fMRI-based inverse analysis of stroke patients' motor functions

The feasibility of automating the evaluation of stroke chronic patients' motor functions has been explored while analyzing their corresponding fMRI studies with statistical parametric analysis, statistical inference analysis and a nonlinear multivoxel pattern-analysis classifier based on a feed-forward backward-propagation neural network. After doing principal component analysis and independent component analysis on an fMRI image data set, acquired after technology-based rehabilitation sessions of patients after stroke, an artificial neural network is trained with noncorrelated independent image-parameter vectors to discriminate statistical patterns of brain activations corresponding to each of the target sign language-like primitive hand movements that patients performed in the fMRI scanner while motor stimuli were being presented. The results look so promising that building a rehabilitation prognostics system could be looked forward.