Bayesian wavelet-based analysis of functional magnetic resonance time series.
Wavelet methods for image regularization offer a data-driven alternative to Gaussian smoothing in functional magnetic resonance (fMRI) analysis. Their impact has been limited by the difficulties in integrating regularization in the wavelet domain and inference in the image domain, precluding the probabilistic decision on which areas are activated by a task. Here we present an integrated framework for Bayesian estimation and regularization in wavelet space that allows the usual voxelwise hypothesis testing. This framework is flexible, being an adaptation to fMRI time series of a more general wavelet-based functional mixed-effect model. Through testing on a combination of simulated and real fMRI data, we show evidence of improved signal recovery, without compromising test accuracy in image space.
Costafreda
Sergi G
SG
Brain Image Analysis Unit, Department of Biostatistics and Computing, Institute of Psychiatry, King's College, DeCrespigny Park, London, UK. s.costafreda@iop.kcl.ac.uk
Barker
Gareth J
GJ
Brammer
Michael J
MJ
D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014793:Visual Cortex / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*