View Item 

Show simple item record

Statistical harmonization corrects site effects in functional connectivity measurements from multi‐site fMRI data
dc.contributor.authorYu, Meichen
dc.contributor.authorLinn, Kristin A.
dc.contributor.authorCook, Philip A.
dc.contributor.authorPhillips, Mary L.
dc.contributor.authorMcInnis, Melvin
dc.contributor.authorFava, Maurizio
dc.contributor.authorTrivedi, Madhukar H.
dc.contributor.authorWeissman, Myrna M.
dc.contributor.authorShinohara, Russell T.
dc.contributor.authorSheline, Yvette I.
dc.date.accessioned2018-11-20T15:36:10Z
dc.date.available2020-01-06T16:40:59Zen
dc.date.issued2018-11
dc.identifier.citationYu, Meichen; Linn, Kristin A.; Cook, Philip A.; Phillips, Mary L.; McInnis, Melvin; Fava, Maurizio; Trivedi, Madhukar H.; Weissman, Myrna M.; Shinohara, Russell T.; Sheline, Yvette I. (2018). "Statistical harmonization corrects site effects in functional connectivity measurements from multi‐site fMRI data." Human Brain Mapping 39(11): 4213-4227.
dc.identifier.issn1065-9471
dc.identifier.issn1097-0193
dc.identifier.urihttps://hdl.handle.net/2027.42/146498
dc.description.abstractAcquiring resting‐state functional magnetic resonance imaging (fMRI) datasets at multiple MRI scanners and clinical sites can improve statistical power and generalizability of results. However, multi‐site neuroimaging studies have reported considerable nonbiological variability in fMRI measurements due to different scanner manufacturers and acquisition protocols. These undesirable sources of variability may limit power to detect effects of interest and may even result in erroneous findings. Until now, there has not been an approach that removes unwanted site effects. In this study, using a relatively large multi‐site (4 sites) fMRI dataset, we investigated the impact of site effects on functional connectivity and network measures estimated by widely used connectivity metrics and brain parcellations. The protocols and image acquisition of the dataset used in this study had been homogenized using identical MRI phantom acquisitions from each of the neuroimaging sites; however, intersite acquisition effects were not completely eliminated. Indeed, in this study, we found that the magnitude of site effects depended on the choice of connectivity metric and brain atlas. Therefore, to further remove site effects, we applied ComBat, a harmonization technique previously shown to eliminate site effects in multi‐site diffusion tensor imaging (DTI) and cortical thickness studies. In the current work, ComBat successfully removed site effects identified in connectivity and network measures and increased the power to detect age associations when using optimal combinations of connectivity metrics and brain atlases. Our proposed ComBat harmonization approach for fMRI‐derived connectivity measures facilitates reliable and efficient analysis of retrospective and prospective multi‐site fMRI neuroimaging studies.
dc.publisherWiley Periodicals, Inc.
dc.publisherSpringer
dc.subject.otherharmonization
dc.subject.otheraging
dc.subject.otheratlas
dc.subject.otherComBat
dc.subject.otherfMRI
dc.subject.otherfunctional connectivity
dc.subject.othergraph theory
dc.subject.othermulti‐site
dc.subject.othernetwork efficiency
dc.titleStatistical harmonization corrects site effects in functional connectivity measurements from multi‐site fMRI data
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelNeurosciences
dc.subject.hlbsecondlevelKinesiology and Sports
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/146498/1/hbm24241.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/146498/2/hbm24241-sup-0002-suppinfo2.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/146498/3/hbm24241_am.pdf
dc.identifier.doi10.1002/hbm.24241
dc.identifier.sourceHuman Brain Mapping
dc.identifier.citedreferenceRath, J., Wurnig, M., Fischmeister, F., Klinger, N., Höllinger, I., Geißler, A., … Beisteiner, R. ( 2016 ). Between‐ and within‐site variability of fMRI localizations. Human Brain Mapping, 37 ( 6 ), 2151 – 2160.
dc.identifier.citedreferenceNoble, S., Scheinost, D., Finn, E. S., Shen, X., Papademetris, X., McEwen, S. C., … Constable, R. T. ( 2017 ). Multisite reliability of MR‐based functional connectivity. NeuroImage, 146, 959 – 970.
