2016.16020201.

  • Brooks, J. C., Faull, O. K., Pattinson, K. T., & Jenkinson, M. (2013). Physiological noise in brainstem FMRI. Frontiers in Human Neuroscience, 7, 623. https://doi.org/10.3389/fnhum.2013.00623.

    Article  PubMed  PubMed Central  Google Scholar 

  • Bustin, S. A. (2014). The reproducibility of biomedical research: sleepers awake! Biomolecular Detection and Quantification, 2, 35–42. https://doi.org/10.1016/j.bdq.2015.01.002.

    Article  PubMed  Google Scholar 

  • Button, K. S., Ioannidis, J. P., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S., et al. (2013). Power failure: why small sample size undermines the reliability of neuroscience. Nature Reviews. Neuroscience, 14(5), 365–376. https://doi.org/10.1038/nrn3475.

    Article  CAS  PubMed  Google Scholar 

  • Calhoun, V. D., & Adali, T. (2012). Multisubject independent component analysis of fMRI: a decade of intrinsic networks, default mode, and neurodiagnostic discovery. IEEE Reviews in Biomedical Engineering, 5, 60–73.

    Article  PubMed  PubMed Central  Google Scholar 

  • Calhoun, V. D., Wager, T. D., Krishnan, A., Rosch, K. S., Seymour, K. E., Nebel, M. B., Mostofsky, S. H., Nyalakanai, P., & Kiehl, K. (2017). Normalization of fMRI data using T1 versus EPI. Human Brain Mapping, 38(11), 5331–5342.

    Article  PubMed  PubMed Central  Google Scholar 

  • Çetin, M. S., Christensen, F., Abbott, C. C., Stephen, J. M., Mayer, A. R., Cañive, J. M., et al. (2014). Thalamus and posterior temporal lobe show greater inter-network connectivity at rest and across sensory paradigms in schizophrenia. Neuroimage, 15(97), 117–126.

    Article  Google Scholar 

  • Chen, M., Lu, W., Chen, Q., Ruchala, K. J., & Olivera, G. H. (2008). A simple fixed-point approach to invert a deformation field. Medical Physics, 35, 81–88.

    Article  CAS  PubMed  Google Scholar 

  • Cheung, M. R., & Krishnan, K. (2009). Interactive deformation registration of endorectal prostate mri using itk thin plate splines. Academic Radiology, 16, 351–357.

    Article  PubMed  Google Scholar 

  • Choudhury, S., Fishman, J. R., McGowan, M. L., & Juengst, E. T. (2014). Big data, open science and the brain: lessons learned from genomics. Frontiers in Human Neuroscience, 8, 239. https://doi.org/10.3389/fnhum.2014.00239.

    Article  PubMed  PubMed Central  Google Scholar 

  • Collins, D., Holmes, C., Peters, T., & Evans, A. (1995). Automatic 3-D model-based neuroanatomical segmentation. Human Brain Mapping, 3(3), 190–208.

    Article  Google Scholar 

  • Cusack, R., Brett, M., & Osswald, K. (2003). An evaluation of the use of magnetic field maps to undistort echo-planar images. Neuroimage, 18, 127–142.

    Article  PubMed  Google Scholar 

  • Du, Y., Allen, E. A., He, H., Sui, J., Wu, L., & Calhoun, V. D. (2016). Artifact removal in the context of group ICA: a comparison of single-subject and group approaches. Human Brain Mapping, 37(3), 1005–1025. https://doi.org/10.1002/hbm.23086.

    Article  PubMed  Google Scholar 

  • Fedorov, A., Li, X., Pohl, K. M., Bouix, S., Styner, M., Addicott, M., Wyatt, C., Daunais, J. B., Wells, W. M., & Kikinis, R. (2011). Atlas-guided segmentation of vervet monkey brain MRI. Open Neuroimaging Journal, 5, 186–197.

    Article  PubMed  Google Scholar 

  • Fox, M. D., & Raichle, M. E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews. Neuroscience, 8(9), 700–711. https://doi.org/10.1038/nrn2201.

