Recent developments in graph theory have heightened the need for investigating the disruptions in the topological structure of functional brain network in major depressive disorder (MDD). In this study, we employed resting-state functional magnetic resonance imaging (fMRI) and graph theory to examine the whole-brain functional networks among 42 MDD patients and 42 healthy controls. Our results showed that compared with healthy controls, MDD patients showed higher local efficiency and modularity. Furthermore, MDD patients showed altered nodal centralities of many brain regions, including hippocampus, temporal cortex, anterior cingulate gyrus and dorsolateral prefrontal gyrus, mainly located in default mode network and cognitive control network. Together, our results suggested that MDD was associated with disruptions in the topological structure of functional brain networks, and provided new insights concerning the pathophysiological mechanisms of MDD.

Neuroimaging techniques such as functional magnetic resonance imaging and positron emission tomography have provided an unprecedented neurobiological perspective for research on personality traits . Evidence from task-related neuroimaging has shown that extraversion is associated with activations in regions of the anterior cingulate cortex, dorsolateral prefrontal cortex, middle temporal gyrus and the amygdala. Currently, resting-state neuroimaging is being widely used in cognitive neuroscience. Initial exploration of extraversion has revealed correlations with the medial prefrontal cortex, anterior cingulate cortex, insular cortex, and the precuneus. Recent research work has indicated that the long-range temporal dependence of the resting-state spontaneous oscillation has high test-retest reliability. Moreover, the long-range temporal dependence of the resting-state networks is highly correlated with personality traits, and this can be used for the prediction of extraversion. As the long-range temporal dependence refl ects real-time information updating in individuals, this method may provide a new approach to research on personality traits.Keywords: extraversion; neuroimaging; resting-state fMRI; default mode network ; scale-free dynamics ·Review· IntroductionIn personality psychology, personality traits are regarded as an individual's lasting and stable behavioral tendencies at different times and in different situations. As one of the most important dimensions of personality traits, extraversion is the best established and validated [1] . Moreover, extraversion is the most stable core trait and a universal component in personality theory. Extraverts are typically described in positive emotional terms such as excitement, engagement, and enthusiasm. In contrast, introverts are described as quiet, conservative, and insensitive to the environment.An individual with high extraversion is more talkative, outgoing, and excited than someone with low extraversion, but even people with low extraversion may occasionally experience talkative and outgoing states. Trait levels of extraversion are known to be highly heritable and are linked to vulnerability to anxiety and major depression disorder [2,3] .Although much is known about the behavioral correlates of extraversion, little is understood about its biological basis.Research on personality traits usually has the ambitious goal of providing a comprehensive understanding of a person's integrated framework [3] . To achieve this goal, various effective research methods have been adopted.Based on the premise that the whole person cannot be understood without understanding the brain, p ersonality neuroscience has emerged as an important direction in the study of personality traits [4] . Generally, personality traits are measured by questionnaire through self-report or peerrating. Then, correlations between personality traits and n eurobiological p arameters are calculated [5] . Currently, a number of techniques are available for personality neuroscience. Anatomical neuroima...

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