dc.identifier.citedreferenceOh, J., Bakshi, R., Calabresi, P. A., Crainiceanu, C., Henry, R. G., Nair, G., … Sicotte, N. L. NAIMS Cooperative Steering Committee ( 2017 ). The NAIMS cooperative pilot project: Design, implementation and future directions. Multiple Sclerosis Journal, 135245851773999.
dc.identifier.citedreferenceOtte, C., Gold, S. M., Penninx, B. W., Pariante, C. M., Etkin, A., Fava, M., … Schatzberg, A. F. ( 2016 ). Major depressive disorder. Nature Reviews. Disease Primers, 2, 16065.
dc.identifier.citedreferencePercival, D. B., & Walden, A. T. ( 2000 ). Wavelet methods for time series analysis. Cambridge University Press.
dc.identifier.citedreferencePower, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., … Petersen, S. E. ( 2011 ). Functional network organization of the human brain. Neuron, 72 ( 4 ), 665 – 678.
dc.identifier.citedreferenceRaichle, M. E. ( 2015 ). The brain’s default mode network. Annual Review of Neuroscience, 38, 433 – 447.
dc.identifier.citedreferenceRosazza, C., … Minati, L. ( 2011 ): Resting‐state brain networks: literature review and clinical applications. Neurol Sci, 32 ( 5 ): 773 – 85.
dc.identifier.citedreferenceRubinov, M., & Sporns, O. ( 2010 ). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52 ( 3 ), 1059 – 1069.
dc.identifier.citedreferenceRush, A. J., Trivedi, M. H., Ibrahim, H. M., Carmody, T. J., Arnow, B., Klein, D. N., … Keller, M. B. ( 2003 ). The 16‐Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS‐C), and self‐report (QIDS‐SR): A psychometric evaluation in patients with chronic major depression. Biological Psychiatry, 54 ( 5 ), 573 – 583.
dc.identifier.citedreferenceSatterthwaite, T. D., Ciric, R., Roalf, D. R., Davatzikos, C., Bassett, D. S., & Wolf, D. H. ( 2017 ). Motion artifact in studies of functional connectivity: Characteristics and mitigation strategies. Human Brain Mapping. https://doi.org/10.1002/hbm.23665. [Epub ahead of print]
dc.identifier.citedreferenceSchaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X. N., Holmes, A. J., … Yeo, B. T. T. ( 2017 ). Local‐global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb Cortex 18. Cerebral Cortex (New York, N.Y.: 1991), 1 – 20.
dc.identifier.citedreferenceSheline, Y. I., Barch, D. M., Price, J. L., Rundle, M. M., Vaishnavi, S. N., Snyder, A. Z., … Raichle, M. E. ( 2009 ). The default mode network and self‐referential processes in depression. Proceedings of the National Academy of Sciences of the United States of America, 106 ( 6 ), 1942 – 1947.
dc.identifier.citedreferenceSheline, Y. I., Price, J. L., Yan, Z., & Mintun, M. A. ( 2010 ). Resting‐state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proceedings of the National Academy of Sciences of the United States of America, 107 ( 24 ), 11020 – 11025.
dc.identifier.citedreferenceShinohara, R. T., Oh, J., Nair, G., Calabresi, P. A., Davatzikos, C., Doshi, J., … Bakshi, R. NAIMS Cooperative ( 2017 ). Volumetric analysis from a harmonized multisite brain MRI study of a single subject with multiple sclerosis. American Journal of Neuroradiology, 38 ( 8 ), 1501 – 1509.
dc.identifier.citedreferenceStam, C. J. ( 2014 ). Modern network science of neurological disorders. Nature Reviews. Neuroscience, 15 ( 10 ), 683 – 695.
dc.identifier.citedreferenceSuckling, J., Ohlssen, D., Andrew, C., Johnson, G., Williams, S. C., Graves, M., … Bullmore, E. ( 2008 ). Components of variance in a multicentre functional MRI study and implications for calculation of statistical power. Human Brain Mapping, 29 ( 10 ), 1111 – 1122.
dc.identifier.citedreferenceSuckling, J., Barnes, A., Job, D., Brenan, D., Lymer, K., Dazzan, P., … Deakin, B. ( 2010 ). Power calculations for multicenter imaging studies controlled by the false discovery rate. Human Brain Mapping, 31 ( 8 ), 1183 – 1195.
