The major depressive disorder (MDD) is a common mental disability, characterized by persistent low mood, reduced vitality and interest, worse sleep, and high suicidal tendency. 1,2 It affects more than 350 million people throughout the world. 3,4 It is predicted that MDD would rank the first cause of disease burden by 2030. 5 The severe impact of MDD on human beings raised the necessity for its treatment. Accordingly, various therapeutic methods have been applied, including antidepressants, psychotherapy, and physical therapy. Antidepressants were the dominant method in MDD treatment, although it was accompanied by side effects such as insomnia, weight gain, loss of appetite, drug dependence, and other physiological responses that would have dramatic influences on people's daily life. [6][7][8] Besides that, the uncertainty of which medicine works well for a particular patient took a lot of time, effort, and money for treatment, resulting in unnecessary side effects and low medication adherence. 7,9 These weaknesses

Temporal variability of the neural signal has been demonstrated to be closely related to healthy brain function. Meanwhile, the evolving brain functions are supported by dynamic relationships among brain regions. We hypothesized that the spatial variability of brain signal might provide important information about brain function. Here we used the spatial sample entropy (SSE) to investigate the spatial variability of neuroimaging signal during a steady-state presented face detection task. Lower SSE was found during task state than during resting state, associating with more repetitive functional interactions between brain regions. The standard deviation (SD) of SSE during the task was negatively related to the SD of reaction time, suggesting that the spatial pattern of neural activity is reorganized according to particular cognitive function and supporting the previous theory that greater variability is associated with better task performance. These results were replicated with reordered data, implying the reliability of SSE in measuring the spatial organization of neural activity. Overall, the present study extends the research scope of brain signal variability from the temporal dimension to the spatial dimension, improving our understanding of the spatiotemporal characteristics of brain activities and the theory of brain signal variability.