Bio


Adam Richie-Halford is a postdoctoral scholar in the Division of Developmental-Behavioral Pediatrics at Stanford University and an affiliate of the eScience Institute at the University of Washington. He received his B.S. in engineering physics from Embry-Riddle Aeronautical University, his M.S. in Physics from the California State University in Long Beach, and his Ph.D. in physics from the University of Washington, where he performed large-scale quantum Monte Carlo simulations of systems encountered in nuclear theory. Along the way he also served as an officer in the Air Force and as a Peace Corps volunteer in the Kingdom of Morocco.

Currently, Adam works with Professor Jason Yeatman to develop new statistical learning techniques for the analysis of neuroimaging data and new browser-based technologies to assist deep phenotyping of dyslexia. He seeks to understand the biophysical properties of the brain’s white matter using large open datasets containing diffusion MRI images, such as the Human Connectome Project (HCP), the Healthy Brain Network (HBN) study, and the Adolescent Brain Cognitive Development (ABCD) study. He is also interested in developing open-source software tools to enable other scientists to analyze and share large datasets. Adam is a member of the Software and Data Carpentry communities.

Stanford Advisors


All Publications


  • QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data. Nature methods Cieslak, M., Cook, P. A., He, X., Yeh, F., Dhollander, T., Adebimpe, A., Aguirre, G. K., Bassett, D. S., Betzel, R. F., Bourque, J., Cabral, L. M., Davatzikos, C., Detre, J. A., Earl, E., Elliott, M. A., Fadnavis, S., Fair, D. A., Foran, W., Fotiadis, P., Garyfallidis, E., Giesbrecht, B., Gur, R. C., Gur, R. E., Kelz, M. B., Keshavan, A., Larsen, B. S., Luna, B., Mackey, A. P., Milham, M. P., Oathes, D. J., Perrone, A., Pines, A. R., Roalf, D. R., Richie-Halford, A., Rokem, A., Sydnor, V. J., Tapera, T. M., Tooley, U. A., Vettel, J. M., Yeatman, J. D., Grafton, S. T., Satterthwaite, T. D. 2021

    Abstract

    Diffusion-weighted magnetic resonance imaging (dMRI) is the primary method for noninvasively studying the organization of white matter in the human brain. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing on a diverse set of software suites to capitalize on their complementary strengths, QSIPrep facilitates the implementation of best practices for processing of diffusion images.

    View details for DOI 10.1038/s41592-021-01185-5

    View details for PubMedID 34155395

  • Multidimensional analysis and detection of informative features in human brain white matter. PLoS computational biology Richie-Halford, A., Yeatman, J., Simon, N., Rokem, A. 2021; 17 (6): e1009136

    Abstract

    The white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to quantify tissue properties along the trajectories of these connections. Statistical inference from tractometry usually either averages these quantities along the length of each fiber bundle or computes regression models separately for each point along every one of the bundles. These approaches are limited in their sensitivity, in the former case, or in their statistical power, in the latter. We developed a method based on the sparse group lasso (SGL) that takes into account tissue properties along all of the bundles and selects informative features by enforcing both global and bundle-level sparsity. We demonstrate the performance of the method in two settings: i) in a classification setting, patients with amyotrophic lateral sclerosis (ALS) are accurately distinguished from matched controls. Furthermore, SGL identifies the corticospinal tract as important for this classification, correctly finding the parts of the white matter known to be affected by the disease. ii) In a regression setting, SGL accurately predicts "brain age." In this case, the weights are distributed throughout the white matter indicating that many different regions of the white matter change over the lifespan. Thus, SGL leverages the multivariate relationships between diffusion properties in multiple bundles to make accurate phenotypic predictions while simultaneously discovering the most relevant features of the white matter.

    View details for DOI 10.1371/journal.pcbi.1009136

    View details for PubMedID 34181648

  • Evaluating the Reliability of Human Brain White Matter Tractometry. Aperture neuro Kruper, J., Yeatman, J. D., Richie-Halford, A., Bloom, D., Grotheer, M., Caffarra, S., Kiar, G., Karipidis, I. I., Roy, E., Chandio, B. Q., Garyfallidis, E., Rokem, A. 1800; 1 (1)

    Abstract

    The validity of research results depends on the reliability of analysis methods. In recent years, there have been concerns about the validity of research that uses diffusion-weighted MRI (dMRI) to understand human brain white matter connections in vivo, in part based on the reliability of analysis methods used in this field. We defined and assessed three dimensions of reliability in dMRI-based tractometry, an analysis technique that assesses the physical properties of white matter pathways: (1) reproducibility, (2) test-retest reliability, and (3) robustness. To facilitate reproducibility, we provide software that automates tractometry (https://yeatmanlab.github.io/pyAFQ). In measurements from the Human Connectome Project, as well as clinical-grade measurements, we find that tractometry has high test-retest reliability that is comparable to most standardized clinical assessment tools. We find that tractometry is also robust: showing high reliability with different choices of analysis algorithms. Taken together, our results suggest that tractometry is a reliable approach to analysis of white matter connections. The overall approach taken here both demonstrates the specific trustworthiness of tractometry analysis and outlines what researchers can do to establish the reliability of computational analysis pipelines in neuroimaging.