dc.identifier.citedreferenceTomasi, D., & Volkow, N. D. ( 2012 ). Aging and functional brain networks. Molecular Psychiatry, 17 ( 5 ), 471, 549 – 458.
dc.identifier.citedreferenceTrivedi, M. H., McGrath, P. J., Fava, M., Parsey, R. V., Kurian, B. T., Phillips, M. L., … Weissman, M. M. ( 2016 ). Establishing moderators and biosignatures of antidepressant response in clinical care (EMBARC): Rationale and design. Journal of Psychiatric Research, 78, 11 – 23.
dc.identifier.citedreferenceTurner, J. A., Damaraju, E., van Erp, T. G., Mathalon, D. H., Ford, J. M., Voyvodic, J., … Calhoun, V. D. ( 2013 ). A multi‐site resting state fMRI study on the amplitude of low frequency fluctuations in schizophrenia. Frontiers in Neuroscience, 7, 137.
dc.identifier.citedreferenceTustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. ( 2010 ). N4ITK: Improved N3 bias correction. IEEE Transactions on Medical Imaging, 29 ( 6 ), 1310 – 1320.
dc.identifier.citedreferenceTustison, N. J., Cook, P. A., Klein, A., Song, G., Das, S. R., Duda, J. T., … Avants, B. B. ( 2014 ). Large‐scale evaluation of ants and freesurfer cortical thickness measurements. NeuroImage, 99, 166 – 179.
dc.identifier.citedreferenceTzourio‐Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., … Joliot, M. ( 2002 ). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single‐subject brain. NeuroImage, 15 ( 1 ), 273 – 289.
dc.identifier.citedreferenceVan Horn, J. D., & Toga, A. W. ( 2009 ). Multi‐site neuroimaging trials. Current Opinion in Neurology, 22 ( 4 ), 370 – 378.
dc.identifier.citedreferenceWatson, D., & Clark, L. A. ( 1991 ). The mood and anxiety symptom questionnaire. Iowa City, IA: University of Iowa.
dc.identifier.citedreferenceWebb, C. A., Dillon, D. G., Pechtel, P., Goer, F. K., Murray, L., Huys, Q. J., … Pizzagalli, D. A. ( 2016 ). Neural correlates of three promising endophenotypes of depression: Evidence from the EMBARC study. Neuropsychopharmacology, 41 ( 2 ), 454 – 463.
dc.identifier.citedreferenceWig, G. S., Laumann, T. O., & Petersen, S. E. ( 2014 ). An approach for parcellating human cortical areas using resting‐state correlations. NeuroImage, 93, 276 – 291.
dc.identifier.citedreferenceWilliams, L. M. ( 2016 ). Precision psychiatry: A neural circuit taxonomy for depression and anxiety. Lancet Psychiatry, 3 ( 5 ), 472 – 480. May
dc.identifier.citedreferenceXia, M., Wang, J., & He, Y. ( 2013 ). BrainNet Viewer: A network visualization tool for human brain connectomics. PLoS One, 8 ( 7 ), e68910.
dc.identifier.citedreferenceYeo, B. T. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., … Polimeni, J. R. ( 2011 ). The organization of the human cerebral cortex estimated by intrinsic functionalconnectivity. Journal of Neurophysiology, 106 ( 3 ), 1125 – 1165.
dc.identifier.citedreferenceYu, M., Gouw, A. A., Hillebrand, A., Tijms, B. M., Stam, C. J., van Straaten, E. C. W., & Pijnenburg, Y. A. L. ( 2016 ). Different functional connectivity and network topology in behavioral variant of frontotemporal dementia and Alzheimer’s disease: An EEG study. Neurobiology of Aging, 42, 150 – 162.
dc.identifier.citedreferenceYu, M., Engels, M. M. A., Hillebrand, A., van Straaten, E. C. W., Gouw, A. A., Teunissen, C., … Stam, C. J. ( 2017 ). Selective impairment of hippocampus and posterior hub areas in Alzheimer’s disease: An MEG‐based multiplex network study. Brain, 140 ( 5 ), 1466 – 1485.