    Article  CAS  PubMed  Google Scholar 

  • Geng, X., Christensen, G. E., Gu, H., Ross, T. J., & Yang, Y. (2009). Implicit reference-based group-wise image registration and its application to structural and functional MRI. Neuroimage, 47(4), 1341–1351.

    Article  PubMed  PubMed Central  Google Scholar 

  • Gonzalez-Castillo, J., Handwerker, D. A., Robinson, M. E., Hoy, C. W., Buchanan, L. C., Saad, Z. S., & Bandettini, P. A. (2014). The spatial structure of resting state connectivity stability on the scale of minutes. Frontiers in Neuroscience, 8, 138. https://doi.org/10.3389/fnins.2014.00138.

    Article  PubMed  PubMed Central  Google Scholar 

  • Greve, D. N., Mueller, B. A., Liu, T., Turner, J. A., Voyvodic, J., Yetter, E., Diaz, M., McCarthy, G., Wallace, S., Roach, B. J., Ford, J. M., Mathalon, D. H., Calhoun, V. D., Wible, C. G., Brown, G. G., Potkin, S. G., & Glover, G. (2011). A novel method for quantifying scanner instability in fMRI. Magnetic Resonance in Medicine, 65(4), 1053–1061. https://doi.org/10.1002/mrm.22691.

    Article  PubMed  Google Scholar 

  • Guadalupe, T., Mathias, S. R., vanErp, T. G., Whelan, C. D., Zwiers, M. P., Abe, Y., et al. (2017). Human subcortical brain asymmetries in 15,847 people worldwide reveal effects of age and sex. Brain Imaging and Behavior, 11(5), 1497–1514. https://doi.org/10.1007/s11682-016-9629-z.

    Article  PubMed  Google Scholar 

  • Gusnard, D. A., & Raichle, M. E. (2001). Searching for a baseline: functional imaging and the resting human brain. Nature Reviews. Neuroscience, 2(10), 685–694.

    Article  CAS  PubMed  Google Scholar 

  • Hayasaka, S. (2013). Functional connectivity networks with and without global signal correction. Frontiers in Human Neuroscience, 7, 880. https://doi.org/10.3389/fnhum.2013.00880.

    Article  PubMed  PubMed Central  Google Scholar 

  • Heiland, S. (2008). From A as in aliasing to Z as in zipper: artifacts in MRI. Clinical Neuroradiology, 18, 25–36.

    Article  Google Scholar 

  • Hibar, D. P., Stein, J. L., Renteria, M. E., Arias-Vasquez, A., Desrivieres, S., Jahanshad, N., et al. (2015). Common genetic variants influence human subcortical brain structures. Nature, 520(7546), 224–229. https://doi.org/10.1038/nature14101.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hibar, D. P., Westlye, L. T., van Erp, T. G., Rasmussen, J., Leonardo, C. D., Faskowitz, J., et al. (2016). Subcortical volumetric abnormalities in bipolar disorder. Molecular Psychiatry, 21(12), 1710–1716. https://doi.org/10.1038/mp.2015.227.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hibar, D. P., Adams, H. H., Jahanshad, N., Chauhan, G., Stein, J. L., Hofer, E., et al. (2017). Novel genetic loci associated with hippocampal volume. Nature Communications, 8, 13624. https://doi.org/10.1038/ncomms13624.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hoogman, M., Bralten, J., Hibar, D. P., Mennes, M., Zwiers, M. P., Schweren, L. S., et al. (2017). Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: a cross-sectional mega-analysis. Lancet Psychiatry, 4(4), 310–319.

    Article  PubMed  PubMed Central  Google Scholar 

  • Hutton, C., Bork, A., Josephs, O., Deichmann, R., Ashburner, J., & Turner, R. (2002). Image distortion correction in fMRI: a quantitative evaluation. Neuroimage, 16, 217–240.

    Article  PubMed  Google Scholar 

  • Ibanez, L., Ng, L., Gee, J., & Aylward, S. (2002). Registration patterns: the generic framework for image registration of the insight toolkit. Proceedings of IEEE International Symposium on Biomedical Imaging, 345–348.

