Neuroimaging studies have shown that autism spectrum disorders (ASDs) may be associated with abnormalities in brain structures and functions at rest as well as during cognitive tasks. However, it remains unclear if functional connectivity (FC) of all brain neural networks is also changed in these subjects. In this study, we acquired functional magnetic resonance imaging scans from 93 children with ASD and 79 matched healthy subjects. Group independent component analysis was executed for all of the participants to estimate FC. One-sample
t
-tests were then performed to obtain the networks for each group. Group differences in the different brain networks were tested using two-sample
t
-tests. Finally, relationships between abnormal FC and clinical variables were investigated with Pearson’s correlation analysis. The results from one-sample
t
-tests revealed nine networks with similar spatial patterns in these two groups. When compared with the controls, children with ASD showed increased connectivity in the right dorsolateral superior frontal gyrus and left middle frontal gyrus (MFG) within the occipital pole network. Children with ASD also showed decreased connectivity in the left gyrus rectus, left middle occipital gyrus, right angular gyrus, right MFG and right inferior frontal gyrus (IFG), orbital part within the lateral visual network (LVN), the left IFG, right precuneus, and right angular gyrus within the left frontoparietal (cognition) network. Furthermore, the mean FC values within the LVN showed significant positive correlations with total score of the Childhood Autism Rating Scale. Our findings indicate that abnormal FC extensively exists within some networks in children with ASD. This abnormal FC may constitute a biomarker of ASD. Our results are an important contribution to the study of neuropathophysiological mechanisms in children with ASD.
With the development of nerve stereotactic technology, brain stereotactic surgery has become an effective method for the current treatment of Parkinson's disease. The accurate localization of the target nuclei is the key issue of the treatment. In this paper, we constructed a dependence tree model to identify the target nuclei further. Theory of fuzzy connectedness was used in the segmentation. Experimental results show it was more desirable and suitable to the clinical applications.
Consumers’ product style perceptions and preference are vague and uncertain. In order to identify consumers’ needs more accurately, this paper established a questionnaire based on fuzzy data, carried out a spot check to consumers’ style preference and perceptions of twelve office chairs with typical form style, then conducted the mean, distances calculation and fuzzy clustering analysis by Excel, SPSS, and Matlab. Comparing with statistics results of traditional questionnaire data, this paper points out that fuzzy data statistics are suitable for the mean calculation of small sample and the clustering algorithm of few preference variables.
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