AIM: To evaluate the spatial distribution of cerebral abnormalities in cirrhotic subjects with and without hepatic encephalopathy (HE) found with magnetization transfer imaging (MTI).METHODS: Nineteen cirrhotic patien...AIM: To evaluate the spatial distribution of cerebral abnormalities in cirrhotic subjects with and without hepatic encephalopathy (HE) found with magnetization transfer imaging (MTI).METHODS: Nineteen cirrhotic patients graded from neurologically normal to HE grade 2 and 18 healthy control subjects underwent magnetic resonance imaging. They gave institutional-review-board-approved written consent. Magnetization transfer ratio (MTR) maps were generated from MTI. We tested for significant differences compared to the control group using statistical non-parametric mapping (SnPM) for a voxelbased evaluation.RESULTS: The MTR of grey and white matter was lower in subjects with more severe HE. Changes were found in patients with cirrhosis without neurological defi cits in the basal ganglia and bilateral white matter. The loss in magnetization transfer increased in severity and spatial extent in patients with overt HE. Patients with HE grade 2 showed an MTR decrease in white and grey matter: the maximum loss of magnetization transfer effect was located in the basal ganglia [SnPM (pseudo-)t = 17.98, P = 0.0001].CONCLUSION: The distribution of MTR changes in HE points to an early involvement of basal ganglia and white matter in HE.展开更多
In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in...In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in an EEG-based brain-computer interface (BCI) was studied. An auto search algorithm was developed to study four datasegment-related parameters in each trial of 12 subjects’ EEG. The length of data segment (LDS), the start position of data (SPD) segment, AR order, and number of trials (NT) were used to build the model. The study showed that, compared with the classification ratio (CR) without parameter selection, the CR was increased by 20% to 30% with proper selection of these data-segment-related parameters, and the optimum parameter values were subject-dependent. This suggests that the data-segment-related parameters should be individualized when building models for BCI.展开更多
文摘AIM: To evaluate the spatial distribution of cerebral abnormalities in cirrhotic subjects with and without hepatic encephalopathy (HE) found with magnetization transfer imaging (MTI).METHODS: Nineteen cirrhotic patients graded from neurologically normal to HE grade 2 and 18 healthy control subjects underwent magnetic resonance imaging. They gave institutional-review-board-approved written consent. Magnetization transfer ratio (MTR) maps were generated from MTI. We tested for significant differences compared to the control group using statistical non-parametric mapping (SnPM) for a voxelbased evaluation.RESULTS: The MTR of grey and white matter was lower in subjects with more severe HE. Changes were found in patients with cirrhosis without neurological defi cits in the basal ganglia and bilateral white matter. The loss in magnetization transfer increased in severity and spatial extent in patients with overt HE. Patients with HE grade 2 showed an MTR decrease in white and grey matter: the maximum loss of magnetization transfer effect was located in the basal ganglia [SnPM (pseudo-)t = 17.98, P = 0.0001].CONCLUSION: The distribution of MTR changes in HE points to an early involvement of basal ganglia and white matter in HE.
文摘In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in an EEG-based brain-computer interface (BCI) was studied. An auto search algorithm was developed to study four datasegment-related parameters in each trial of 12 subjects’ EEG. The length of data segment (LDS), the start position of data (SPD) segment, AR order, and number of trials (NT) were used to build the model. The study showed that, compared with the classification ratio (CR) without parameter selection, the CR was increased by 20% to 30% with proper selection of these data-segment-related parameters, and the optimum parameter values were subject-dependent. This suggests that the data-segment-related parameters should be individualized when building models for BCI.