  • Ikram, M. A., Fornage, M., Smith, A. V., Seshadri, S., Schmidt, R., Debette, S., Vrooman, H. A., Sigurdsson, S., Ropele, S., Taal, H. R., Mook-Kanamori, D. O., Coker, L. H., Longstreth WT Jr, Niessen, W. J., DeStefano, A., Beiser, A., Zijdenbos, A. P., Struchalin, M., Jack CR Jr, Rivadeneira, F., Uitterlinden, A. G., Knopman, D. S., Hartikainen, A. L., Pennell, C. E., Thiering, E., Steegers, E. A., Hakonarson, H., Heinrich, J., Palmer, L. J., Jarvelin, M. R., McCarthy, M., Grant, S. F., St Pourcain, B., Timpson, N. J., Smith, G. D., Sovio, U., Early Growth Genetics Consortium, Nalls, M. A., Au, R., Hofman, A., Gudnason, H., van der Lugt, A., Harris, T. B., Meeks, W. M., Vernooij, M. W., van Buchem, M., Catellier, D., Jaddoe, V. W., Gudnason, V., Windham, B. G., Wolf, P. A., van Duijn, C., Mosley TH Jr, Schmidt, H., Launer, L. J., Breteler, M. M., DeCarli, C., & Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium. (2012). Common variants at 6q22 and 17q21 are associated with intracranial volume. Nature Genetics, 44(5), 539–544. https://doi.org/10.1038/ng.2245.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jahanshad, N., Kochunov, P. V., Sprooten, E., Mandl, R. C., Nichols, T. E., Almasy, L., Blangero, J., Brouwer, R. M., Curran, J. E., de Zubicaray, G. I., Duggirala, R., Fox, P. T., Hong, L. E., Landman, B. A., Martin, N. G., McMahon, K. L., Medland, S. E., Mitchell, B. D., Olvera, R. L., Peterson, C. P., Starr, J. M., Sussmann, J. E., Toga, A. W., Wardlaw, J. M., Wright, M. J., Hulshoff Pol, H. E., Bastin, M. E., McIntosh, A. M., Deary, I. J., Thompson, P. M., & Glahn, D. C. (2013). Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: a pilot project of the ENIGMA-DTI working group. Neuroimage, 81, 455–469.

    Article  PubMed  PubMed Central  Google Scholar 

  • Jahanshad, N., Roshchupkin, G., Faskowitz, J., Hibar, D. P., Gutman, B. A., Adams, A. H. H., et al. (2018). Multisite metaanalysis of image-wide genome-wide associations with morphometry (Imaging genetics ). Elsvier Inc.

  • Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., & Smith, S. M. (2012). FSL. Neuroimage, 62, 782–790.

    Article  PubMed  Google Scholar 

  • Jezzard, P. (2012). Correction of geometric distortion in fMRI data. Neuroimage, 62(2), 648–651. https://doi.org/10.1016/j.neuroimage.2011.09.010.

    Article  PubMed  Google Scholar 

  • Kikinis, R., & Pieper, S. (2011). 3d slicer as a tool for interactive brain tumor segmentation. Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 6982–6984.

  • Klein, A., Andersson, J., Ardekani, B. A., Ashburner, J., Avants, B., Chiang, M. C., Christensen, G. E., Collins, D. L., Gee, J., Hellier, P., Song, J. H., Jenkinson, M., Lepage, C., Rueckert, D., Thompson, P., Vercauteren, T., Woods, R. P., Mann, J. J., & Parsey, R. V. (2009). Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage, 46(3), 786–802. https://doi.org/10.1016/j.neuroimage.2008.12.037.

    Article  PubMed  PubMed Central  Google Scholar 

  • Kochunov, P., Lancaster, J. L., Thompson, P., Woods, R., Mazziotta, J., Hardies, J., & Fox, P. (2001). Regional spatial normalization: Toward an optimal target. Journal of Computer Assisted Tomography, 25(5), 805–816.

    Article  CAS  PubMed  Google Scholar 

  • Kruger, G., & Glover, G. H. (2001). Physiological noise in oxygenation- sensitive magnetic resonance imaging. Magnetic Resonance in Medicine, 46, 631–637.

