Name
Abstract | ||
|---|---|---|
The vast majority of methods employed in the analysis of functional Magnetic Resonance Imaging (fMRI) produce exclusively linear models; however, it is clear that linear models cannot fully describe a system with the observed behavioral complexity of the human brain --- an intrinsically nonlinear system. By using tools embracing the possibility of modeling the underlying nonlinear system we may uncover meaningful undiscovered relationships which further our understanding of the brain. We employ genetic programming, an artificial intelligence technique, to perform symbolic regression for the discovery of nonlinear models better suited to capturing the complexities of a high dimensional dynamic system: the human brain. fMRI data for multiple subjects performing different tasks were segmented into regions of interest and nonlinear models were generated which effectively described the system succinctly. The nonlinear models contained undiscovered relationships and selected different sets of regions of interest than traditional tools, which leads to more accurate understanding of the functional networks. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3071178.3071209 | GECCO |
Keywords | Field | DocType |
Symbolic regression, Computational neuroscience, Functional magnetic resonance imaging, Nonlinear Modelling | Computational neuroscience,Nonlinear system,Functional magnetic resonance imaging,Linear model,Computer science,Functional networks,Genetic programming,Artificial intelligence,Nonlinear modelling,Symbolic regression,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| James Hughes | 1 | 14 | 4.55 |
| Mark Daley | 2 | 166 | 22.18 |