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Decoding Brain’s Global Spatiotemporal Dynamics using a scalable k-means clustering method

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Our method effectively analyzes terabyte-sized fMRI datasets, uncovering six distinct global spatiotemporal patterns.
The scalable k-means clustering technique allows for efficient processing of high-resolution hemodynamic data.
Results indicate significant insights into the characteristics of BOLD dynamics during resting state in large datasets.
The approach highlights the potential for joint spatial and temporal pattern estimation in brain activity analysis.

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Scalable method analyzes high-resolution fMRI data, revealing six spatiotemporal patterns, suggesting improved insights into hemodynamic activity.

Ma et al. (2025) studied this question.

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Scalable method analyzes high-resolution fMRI data, revealing six spatiotemporal patterns, suggesting improved insights into hemodynamic activity.