Major depressive disorder is associated with aberrant topological organizations of brain networks. However, whether this aberrance is shown in broader frequency bands or in a specific frequency band remains unknown. Fifty patients and fifty gender, age and education matched normal controls underwent resting state functional magnetic resonance imaging. Frequency dependent topological measures based on graph theory were calculated from wavelet decomposed resting state functional brain signals. In the specific frequency band of 0.03–0.06 14Hz, the clustering coefficient and the global efficiency were reduced while the characteristic path length was increased. Furthermore, patients showed aberrant nodal centralities in the default mode network, executive network and occipital network. Network based statistical analysis revealed system-wise topological alterations in these networks. The finding provides the first systematic evidence that depression is associated with frequency specific global and local topological disruptions and highlights the importance of frequency information in investigating major depressive disorders.

Major depressive disorder is associated with abnormal anatomical and functional connectivity, yet alterations in whole cortical thickness topology remain unknown. Here, we examined cortical thickness in medication-free adult depression patients (n = 76) and matched healthy controls (n = 116). Inter-regional correlation was performed to construct brain networks. By applying graph theory analysis, global (i.e., small-worldness) and regional (centrality) topology was compared between major depressive disorder patients and healthy controls. We found that in depression patients, topological organization of the cortical thickness network shifted towards randomness, and lower small-worldness was driven by a decreased clustering coefficient. Consistently, altered nodal centrality was identified in the isthmus of the cingulate cortex, insula, supra-marginal gyrus, middle temporal gyrus and inferior parietal gyrus, all of which are components within the default mode, salience and central executive networks. Disrupted nodes anchored in the default mode and executive networks were associated with depression severity. The brain systems involved sustain core symptoms in depression and implicate a structural basis for depression. Our results highlight the possibility that developmental and genetic factors are crucial to understand the neuropathology of depression.

No abstract

This Compared with the traditional text classification model, the Tibetan text classification based on N-Gram model has adopted N-Gram model in terms of the level of word. In other words, during the text classification, word segmentation is not required. Also, feature selection and abundant pre-treatment processes are avoided. This paper not only carried out profound research on N-Gram models, but also discusses the selection of parameter N in the model by adopting Naïve Bayes Multinomial classifier.