Although some patients have successful peripheral nerve regeneration,a poor recovery of hand function often occurs after peripheral nerve injury.It is believed that the capability of brain plasticity is crucial for th...Although some patients have successful peripheral nerve regeneration,a poor recovery of hand function often occurs after peripheral nerve injury.It is believed that the capability of brain plasticity is crucial for the recovery of hand function.The supplementary motor area may play a key role in brain remodeling after peripheral nerve injury.In this study,we explored the activation mode of the supplementary motor area during a motor imagery task.We investigated the plasticity of the central nervous system after brachial plexus injury,using the motor imagery task.Results from functional magnetic resonance imaging showed that after brachial plexus injury,the motor imagery task for the affected limbs of the patients triggered no obvious activation of bilateral supplementary motor areas.This result indicates that it is difficult to excite the supplementary motor areas of brachial plexus injury patients during a motor imagery task,thereby impacting brain remodeling.Deactivation of the supplementary motor area is likely to be a serious problem for brachial plexus injury patients in terms of preparing,initiating and executing certain movements,which may be partly responsible for the unsatisfactory clinical recovery of hand function.展开更多
Hemodynamic response during motor imagery （MI） is studied extensively by functional magnetic resonance imaging （fMRI） technologies. To further understand the human brain functions under MI, a more precise classifi...Hemodynamic response during motor imagery （MI） is studied extensively by functional magnetic resonance imaging （fMRI） technologies. To further understand the human brain functions under MI, a more precise classification of the brain regions corresponding to each brain function is desired. In this study, a Bayesian trained radial basis function （RBF） neural network, which determines the weights and regularization parameters automatically by Bayesian learning, is applied to make a precise classification of the hemodynamic response to the tasks during the MI experiment. To illustrate the proposed method, data with MI task performance from 1 subject was used. The results demonstrate that this approach splits the hemodynamic response to different tasks successfully.展开更多
基金supported by the Youth Researcher Foundation of Shanghai Health Development Planning Commission,No.20124319
文摘Although some patients have successful peripheral nerve regeneration,a poor recovery of hand function often occurs after peripheral nerve injury.It is believed that the capability of brain plasticity is crucial for the recovery of hand function.The supplementary motor area may play a key role in brain remodeling after peripheral nerve injury.In this study,we explored the activation mode of the supplementary motor area during a motor imagery task.We investigated the plasticity of the central nervous system after brachial plexus injury,using the motor imagery task.Results from functional magnetic resonance imaging showed that after brachial plexus injury,the motor imagery task for the affected limbs of the patients triggered no obvious activation of bilateral supplementary motor areas.This result indicates that it is difficult to excite the supplementary motor areas of brachial plexus injury patients during a motor imagery task,thereby impacting brain remodeling.Deactivation of the supplementary motor area is likely to be a serious problem for brachial plexus injury patients in terms of preparing,initiating and executing certain movements,which may be partly responsible for the unsatisfactory clinical recovery of hand function.
基金supported by the National Natural Science Foundation of China under Grant No. 9082006 and 30770590Key Research Project of Science and Technology of MOE under Grant No. 107097863 Program under Grant No. 2008AA02Z4080
文摘Hemodynamic response during motor imagery （MI） is studied extensively by functional magnetic resonance imaging （fMRI） technologies. To further understand the human brain functions under MI, a more precise classification of the brain regions corresponding to each brain function is desired. In this study, a Bayesian trained radial basis function （RBF） neural network, which determines the weights and regularization parameters automatically by Bayesian learning, is applied to make a precise classification of the hemodynamic response to the tasks during the MI experiment. To illustrate the proposed method, data with MI task performance from 1 subject was used. The results demonstrate that this approach splits the hemodynamic response to different tasks successfully.
