Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples
Abstract
At its best, connectivity mapping can offer researchers great insight into how spatially disparate regions of the human brain coordinate activity during brain processing. A recent investigation conducted by Smith and colleagues (2011) on methods for estimating connectivity maps suggested that those which attempt to ascertain the direction of influence among ROIs rarely provide reliable results. Another problem gaining increasing attention is heterogeneity in connectivity maps. Most group-level methods require that the data come from homogeneous samples, and misleading findings may arise from current methods if the connectivity maps for individuals vary across the sample (which is likely the case). The utility of maps resulting from effective connectivity on the individual or group levels is thus diminished because they do not accurately inform researchers. The present paper introduces a novel estimation technique for fMRI researchers, Group Iterative Multiple Model Estimation (GIMME), which demonstrates that using information across individuals assists in the recovery of the existence of connections among ROIs used by Smith and colleagues (2011) and the direction of the influence. Using heterogeneous in-house data, we demonstrate that GIMME offers a unique improvement over current approaches by arriving at reliable group and individual structures even when the data are highly heterogeneous across individuals comprising the group. An added benefit of GIMME is that it obtains reliable connectivity map estimates equally well using the data from resting state, block, or event-related designs. GIMME provides researchers with a powerful, flexible tool for identifying directed connectivity maps at the group and individual levels.
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GIMME is one of the only methods to date that can reliably recover both the presence and direction of directed functional connectivity maps in heterogeneous and homogeneous samples.
“GIMME is one of the only methods to date that can recover both the direction and presence of directed functional connectivity maps in heterogeneous data, making it an ideal place to start since it addresses the problem of heterogeneity.”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [10]
Using group-derived parameter patterns as a starting point vastly improves reliability and precision of individual-level connections.
“This has been shown to vastly improve reliability and precision of individual-level connections [10].”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [10]
GIMME reliably recovered the presence and true direction of connections at the group and individual levels and is one of the few approaches that can do so.
“As seen previously [10], GIMME reliably recovered the presence and true direction of connections at the group and individual levels and is one of the few approaches that can do so [46].”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [10]
Cites
Confirmatory factor analysis for applied research
Brown, T. A. (2007) · Choice Reviews Online · doi:10.5860/choice.44-2769 · Cited by 15,613 · see citing works
Friston, K. J., Harrison, L., & Penny, W. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00202-7 · Cited by 4,583 · see citing works
Statistical Parametric Mapping: The Analysis of Functional Brain Images
Penny, W. D., Friston, K. J., Ashburner, J., Kiebel, S. J., & Thomas, N. (2007) · UCL Discovery (University College London) · Cited by 3,339 · see citing works
Molenaar, P. C. M. (2004) · Measurement: Interdisciplinary Research & Perspective · doi:10.1207/s15366359mea0204_1 · Cited by 1,943 · see citing works
Self-citationNetwork modelling methods for FMRI
Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., Webster, M., Beckmann, C. F., Nichols, T. E., Ramsey, J. D., & Woolrich, M. W. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.08.063 · Cited by 1,878 · see citing works
Dynamics of blood flow and oxygenation changes during brain activation: the Balloon model
Buxton, R. B., Wong, E. C., & Frank, L. R. (1998) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910390602 · Cited by 1,665 · see citing works
The new person-specific paradigm in psychology
Molenaar, P. C. M., & Campbell, C. G. (2009) · Current Directions in Psychological Science · doi:10.1111/j.1467-8721.2009.01619.x · Cited by 1,088 · see citing works
