Ming Ke

Publications (4)2.52 Total impact

• Chapter: Brain Functional Network for Chewing of Gum

  Ming Ke, Hui Shen, Zongtan Zhou, Xiaolin Zhou, Dewen Hu, Xuhui Chen
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  ABSTRACT: Recent studies showed that gum-chewing induced significant increases in cerebral blood flow and blood-oxygenation level in the widespread brain regions. However, little is known about the underlying mechanism of chewing-induced regional interconnection and interaction within the brain. In this study, we investigated the human brain functional network during chewing of gum by using functional magnetic resonance imaging and complex network theory. Adjacency matrix of the network was constructed by the active voxels of chewing-related. The global statistical properties of the network revealed the brain functional network for chewing of gum had small-world effect and scale-free property. Computing the degree and betweenness which belong to the centrality indices, we found that the neocortical hubs of the network were distributed in the sense and motor cortex, and the nodes in the thalamus and lentiform nucleus held the largest betweenness. The sense and motor cortices as well as thalamus and lentiform nucleus have the important roles in dispatch and transfer information of network. KeywordsFunctional network–Gum-chewing–Small-world network–Functional magnetic resonance imaging
  06/2011: pages 169-178;
• Article: Bilateral functional asymmetry disparity in positive and negative schizophrenia revealed by resting-state fMRI.

  Ming Ke, Ran Zou, Hui Shen, Xing Huang, Zongtan Zhou, Zhening Liu, Zhimin Xue, Dewen Hu
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  ABSTRACT: Brain functional asymmetry abnormalities have previously been reported in schizophrenia. In the present study, we hypothesized that the pattern of functional asymmetry in schizophrenia may differ between patients suffering from positive and negative symptoms. We examined the relationship between altered asymmetry of functional connectivity and symptom type (positive/negative) using resting-state functional magnetic resonance imaging. We selected the dorsolateral prefrontal cortex, superior temporal gyrus, middle temporal gyrus and hippocampus as regions of interest, and analyzed functional connectivity patterns between these and other brain regions. Furthermore, a voxel-based two-level asymmetry analysis was conducted to investigate differences in the asymmetry of functional connectivity patterns within and between groups. Our results showed that patients exhibiting positive symptoms had significantly increased leftward asymmetry of functional connectivity. The negative symptom group, in contrast, exhibited increased rightward asymmetry of functional connectivity. The strength of the asymmetry in these regions was found to be significantly correlated with symptom ratings obtained using the Positive and Negative Syndrome Scale. These results suggest that predominantly positive and predominantly negative schizophrenia may have different neural underpinnings, and that certain regions in the frontal and temporal lobes, as well as the cingulate gyrus and precuneus, play important roles in mediating the symptoms of this complex disease. Our study also provided further evidence for the hypothesis that schizophrenia is related to abnormalities in functional brain networks.
  Psychiatry Research 03/2010; 182(1):30-9. · 2.52 Impact Factor
• Conference Proceeding: Combined Analysis of Resting-State fMRI and DTI Data Reveals Abnormal Development of Function-Structure in Early-Onset Schizophrenia.

  Ming Ke, Xing Huang, Hui Shen, Zongtan Zhou, Xiaogang Chen, Dewen Hu
  Rough Sets and Knowledge Technology, Third International Conference, RSKT 2008, Chengdu, China, May 17-19, 2008. Proceedings; 01/2008
• Chapter: Differentiate Negative and Positive Schizophrenia Using Support Vector Machine

  Ming Ke, Hui Shen, Baojuan Li, Zongtan Zhou, Dewen Hu
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  ABSTRACT: In the present study, we identified the correlative pattern of gray matter distribution that best discriminates between positive and negative schizophrenia patients, which might provide additional information to psychiatric diagnostic system for mental disorders. First, we applied the voxel-based morphometry (VBM) to compare the gray matter distribution between negative and positive schizophrenia patients. Second, we trained the support vector machine (SVM) to obtain a classification model that classified 20 positive and 11 negative schizophrenic patients. The results showed that 84% subjects were correctly classified. We demonstrated that the united method of VBM and SVM would provide a useful tool for clinical diagnostic systems.
  12/2007: pages 863-866;