Publications

\(\dagger\) denotes supported trainee co-author at time of publication | \(*\) denotes senior/corresponding author role

A selection of key publication models and results.

Preprints

5. Schaaf, J.V., Sørensen, Ø., McCormick, E.M., Aristodemou, M., & Kievit, R.A. (preprint). Advocating a new default for time series modeling: Asymmetric temporal dynamics using threshold dynamics parameters. Preprint Supplemental Material

4. McCormick, E.M. (preprint). A tutorial on formalizing and testing specific psychological theory using nonlinear models. Preprint Supplemental Material

3. Sadeghi, N., Van Der Velpen, I., Baker, B., Batta, I., Genon, S., McCormick, E. M., Michel, L., Moraczewski, D., Morton, J.B., Seraji, M., Shaw, P., Silva, R.F., Soleimani, N., Sprooten, E., Sørensen, Ø., Thomas, A.G., Wazana, A., Zhou, Z., Calhoun, V., Chakravarty, M.M., Kievit, R.A., Plachti, A., Zuo, X., & White, T. (preprint). What do we really know about the interplay between brain, behavior, and cognition from childhood to early adulthood? An international group effort to generate and share simulated datasets. Preprint | Supplemental Material

2. \(^{\dagger}\)König, M., Smith, A., Moreno-Lopez L., Davidson, E., Dauvermann, M., Orellana, S., McCormick, E. M., Kaçer, M., Ioannidis, K., & Van Harmelen, A. L. (preprint). Friendship buffering effects on mental health symptoms before and during the COVID-19 pandemic: a UK longitudinal study of young people with childhood adversity. Preprint | Supplemental Material

1. McCormick, E. M., Borgeest, G. S., & Kievit, R. A. (preprint). Interrupted mediation: A cautionary note on using derived metrics as intervening variables in path models. Preprint | Supplemental Material

Published

In press

40. McCormick, E. M. (in press). Deriving models of change with interpretable parameters: linear estimation with nonlinear inference. Psychometrika, Accepted September 2024. Supplemental Material | Preprint | LENI Package

2024

39. Brandmaier, A. M., Lindenberger, U., & \(\mathbf{^*}\)McCormick, E. M. (2024). Optimal Two-Time Point Longitudinal Models for Estimating Individual-Level Change: Asymptotic Insights and Practical Implications. Developmental Cognitive Neuroscience, 70, 101450. https://doi.org/10.1016/j.dcn.2024.101450
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38. McCormick, E. M. & Bauer, D. J. (2024). How should we model the effect of “change” - or should we?. Psychological Methods, Advance online publication. https://doi.org/10.1037/met0000663
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37. McCormick, E. M., Curran, P. J. & Hancock G. R. (2024). Latent Growth Factors as Predictors of Distal Outcomes. Psychological Methods, Advance online publication. https://doi.org/10.1037/met0000642
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36. \(^\dagger\)Michel L. C., McCormick, E. M., & Kievit, R. A. (2024). Grey and white matter metrics demonstrate distinct and complementary prediction of differences in cognitive performance in children: Findings from ABCD (N= 11 876). Journal of Neuroscience, 44(12), e0465232023oi. https://doi.org/10.1523/JNEUROSCI.0465-23.2023
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35. \(^{\dagger}\)Parsons, S., & McCormick, E. M. (2024). Limitations of two time point data for understanding individual differences in longitudinal modeling — What can difference reveal about change?. Developmental Cognitive Neuroscience, 66, 101353. https://doi.org/10.1016/j.dcn.2024.101353
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2023

34. Duell, N., Perino, M. T., McCormick, E. M., & Telzer, E. H. (2023). Differential processing of risk and reward in delinquent and non-delinquent youth. Social Cognitive and Affective Neuroscience, 18(1), 1-9. https://doi.org/10.1093/scan/nsad040
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33. McCormick, E. M., Byrne, M. L., Flournoy, J. C., Mills, K. L., & Pfeifer, J. H. (2023). The Hitchhiker’s Guide to Longitudinal Models: A Primer on Model Selection for Repeated-Measures Methods. Developmental Cognitive Neuroscience, 63, 101281. https://doi.org/10.1016/j.dcn.2023.101281
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32. McCormick, E. M., Cam-CAN, & Kievit, R. A. (2023). Poorer white matter microstructure predicts slower and more variable reaction time performance: evidence for a neural noise hypothesis in a large lifespan cohort. Journal of Neuroscience, 43(19), 3557-3566. https://doi.org/10.1523/JNEUROSCI.1042-22.2023
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31. \(^{\dagger}\)Jorgensen, N. A., Muscatell, K. A., McCormick, E. M., Prinstein, M. J., Lindquist, K. A., & Telzer, E. H. (2023). Neighborhood Disadvantage, Race, and Neural Sensitivity to Social Threat and Reward among Adolescents. Social Cognitive and Affective Neuroscience, 18(1), 1-12. https://doi.org/10.1093/scan/nsac053
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2022

