Why the study?
Temporal neural signal variability is linked to brain function, and investigators hypothesized that spatial variability of brain signals might provide important information about brain function.
Does a face detection task alter the spatial sample entropy of neuroimaging signals compared to resting state?
Does a face detection task alter the spatial sample entropy of neuroimaging signals compared to resting state?
Spatial variability of brain signals, measured by spatial sample entropy, differentiates task states from resting states and correlates with task performance.
SSE may index task-related spatial brain dynamics; leaves open its broader utility in cognitive neuroscience.
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.
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Wang et al. (2020) studied this question.
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