Academic Appointments


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  • The Impact of Mindfulness Training on Police Officer Stress, Mental Health, and Salivary Cortisol Levels. Frontiers in psychology Grupe, D. W., Stoller, J. L., Alonso, C., McGehee, C., Smith, C., Mumford, J. A., Rosenkranz, M. A., Davidson, R. J. 2021; 12: 720753

    Abstract

    Unaddressed occupational stress and trauma contribute to elevated rates of mental illness and suicide in policing, and to violent and aggressive behavior that disproportionately impacts communities of color. Emerging evidence suggests mindfulness training with police may reduce stress and aggression and improve mental health, but there is limited evidence for changes in biological outcomes or the lasting benefits of mindfulness training. We conducted a randomized controlled trial (RCT) of 114 police officers from three Midwestern U.S. law enforcement agencies. We assessed stress-related physical and mental health symptoms, blood-based inflammatory markers, and hair and salivary cortisol. Participants were then randomized to an 8-week mindfulness intervention or waitlist control (WLC), and the same assessments were repeated post-intervention and at 3-month follow-up. Relative to waitlist control, the mindfulness group had greater improvements in psychological distress, mental health symptoms, and sleep quality post-training, gains that were maintained at 3-month follow-up. Intervention participants also had a significantly lower cortisol awakening response (CAR) at 3-month follow-up relative to waitlist control. Contrary to hypotheses, there were no intervention effects on hair cortisol, diurnal cortisol slope, or inflammatory markers. In summary, an 8-week mindfulness intervention for police officers led to self-reported improvements in distress, mental health, and sleep, and a lower CAR. These benefits persisted (or emerged) at 3-month follow-up, suggesting that this training may buffer against the long-term consequences of chronic stress. Future research should assess the persistence of these benefits over a longer period while expanding the scope of outcomes to consider the broader community of mindfulness training for police. Clinical Trial Registration: ClinicalTrials.gov#NCT03488875.

