Professional Education


  • Doctor of Philosophy, University of Minnesota Twin Cities (2014)
  • Bachelor of Arts, Macalester College (2006)

Stanford Advisors


Current Research and Scholarly Interests


My doctoral research primarily explored neurometric encoding and decoding based on the brain's in-vivo functional connectivity. Most of my projects centered around developing and implementing novel multivariate functional connectivity approaches for fcMRI research. I applied these techniques to two behavioral genetics studies of cognitive processes, MR pulse sequence development and implementation, as well as the development of psychiatric diagnostic tools for schizophrenia, Parkinson's and substance abuse. The post-doctoral research that I am now conducting extends this work, in that I am currently developing multivariate models that add population genetics via extended pedigrees, and structural connectivity to the neuropsychological and functional connectivity parameters that I had previously been assessing. The overarching goal is to be able to use this entire complement of data to understanding the genetic and neurobiological substrates of the functioning and dysfunction of higher-order cognitive processes. By advancing our understanding the interaction and distribution of cognitive and neurobiological functionality, this work should inform the development of both individualized medicine strategies and epidemiological models of psychopathology.

Lab Affiliations


All Publications


  • The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance. Neuron Shine, J. M., Bissett, P. G., Bell, P. T., Koyejo, O., Balsters, J. H., Gorgolewski, K. J., Moodie, C. A., Poldrack, R. A. 2016; 92 (2): 544-554

    Abstract

    Higher brain function relies upon the ability to flexibly integrate information across specialized communities of brain regions; however, it is unclear how this mechanism manifests over time. In this study, we used time-resolved network analysis of fMRI data to demonstrate that the human brain traverses between functional states that maximize either segregation into tight-knit communities or integration across otherwise disparate neural regions. Integrated states enable faster and more accurate performance on a cognitive task, and are associated with dilations in pupil diameter, suggesting that ascending neuromodulatory systems may govern the transition between these alternative modes of brain function. Together, our results confirm a direct link between cognitive performance and the dynamic reorganization of the network structure of the brain.

    View details for DOI 10.1016/j.neuron.2016.09.018

    View details for PubMedID 27693256

    View details for PubMedCentralID PMC5073034

  • Bootstrap Enhanced Penalized Regression for Variable Selection with Neuroimaging Data. Frontiers in neuroscience Abram, S. V., Helwig, N. E., Moodie, C. A., DeYoung, C. G., MacDonald, A. W., Waller, N. G. 2016; 10: 344-?

    Abstract

    Recent advances in fMRI research highlight the use of multivariate methods for examining whole-brain connectivity. Complementary data-driven methods are needed for determining the subset of predictors related to individual differences. Although commonly used for this purpose, ordinary least squares (OLS) regression may not be ideal due to multi-collinearity and over-fitting issues. Penalized regression is a promising and underutilized alternative to OLS regression. In this paper, we propose a nonparametric bootstrap quantile (QNT) approach for variable selection with neuroimaging data. We use real and simulated data, as well as annotated R code, to demonstrate the benefits of our proposed method. Our results illustrate the practical potential of our proposed bootstrap QNT approach. Our real data example demonstrates how our method can be used to relate individual differences in neural network connectivity with an externalizing personality measure. Also, our simulation results reveal that the QNT method is effective under a variety of data conditions. Penalized regression yields more stable estimates and sparser models than OLS regression in situations with large numbers of highly correlated neural predictors. Our results demonstrate that penalized regression is a promising method for examining associations between neural predictors and clinically relevant traits or behaviors. These findings have important implications for the growing field of functional connectivity research, where multivariate methods produce numerous, highly correlated brain networks.

    View details for DOI 10.3389/fnins.2016.00344

    View details for PubMedID 27516732

    View details for PubMedCentralID PMC4964314