Application of artificial neural network to fMRI regression analysis.
We used an artificial neural network (ANN) to detect correlations between event sequences and fMRI (functional magnetic resonance imaging) signals. The layered feed-forward neural network, given a series of events as inputs and the fMRI signal as a supervised signal, performed a non-linear regression analysis. This type of ANN is capable of approximating any continuous function, and thus this analysis method can detect any fMRI signals that correlated with corresponding events. Because of the flexible nature of ANNs, fitting to autocorrelation noise is a problem in fMRI analyses. We avoided this problem by using cross-validation and an early stopping procedure. The results showed that the ANN could detect various responses with different time courses. The simulation analysis also indicated an additional advantage of ANN over non-parametric methods in detecting parametrically modulated responses, i.e., it can detect various types of parametric modulations without a priori assumptions. The ANN regression analysis is therefore beneficial for exploratory fMRI analyses in detecting continuous changes in responses modulated by changes in input values.
Misaki
Masaya
M
Brain Information Group, Kansai Advanced Research Center, National Institute of Information and Communications Technology, 588-2 Iwaoka, Kobe-shi, Hyogo 651-2429, Japan. misaki@po.nict.go.jp
Miyauchi
Satoru
S
D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D002560:Cerebrovascular Circulation / Q000502:physiology; D003199:Computer Systems; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008297:Male; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010100:Oxygen / Q000097:blood; D011597:Psychomotor Performance / Q000502:physiology; D012044:Regression Analysis; D015203:Reproducibility of Results; D012438:Saccades
D010100:Oxygen