    View details for DOI 10.3389/fpsyg.2021.720753

    View details for PubMedID 34539521

  • Variability in the analysis of a single neuroimaging dataset by many teams NATURE Botvinik-Nezer, R., Holzmeister, F., Camerer, C. F., Dreber, A., Huber, J., Johannesson, M., Kirchler, M., Iwanir, R., Mumford, J. A., Adcock, R., Avesani, P., Baczkowski, B. M., Bajracharya, A., Bakst, L., Ball, S., Barilari, M., Bault, N., Beaton, D., Beitner, J., Benoit, R. G., Berkers, R. J., Bhanji, J. P., Biswal, B. B., Bobadilla-Suarez, S., Bortolini, T., Bottenhorn, K. L., Bowring, A., Braem, S., Brooks, H. R., Brudner, E. G., Calderon, C. B., Camilleri, J. A., Castrellon, J. J., Cecchetti, L., Cieslik, E. C., Cole, Z. J., Collignon, O., Cox, R. W., Cunningham, W. A., Czoschke, S., Dadi, K., Davis, C. P., Luca, A., Delgado, M. R., Demetriou, L., Dennison, J. B., Di, X., Dickie, E. W., Dobryakova, E., Donnat, C. L., Dukart, J., Duncan, N. W., Durnez, J., Eed, A., Eickhoff, S. B., Erhart, A., Fontanesi, L., Fricke, G., Fu, S., Galvan, A., Gau, R., Genon, S., Glatard, T., Glerean, E., Goeman, J. J., Golowin, S. E., Gonzalez-Garcia, C., Gorgolewski, K. J., Grady, C. L., Green, M. A., Guassi Moreira, J. F., Guest, O., Hakimi, S., Hamilton, J., Hancock, R., Handjaras, G., Harry, B. B., Hawco, C., Herholz, P., Herman, G., Heunis, S., Hoffstaedter, F., Hogeveen, J., Holmes, S., Hu, C., Huettel, S. A., Hughes, M. E., Iacovella, V., Iordan, A. D., Isager, P. M., Isik, A. I., Jahn, A., Johnson, M. R., Johnstone, T., Joseph, M. E., Juliano, A. C., Kable, J. W., Kassinopoulos, M., Koba, C., Kong, X., Koscik, T. R., Kucukboyaci, N., Kuhl, B. A., Kupek, S., Laird, A. R., Lamm, C., Langner, R., Lauharatanahirun, N., Lee, H., Lee, S., Leemans, A., Leo, A., Lesage, E., Li, F., Li, M. C., Lim, P., Lintz, E. N., Liphardt, S. W., Losecaat Vermeer, A. B., Love, B. C., Mack, M. L., Malpica, N., Marins, T., Maumet, C., McDonald, K., McGuire, J. T., Melero, H., Mendez Leal, A. S., Meyer, B., Meyer, K. N., Mihai, G., Mitsis, G. D., Moll, J., Nielson, D. M., Nilsonne, G., Notter, M. P., Olivetti, E., Onicas, A. I., Papale, P., Patil, K. R., Peelle, J. E., Perez, A., Pischedda, D., Poline, J., Prystauka, Y., Ray, S., Reuter-Lorenz, P. A., Reynolds, R. C., Ricciardi, E., Rieck, J. R., Rodriguez-Thompson, A. M., Romyn, A., Salo, T., Samanez-Larkin, G. R., Sanz-Morales, E., Schlichting, M. L., Schultz, D. H., Shen, Q., Sheridan, M. A., Silvers, J. A., Skagerlund, K., Smith, A., Smith, D. V., Sokol-Hessner, P., Steinkamp, S. R., Tashjian, S. M., Thirion, B., Thorp, J. N., Tinghog, G., Tisdall, L., Tompson, S. H., Toro-Serey, C., Torre Tresols, J., Tozzi, L., Truong, V., Turella, L., van 't Veer, A. E., Verguts, T., Vettel, J. M., Vijayarajah, S., Vo, K., Wall, M. B., Weeda, W. D., Weis, S., White, D. J., Wisniewski, D., Xifra-Porxas, A., Yearling, E. A., Yoon, S., Yuan, R., Yuen, K. L., Zhang, L., Zhang, X., Zosky, J. E., Nichols, T. E., Poldrack, R. A., Schonberg, T. 2020
  • Variability in the analysis of a single neuroimaging dataset by many teams. Nature Botvinik-Nezer, R. n., Holzmeister, F. n., Camerer, C. F., Dreber, A. n., Huber, J. n., Johannesson, M. n., Kirchler, M. n., Iwanir, R. n., Mumford, J. A., Adcock, R. A., Avesani, P. n., Baczkowski, B. M., Bajracharya, A. n., Bakst, L. n., Ball, S. n., Barilari, M. n., Bault, N. n., Beaton, D. n., Beitner, J. n., Benoit, R. G., Berkers, R. M., Bhanji, J. P., Biswal, B. B., Bobadilla-Suarez, S. n., Bortolini, T. n., Bottenhorn, K. L., Bowring, A. n., Braem, S. n., Brooks, H. R., Brudner, E. G., Calderon, C. B., Camilleri, J. A., Castrellon, J. J., Cecchetti, L. n., Cieslik, E. C., Cole, Z. J., Collignon, O. n., Cox, R. W., Cunningham, W. A., Czoschke, S. n., Dadi, K. n., Davis, C. P., Luca, A. D., Delgado, M. R., Demetriou, L. n., Dennison, J. B., Di, X. n., Dickie, E. W., Dobryakova, E. n., Donnat, C. L., Dukart, J. n., Duncan, N. W., Durnez, J. n., Eed, A. n., Eickhoff, S. B., Erhart, A. n., Fontanesi, L. n., Fricke, G. M., Fu, S. n., Galván, A. n., Gau, R. n., Genon, S. n., Glatard, T. n., Glerean, E. n., Goeman, J. J., Golowin, S. A., González-García, C. n., Gorgolewski, K. J., Grady, C. L., Green, M. A., Guassi Moreira, J. F., Guest, O. n., Hakimi, S. n., Hamilton, J. P., Hancock, R. n., Handjaras, G. n., Harry, B. B., Hawco, C. n., Herholz, P. n., Herman, G. n., Heunis, S. n., Hoffstaedter, F. n., Hogeveen, J. n., Holmes, S. n., Hu, C. P., Huettel, S. A., Hughes, M. E., Iacovella, V. n., Iordan, A. D., Isager, P. M., Isik, A. I., Jahn, A. n., Johnson, M. R., Johnstone, T. n., Joseph, M. J., Juliano, A. C., Kable, J. W., Kassinopoulos, M. n., Koba, C. n., Kong, X. Z., Koscik, T. R., Kucukboyaci, N. E., Kuhl, B. A., Kupek, S. n., Laird, A. R., Lamm, C. n., Langner, R. n., Lauharatanahirun, N. n., Lee, H. n., Lee, S. n., Leemans, A. n., Leo, A. n., Lesage, E. n., Li, F. n., Li, M. Y., Lim, P. C., Lintz, E. N., Liphardt, S. W., Losecaat Vermeer, A. B., Love, B. C., Mack, M. L., Malpica, N. n., Marins, T. n., Maumet, C. n., McDonald, K. n., McGuire, J. T., Melero, H. n., Méndez Leal, A. S., Meyer, B. n., Meyer, K. N., Mihai, G. n., Mitsis, G. D., Moll, J. n., Nielson, D. M., Nilsonne, G. n., Notter, M. P., Olivetti, E. n., Onicas, A. I., Papale, P. n., Patil, K. R., Peelle, J. E., Pérez, A. n., Pischedda, D. n., Poline, J. B., Prystauka, Y. n., Ray, S. n., Reuter-Lorenz, P. A., Reynolds, R. C., Ricciardi, E. n., Rieck, J. R., Rodriguez-Thompson, A. M., Romyn, A. n., Salo, T. n., Samanez-Larkin, G. R., Sanz-Morales, E. n., Schlichting, M. L., Schultz, D. H., Shen, Q. n., Sheridan, M. A., Silvers, J. A., Skagerlund, K. n., Smith, A. n., Smith, D. V., Sokol-Hessner, P. n., Steinkamp, S. R., Tashjian, S. M., Thirion, B. n., Thorp, J. N., Tinghög, G. n., Tisdall, L. n., Tompson, S. H., Toro-Serey, C. n., Torre Tresols, J. J., Tozzi, L. n., Truong, V. n., Turella, L. n., van 't Veer, A. E., Verguts, T. n., Vettel, J. M., Vijayarajah, S. n., Vo, K. n., Wall, M. B., Weeda, W. D., Weis, S. n., White, D. J., Wisniewski, D. n., Xifra-Porxas, A. n., Yearling, E. A., Yoon, S. n., Yuan, R. n., Yuen, K. S., Zhang, L. n., Zhang, X. n., Zosky, J. E., Nichols, T. E., Poldrack, R. A., Schonberg, T. n. 2020; 582 (7810): 84–88