dc.identifier.citedreferenceZhang, Z., Telesford, Q. K., Giusti, C., Lim, K. O., & Bassett, D. S. ( 2016 ). Choosing wavelet methods, filters, and lengths for functional brain network construction. PLoS One, 11 ( 6 ), e0157243.
dc.identifier.citedreferenceAbraham, A., Milham, M. P., Di Martino, A., Cameron Craddock, R., Samaras, D., Thirion, B., & Varoquaux, G. ( 2017 ). Deriving reproducible biomarkers from multi‐site resting‐state data: An Autism‐based example. NeuroImage, 147, 736 – 745.
dc.identifier.citedreferenceAchard, S., Salvador, R., Whitcher, B., Suckling, J., & Bullmore, E. ( 2006 ). A resilient, low‐frequency, small‐world human brain functional network with highly connected association cortical hubs. Journal of Neuroscience, 26 ( 1 ), 63 – 72.
dc.identifier.citedreferenceAchard, S., & Bullmore, E. ( 2007 ). Efficiency and cost of economical brain brain functional networks. PLoS Computational Biology, 3 ( 2 ), e17.
dc.identifier.citedreferenceAjilore, O., Lamar, M., & Kumar, A. ( 2014 ). Association of brain network efficiency with aging, depression, and cognition. American Journal of Geriatric Psychiatry, 22 ( 2 ), 102 – 110.
dc.identifier.citedreferenceAvants, 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.
dc.identifier.citedreferenceAvants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A., & Gee, J. C. ( 2011 ). A reproducible evaluation of ants similarity metric performance in brain image registration. NeuroImage, 54 ( 3 ), 2033 – 2044.
dc.identifier.citedreferenceAvants, B. B., Tustison, N. J., Wu, J., Cook, P. A., & Gee, J. C. ( 2011 ). An open source multivariate framework for n‐tissue segmentation with evaluation on public data. Neuroinformatics, 9 ( 4 ), 381 – 400.
dc.identifier.citedreferenceBassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M., & Grafton, S. T. ( 2011 ). Dynamic reconfiguration of human brain networks during learning. Proceedings of the National Academy of Sciences of the United States of America, 108 ( 18 ), 7641 – 7646.
dc.identifier.citedreferenceBenjamini, Y., & Hochberg, Y. ( 1995 ). Controling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B, Statistical Methodology, 57, 289 – 300.
dc.identifier.citedreferenceBiswal, 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 ( 4 ), 537 – 541.
dc.identifier.citedreferenceBiswal, B. B., Mennes, M., Zuo, X. N., Gohel, S., Kelly, C., Smith, S. M., … Milham, M. P. ( 2010 ). Toward discovery science of human brain function. Proceedings of the National Academy of Sciences of the United States of America, 107 ( 10 ), 4734 – 4739.
dc.identifier.citedreferenceBressler, S. L., & Menon, V. ( 2010 ). Large‐scale brain networks in cognition: Emerging methods and principles. Trends in Cognitive Sciences, 14 ( 6 ), 277 – 290.
dc.identifier.citedreferenceBrodmann, K. ( 1909 ). Localization in the cerebral cortex, translated by Garey LJ. New York, NY: Springer.
dc.identifier.citedreferenceBrown, G. G., Mathalon, D. H., Stern, H., Ford, J., Mueller, B., Greve, D. N., … Potkin, S. G. Function Biomedical Informatics Research Network ( 2011 ). Multisite reliability of cognitive BOLD data. NeuroImage, 54 ( 3 ), 2163 – 2175.
dc.identifier.citedreferenceBullmore, E., & Sporns, O. ( 2009 ). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews. Neuroscience, 10 ( 3 ), 186 – 198.
dc.identifier.citedreferenceBullmore, E., & Sporns, O. ( 2012 ). The economy of brain network organization. Nature Reviews. Neuroscience, 13 ( 5 ), 336 – 349.
dc.identifier.citedreferenceChavez, S., Viviano, J., Zamyadi, M., Kingsley, P. B., Kochunov, P., Strother, S., & Voineskos, A. ( 2018 ). A novel DTI‐QA tool: Automated metric extraction exploiting the sphericity of an agar filled phantom. Magnetic Resonance Imaging, 46, 28 – 39.