    Article  CAS  PubMed  Google Scholar 

  • Lancaster, J. L., Fox, P., Downs, H., Nickerson, D., Hander, T., Mallah, M., et al. (1999). Global spatial normalization of the human brain using convex hulls. Journal of Nuclear Medicine, 40(6), 942–955.

    CAS  PubMed  Google Scholar 

  • Major Depressive Disorder Working Group of the Psychiatric, G. C, Ripke, S., Wray, N. R., Lewis, C. M., Hamilton, S. P., Weissman, M. M., et al. (2013). A mega-analysis of genome-wide association studies for major depressive disorder. Molecular Psychiatry, 18(4), 497–511. https://doi.org/10.1038/mp.2012.21.

    Article  CAS  Google Scholar 

  • Marchenko, V. A., & Pastur, L. A. (1967). Distribution of eigenvalues for some sets of random matrices. Mathematics of the USSR-Sbornik, 1(4).

  • Margulies, D. S., Clare Kellya, A. M., Uddin, L. Q., Biswal, B. B., Xavier Castellanos, F., & Milham, M. P. (2007). Mapping the functional connectivity of anterior cingulate cortex. Neuroimage, 37, 579–588.

    Article  PubMed  Google Scholar 

  • Miller, M. I., Beg, M. F., Ceritoglu, C., & Stark, C. (2005). Increasing the power of functional maps of the medial temporal lobe by using large deformation diffeomorphic metric mapping. Proceedings of the National Academy of Sciences of the United States of America, 102, 9685–9690.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Molloy, E. K., Meyerand, M. E., & Birn, R. M. (2014). The influence of spatial resolution and smoothing on the detectability of resting-state and task fMRI. Neuroimage, 86, 221–230. https://doi.org/10.1016/j.neuroimage.2013.09.001.

    Article  PubMed  Google Scholar 

  • Murphy, K., Bodurka, J., & Bandettini, P. A. (2007). How long to scan? The relationship between fMRI temporal signal to noise ratio and necessary scan duration. Neuroimage, 34(2), 565–574. https://doi.org/10.1016/j.neuroimage.2006.09.032.

    Article  PubMed  Google Scholar 

  • Murphy, K., van Ginneken, B., Reinhardt, J. M., Kabus, S., Ding, K., Deng, X., et al. (2011). Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge. IEEE Transactions on Medical Imaging, 30, 1901–1920.

    Article  PubMed  Google Scholar 

  • Peltier, S. J., & Noll, D. C. (2002). T2* dependence of low frequency functional connectivity. Neuroimage, 16, 985–992.

    Article  CAS  PubMed  Google Scholar 

  • Peyrat, J.-M., Delingette, H., Sermesant, M., Xu, C., & Ayache, N. (2010). Registration of 4d cardiac ct sequences under trajectory constraints with multi-channel diffeomorphic demons. IEEE Transactions on Medical Imaging, 29, 1351–1368.

    Article  PubMed  Google Scholar 

  • Poldrack, R. A., & Gorgolewski, K. J. (2014). Making big data open: data sharing in neuroimaging. Nature Neuroscience, 17(11), 1510–1517. https://doi.org/10.1038/nn.3818.

    Article  CAS  PubMed  Google Scholar 

  • Poldrack, R. A., Baker, C. I., Durnez, J., Gorgolewski, K. J., Matthews, P. M., Munafo, M. R., et al. (2017). Scanning the horizon: towards transparent and reproducible neuroimaging research. Nature Reviews. Neuroscience, 18(2), 115–126. https://doi.org/10.1038/nrn.2016.167.

    Article  CAS  PubMed  Google Scholar 

  • Purdon, P. L., & Weisskoff, R. M. (1998). Effect of temporal autocorrelation due to physiological noise and stimulus paradigm on voxel-level false-positive rates in fMRI. Human Brain Mapping, 6, 239–249.