    Abstract

    Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

    View details for DOI 10.1038/s41586-020-2314-9

    View details for PubMedID 32483374

  • Neural correlates of state-based decision-making in younger and older adults NEUROIMAGE Worthy, D. A., Davis, T., Gorlick, M. A., Cooper, J. A., Bakkour, A., Mumford, J. A., Poldrack, R. A., Maddox, W. T. 2016; 130: 13-23

    Abstract

    Older and younger adults performed a state-based decision-making task while undergoing functional MRI (fMRI). We proposed that younger adults would be more prone to base their decisions on expected value comparisons, but that older adults would be more reactive decision-makers who would act in response to recent changes in rewards or states, rather than on a comparison of expected values. To test this we regressed BOLD activation on two measures from a sophisticated reinforcement learning (RL) model. A value-based regressor was computed by subtracting the immediate value of the selected alternative from its long-term value. The other regressor was a state-change uncertainty signal that served as a proxy for whether the participant's state improved or declined, relative to the previous trial. Younger adults' activation was modulated by the value-based regressor in ventral striatal and medial PFC regions implicated in reinforcement learning. Older adults' activation was modulated by state-change uncertainty signals in right dorsolateral PFC, and activation in this region was associated with improved performance in the task. This suggests that older adults may depart from standard expected-value based strategies and recruit lateral PFC regions to engage in reactive decision-making strategies.