Self-citationMarreiros, A. C., Stephan, K. E., & Friston, K. J. (2010) · Scholarpedia · doi:10.4249/scholarpedia.9568 · Cited by 565 · see citing works
A Dynamic Factor Model for the Analysis of Multivariate Time Series
Molenaar, P. C. M. (1985) · Psychometrika · doi:10.1007/bf02294246 · Cited by 480 · see citing works
Self-citationSörbom, D. (1989) · Psychometrika · doi:10.1007/bf02294623 · Cited by 424 · see citing works
Normalized Cut Group Clustering of Resting-State fMRI Data
van den Heuvel, M. P., Mandl, R. C. W., & Pol, H. E. H. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002001 · Cited by 403 · see citing works
The future of fMRI connectivity
Smith, S. M. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.022 · Cited by 355 · see citing works
Atypical default network connectivity in youth with attention-deficit/hyperactivity disorder
Fair, D. A., Posner, J., Nagel, B. J., Bathula, D., Dias, T. G. C., Mills, K. L., Blythe, M. S., Giwa, A., Schmitt, C. F., & Nigg, J. T. (2010) · Biological Psychiatry · doi:10.1016/j.biopsych.2010.07.003 · Cited by 354 · see citing works
Six problems for causal inference from fMRI
Ramsey, J. D., Hanson, S. J., Hanson, C., Halchenko, Y. O., Poldrack, R. A., & Glymour, C. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.08.065 · Cited by 337 · see citing works
Modelling functional integration: a comparison of structural equation and dynamic causal models
Penny, W. D., Stephan, K. E., Mechelli, A., & Friston, K. J. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.041 · Cited by 325 · see citing works
Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T. (2007) · Human Brain Mapping · doi:10.1002/hbm.20259 · Cited by 236 · see citing works
Gates, K. M., Molenaar, P. C. M., Hillary, F. G., Ram, N., & Rovine, M. J. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.117 · Cited by 171 · see citing works
Self-citationMiller, M. B., Horn, J. D. V., Wolford, G., Handy, T. C., Valsangkar-Smyth, M., Inati, S., Grafton, S. T., & Gazzaniga, M. S. (2002) · Journal of Cognitive Neuroscience · doi:10.1162/089892902760807203 · Cited by 158 · see citing works
Graphical Models: Selecting causal and statistical models
Meek, C. (2023) · Figshare · doi:10.1184/r1/22696393 · Cited by 133 · see citing works
Extended unified SEM approach for modeling event-related fMRI data
Gates, K. M., Molenaar, P. C. M., Hillary, F. G., & Slobounov, S. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.08.051 · Cited by 125 · see citing works
Self-citationIdentifying abnormal connectivity in patients using Dynamic Causal Modelling of fMRI responses
Seghier, M. L. (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00142 · Cited by 107 · see citing works
Ramsey, J., Hanson, S. J., & Glymour, C. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.06.068 · Cited by 102 · see citing works
Group analysis in functional neuroimaging: selecting subjects using similarity measures
Kherif, F. (2003) · NeuroImage · doi:10.1016/j.neuroimage.2003.08.018 · Cited by 100 · see citing works
Examining working memory task acquisition in a disrupted neural network
Hillary, F. G., Medaglia, J. D., Gates, K., Molenaar, P. C., Slocomb, J., Peechatka, A., & Good, D. C. (2011) · Brain · doi:10.1093/brain/awr043 · Cited by 74 · see citing works
Self-citationJames, G. A., Kelley, M. E., Craddock, R. C., Holtzheimer, P. E., Dunlop, B. W., Nemeroff, C. B., Mayberg, H. S., & Hu, X. P. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.12.049 · Cited by 74 · see citing works
Seghier, M. L., Lazeyras, F., Pegna, A. J., Annoni, J., & Khateb, A. (2007) · Human Brain Mapping · doi:10.1002/hbm.20410 · Cited by 59 · see citing works
The Main Sources of Intersubject Variability in Neuronal Activation for Reading Aloud
Kherif, F., Josse, G., Seghier, M. L., & Price, C. J. (2008) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2009.21084 · Cited by 58 · see citing works
Guttman, M., Mies, C., Dudycz-Sulicz, K., Diskin, S. J., Baldwin, D. A., Stoeckert, C. J., & Grant, G. R. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030143 · Cited by 47 · see citing works
Miller, M. B., & Horn, J. D. V. (2006) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2006.03.019 · Cited by 44 · see citing works
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1 of the 30 references couldn’t be matched to a known paper.
