Algorithm Development
Fisher, Z. F., Parsons, J., Gates, K. M., & Hopfinger, J. B. (2023). Blind Subgrouping of Task-based fMRI. Psychometrika, 88(2), 434-455.
Luo, L., Fisher, Z. F., Arizmendi, C., Molenaar, P., Beltz, A., & Gates, K. M. (2022). Estimating both directed and undirected contemporaneous relations in time series data using hybrid-group iterative multiple model estimation. Psychological Methods, 28(1), 189–206. https://doi.org/10.1037/met0000485
Gates, K. M., Fisher, Z. F., & Bollen, K. A. (2019). Latent variable GIMME using model implied instrumental variables (MIIVs). Psychological methods, 25(2), 227.
Lane, S. T., Gates, K. M., Pike, H. K., Beltz, A. M., & Wright, A. G. (2019). Uncovering general, shared, and unique temporal patterns in ambulatory assessment data. Psychological Methods, 24(1), 54.
Henry, T. R., Feczko, E., Cordova, M., Earl, E., Williams, S., Nigg, J. T., … & Gates, K. M. (2019). Comparing directed functional connectivity between groups with confirmatory subgrouping GIMME. Neuroimage, 188, 642-653.
Gates, K. M., Lane, S. T., Varangis, E., Giovanello, K., & Guiskewicz, K. (2017). Unsupervised classification during time-series model building. Multivariate behavioral research, 52(2), 129-148.
Beltz, A. M., & Molenaar, P. C. (2016). Dealing with multiple solutions in structural vector autoregressive models. Multivariate behavioral research, 51(2-3), 357-373.
Gates, K. M., & Molenaar, P. C. (2012). Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. NeuroImage, 63(1), 310-319.
Textbook
Gates, K.M., Chow, S.-M., & Molenaar, P.C.M. (2023). Intensive Longitudinal Analysis of Human Processes. CRC Press. (Online material freely available)
Tutorials
Beltz, A. M., & Gates, K. M. (2017). Network mapping with GIMME. Multivariate behavioral research, 52(6), 789-804.
Lane, S. T., & Gates, K. M. (2017). Automated selection of robust individual-level structural equation models for time series data. Structural Equation Modeling: A Multidisciplinary Journal, 24(5), 768-782.
Zinszer, B. (2013). Effective Connectivity modeling with the euSEM and GIMME. University TPS, editor.
Applications – fMRI
Zhang, X., Yang, J., Wang, R., & Li, P. (2020). A neuroimaging study of semantic representation in first and second languages. Language, Cognition and Neuroscience, 1-16.
Dajani, D. R., Odriozola, P., Winters, M., Voorhies, W., Marcano, S., Baez, A., … & Uddin, L. Q. (2020). Measuring Cognitive Flexibility with the Flexible Item Selection Task: From fMRI Adaptation to Individual Connectome Mapping. Journal of Cognitive Neuroscience, 32(6), 1026-1045.
Elbich, D. B., Molenaar, P. C., & Scherf, K. S. (2019). Evaluating the organizational structure and specificity of network topology within the face processing system. Human brain mapping, 40(9), 2581-2595.
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.
Beltz, A. M., Moser, J. S., Zhu, D. C., Burt, S. A., & Klump, K. L. (2018). Using person‐specific neural networks to characterize heterogeneity in eating disorders: Illustrative links between emotional eating and ovarian hormones. International Journal of Eating Disorders, 51(7), 730-740.
Yang, J., Ye, J., Wang, R., Zhou, K., & Wu, Y. J. (2018). Bilingual contexts modulate the inhibitory control network. Frontiers in psychology, 9, 395.
Price, R. B., Lane, S., Gates, K., Kraynak, T. E., Horner, M. S., Thase, M. E., & Siegle, G. J. (2017). Parsing heterogeneity in the brain connectivity of depressed and healthy adults during positive mood. Biological psychiatry, 81(4), 347-357.
