Keywords: fMRI (resting state), Brain, SpatiotemporalThe human brain, at rest, is complex and many functional studies focus on the brain patterns around criticality. This work addresses a novel perspective, the avalanche, in resting-state fMRI. In this work, we design two data-driven signal modeling approaches that dynamically measure and visualize the signal entropy from both spatial and temporal aspects. The first approach applies a clustering-based scheme with the Markov chain. The second method utilizes the autoregressive model with a sliding window. The results show a consistent, less complex pattern at the avalanche state, from which the interpretation of the brain can be clearer than at criticality.
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