    Article  CAS  PubMed  Google Scholar 

  • Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L., Leach, M. O., & Hawkes, D. J. (1999). Nonrigid registration using free-form deformations: application to breast mr images. IEEE Transactions on Medical Imaging, 18, 712–721.

    Article  CAS  PubMed  Google Scholar 

  • Russell, J. F. (2013). If a job is worth doing, it is worth doing twice. Nature, 496, 7.

    Article  CAS  PubMed  Google Scholar 

  • Schizophrenia Working Group of the Psychiatric Genomics, C. (2014). Biological insights from 108 schizophrenia-associated genetic loci. Nature, 511(7510), 421–427. https://doi.org/10.1038/nature13595.

    Article  CAS  Google Scholar 

  • Schmaal, L., Hibar, D. P., Samann, P. G., Hall, G. B., Baune, B. T., Jahanshad, N., et al. (2016). Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group. Molecular Psychiatry, 22(6), 900–909. https://doi.org/10.1038/mp.2016.60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shelton, D., Stetten, G., Aylward, S., Ibáñez, L., Cois, A., & Stewart, C. (2005). Teaching medical image analysis with the insight toolkit. Medical Image Analysis, 9, 605–611.

    Article  PubMed  Google Scholar 

  • Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., de Luca, M., Drobnjak, I., Flitney, D. E., Niazy, R. K., Saunders, J., Vickers, J., Zhang, Y., de Stefano, N., Brady, J. M., & Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage, 23, S208–S219. https://doi.org/10.1016/j.neuroimage.2004.07.051.

    Article  Google Scholar 

  • Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., & Beckmann, C. F. (2009). Correspondence of the brain’s functional architecture during activation and rest. Proceedings of the National Academy of Sciences of the United States of America, 106, 13040–13045.

    Article  PubMed  PubMed Central  Google Scholar 

  • Smith, S. M., Beckmann, C. F., Andersson, J., Auerbach, E. J., Bijsterbosch, J., Douaud, G., Duff, E., Feinberg, D. A., Griffanti, L., Harms, M. P., Kelly, M., Laumann, T., Miller, K. L., Moeller, S., Petersen, S., Power, J., Salimi-Khorshidi, G., Snyder, A. Z., Vu, A. T., Woolrich, M. W., Xu, J., Yacoub, E., Uğurbil, K., van Essen, D., Glasser, M. F., & WU-Minn HCP Consortium. (2013). Resting-state fMRI in the human connectome project. Neuroimage, 80, 144–168. https://doi.org/10.1016/j.neuroimage.2013.05.039.

    Article  PubMed  PubMed Central  Google Scholar 

  • Song, X., Zhang, Y., & Liu, Y. (2014). Frequency specificity of regional homogeneity in the resting-state human brain. PLoS One, 9(1), e86818.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Stanley, M. L., Moussa, M. N., Paolini, B. M., Lyday, R. G., Burdette, J. H., & Laurienti, P. J. (2013). Defining nodes in complex brain networks. Frontiers in Computational Neuroscience, 7, 169. https://doi.org/10.3389/fncom.2013.00169.

    Article  PubMed  PubMed Central  Google Scholar 

  • Stein, J. L., Medland, S. E., Vasquez, A. A., Hibar, D. P., Senstad, R. E., Winkler, A. M., et al. (2012). Identification of common variants associated with human hippocampal and intracranial volumes. Nature Genetics, 44(5), 552–561. https://doi.org/10.1038/ng.2250.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Thompson, P. M., Stein, J. L., Medland, S. E., Hibar, D. P., Vasquez, A. A., Renteria, M. E., et al. (2014). The ENIGMA consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging and Behavior, 8, 153–182. https://doi.org/10.1007/s11682-013-9269-5.

    Article  PubMed  PubMed Central  Google Scholar 

  • Triantafyllou, C., Hoge, R. D., Krueger, G., Wiggins, C. J., Potthast, A., Wiggins, G. C., & Wald, L. L. (2005). Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters. Neuroimage, 26(1), 243–250. https://doi.org/10.1016/j.neuroimage.2005.01.007.