    View details for DOI 10.1016/j.neuroimage.2015.12.004

    View details for Web of Science ID 000372745600002

    View details for PubMedCentralID PMC4808466

  • Long-term neural and physiological phenotyping of a single human NATURE COMMUNICATIONS Poldrack, R. A., Laumann, T. O., Koyejo, O., Gregory, B., Hover, A., Chen, M., Gorgolewski, K. J., Luci, J., Joo, S. J., Boyd, R. L., Hunicke-Smith, S., Simpson, Z. B., Caven, T., Sochat, V., Shine, J. M., Gordon, E., Snyder, A. Z., Adeyemo, B., Petersen, S. E., Glahn, D. C., McKay, D. R., Curran, J. E., Goering, H. H., Carless, M. A., Blangero, J., Dougherty, R., Leemans, A., Handwerker, D. A., Frick, L., Marcotte, E. M., Mumford, J. A. 2015; 6

    Abstract

    Psychiatric disorders are characterized by major fluctuations in psychological function over the course of weeks and months, but the dynamic characteristics of brain function over this timescale in healthy individuals are unknown. Here, as a proof of concept to address this question, we present the MyConnectome project. An intensive phenome-wide assessment of a single human was performed over a period of 18 months, including functional and structural brain connectivity using magnetic resonance imaging, psychological function and physical health, gene expression and metabolomics. A reproducible analysis workflow is provided, along with open access to the data and an online browser for results. We demonstrate dynamic changes in brain connectivity over the timescales of days to months, and relations between brain connectivity, gene expression and metabolites. This resource can serve as a testbed to study the joint dynamics of human brain and metabolic function over time, an approach that is critical for the development of precision medicine strategies for brain disorders.

    View details for DOI 10.1038/ncomms9885

    View details for Web of Science ID 000367577400002

    View details for PubMedCentralID PMC4682164

  • Functional System and Areal Organization of a Highly Sampled Individual Human Brain NEURON Laumann, T. O., Gordon, E. M., Adeyemo, B., Snyder, A. Z., Joo, S. J., Chen, M., Gilmore, A. W., McDermott, K. B., Nelson, S. M., Dosenbach, N. U., Schlaggar, B. L., Mumford, J. A., Poldrack, R. A., Petersen, S. E. 2015; 87 (3): 657-670

    Abstract

    Resting state functional MRI (fMRI) has enabled description of group-level functional brain organization at multiple spatial scales. However, cross-subject averaging may obscure patterns of brain organization specific to each individual. Here, we characterized the brain organization of a single individual repeatedly measured over more than a year. We report a reproducible and internally valid subject-specific areal-level parcellation that corresponds with subject-specific task activations. Highly convergent correlation network estimates can be derived from this parcellation if sufficient data are collected-considerably more than typically acquired. Notably, within-subject correlation variability across sessions exhibited a heterogeneous distribution across the cortex concentrated in visual and somato-motor regions, distinct from the pattern of intersubject variability. Further, although the individual's systems-level organization is broadly similar to the group, it demonstrates distinct topological features. These results provide a foundation for studies of individual differences in cortical organization and function, especially for special or rare individuals. VIDEO ABSTRACT.

    View details for DOI 10.1016/j.neuron.2015.06.037

    View details for Web of Science ID 000361145000016

    View details for PubMedID 26212711

  • Orthogonalization of Regressors in fMRI Models PLOS ONE Mumford, J. A., Poline, J., Poldrack, R. A. 2015; 10 (4)

    Abstract

    The occurrence of collinearity in fMRI-based GLMs (general linear models) may reduce power or produce unreliable parameter estimates. It is commonly believed that orthogonalizing collinear regressors in the model will solve this problem, and some software packages apply automatic orthogonalization. However, the effects of orthogonalization on the interpretation of the resulting parameter estimates is widely unappreciated or misunderstood. Here we discuss the nature and causes of collinearity in fMRI models, with a focus on the appropriate uses of orthogonalization. Special attention is given to how the two popular fMRI data analysis software packages, SPM and FSL, handle orthogonalization, and pitfalls that may be encountered in their usage. Strategies are discussed for reducing collinearity in fMRI designs and addressing their effects when they occur.