dc.identifier.citedreferenceCiric, R., Wolf, D. H., Power, J. D., Roalf, D. R., Baum, G. L., Ruparel, K., … Satterthwaite, T. D. ( 2017 ). Benchmarking of participant‐level confound regression strategies for the control of motion artifact in studies of functional connectivity. NeuroImage, 154, 174 – 187.
dc.identifier.citedreferenceCohen, J. R., & D’Esposito, M. ( 2016 ). The segregation and integration of distinct brain networks and their relationship to cognition. Journal of Neuroscience, 36 ( 48 ), 12083 – 12094.
dc.identifier.citedreferenceDamoiseaux, J. S., Beckmann, C. F., Arigita, E. J., Barkhof, F., Scheltens, P., Stam, C. J., … Rombouts, S. A. ( 2008 ). Reduced resting‐state brain activity in the “default network” in normal aging. Cerebral Cortex (New York, N.Y.: 1991), 18 ( 8 ), 1856 – 1864.
dc.identifier.citedreferenceDamoiseaux, J. S. ( 2017 ). Effects of aging on functional and structural brain connectivity. NeuroImage, 160, 32 – 40.
dc.identifier.citedreferenceDansereau, C., Benhajali, Y., Risterucci, C., Pich, E. M., Orban, P., Arnold, D., & Bellec, P. ( 2017 ). Statistical power and prediction accuracy in multisite resting‐state fMRI connectivity. NeuroImage, 149, 220 – 232.
dc.identifier.citedreferenceDas, S. R., Avants, B. B., Grossman, M., & Gee, J. C. ( 2009 ). Registration based cortical thickness measurement. NeuroImage, 45 ( 3 ), 867 – 879.
dc.identifier.citedreferenceDelaparte, L., Yeh, F. C., Adams, P., Malchow, A., Trivedi, M. H., Oquendo, M. A., … DeLorenzo, C. ( 2017 ). A comparison of structural connectivity in anxious depression versus non‐anxious depression. Journal of Psychiatric Research, 89, 38 – 47.
dc.identifier.citedreferenceDesikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., … Killiany, R. J. ( 2006 ). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31 ( 3 ), 968.
dc.identifier.citedreferenceDi Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., … Milham, M. P. ( 2014 ). The autism brain imaging data exchange: Towards a large‐scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19 ( 6 ), 659 – 667.
dc.identifier.citedreferenceDijkstra, E. W. ( 1959 ). A note on two problems in connection with graphs. Numerische Mathematik, 1 ( 1 ), 269 – 271.
dc.identifier.citedreferenceDoucet, G. E., Bassett, D. S., Yao, N., Glahn, D. C., & Frangou, S. ( 2017 ). The role of intrinsic brain functional connectivity in vulnerability and resilience to bipolar disorder. American Journal of Psychiatry, 174 ( 12 ), 1214 – 1222.
dc.identifier.citedreferenceFeis, R. A., Smith, S. M., Filippini, N., Douaud, G., Dopper, E. G., Heise, V., … Mackay, C. E. ( 2015 ). ICA‐based artifact removal diminishes scan site differences in multi‐center resting‐state fMRI. Frontiers in Neuroscience, 9, 395.
dc.identifier.citedreferenceFerreira, L. K., & Busatto, G. F. ( 2013 ). Resting‐state functional connectivity in normal brain aging. Neuroscience and Biobehavioral Reviews, 37 ( 3 ), 384 – 400.
dc.identifier.citedreferenceFirst, M. B., Spitzer, R. L., & Gibbon, M. ( 2002 ). Structured clinical interview for DSM‐IV‐TR axis I disorders, research version, patient edition (SCID‐I/P). New York, NY: Biometrics Research, New York State Psychiatric Institute.
dc.identifier.citedreferenceFornito, A., Zalesky, A., & Breakspear, M. ( 2015 ). The connectomics of brain disorders. Nature Reviews. Neuroscience, 16 ( 3 ), 159 – 172.
dc.identifier.citedreferenceFornito, A., Zalesky, A., & Bullmore, E. ( 2016 ). Fundamentals of brain network analysis. Cambridge: Academic Press.
