  Functional Magnetic Resonance Imaging (fMRI) data is a widely used kind of
four-dimensional biomedical data, which requires effective compression.
However, fMRI compressing poses unique challenges due to its intricate temporal
dynamics, low signal-to-noise ratio, and complicated underlying redundancies.
This paper reports a novel compression paradigm specifically tailored for fMRI
data based on Implicit Neural Representation (INR). The proposed approach
focuses on removing the various redundancies among the time series by employing
several methods, including (i) conducting spatial correlation modeling for
intra-region dynamics, (ii) decomposing reusable neuronal activation patterns,
and (iii) using proper initialization together with nonlinear fusion to
describe the inter-region similarity. This scheme appropriately incorporates
the unique features of fMRI data, and experimental results on publicly
available datasets demonstrate the effectiveness of the proposed method,
surpassing state-of-the-art algorithms in both conventional image quality
evaluation metrics and fMRI downstream tasks. This work in this paper paves the
way for sharing massive fMRI data at low bandwidth and high fidelity.
