Finding Nonlinear Relationships in fMRI Time Series with Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "James Alexander Hughes and Mark Daley",
  title =        "Finding Nonlinear Relationships in {fMRI} Time Series
                 with Symbolic Regression",
  booktitle =    "GECCO '16 Companion: Proceedings of the Companion
                 Publication of the 2016 Annual Conference on Genetic
                 and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich and Frank Neumann and 
                 Andrew M. Sutton and Martin Middendorf and Xiaodong Li and 
                 Emma Hart and Mengjie Zhang and Youhei Akimoto and 
                 Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and 
                 Daniele Loiacono and Julian Togelius and 
                 Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and 
                 Faustino Gomez and Carlos M. Fonseca and 
                 Heike Trautmann and Alberto Moraglio and William F. Punch and 
                 Krzysztof Krawiec and Zdenek Vasicek and 
                 Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and 
                 Boris Naujoks and Enrique Alba and Gabriela Ochoa and 
                 Simon Poulding and Dirk Sudholt and Timo Koetzing",
  pages =        "101--102",
  keywords =     "genetic algorithms, genetic programming: Poster",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4323-7",
  DOI =          "doi:10.1145/2908961.2909021",
  abstract =     "The brain is an intrinsically nonlinear system, yet
                 the dominant methods used to generate network models of
                 functional connectivity from fMRI data use linear
                 methods. Although these approaches have been used
                 successfully, they are limited in that they can find
                 only linear relations within a system we know to be

                 This study employs a highly specialized genetic
                 programming system which incorporates multiple
                 enhancements to perform symbolic regression, a type of
                 regression analysis that searches for declarative
                 mathematical expressions to describe relationships in
                 observed data.

                 Publicly available fMRI data from the Human Connectome
                 Project were segmented into meaningful regions of
                 interest and highly nonlinear mathematical expressions
                 describing functional connectivity were generated.
                 These nonlinear expressions exceed the explanatory
                 power of traditional linear models and allow for more
                 accurate investigation of the underlying physiological
  notes =        "Distributed at GECCO-2016.",

Genetic Programming entries for James Alexander Hughes Mark Daley