Individual trial analysis for 7T fMRI data by a data-driven multi scale approach

An important interest in event-related single trial fMRI is the possibility of studying cognitive processes that vary in time (e.g. learning or adaptation). Region-specific modelling and the inter-trial variability of the evoked response play an important role. We showed how the use of the iterated multigrid priors (iMGP) method, a previously introduced data-driven multi scale Bayesian iterative approach, may be extended for a trial-by-trial analysis on ultra-high magnetic field data. We used both artificial (present real physiological noise) and real (unilateral finger tapping experiment) data at 7T and compared to other methods. Since the iMGP does not need to spatially smooth the data, avoiding a loss of sensitivity, we take advantage of the high SNR available at 7T. For artificial data, we showed receiver operating characteristic curves parametrized by the activity threshold and by the addition of extra thermal noise and compared with correlation technique results.The method showed be very robust in terms of specificity for very noisy data and capable of capturing the temporal variability imposed artificially across regions. For real data, we examined the inter-trial spatial relationships for four subjects and the time-to-peak of the evoked response estimated by the iMGP across trials, regions and subjects. To stress the reliability of the iMGP in single trial studies, an illustrative comparison with the variational Bayes approach (implemented in the very popular Statistical Parametric Mapping software) was done for a single subject. Despite the extravascular signals are still present at 7T and the confounds of physiological noise and hemodynamic variability affecting single trial approaches, we showed that with the iMGP method it is possible to detect individual HR robustly.