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Network analysis of multivariate data in psychological science
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The Gaussian Graphical Model in Cross-Sectional and Time-Series Data
Epskamp, S., Waldorp, L., Mõttus, R., & Borsboom, D. (2018) · Multivariate Behavioral Research · doi:10.1080/00273171.2018.1454823 · Cited by 1,182 · see citing works
Variable-Centered, Person-Centered, and Person-Specific Approaches: Where Theory Meets the Method
Howard, M. C., & Hoffman, M. E. (2018) · Organizational Research Methods · doi:10.1177/1094428117744021 · Cited by 1,121 · see citing works
Moving Forward: Challenges and Directions for Psychopathological Network Theory and Methodology
Fried, E. I., & Cramer, A. O. J. (2017) · Perspectives on Psychological Science · doi:10.1177/1745691617705892 · Cited by 957 · see citing works
A Network Approach to Psychopathology: New Insights into Clinical Longitudinal Data
Bringmann, L. F., Vissers, N., Wichers, M., Geschwind, N., Kuppens, P., Peeters, F. P. M. L., Borsboom, D., & Tuerlinckx, F. (2013) · PLoS ONE · doi:10.1371/journal.pone.0060188 · Cited by 679 · see citing works
Generalized Network Psychometrics: Combining Network and Latent Variable Models
Epskamp, S., Rhemtulla, M., & Borsboom, D. (2017) · Psychometrika · doi:10.1007/s11336-017-9557-x · Cited by 642 · see citing works
Reporting standards for psychological network analyses in cross-sectional data.
Burger, J., Isvoranu, A., Lunansky, G., Haslbeck, J. M. B., Epskamp, S., Hoekstra, R. H. A., Fried, E. I., Borsboom, D., & Blanken, T. F. (2022) · Psychological Methods · doi:10.1037/met0000471 · Cited by 555 · see citing works
Hamaker, E. L., Asparouhov, T., Brose, A., Schmiedek, F., & Muthén, B. (2018) · Multivariate Behavioral Research · doi:10.1080/00273171.2018.1446819 · Cited by 549 · see citing works
Epskamp, S., van Borkulo, C. D., van der Veen, D. C., Servaas, M. N., Isvoranu, A.-M., Riese, H., & Cramer, A. O. J. (2018) · Clinical Psychological Science · doi:10.1177/2167702617744325 · Cited by 448 · see citing works
Cognitive Network Neuroscience
Medaglia, J. D., Lynall, M.-E., & Bassett, D. S. (2015) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00810 · Cited by 419 · see citing works
Psychometric Network Models from Time-Series and Panel Data
Epskamp, S. (2020) · Psychometrika · doi:10.1007/s11336-020-09697-3 · Cited by 365 · see citing works
Task-Based Core-Periphery Organization of Human Brain Dynamics
Bassett, D. S., Wymbs, N. F., Rombach, M. P., Porter, M. A., Mucha, P. J., & Grafton, S. T. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003171 · Cited by 356 · see citing works
Psychopathological networks: Theory, methods and practice
Bringmann, L. F., Albers, C. J., Bockting, C., Borsboom, D., Ceulemans, E., Cramer, A. O. J., Epskamp, S., Eronen, M. I., Hamaker, E. L., Kuppens, P., et al. (2021) · Behaviour Research and Therapy · doi:10.1016/j.brat.2021.104011 · Cited by 297 · see citing works
Self-citationThe role of the individual in the coming era of process-based therapy
Hayes, S. C., Hofmann, S. G., Stanton, C. E., Carpenter, J. K., Sanford, B. T., Curtiss, J. E., & Ciarrochi, J. (2019) · Behaviour Research and Therapy · doi:10.1016/j.brat.2018.10.005 · Cited by 295 · see citing works
Bridging the Nomothetic and Idiographic Approaches to the Analysis of Clinical Data
Beltz, A. M., Wright, A. G. C., Sprague, B. N., & Molenaar, P. C. M. (2016) · Assessment · doi:10.1177/1073191116648209 · Cited by 293 · see citing works
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Wright, A. G. C., & Woods, W. C. (2020) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-102419-125032 · Cited by 281 · see citing works
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Soares, J. M., Magalhães, R. J. D. S., Moreira, P. S., Sousa, A., Ganz, E., Sampaio, A., Alves, V., Marques, P., & Sousa, N. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00515 · Cited by 270 · see citing works
Toward a dynamic model of psychological assessment: Implications for personalized care.
