Cesar Caballero-Gaudes1, Natalia Petridou, 12, Susan Francis1, Penny Gowland1
1Sir Peter Mansfield Magnetic Resonance Centre, University of Nottingham, Nottingham, Nottinghamshire, United Kingdom; 2University Medical Centre Utrecht, Utrecht, Netherlands
In recent work we showed that by means of sparse estimation techniques the spatial and temporal evolution of single-trial BOLD responses can be automatically detected without any prior knowledge of the stimulus timing and without thresholding: paradigm free mapping (PFM). However, fMRI time series also contain physiological and instrumental fluctuations which can hinder the detection of BOLD responses associated to neuronal activity. Physiological fluctuations can be removed prior to PFM via high-pass filtering, or by RETROICOR, RVT or RVHRCOR, but these techniques must be employed in a pre-processing stage and require the additional recording of physiological respiratory and cardiac waveforms. Here, extending on our previous work, we present a novel technique which by decomposing the fMRI signal enables automatic detection of fMRI BOLD responses without prior stimulus information and automatic fitting of significant frequency fluctuations present in the signal, such as non-neuronal cardiac and respiratory fluctuations (semiparametric PFM, sPFM). This technique is based on a semiparametric linear representation of the fMRI signal which is recursively fitted using a morphological component analysis algorithm. The feasibility of this technique was evaluated in simulations and real fMRI data acquired at 7T, and its performance validated to RETROICOR.