  High spatial and temporal resolution across the whole brain is essential to
accurately resolve neural activities in fMRI. Therefore, accelerated imaging
techniques target improved coverage with high spatio-temporal resolution.
Simultaneous multi-slice (SMS) imaging combined with in-plane acceleration are
used in large studies that involve ultrahigh field fMRI, such as the Human
Connectome Project. However, for even higher acceleration rates, these methods
cannot be reliably utilized due to aliasing and noise artifacts. Deep learning
(DL) reconstruction techniques have recently gained substantial interest for
improving highly-accelerated MRI. Supervised learning of DL reconstructions
generally requires fully-sampled training datasets, which is not available for
high-resolution fMRI studies. To tackle this challenge, self-supervised
learning has been proposed for training of DL reconstruction with only
undersampled datasets, showing similar performance to supervised learning. In
this study, we utilize a self-supervised physics-guided DL reconstruction on a
5-fold SMS and 4-fold in-plane accelerated 7T fMRI data. Our results show that
our self-supervised DL reconstruction produce high-quality images at this
20-fold acceleration, substantially improving on existing methods, while
showing similar functional precision and temporal effects in the subsequent
analysis compared to a standard 10-fold accelerated acquisition.
