A Data-Driven Multi-scale Technique for fMRI Mapping of the Human Somatosensory Cortex.

A previously introduced Bayesian non-parametric multi-scale technique, called iterated Multigrid Priors (iMGP) method, is used to map the topographic organization of human primary somatosensory cortex (S1). We analyze high spatial resolution fMRI data acquired at ultra-high field (UHF, 7T) in individual subjects during vibrotactile stimulation applied to each distal phalange of the left hand digits using both a travelling-wave (TW) and event-related (ER) paradigm design. We compare the somatotopic digit representations generated in S1 using the iMGP method with those obtained using established fMRI paradigms and analysis techniques: Fourier-based analysis of travelling-wave data and General Linear Model (GLM) analysis of event-related data.

Backward renormalization-group inference of cortical dipole sources and neural connectivity efficacy.

Proper neural connectivity inference has become essential for understanding cognitive processes associated with human brain function. Its efficacy is often hampered by the curse of dimensionality. In the electroencephalogram case, which is a noninvasive electrophysiological monitoring technique to record electrical activity of the brain, a possible way around this is to replace multichannel electrode information with dipole reconstructed data.

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).

BOLD response analysis by iterated local multigrid priors.

We present a non parametric Bayesian multiscale method to characterize the Hemodynamic Response HR as function of time. This is done by extending and adapting the Multigrid Priors (MGP) method proposed in (S.D.

Multigrid priors for a Bayesian approach to fMRI.

We introduce multigrid priors to construct a Bayesian-inspired method to asses brain activity in functional magnetic resonance imaging (fMRI). A sequence of different scale grids is constructed over the image. Starting from the finest scale, coarse grain data variables are sequentially defined for each scale.