30. \(^{\dagger}\)Metherell, T. E., Ghai, S., McCormick, E. M., Ford T. J., & Orben, A. (2022). Digital exclusion predicts worse mental health among adolescents during COVID-19. Scientific Reports, 12. 19088. https://doi.org/10.1038/s41598-022-23899-y
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29. \(^{\dagger}\)Do, K. T., McCormick, E. M., Prinstein, M. J., Lindquist, K. A., & Telzer, E. H. (2022). Intrinsic connectivity within the affective salience network moderates adolescent susceptibility to negative and positive peer norms. Scientific Reports, 12. 17463. https://doi.org/10.1038/s41598-022-17780-1
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28. McCormick, E. M., Arnemann, K. L., Ito, T., Hanson, S. J., & Cole, M. W. (2022). Latent functional connectivity underlying multiple brain states. Network Neuroscience, 6(2), 570-590. https://doi.org/10.1162/netn_a_00234
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27. Kievit, R. A, McCormick, E. M.*, Fuhrmann, D.*, Deserno, M.*, & Orben, A*. (2022). Using large, publicly available data sets to study adolescent development: opportunities and challenges. Current Opinion in Psychology, 44, 303-308. * denotes equal contribution https://doi.org/10.1016/j.copsyc.2021.10.003
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2021

26. McCormick, E. M. (2021). Multi-Level Multi-Growth Models: New opportunities for addressing developmental theory using advanced longitudinal designs with planned missingness. Developmental Cognitive Neuroscience, 51, 101001. https://doi.org/10.1016/j.dcn.2021.101001
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25. McCormick, E. M., Peters, S., Crone, E. A., & Telzer, E. H. (2021). Longitudinal Network Re-organization Across Learning and Development. NeuroImage, 229, 117784. https://doi.org/10.1016/j.neuroimage.2021.117784
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24. Duell, N., van Hoorn, J., McCormick, E. M., Prinstein, M. J., & Telzer, E. H. (2021). Hormonal and neural correlates of prosocial conformity in adolescents. Developmental Cognitive Neuroscience, 48, 100936. https://doi.org/10.1016/j.dcn.2021.100936
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2020

23. Kwon, S-J. Do, K. T., McCormick, E. M., & Telzer, E. H. (2020). Neural correlates of conflicting social influence on adolescent risk-taking. Journal of Research on Adolescence, 31(1), 139-152. https://doi.org/10.1111/jora.12587
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22. Do, K. T., McCormick, E. M., & Telzer, E. H. (2020). Neural sensitivity to conflicting attitudes supports greater conformity toward positive over negative influence in early adolescence. Developmental Cognitive Neuroscience, 45, 100837. https://doi.org/10.1016/j.dcn.2020.100837
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21. van Hoorn, J., McCormick, E. M., Perino, M. T., Rogers, C. R., & Telzer, E.H. (2020). Differential behavioral and neural profiles in high-risk youth with conduct problems during risky decision-making. Journal of Research on Adolescence, 30(3), 599-615. https://doi.org/10.1111/jora.12546
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20. Chen, X., McCormick, E. M., Ravindran, N., Telzer, E. H., & McElwain, N. L. (2020). Maternal emotion socialization in early childhood predicts adolescents’ amygdala-vmPFC functional connectivity to emotion faces. Developmental Psychology, 56(3), 503-515. http://dx.doi.org/10.1037/dev0000852
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2019

19. Perino, M. T., Guassi Moreira, J., McCormick, E. M., & Telzer, E. H. (2019). Apples to apples? Neural correlates of emotion regulation differences between high and low risk adolescents. Social Cognitive and Affective Neuroscience, 14(8), 827-836. https://doi.org/10.1093/scan/nsz063
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18. Kwon, S-J., Ivory, S. L., McCormick, E. M., & Telzer, E. H. (2019). Behavioral and neural dysregulation to social reward and links to internalizing symptoms in adolescents. Frontiers in Behavioral Neuroscience, 13, 158. https://doi.org/10.3389/fnbeh.2019.00158
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17. McCormick, E. M., McElwain, N. L., & Telzer, E. H. (2019). Alterations in adolescent dopaminergic systems as a function of early mother-toddler attachment: a prospective longitudinal examination. International Journal of Developmental Neuroscience, 78, 122-129. https://doi.org/10.1016/j.ijdevneu.2019.06.010
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16. Do, K. T., McCormick, E. M., & Telzer, E. H. (2019). The neural development of prosocial behavior from childhood to adolescence. Social Cognitive and Affective Neuroscience, 14(2), 129-139. https://doi.org/10.1093/scan/nsy117
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15. McCormick, E. M., Gates, K. M., & Telzer, E. H. (2019). Model-based network discovery of developmental and performance-related differences during risky decision-making. NeuroImage, 188, 456-464. https://doi.org/10.1016/j.neuroimage.2018.12.042
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2018