    Article  CAS  PubMed  Google Scholar 

  • Triantafyllou, C., Hoge, R. D., & Wald, L. L. (2006). Effect of spatial smoothing on physiological noise in high-resolution fMRI. Neuroimage, 32(2), 551–557.

    Article  PubMed  Google Scholar 

  • Turner, G. H., & Twieg, D. B. (2005). Study of temporal stationarity and spatial consistency of fMRI noise using independent component analysis. IEEE Transactions on Medical Imaging, 24(6), 712–718.

    Article  PubMed  Google Scholar 

  • van Dalen, J. A., Vogel, W., Huisman, H. J., Oyen, W. J. G., Jager, G. J., & Karssemeijer, N. (2004). Accuracy of rigid CT-FDG-PET image registration of the liver. Physics in Medicine and Biology, 49, 5393–5405.

    Article  PubMed  Google Scholar 

  • van Erp, T. G., Hibar, D. P., Rasmussen, J. M., Glahn, D. C., Pearlson, G. D., Andreassen, O. A., et al. (2016). Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Molecular Psychiatry, 21(4), 547–553. https://doi.org/10.1038/mp.2015.63.

    Article  PubMed  Google Scholar 

  • Veraart, J., Fieremans, E., & Novikov, D. S. (2016a). Diffusion MRI noise mapping using random matrix theory. Magnetic Resonance in Medicine, 76, 1582–1593. https://doi.org/10.1002/mrm.26059.

    Article  CAS  PubMed  Google Scholar 

  • Veraart, J., Novikov, D. S., Christiaens, D., Ades-Aron, B., Sijbers, J., & Fieremans, E. (2016b). Denoising of diffusion MRI using random matrix theory. Neuroimage, 142, 394–406. https://doi.org/10.1016/j.neuroimage.2016.08.016.

    Article  PubMed  PubMed Central  Google Scholar 

  • Villain, N., Landeau, B., Groussard, M., Mevel, K., Fouquet, M., Dayan, J., Eustache, F., Desgranges, B., & Chételat, G. (2010). A simple way to improve anatomical mapping of functional brain imaging. Journal of Neuroimaging, 20(4), 324–333. https://doi.org/10.1111/j.1552-6569.2010.00470.x.

    Article  PubMed  PubMed Central  Google Scholar 

  • Weisskoff, R. M. (1996). Simple measurement of scanner stability for functional NMR imaging of activation in the brain. Magnetic Resonance in Medicine, 36(4), 643–645.

    Article  CAS  PubMed  Google Scholar 

  • Welvaert, M., & Rosseel, Y. (2013). On the definition of signal-to-noise ratio and contrast-to-noise ratio for FMRI data. PLoS One, 8(11), e77089. https://doi.org/10.1371/journal.pone.0077089.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Woods, R. (1996). Correlation of brain structure and function. In A. Toga, J. Mazziotta (Ed.), Brain mapping: The methods (pp. 313–342). Academic Press.

  • Yoo, T. S., Ackerman, M. J., Lorensen, W. E., Schroeder, W., Chalana, V., Aylward, S., Metaxas, D., & Whitaker, R. (2002). Engineering and algorithm design for an image processing API: a technical report on ITK - the insight toolkit. Studies in Health Technology and Informatics, 85, 586–592.

    PubMed  Google Scholar 

  • Zhang, S., Li, X., Lv, J., Jiang, X., Guo, L., & Liu, T. (2016). Characterizing and differentiating task-based and resting state fMRI signals via two-stage sparse representations. Brain Imaging and Behavior, 10(1), 21–32. https://doi.org/10.1007/s11682-015-9359-7.

