Sparse linear regression for reconstructing muscle activity from human cortical fMRI.

In humans, it is generally not possible to use invasive techniques in order to identify brain activity corresponding to activity of individual muscles. Further, it is believed that the spatial resolution of non-invasive brain imaging modalities is not sufficient to isolate neural activity related to individual muscles. However, this study shows that it is possible to reconstruct muscle activity from functional magnetic resonance imaging (fMRI). We simultaneously recorded surface electromyography (EMG) from two antagonist muscles and motor cortices activity using fMRI, during an isometric task requiring both reciprocal activation and co-activation of the wrist muscles. Bayesian sparse regression was used to identify the parameters of a linear mapping from the fMRI activity in areas 4 (M1) and 6 (pre-motor, SMA) to EMG, and to reconstruct muscle activity in an independent test data set. The mapping obtained by the sparse regression algorithm showed significantly better generalization than those obtained from algorithms commonly used in decoding, i.e., support vector machine and least square regression. The two voxel sets corresponding to the activity of the antagonist muscles were intermingled but disjoint. They were distributed over a wide area of pre-motor cortex and M1 and not limited to regions generally associated with wrist control. These results show that brain activity measured by fMRI in humans can be used to predict individual muscle activity through Bayesian linear models, and that our algorithm provides a novel and non-invasive tool to investigate the brain mechanisms involved in motor control and learning in humans.