14. McCormick, E. M. & Telzer, E. H. (2018b). Contributions of default mode network stability and deactivation to adolescent task engagement. Scientific Reports, 8(1), 18049. https://doi.org/10.1038/s41598-018-36269-4
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13. van Hoorn, J., McCormick, E. M., Rogers, C. R., Ivory, S. L., & Telzer, E. H. (2018). Differential effects of parent and peer presence on neural correlates of risk taking in adolescence. Social Cognitive and Affective Neuroscience, 13(9), 945-955. https://doi.org/10.1093/scan/nsy071
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12. McCormick, E. M., van Hoorn, J., Cohen, J. R., & Telzer, E. H. (2018). Functional connectivity in the social brain across childhood and adolescence. Social Cognitive and Affective Neuroscience, 13(9), 819-830. https://doi.org/10.1093/scan/nsy064
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11. Rogers, C. R., McCormick, E. M., Van Hoorn, J., & Telzer, E.H. (2018). Neural correlates of sibling closeness and association with externalizing behavior in adolescence. Social Cognitive and Affective Neuroscience, 13(9), 977-988. https://doi.org/10.1093/scan/nsy063
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10. Muscatell, K. A., McCormick, E. M., & Telzer, E. H. (2018). Subjective social status and neural processing of race in Mexican American adolescents. Development and Psychopathology, 30(5), 1837-1848. https://doi.org/10.1017/s0954579418000949
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9. van Hoorn, J., McCormick, E. M., & Telzer, E. H. (2018). Moderate social sensitivity in a risky context supports adaptive decision-making in adolescence: Evidence from brain and behavior. Social Cognitive and Affective Neuroscience, 13(5), 546-556. https://doi.org/10.1093/scan/nsy016
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8. Telzer, E. H., McCormick, E. M., Peters, S., Cosme, D., Pfeifer, J. H., & van Duijvenvoorde, A. C. K. (2018). Methodological considerations for developmental longitudinal fMRI research. Developmental Cognitive Neuroscience, 33, 149-160. https://doi.org/10.1016/j.dcn.2018.02.004
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7. McCormick, E. M., Perino, M. T., & Telzer, E. H. (2018). Not just social sensitivity: Selective adolescent suppression of social feedback during risk taking}. Developmental Cognitive Neuroscience, 30, 134-141. https://doi.org/10.1016/j.dcn.2018.01.012
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6. McCormick, E. M., & Telzer, E. H. (2018a). Not doomed to repeat: Enhanced neural tracking of errors promotes adaptive task performance during adolescence. Journal of Cognitive Neuroscience. 30(3), 281-289. https://doi.org/10.1162/jocn_a_01206
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5. Qu, Y., Pomerantz, E. M., McCormick, E. M., & Telzer, E. H. (2018). Youth’s conceptions of adolescence predict longitudinal changes in prefrontal cortex activation and risk taking. Child Development, 89(3), 773-783. https://doi.org/10.1111/cdev.13017
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2017

4. McCormick, E. M., Qu, Y., & Telzer, E. H. (2017). Activation in context: Differential conclusions drawn from cross-sectional and longitudinal analyses of adolescents’ cognitive control-related neural activity. Frontiers in Human Neuroscience, 11, 141. https://doi.org/10.3389/fnhum.2017.00141
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3. McCormick, E. M. & Telzer, E. H. (2017b). Failure to retreat: Blunted sensitivity to negative feedback supports risky behavior in adolescents. NeuroImage, 147, 381-389. http://dx.doi.org/10.1016/j.neuroimage.2016.12.041
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2. McCormick, E. M. & Telzer, E. H. (2017a). Adaptive adolescent flexibility: Neurodevelopment of decision-making and learning in a risky context. Journal of Cognitive Neuroscience, 29, 413-423. https://doi.org/10.1162/jocn_a_01061
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2016

1. McCormick, E. M., Qu, Y., & Telzer, E. H. (2016). Adolescent neurodevelopment of cognitive control and risk-taking in negative family contexts. NeuroImage, 124, 989-996. http://dx.doi.org/10.1016/j.neuroimage.2015.09.063
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Chapters

1. Curran, P.J., Strauss, C., McCormick, E.M., & McGinley, J.S. (2023). A multivariate growth curve model for three-level data. In H. Cooper (Ed.) APA Handbook of Research Methods in Psychology, Second Edition. Washington, DC: American Psychological Association.