- author = "James Hughes and Mark Daley",
- title = "Generating Nonlinear Models of Functional Connectivity from Functional Magnetic Resonance Imaging Data with Genetic Programming",
- booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019",
- year = "2019",
- editor = "Carlos A. Coello Coello",
- pages = "3252--3261",
- month = "10-13 " # jun,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, Computational Neuroscience, Functional Connectivity, Functional Magnetic Resonance Imaging, Symbolic Regression",
- isbn13 = "978-1-7281-2152-6",
-
DOI = "
doi:10.1109/CEC.2019.8790120",
- size = "8 pages",
-
abstract = "The brain is a nonlinear computational system;
however, most methods employed in finding functional
connectivity models with functional magnetic resonance
imaging (fMRI) data produce strictly linear models -
models incapable of truly describing the underlying
system.
Genetic programming is used to develop non-linear models of functional connectivity from fMRI data. The study builds on previous work and observes that non linear models contain relationships not found by traditional linear methods. When compared to linear models, the nonlinear models contained fewer regions of interest and were never significantly worse when applied to data the models were fit to. Nonlinear models could generalize to unseen data from the same subject better than traditional linear models (intra-subject). Nonlinear models could not generalize to unseen data recorded from other subjects (intersubject) as well as the linear models, and reasons for this are discussed. This study presents the problem that many, manifestly different models in both operators and features, can effectively describe the system with acceptable metrics.",
-
notes = "also known as \cite{8790120},
IEEE Catalog Number: CFP19ICE-ART",