    Article  PubMed  PubMed Central  Google Scholar 

  • Zuo, X. N., Anderson, J. S., Bellec, P., Birn, R. M., Biswal, B. B., Blautzik, J., Breitner, J. C. S., Buckner, R. L., Calhoun, V. D., Castellanos, F. X., Chen, A., Chen, B., Chen, J., Chen, X., Colcombe, S. J., Courtney, W., Craddock, R. C., di Martino, A., Dong, H. M., Fu, X., Gong, Q., Gorgolewski, K. J., Han, Y., He, Y., He, Y., Ho, E., Holmes, A., Hou, X. H., Huckins, J., Jiang, T., Jiang, Y., Kelley, W., Kelly, C., King, M., LaConte, S. M., Lainhart, J. E., Lei, X., Li, H. J., Li, K., Li, K., Lin, Q., Liu, D., Liu, J., Liu, X., Liu, Y., Lu, G., Lu, J., Luna, B., Luo, J., Lurie, D., Mao, Y., Margulies, D. S., Mayer, A. R., Meindl, T., Meyerand, M. E., Nan, W., Nielsen, J. A., O’Connor, D., Paulsen, D., Prabhakaran, V., Qi, Z., Qiu, J., Shao, C., Shehzad, Z., Tang, W., Villringer, A., Wang, H., Wang, K., Wei, D., Wei, G. X., Weng, X. C., Wu, X., Xu, T., Yang, N., Yang, Z., Zang, Y. F., Zhang, L., Zhang, Q., Zhang, Z., Zhang, Z., Zhao, K., Zhen, Z., Zhou, Y., Zhu, X. T., & Milham, M. P. (2014). An open science resource for establishing reliability and reproducibility in functional connectomics. Scientific Data, 1, 140049. https://doi.org/10.1038/sdata.2014.49.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Funding

Support was received from NIH grants U54 EB020403, U01MH108148, 2R01EB015611, R01MH112180, R01DA027680, R01MH085646. MarbG and MuenG (FOR2107 study): Work was supported by the German Research Foundation (DFG), grant numbers FOR2107; KI 588/15–1 to TK, DA 1151/5–1 to UD, KO 4291/4–1 and KR 3822/5–1 to AK. JV is a Postdoctoral Fellow of the Research Foundation - Flanders (FWO; grant number 12S1615N).

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Authors and Affiliations

  1. Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA

    Bhim M. Adhikari, Dinesh Shukla, Robert W. Buchanan, L. Elliot Hong & Peter Kochunov

  2. Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA

    Neda Jahanshad, Fabrizio Pizzagalli & Paul M. Thompson

  3. Department of Psychology, Georgia State University, Atlanta, GA, USA

    Jessica Turner

  4. Department of Psychiatry, University of Münster, Münster, Germany

    Dominik Grotegerd & Udo Dannlowski

  5. Department of Clinical Radiology, University of Münster, Münster, Germany

    Harald Kugel

  6. Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany

    Jennifer Engelen, Bruno Dietsche, Axel Krug & Tilo Kircher

  7. Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA

    Els Fieremans, Jelle Veraart & Dmitry S. Novikov

  8. Department of Psychiatry, Department of Anatomy & Neurosciences, VU University Medical Center, Amsterdam, Netherlands

    Premika S. W. Boedhoe, Ysbrand D. van der Werf & Odile A. van den Heuvel

  9. Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa

    Jonathan Ipser, Anne Uhlmann & Dan J. Stein

  10. Centre for Addiction and Mental Health, Toronto, ON, Canada

    Erin Dickie

  11. Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Toronto, ON, Canada

    Aristotle N. Voineskos

  12. Department of Psychiatry, University of Toronto, Toronto, ON, Canada

    Aristotle N. Voineskos

  13. Department of Psychiatry, The Zucker Hillside Hospital, Glen Oaks, New York, NY, USA

    Anil K. Malhotra

  14. The Mind Research Network & The University of New Mexico, Albuquerque, NM, USA

    Vince D. Calhoun

  15. Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Matte, Berlin, Germany

    Lea Waller, Ilja M. Veer & Hernik Walter

  16. Department of Psychiatry, Yale University, School of Medicine, New Haven, CT, USA

    David C. Glahn

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  1. Bhim M. Adhikari
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  33. Peter Kochunov
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Corresponding author

Correspondence to Bhim M. Adhikari.

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There is no conflict of interest for any of the authors.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Adhikari, B.M., Jahanshad, N., Shukla, D. et al. A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol. Brain Imaging and Behavior 13, 1453–1467 (2019). https://doi.org/10.1007/s11682-018-9941-x

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