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Changes in brain functional network connectivity after stroke 被引量:3
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作者 Wei Li Yapeng Li +1 位作者 Wenzhen Zhu Xi Chen 《Neural Regeneration Research》 SCIE CAS CSCD 2014年第1期51-60,共10页
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
关键词 nerve regeneration brain injury STROKE motor areas functional magnetic resonanceimaging brain network independent component analysis functional network connectivity neuralplasticity NSFC grant neural regeneration
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Depression discrimination using fMRI and DTI data by wavelet based fusion scheme 被引量:1
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作者 赵竟 罗国平 +1 位作者 姚志剑 卢青 《Journal of Southeast University(English Edition)》 EI CAS 2012年第1期25-28,共4页
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
关键词 classification functional magnetic resonanceimaging (fMRI) diffusion tensor imaging (DTI) medicalimage fusion DEPRESSION
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Abnormal functional connectivity with mood regulating circuit in unmedicated individual with major depression: a resting-state functional magnetic resonance study 被引量:12
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作者 PENG Dai-hui SHEN Ting +6 位作者 ZHANG Jie HUANG Jia LIU Jun LIU Shu-yong JIANG Kai-da XU Yi-feng FANG Yi-ru 《Chinese Medical Journal》 SCIE CAS CSCD 2012年第20期3701-3706,共6页
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
关键词 DEPRESSION anterior cingulate cortex thalamus functional connectivity functional magnetic resonanceimaging
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