    View details for DOI 10.52294/e6198273-b8e3-4b63-babb-6e6b0da10669

    View details for PubMedID 35079748

  • Emergence of a Pseudogap in the BCS-BEC Crossover PHYSICAL REVIEW LETTERS Richie-Halford, A., Drut, J. E., Bulgac, A. 2020; 125 (6): 060403

    Abstract

    Strongly correlated Fermi systems with pairing interactions become superfluid below a critical temperature T_{c}. The extent to which such pairing correlations alter the behavior of the liquid at temperatures T>T_{c} is a subtle issue that remains an area of debate, in particular regarding the appearance of the so-called pseudogap in the BCS-BEC crossover of unpolarized spin-1/2 nonrelativistic matter. To shed light on this, we extract several quantities of crucial importance at and around the unitary limit, namely, the odd-even staggering of the total energy, the spin susceptibility, the pairing correlation function, the condensate fraction, and the critical temperature T_{c}, using a nonperturbative, constrained-ensemble quantum Monte Carlo algorithm.

    View details for DOI 10.1103/PhysRevLett.125.060403

    View details for Web of Science ID 000555324500001

    View details for PubMedID 32845679

  • A browser-based tool for visualization and analysis of diffusion MRI data NATURE COMMUNICATIONS Yeatman, J. D., Richie-Halford, A., Smith, J. K., Keshavan, A., Rokem, A. 2018; 9: 940

    Abstract

    Human neuroscience research faces several challenges with regards to reproducibility. While scientists are generally aware that data sharing is important, it is not always clear how to share data in a manner that allows other labs to understand and reproduce published findings. Here we report a new open source tool, AFQ-Browser, that builds an interactive website as a companion to a diffusion MRI study. Because AFQ-Browser is portable-it runs in any web-browser-it can facilitate transparency and data sharing. Moreover, by leveraging new web-visualization technologies to create linked views between different dimensions of the dataset (anatomy, diffusion metrics, subject metadata), AFQ-Browser facilitates exploratory data analysis, fueling new discoveries based on previously published datasets. In an era where Big Data is playing an increasingly prominent role in scientific discovery, so will browser-based tools for exploring high-dimensional datasets, communicating scientific discoveries, aggregating data across labs, and publishing data alongside manuscripts.

    View details for DOI 10.1038/s41467-018-03297-7

    View details for Web of Science ID 000426543800007

    View details for PubMedID 29507333

    View details for PubMedCentralID PMC5838108

  • Classification of magnetic inhomogeneities and 0-pi transitions in superconducting-magnetic hybrid structures (vol 94, 104518, 2016) PHYSICAL REVIEW B Baker, T. E., Richie-Halford, A., Bill, A. 2016; 94 (13)
  • MADNESS: A MULTIRESOLUTION, ADAPTIVE NUMERICAL ENVIRONMENT FOR SCIENTIFIC SIMULATION SIAM JOURNAL ON SCIENTIFIC COMPUTING Harrison, R. J., Beylkin, G., Bischoff, F. A., Calvin, J. A., Fann, G. I., Fosso-Tande, J., Galindo, D., Hammond, J. R., Hartman-Baker, R., Hill, J. C., Jia, J., Kottmann, J. S., Ou, M., Pei, J., Ratcliff, L. E., Reuter, M. G., Richie-Halford, A. C., Romero, N. A., Sekino, H., Shelton, W. A., Sundahl, B. E., Thornton, W., Valeev, E. F., Vazquez-Mayagoitia, A., Vence, N., Yanai, T., Yokoi, Y. 2016; 38 (5): S123-S142

    View details for DOI 10.1137/15M1026171

    View details for Web of Science ID 000387347700008

  • Long range triplet Josephson current and 0-pi transitions in tunable domain walls NEW JOURNAL OF PHYSICS Baker, T. E., Richie-Halford, A., Bill, A. 2014; 16
  • Cascading proximity effects in rotating magnetizations EPL Baker, T. E., Richie-Halford, A., Icreverzi, O. E., Bill, A. 2014; 107 (1)
  • Classical Mechanical Analogies in Wide Dirty SFS Junctions Baker, T. E., Icreverzi, O. E., Richie-Halford, A., Bill, A. SPRINGER. 2012: 2183-2185
  • Properties of Magnetic-Superconducting Proximity Systems Bill, A., de Rojas, J., Baker, T. E., Richie-Halford, A. SPRINGER. 2012: 2177-2182
  • Space-time localization of inner heliospheric plasma turbulence using multiple spacecraft radio links SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS Richie-Halford, A. C., Iess, L., Tortora, P., Armstrong, J. W., Asmar, S. W., Woo, R., Habbal, S., Morgan, H. 2009; 7