Price, R. B., Gates, K., Kraynak, T. E., Thase, M. E., & Siegle, G. J. (2017). Data-driven subgroups in depression derived from directed functional connectivity paths at rest. Neuropsychopharmacology, 42(13), 2623-2632.
Zelle, S. L., Gates, K. M., Fiez, J. A., Sayette, M. A., & Wilson, S. J. (2017). The first day is always the hardest: Functional connectivity during cue exposure and the ability to resist smoking in the initial hours of a quit attempt. NeuroImage, 151, 24-32.
Martinez, B., Karunanayaka, P., Wang, J., Tobia, M. J., Vasavada, M., Eslinger, P. J., & Yang, Q. X. (2017). Different patterns of age-related central olfactory decline in men and women as quantified by olfactory fMRI. Oncotarget, 8(45), 79212.
Yang, J., Gates, K. M., Molenaar, P., & Li, P. (2015). Neural changes underlying successful second language word learning: An fMRI study. Journal of Neurolinguistics, 33, 29-49.
Nichols, T. T., Gates, K. M., Molenaar, P. C., & Wilson, S. J. (2014). Greater BOLD activity but more efficient connectivity is associated with better cognitive performance within a sample of nicotine‐deprived smokers. Addiction biology, 19(5), 931-940.
Hillary, F. G., Medaglia, J. D., Gates, K. M., Molenaar, P. C., & Good, D. C. (2014). Examining network dynamics after traumatic brain injury using the extended unified SEM approach. Brain imaging and behavior, 8(3), 435-445.
Karunanayaka, P., Eslinger, P. J., Wang, J. L., Weitekamp, C. W., Molitoris, S., … & Yang, Q. X. (2014). Networks involved in olfaction and their dynamics using independent component analysis and unified structural equation modeling. Human brain mapping, 35(5), 2055-2072.
Beltz, A. M., Gates, K. M., Engels, A. S., Molenaar, P. C., Pulido, C., Turrisi, R., … & Wilson, S. J. (2013). Changes in alcohol-related brain networks across the first year of college: a prospective pilot study using fMRI effective connectivity mapping. Addictive behaviors, 38(4), 2052-2059.
Gates, K. M., Molenaar, P. C., Iyer, S. P., Nigg, J. T., & Fair, D. A. (2014). Organizing heterogeneous samples using community detection of GIMME-derived resting state functional networks. PloS one, 9(3), e91322.
Applications – EMA data
Ellison, W. D., Levy, K. N., Newman, M. G., Pincus, A. L., Wilson, S. J., & Molenaar, P. (2019). Dynamics among borderline personality and anxiety features in psychotherapy outpatients: An exploration of nomothetic and idiographic patterns. Personality Disorders: Theory, Research, and Treatment.
Wright, A. G., Gates, K. M., Arizmendi, C., Lane, S. T., Woods, W. C., & Edershile, E. A. (2019). Focusing personality assessment on the person: Modeling general, shared, and person specific processes in personality and psychopathology. Psychological Assessment, 31(4), 502.
Dotterer, H. L., Beltz, A. M., Foster, K. T., Simms, L. J., & Wright, A. G. (2019). Personalized models of personality disorders: Using a temporal network method to understand symptomatology and daily functioning in a clinical sample. Psychological Medicine, 1-9.
Bouwmans, M. E., Beltz, A. M., Bos, E. H., Oldehinkel, A. J., de Jonge, P., & Molenaar, P. C. (2016). The dynamic interplay of melatonin, affect, and fatigue in the context of sleep and depression. A sad day’s night, 31.
Beltz, A. M., Wright, A. G., Sprague, B. N., & Molenaar, P. C. (2016). Bridging the nomothetic and idiographic approaches to the analysis of clinical data. Assessment, 23(4), 447-458.
Wright, A. G., Beltz, A. M., Gates, K. M., Molenaar, P., & Simms, L. J. (2015). Examining the dynamic structure of daily internalizing and externalizing behavior at multiple levels of analysis. Frontiers in psychology, 6, 1914.