Aphasia is an acquired language disorder that is a common consequence of stroke.The pathogenesis of the disease is not fully understood,and as a result,current treatment options are not satisfactory.Here,we used blood...Aphasia is an acquired language disorder that is a common consequence of stroke.The pathogenesis of the disease is not fully understood,and as a result,current treatment options are not satisfactory.Here,we used blood oxygenation level-dependent functional magnetic resonance imaging to evaluate the activation of bilateral cortices in patients with Broca＇s aphasia 1 to 3 months after stroke.Our results showed that language expression was associated with multiple brain regions in which the right hemisphere participated in the generation of language.The activation areas in the left hemisphere of aphasia patients were significantly smaller compared with those in healthy adults.The activation frequency,volumes,and intensity in the regions related to language,such as the left inferior frontal gyrus（Broca＇s area）,the left superior temporal gyrus,and the right inferior frontal gyrus（the mirror region of Broca＇s area）,were lower in patients compared with healthy adults.In contrast,activation in the right superior temporal gyrus,the bilateral superior parietal lobule,and the left inferior temporal gyrus was stronger in patients compared with healthy controls.These results suggest that the right inferior frontal gyrus plays a role in the recovery of language function in the subacute stage of stroke-related aphasia by increasing the engagement of related brain areas.展开更多
Purpose-Aiming at the shortcomings of EEG signals generated by brain’s sensorimotor region activated tasks,such as poor performance,low efficiency and weak robustness,this paper proposes an EEG signals classification...Purpose-Aiming at the shortcomings of EEG signals generated by brain’s sensorimotor region activated tasks,such as poor performance,low efficiency and weak robustness,this paper proposes an EEG signals classification method based on multi-dimensional fusion features.Design/methodology/approach-First,the improved Morlet wavelet is used to extract the spectrum feature maps from EEG signals.Then,the spatial-frequency features are extracted from the PSD maps by using the three-dimensional convolutional neural networks(3DCNNs)model.Finally,the spatial-frequency features are incorporated to the bidirectional gated recurrent units(Bi-GRUs)models to extract the spatial-frequencysequential multi-dimensional fusion features for recognition of brain’s sensorimotor region activated task.Findings-In the comparative experiments,the data sets of motor imagery(MI)/action observation(AO)/action execution(AE)tasks are selected to test the classification performance and robustness of the proposed algorithm.In addition,the impact of extracted features on the sensorimotor region and the impact on the classification processing are also analyzed by visualization during experiments.Originality/value-The experimental results show that the proposed algorithm extracts the corresponding brain activation features for different action related tasks,so as to achieve more stable classification performance in dealing with AO/MI/AE tasks,and has the best robustness on EEGsignals of different subjects.展开更多
基金supported by the Natural Science Foundation of Guangdong Province of China,No.2016A030313327the Science and Technology Planning Project of Guangzhou City of China,No.201607010185+1 种基金the Science and Technology Planning Project of Guangdong Province of China,No.2016A020215226the National Natural Science Foundation of China,No.81401869
文摘Aphasia is an acquired language disorder that is a common consequence of stroke.The pathogenesis of the disease is not fully understood,and as a result,current treatment options are not satisfactory.Here,we used blood oxygenation level-dependent functional magnetic resonance imaging to evaluate the activation of bilateral cortices in patients with Broca＇s aphasia 1 to 3 months after stroke.Our results showed that language expression was associated with multiple brain regions in which the right hemisphere participated in the generation of language.The activation areas in the left hemisphere of aphasia patients were significantly smaller compared with those in healthy adults.The activation frequency,volumes,and intensity in the regions related to language,such as the left inferior frontal gyrus（Broca＇s area）,the left superior temporal gyrus,and the right inferior frontal gyrus（the mirror region of Broca＇s area）,were lower in patients compared with healthy adults.In contrast,activation in the right superior temporal gyrus,the bilateral superior parietal lobule,and the left inferior temporal gyrus was stronger in patients compared with healthy controls.These results suggest that the right inferior frontal gyrus plays a role in the recovery of language function in the subacute stage of stroke-related aphasia by increasing the engagement of related brain areas.
基金The education and scientific research project of young and middle-aged teachers of Fujian provincial department of education(No.JAT171070).
文摘Purpose-Aiming at the shortcomings of EEG signals generated by brain’s sensorimotor region activated tasks,such as poor performance,low efficiency and weak robustness,this paper proposes an EEG signals classification method based on multi-dimensional fusion features.Design/methodology/approach-First,the improved Morlet wavelet is used to extract the spectrum feature maps from EEG signals.Then,the spatial-frequency features are extracted from the PSD maps by using the three-dimensional convolutional neural networks(3DCNNs)model.Finally,the spatial-frequency features are incorporated to the bidirectional gated recurrent units(Bi-GRUs)models to extract the spatial-frequencysequential multi-dimensional fusion features for recognition of brain’s sensorimotor region activated task.Findings-In the comparative experiments,the data sets of motor imagery(MI)/action observation(AO)/action execution(AE)tasks are selected to test the classification performance and robustness of the proposed algorithm.In addition,the impact of extracted features on the sensorimotor region and the impact on the classification processing are also analyzed by visualization during experiments.Originality/value-The experimental results show that the proposed algorithm extracts the corresponding brain activation features for different action related tasks,so as to achieve more stable classification performance in dealing with AO/MI/AE tasks,and has the best robustness on EEGsignals of different subjects.
