Ben Kandel


Research Overview

I am a Bioengineering doctoral student in the HHMI/NIBIB Interfaces program, researching statistical representations of high-dimensional neuroimaging data and multimodality data integration.  In particular, my research focuses on novel methods to incorporate functional and structural imaging modalities to construct biologically interpretable and statistically accurate multivariate models of the brain.  Because multivariate models more accurately reflect the brain’s organization than univariate models, they have the potential to increase power in observational studies and provide greater insight into how different aspects of the brain integrate into a cohesive whole.



  • Ph.D. in Bioengineering, University of Pennsylvania, 2010-present.  HHMI-NIBIB Interfaces Scholar, Department of Defense NDSEG Fellow.
  • B.A. in Physics (minors in Mathematics and Chemistry), Yeshiva University, 2010.

Peer-Reviewed Articles

Google Scholar page.

  • B. M. Kandel, D. A. Wolk, J. C. Gee, and B. Avants, “Predicting cognitive data from medical images using sparse linear regression,” in Information Processing in Medical Imaging, Springer Berlin Heidelberg, 2013, pp. 86-97.
    author = {Kandel, Benjamin M and Wolk, David A and Gee, James C and Avants,
    title = {{P}redicting cognitive data from medical images using sparse linear
    booktitle = {{I}nformation {P}rocessing in {M}edical {I}maging},
    publisher = {Springer Berlin Heidelberg},
    year = {2013},
    pages = {86--97},
    owner = {ben},
    timestamp = {2014.03.28}
  • B. Avants, P. Dhillon, B. M. Kandel, P. A. Cook, C. T. McMillan, M. Grossman, and J. C. Gee, “Eigenanatomy improves detection power for longitudinal cortical change.,” Med Image Comput Comput Assist Interv, vol. 15, iss. Pt 3, pp. 206-213, 2012.
    author = {Avants, Brian and Dhillon, Paramveer and Kandel, Benjamin M. and
    Cook, Philip A. and McMillan, Corey T. and Grossman, Murray and Gee,
    James C.},
    title = {{E}igenanatomy improves detection power for longitudinal cortical
    journal = {{M}ed {I}mage {C}omput {C}omput {A}ssist {I}nterv},
    year = {2012},
    volume = {15},
    pages = {206--213},
    number = {Pt 3},
    abstract = {We contribute a novel and interpretable dimensionality reduction strategy,
    eigenanatomy, that is tuned for neuroimaging data. The method approximates
    the eigendecomposition of an image set with basis functions (the
    eigenanatomy vectors) that are sparse, unsigned and are anatomically
    clustered. We employ the eigenanatomy vectors as anatomical predictors
    to improve detection power in morphometry. Standard voxel-based morphometry
    (VBM) analyzes imaging data voxel-by-voxel--and follows this with
    cluster-based or voxel-wise multiple comparisons correction methods
    to determine significance. Eigenanatomy reverses the standard order
    of operations by first clustering the voxel data and then using standard
    linear regression in this reduced dimensionality space. As with traditional
    region-of-interest (ROI) analysis, this strategy can greatly improve
    detection power. Our results show that eigenanatomy provides a principled
    objective function that leads to localized, data-driven regions of
    interest. These regions improve our ability to quantify biologically
    plausible rates of cortical change in two distinct forms of neurodegeneration.
    We detail the algorithm and show experimental evidence of its efficacy.},
    institution = {{P}hiladelphia, {PA} 19104, {USA}.},
    keywords = {Aging, physiology; Algorithms; Brain, anatomy /&/ histology/physiology;
    Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted,
    methods; Information Storage and Retrieval, methods; Longitudinal
    Studies; Magnetic Resonance Imaging, methods; Pattern Recognition,
    Automated, methods; Reproducibility of Results; Sensitivity and Specificity},
    language = {eng},
    medline-pst = {ppublish},
    owner = {pcook},
    pmid = {23286132},
    timestamp = {2013.02.19}
  • B. M. Kandel and T. E. Hullar, “The relationship of head movements to semicircular canal size in cetaceans,” The Journal of experimental biology, vol. 213, iss. 7, p. 1175–1181w, 2010.
    author = {Kandel, Benjamin M and Hullar, Timothy E},
    title = {{T}he relationship of head movements to semicircular canal size in
    journal = {{T}he {J}ournal of experimental biology},
    year = {2010},
    volume = {213},
    pages = {1175--1181w},
    number = {7},
    owner = {ben},
    publisher = {The Company of Biologists Ltd},
    timestamp = {2014.03.27}