Fisher, A. J. (2015) · Journal of Consulting and Clinical Psychology · doi:10.1037/ccp0000026 · Cited by 263 · see citing works
Hyperconnectivity is a fundamental response to neurological disruption.
Hillary, F. G., Román, C. A. F., Venkatesan, U. M., Rajtmajer, S. M., Bajo, R., & Castellanos, N. P. (2014) · Neuropsychology · doi:10.1037/neu0000110 · Cited by 248 · see citing works
Wysocki, A. C., van Bork, R., Cramer, A. O. J., & Rhemtulla, M. (2025) · advances in/psychology · doi:10.56296/aip00037 · Cited by 237 · see citing works
Hayes, S. C., Ciarrochi, J., Hofmann, S. G., Chin, F., & Sahdra, B. (2022) · Behaviour Research and Therapy · doi:10.1016/j.brat.2022.104155 · Cited by 234 · see citing works
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Hofmann, S. G., Curtiss, J. E., & Hayes, S. C. (2020) · Clinical Psychology Review · doi:10.1016/j.cpr.2020.101824 · Cited by 232 · see citing works
Cao, H., Plichta, M. M., Schäfer, A., Haddad, L., Grimm, O., Schneider, M., Esslinger, C., Kirsch, P., Meyer-Lindenberg, A., & Tost, H. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.09.013 · Cited by 227 · see citing works
Groen, R. N., Ryan, O., Wigman, J. T. W., Riese, H., Penninx, B. W. J. H., Giltay, E. J., Wichers, M., & Hartman, C. A. (2020) · BMC Medicine · doi:10.1186/s12916-020-01738-z · Cited by 222 · see citing works
Applied ambulatory assessment: Integrating idiographic and nomothetic principles of measurement.
Wright, A. G. C., & Zimmermann, J. (2019) · Psychological Assessment · doi:10.1037/pas0000685 · Cited by 204 · see citing works
Connectotyping: Model Based Fingerprinting of the Functional Connectome
Miranda-Dominguez, O., Mills, B. D., Carpenter, S. D., Grant, K. A., Kroenke, C. D., Nigg, J. T., & Fair, D. A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0111048 · Cited by 197 · see citing works
Voelkle, M. C., Brose, A., Schmiedek, F., & Lindenberger, U. (2014) · Multivariate Behavioral Research · doi:10.1080/00273171.2014.889593 · Cited by 195 · see citing works
Roefs, A., Fried, E. I., Kindt, M., Martijn, C., Elzinga, B., Evers, A. W. M., Wiers, R. W., Borsboom, D., & Jansen, A. (2022) · Behaviour Research and Therapy · doi:10.1016/j.brat.2022.104096 · Cited by 185 · see citing works
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Gates, K. M., Lane, S., Varangis, E. M. L., Giovanello, K. S., & Guiskewicz, K. (2016) · Multivariate Behavioral Research · doi:10.1080/00273171.2016.1256187 · Cited by 136 · see citing works
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Second language lexical development and cognitive control: A longitudinal fMRI study.