dc.identifier.citedreferenceForsyth, J. K., McEwen, S. C., Gee, D. G., Bearden, C. E., Addington, J., Goodyear, B., … Cannon, T. D. ( 2014 ). Reliability of functional magnetic resonance imaging activation during working memory in a multi‐site study: Analysis from the North American Prodrome Longitudinal Study. NeuroImage, 97, 41 – 52.
dc.identifier.citedreferenceFortin, J. P., Parker, D., Tunç, B., Watanabe, T., Elliott, M. A., Ruparel, K., … Shinohara, R. T. ( 2017 ). Harmonization of multi‐site diffusion tensor imaging data. NeuroImage, 161, 149 – 170.
dc.identifier.citedreferenceFortin, J. P., Cullen, N., Sheline, Y. I., Taylor, W. D., Aselcioglu, I., Cook, P. A., … Shinohara, R. T. ( 2018 ). Harmonization of cortical thickness measurements across scanners and sites. NeuroImage, 167, 104 – 120.
dc.identifier.citedreferenceFox, M. D., & Raichle, M. E. ( 2007 ). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews. Neuroscience, 8 ( 9 ), 700 – 711.
dc.identifier.citedreferenceFriedman, L., & Glover, G. H. Fbirn Consortium ( 2006 ). Reducing interscanner variability of activation in a multicenter fMRI study: Controlling for signal‐to‐fluctuation‐noise‐ratio (SFNR) differences. NeuroImage, 33 ( 2 ), 471 – 481.
dc.identifier.citedreferenceFriedman, L., Stern, H., Brown, G. G., Mathalon, D. H., Turner, J., Glover, G. H., … Potkin, S. G. ( 2008 ). Test‐retest and between‐site reliability in a multicenter fMRI study. Human Brain Mapping, 29 ( 8 ), 958 – 972.
dc.identifier.citedreferenceGlasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., … Van Essen, D. C. ( 2016 ). A multi‐modal parcellation of human cerebral cortex. Nature, 536 ( 7615 ), 171 – 178.
dc.identifier.citedreferenceGlover, G. H., Mueller, B. A., Turner, J. A., van Erp, T. G., Liu, T. T., Greve, D. N., … Potkin, S. G. ( 2012 ). Function biomedical informatics research network recommendations for prospective multicenter functional MRI studies. Journal of Magnetic Resonance Imaging, 36 ( 1 ), 39 – 54.
dc.identifier.citedreferenceGong, Q., & He, Y. ( 2015 ). Depression, neuroimaging and connectomics: A selective overview. Biological Psychiatry, 77 ( 3 ), 223 – 235.
dc.identifier.citedreferenceGordon, E. M., Laumann, T. O., Adeyemo, B., Huckins, J. F., Kelley, W. M., & Petersen, S. E. ( 2016 ). Generation and evaluation of a cortical area parcellation from resting‐state correlations. Cerebral Cortex, 26 ( 1 ), 288 – 303.
dc.identifier.citedreferenceGountouna, V. E., Job, D. E., McIntosh, A. M., Moorhead, T. W., Lymer, G. K., Whalley, H. C., … Lawrie, S. M. ( 2010 ). Functional magnetic resonance imaging (fMRI) reproducibility and variance components across visits and scanning sites with a finger tapping task. NeuroImage, 49 ( 1 ), 552 – 560.
dc.identifier.citedreferenceGradin, V., Gountouna, V. E., Waiter, G., Ahearn, T. S., Brennan, D., Condon, B., … Steele, J. D. ( 2010 ). Between‐ and within‐scanner variability in the CaliBrain study n‐back cognitive task. Psychiatry Research, 184 ( 2 ), 86 – 95.
dc.identifier.citedreferenceGrady, C. L., Protzner, A. B., Kovacevic, N., Strother, S. C., Afshin‐Pour, B., Wojtowicz, M., … McIntosh, A. R. ( 2010 ). A multivariate analysis of age‐related differences in default mode and task‐positive networks across multiple cognitive domains. Cerebral Cortex (New York, N.Y.: 1991), 20 ( 6 ), 1432 – 1447.
