Studies have shown that functional network connection models can be used to study brain net- work changes in patients with schizophrenia. In this study, we inferred that these models could also be used to explore func...Studies have shown that functional network connection models can be used to study brain net- work changes in patients with schizophrenia. In this study, we inferred that these models could also be used to explore functional network connectivity changes in stroke patients. We used independent component analysis to find the motor areas of stroke patients, which is a novel way to determine these areas. In this study, we collected functional magnetic resonance imaging datasets from healthy controls and right-handed stroke patients following their first ever stroke. Using independent component analysis, six spatially independent components highly correlat- ed to the experimental paradigm were extracted. Then, the functional network connectivity of both patients and controls was established to observe the differences between them. The results showed that there were 11 connections in the model in the stroke patients, while there were only four connections in the healthy controls. Further analysis found that some damaged connections may be compensated for by new indirect connections or circuits produced after stroke. These connections may have a direct correlation with the degree of stroke rehabilitation. Our findings suggest that functional network connectivity in stroke patients is more complex than that in hea- lthy controls, and that there is a compensation loop in the functional network following stroke. This implies that functional network reorganization plays a very important role in the process of rehabilitation after stroke.展开更多
Both functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) can provide different information of the human brain, so using the wavelet transform method can achieve a fusion of these two ty...Both functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) can provide different information of the human brain, so using the wavelet transform method can achieve a fusion of these two types of image data and can effectively improve the depression recognition accuracy. Multi-resolution wavelet decomposition is used to transform each type of images to the frequency domain in order to obtain the frequency components of the images. To each subject, decomposition components of two images are then added up separately according to their frequencies. The inverse discrete wavelet transform is used to reconstruct the fused images. After that, principal component analysis (PCA) is applied to reduce the dimension and obtain the features of the fusion data before classification. Based on the features of the fused images, an accuracy rate of 80. 95 % for depression recognition is achieved using a leave-one-out cross-validation test. It can be concluded that this wavelet fusion scheme has the ability to improve the current diagnosis of depression.展开更多
Background Reports on mood regulating circuit (MRC) indicated different activities between depressed patients and healthy controls. The functional networks based on MRC have not been described in major depression di...Background Reports on mood regulating circuit (MRC) indicated different activities between depressed patients and healthy controls. The functional networks based on MRC have not been described in major depression disorder (MDD). Both the anterior cingulate cortex (ACC) and thalamus are all the key regions of MRC. This study was to investigate the two functional networks related to ACC and thalamus in MDD. Methods Sixteen patients with MDD on first episode which never got any medication and sixteen matched health controls were scanned by 3.0 T functional magnetic resonance imaging (fMRI) during resting-state. The pregenual anterior cingulate cortex (pgACC) was used as seed region to construct the functional network by cortex section. The thalamus was used as seed region to construct the functional network by limbic section. Paired-t tests between-groups were performed for the seed-target correlations based on the individual fisher z-transformed correlation maps by SPM2. Results Depressed subjects exhibited significantly great functional connectivity (FC) between pgACC and the parahippocampus gyrus in one cluster (size 923) including left parahippocampus gyrus (-21, -49,7), left parietal lobe (-3, -46, 52) and left frontal lobe (-27, -46, 28). The one cluster (size 962) of increased FC on thalamus network overlapped the precuneus near to right parietal lobe (9, -52,46) and right cingulate gyrus (15, -43, 43) in health controls. Conclusions Abnormal functional networks exist in earlier manifestation of MDD related to MRC by both cortex and limbic sections. The increased functional connectivity of pgACC and decreased functional connectivity of thalamus is mainly involved in bias mood processing and cognition.展开更多
基金supported by the National Natural Science Foundation of China,No.60905024
文摘Studies have shown that functional network connection models can be used to study brain net- work changes in patients with schizophrenia. In this study, we inferred that these models could also be used to explore functional network connectivity changes in stroke patients. We used independent component analysis to find the motor areas of stroke patients, which is a novel way to determine these areas. In this study, we collected functional magnetic resonance imaging datasets from healthy controls and right-handed stroke patients following their first ever stroke. Using independent component analysis, six spatially independent components highly correlat- ed to the experimental paradigm were extracted. Then, the functional network connectivity of both patients and controls was established to observe the differences between them. The results showed that there were 11 connections in the model in the stroke patients, while there were only four connections in the healthy controls. Further analysis found that some damaged connections may be compensated for by new indirect connections or circuits produced after stroke. These connections may have a direct correlation with the degree of stroke rehabilitation. Our findings suggest that functional network connectivity in stroke patients is more complex than that in hea- lthy controls, and that there is a compensation loop in the functional network following stroke. This implies that functional network reorganization plays a very important role in the process of rehabilitation after stroke.
基金The National Natural Science Foundation of China(No.30900356,81071135)
文摘Both functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) can provide different information of the human brain, so using the wavelet transform method can achieve a fusion of these two types of image data and can effectively improve the depression recognition accuracy. Multi-resolution wavelet decomposition is used to transform each type of images to the frequency domain in order to obtain the frequency components of the images. To each subject, decomposition components of two images are then added up separately according to their frequencies. The inverse discrete wavelet transform is used to reconstruct the fused images. After that, principal component analysis (PCA) is applied to reduce the dimension and obtain the features of the fusion data before classification. Based on the features of the fused images, an accuracy rate of 80. 95 % for depression recognition is achieved using a leave-one-out cross-validation test. It can be concluded that this wavelet fusion scheme has the ability to improve the current diagnosis of depression.
文摘Background Reports on mood regulating circuit (MRC) indicated different activities between depressed patients and healthy controls. The functional networks based on MRC have not been described in major depression disorder (MDD). Both the anterior cingulate cortex (ACC) and thalamus are all the key regions of MRC. This study was to investigate the two functional networks related to ACC and thalamus in MDD. Methods Sixteen patients with MDD on first episode which never got any medication and sixteen matched health controls were scanned by 3.0 T functional magnetic resonance imaging (fMRI) during resting-state. The pregenual anterior cingulate cortex (pgACC) was used as seed region to construct the functional network by cortex section. The thalamus was used as seed region to construct the functional network by limbic section. Paired-t tests between-groups were performed for the seed-target correlations based on the individual fisher z-transformed correlation maps by SPM2. Results Depressed subjects exhibited significantly great functional connectivity (FC) between pgACC and the parahippocampus gyrus in one cluster (size 923) including left parahippocampus gyrus (-21, -49,7), left parietal lobe (-3, -46, 52) and left frontal lobe (-27, -46, 28). The one cluster (size 962) of increased FC on thalamus network overlapped the precuneus near to right parietal lobe (9, -52,46) and right cingulate gyrus (15, -43, 43) in health controls. Conclusions Abnormal functional networks exist in earlier manifestation of MDD related to MRC by both cortex and limbic sections. The increased functional connectivity of pgACC and decreased functional connectivity of thalamus is mainly involved in bias mood processing and cognition.