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Self-citationGates, K. M., Molenaar, P. C. M., Iyer, S. P., Nigg, J. T., & Fair, D. A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0091322 · Cited by 107 · see citing works
Self-citationWright, A. G. C., Gates, K. M., Arizmendi, C., Lane, S., Woods, W. C., & Edershile, E. A. (2019) · Psychological Assessment · doi:10.1037/pas0000617 · Cited by 107 · see citing works
Self-citationThe dynamics, processes, mechanisms, and functioning of personality: An overview of the field
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Parsing Heterogeneity in the Brain Connectivity of Depressed and Healthy Adults During Positive Mood
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A process-based approach to cognitive behavioral therapy: A theory-based case illustration
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Wright, A. G. C., Beltz, A. M., Gates, K. M., Molenaar, P. C. M., & Simms, L. J. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.01914 · Cited by 82 · see citing works
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Fernández‐de‐las‐Peñas, C., Rodríguez‐Jiménez, J., Palacios‐Ceña, M., De-la-Llave-Rincón, A. I., Fuensalida‐Novo, S., Florêncio, L. L., Ambite‐Quesada, S., Ortega‐Santiago, R., Arias‐Buría, J. L., Liew, B. X. W., et al. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19159273 · Cited by 77 · see citing works
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van Hoorn, J., McCormick, E. M., Rogers, C. R., Ivory, S. L., & Telzer, E. H. (2018) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsy071 · Cited by 73 · see citing works
Bielczyk, N., Uithol, S., van Mourik, T., Anderson, P., Glennon, J. C., & Buitelaar, J. K. (2018) · Network Neuroscience · doi:10.1162/netn_a_00062 · Cited by 70 · see citing works
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Wu, J., Yang, J., Chen, M., Li, S., Zhang, Z., Kang, C., Ding, G., & Guo, T. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.06.022 · Cited by 70 · see citing works
van der Krieke, L., Blaauw, F. J., Emerencia, A. C., Schenk, H., Slaets, J. P. J., Bos, E. H., de Jonge, P., & Jeronimus, B. F. (2016) · Psychosomatic Medicine · doi:10.1097/psy.0000000000000378 · Cited by 69 · see citing works
Kleiman, E. M., Glenn, C. R., & Liu, R. T. (2019) · Journal of Clinical Child & Adolescent Psychology · doi:10.1080/15374416.2019.1666400 · Cited by 68 · see citing works
Ariens, S., Ceulemans, E., & Adolf, J. (2020) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2020.110191 · Cited by 67 · see citing works
Longitudinal changes in reading network connectivity related to skill improvement
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Automated Selection of Robust Individual-Level Structural Equation Models for Time Series Data
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Second language learning success revealed by brain networks
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Methods for Quantifying Patterns of Dynamic Interactions in Dyads
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Self-citationLatent variable GIMME using model implied instrumental variables (MIIVs).
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Self-citationComparing directed functional connectivity between groups with confirmatory subgrouping GIMME
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Self-citationDealing with Multiple Solutions in Structural Vector Autoregressive Models
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Self-citationInsights from Personalized Models of Brain and Behavior for Identifying Biomarkers in PsychiatryPreprint
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Heshmati, S., Oravecz, Z., Brick, T. R., & Roeser, R. W. (2020) · Applied Developmental Science · doi:10.1080/10888691.2020.1766356 · Cited by 42 · see citing works
- Authors
- Gates, K. M., & Molenaar, P. C. M.
- Affiliations
- Pennsylvania State University (US)
- Corresponding author
- Gates, Kathleen M.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-10
- Volume
- 63
- Issue
- 1
- Pages
- 310-319
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2085423000
- Cited by
- 478
- References
- 30
- Citations by year
- 2026: 31 · 2025: 50 · 2024: 53 · 2023: 45 · 2022: 61 · 2021: 51 · 2020: 39 · 2019: 29 · 2018: 34 · 2017: 24 · 2016: 24 · 2015: 15 · 2014: 10 · 2013: 10 · 2012: 2
- FWCI
- 6.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (0852147, 1157220)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- effective connectivity, GIMME, directed connectivity, heterogeneous samples, resting-state fMRI, connectivity mapping
- MeSH terms
- Algorithms, Artificial Intelligence, Brain, Brain Mapping, Data Interpretation, Statistical, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Nerve Net, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results, Sample Size, Connectome
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