dc.identifier.citedreferenceGreenberg, T., Chase, H. W., Almeida, J. R., Stiffler, R., Zevallos, C. R., Aslam, H. A., … Phillips, M. L. 1. ( 2015 ). Moderation of the relationship between reward expectancy and prediction error‐related ventral striatal reactivity by anhedonia in unmedicated major depressive disorder: Findings from the EMBARC study. American Journal of Psychiatry, 172 ( 9 ), 881 – 891.
dc.identifier.citedreferenceGreve, D. N., & Fischl, B. ( 2009 ). Accurate and robust brain image alignment using boundary‐based registration. NeuroImage, 48 ( 1 ), 63 – 72.
dc.identifier.citedreferenceHallquist, M. N., Hwang, K., & Luna, B. ( 2013 ). The nuisance of nuisance regression: Spectral misspecification in a common approach to resting‐state fmri preprocessing reintroduces noise and obscures functional connectivity. NeuroImage, (2013). 82, 208 – 225.
dc.identifier.citedreferenceHamilton, M. ( 1960 ). A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry, 23 ( 1 ), 56 – 62.
dc.identifier.citedreferenceHuber, P. J. ( 2004 ). Robust statistics. Wiley.
dc.identifier.citedreferenceJenkinson, M., Bannister, P., Brady, M., & Smith, S. ( 2002 ). Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17 ( 2 ), 825 – 841.
dc.identifier.citedreferenceJohnson, W. E., Li, C., & Rabinovic, A. ( 2007 ). Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics (Oxford, England), 8 ( 1 ), 118 – 127.
dc.identifier.citedreferenceJovicich, J., Minati, L., Marizzoni, M., Marchitelli, R., Sala‐Llonch, R., Bartrés‐Faz, D., … Frisoni, G. B. PharmaCog Consortium ( 2016 ). Longitudinal reproducibility of default‐mode network connectivity in healthy elderly participants: A multicentric resting‐state fMRI study. NeuroImage, 124 ( Pt A ), 442 – 454.
dc.identifier.citedreferenceKaiser, R. H., Andrews‐Hanna, J. R., Wager, T. D., & Pizzagalli, D. A. ( 2015 ). Large‐scale network dysfunction in major depressive disorder: A meta‐analysis of resting‐state functional connectivity. JAMA Psychiatry, 72 ( 6 ), 603 – 611.
dc.identifier.citedreferenceKeshavan, A., Paul, F., Beyer, M. K., Zhu, A. H., Papinutto, N., Shinohara, R. T., … Henry, R. G. ( 2016 ). Power estimation for non‐standardized multisite studies. NeuroImage, 134, 281 – 294.
dc.identifier.citedreferenceKlein, A., Andersson, J., Ardekani, B. A., Ashburner, J., Avants, B., Chiang, M.‐C., … Parsey, R. V. ( 2009 ). Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage, 46 ( 3 ), 786 – 802.
dc.identifier.citedreferenceKoch, W., Teipel, S., Mueller, S., Buerger, K., Bokde, A. L., Hampel, H., … Meindl, T. ( 2010 ). Effects of aging on default mode network activity in resting state fMRI: Does the method of analysis matter? NeuroImage, 51 ( 1 ), 280 – 287.
dc.identifier.citedreferenceKochunov, P., Dickie, E. W., Viviano, J. D., Turner, J., Kingsley, P. B., Jahanshad, N., … Voineskos, A. N. ( 2018 ). Integration of routine QA data into mega‐analysis may improve quality and sensitivity of multisite diffusion tensor imaging studies. Human Brain Mapping, 39 ( 2 ), 1015 – 1023.
dc.identifier.citedreferenceLatora, V., & Marchiori, M. ( 2001 ). Efficient behavior of small‐world networks. Physical Review Letters, 87 ( 19 ), 198701.
dc.identifier.citedreferenceMcGonigle, D. J. ( 2012 ). Test‐retest reliability in fMRI: Or how I learned to stop worrying and love the variability. NeuroImage, 62 ( 2 ), 1116 – 1120.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
hbm24241.pdf
Size:
1.132MB
Format:
PDF
View/Open
Name:
hbm24241-sup-0002-suppinfo2.pdf
Size:
179.2KB
Format:
PDF
View/Open
Name:
hbm24241_am.pdf
Size:
1.192MB
Format:
PDF
View/Open

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.