    View details for DOI 10.1371/journal.pone.0126255

    View details for Web of Science ID 000353659400093

    View details for PubMedID 25919488

    View details for PubMedCentralID PMC4412813

  • If all your friends jumped off a bridge: The effect of others' actions on engagement in and recommendation of risky behaviors. Journal of experimental psychology. General Helfinstein, S. M., Mumford, J. A., Poldrack, R. A. 2015; 144 (1): 12-17

    Abstract

    There is a large gap between the types of risky behavior we recommend to others and those we engage in ourselves. In this study, we hypothesized that a source of this gap is greater reliance on information about others' behavior when deciding whether to take a risk oneself than when deciding whether to recommend it to others. To test this hypothesis, we asked participants either to report their willingness to engage in a series of risky behaviors themselves; their willingness to recommend those behaviors to a loved one; or, how good of an idea it would be for either them or a loved one to engage in the behaviors. We then asked them to evaluate those behaviors on criteria related to the expected utility of the risk (benefits, costs, and likelihood of costs), and on engagement in the activity by people they knew. We found that, after accounting for effects of perceived benefit, cost, and likelihood of cost, perceptions of others' behavior had a dramatically larger impact on participants' willingness to engage in a risk than on their willingness to recommend the risk or their prescriptive evaluation of the risk. These findings indicate that the influence of others' choices on risk-taking behavior is large, direct, cannot be explained by an economic utility model of risky decision-making, and goes against one's own better judgment. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

    View details for DOI 10.1037/xge0000043

    View details for PubMedID 25485604

  • What do differences between multi-voxel and univariate analysis mean? How subject-, voxel-, and trial-level variance impact fMRI analysis NEUROIMAGE Davis, T., LaRocque, K. F., Mumford, J. A., Norman, K. A., Wagner, A. D., Poldrack, R. A. 2014; 97: 271-283

    Abstract

    Multi-voxel pattern analysis (MVPA) has led to major changes in how fMRI data are analyzed and interpreted. Many studies now report both MVPA results and results from standard univariate voxel-wise analysis, often with the goal of drawing different conclusions from each. Because MVPA results can be sensitive to latent multidimensional representations and processes whereas univariate voxel-wise analysis cannot, one conclusion that is often drawn when MVPA and univariate results differ is that the activation patterns underlying MVPA results contain a multidimensional code. In the current study, we conducted simulations to formally test this assumption. Our findings reveal that MVPA tests are sensitive to the magnitude of voxel-level variability in the effect of a condition within subjects, even when the same linear relationship is coded in all voxels. We also find that MVPA is insensitive to subject-level variability in mean activation across an ROI, which is the primary variance component of interest in many standard univariate tests. Together, these results illustrate that differences between MVPA and univariate tests do not afford conclusions about the nature or dimensionality of the neural code. Instead, targeted tests of the informational content and/or dimensionality of activation patterns are critical for drawing strong conclusions about the representational codes that are indicated by significant MVPA results.

    View details for DOI 10.1016/j.neuroimage.2014.04.037

    View details for Web of Science ID 000337988700028

    View details for PubMedCentralID PMC4115449

  • Functional imaging of sleep vertex sharp transients CLINICAL NEUROPHYSIOLOGY Stern, J. M., Caporro, M., Haneef, Z., Yeh, H. J., Buttinelli, C., Lenartowicz, A., Mumford, J. A., Parvizi, J., Poldrack, R. A. 2011; 122 (7): 1382-1386

    Abstract

    The vertex sharp transient (VST) is an electroencephalographic (EEG) discharge that is an early marker of non-REM sleep. It has been recognized since the beginning of sleep physiology research, but its source and function remain mostly unexplained. We investigated VST generation using functional MRI (fMRI).Simultaneous EEG and fMRI were recorded from seven individuals in drowsiness and light sleep. VST occurrences on EEG were modeled with fMRI using an impulse function convolved with a hemodynamic response function to identify cerebral regions correlating to the VSTs. A resulting statistical image was thresholded at Z>2.3.Two hundred VSTs were identified. Significantly increased signal was present bilaterally in medial central, lateral precentral, posterior superior temporal, and medial occipital cortex. No regions of decreased signal were present.The regions are consistent with electrophysiologic evidence from animal models and functional imaging of human sleep, but the results are specific to VSTs. The regions principally encompass the primary sensorimotor cortical regions for vision, hearing, and touch.The results depict a network comprising the presumed VST generator and its associated regions. The associated regions functional similarity for primary sensation suggests a role for VSTs in sensory experience during sleep.

    View details for DOI 10.1016/j.clinph.2010.12.049

    View details for Web of Science ID 000291102300017

    View details for PubMedID 21310653

    View details for PubMedCentralID PMC3105179