Functional Parcellation of fMRI data using multistage k-means clustering. (arXiv:2202.11206v1 [eess.IV])

Purpose: Functional Magnetic Resonance Imaging (fMRI) data acquired through
resting-state studies have been used to obtain information about the
spontaneous activations inside the brain. One of the approaches for analysis
and interpretation of resting-state fMRI data require spatially and
functionally homogenous parcellation of the whole brain based on underlying
temporal fluctuations. Clustering is often used to generate functional
parcellation. However, major clustering algorithms, when used for fMRI data,
have their limitations. Among commonly used parcellation schemes, a tradeoff
exists between …

arxiv clustering data k-means