10 citations · 30 across the 6 of their papers we have counts for
6 papers
Structured Sparse Principal Components Analysis with the TV-Elastic Net penalty
Amicie de Pierrefeu, Tommy Löfstedt, Fouad Hadj-Selem +4
Principal component analysis (PCA) is an exploratory tool widely used in data analysis to uncover dominant patterns of variability within a population. Despite its ability to repre…
Physiologically Informed Bayesian Analysis of ASL fMRI Data
Aina Frau-Pascual, Thomas Vincent, Jennifer Sloboda +2
Arterial Spin Labelling (ASL) functional Magnetic Resonance Imaging (fMRI) data provides a quantitative measure of blood perfusion, that can be correlated to neuronal activation. I…
Hemodynamically informed parcellation of cerebral FMRI data
Aina Frau-Pascual, Thomas Vincent, Florence Forbes +1
Standard detection of evoked brain activity in functional MRI (fMRI) relies on a fixed and known shape of the impulse response of the neurovascular coupling, namely the hemodynamic…
Gradient waveform design for variable density sampling in Magnetic Resonance Imaging
Nicolas Chauffert, Pierre Weiss, Jonas Kahn +1
Fast coverage of k-space is a major concern to speed up data acquisition in Magnetic Resonance Imaging (MRI) and limit image distortions due to long echo train durations. The hardw…
Fast joint detection-estimation of evoked brain activity in event-related fMRI using a variational approach
Lotfi Chaari, Thomas Vincent, Florence Forbes +2
In standard clinical within-subject analyses of event-related fMRI data, two steps are usually performed separately: detection of brain activity and estimation of the hemodynamic r…
ICA-based sparse feature recovery from fMRI datasets
Gaël Varoquaux, Merlin Keller, Jean Baptiste Poline +2
Spatial Independent Components Analysis (ICA) is increasingly used in the context of functional Magnetic Resonance Imaging (fMRI) to study cognition and brain pathologies. Salient…