  Functional MRI (fMRI) is commonly used for interpreting neural activities
across the brain. Numerous accelerated fMRI techniques aim to provide improved
spatiotemporal resolutions. Among these, simultaneous multi-slice (SMS) imaging
has emerged as a powerful strategy, becoming a part of large-scale studies,
such as the Human Connectome Project. However, when SMS imaging is combined
with in-plane acceleration for higher acceleration rates, conventional SMS
reconstruction methods may suffer from noise amplification and other artifacts.
Recently, deep learning (DL) techniques have gained interest for improving MRI
reconstruction. However, these methods are typically trained in a supervised
manner that necessitates fully-sampled reference data, which is not feasible in
highly-accelerated fMRI acquisitions. Self-supervised learning that does not
require fully-sampled data has recently been proposed and has shown similar
performance to supervised learning. However, it has only been applied for
in-plane acceleration. Furthermore the effect of DL reconstruction on
subsequent fMRI analysis remains unclear. In this work, we extend
self-supervised DL reconstruction to SMS imaging. Our results on prospectively
10-fold accelerated 7T fMRI data show that self-supervised DL reduces
reconstruction noise and suppresses residual artifacts. Subsequent fMRI
analysis remains unaltered by DL processing, while the improved temporal
signal-to-noise ratio produces higher coherence estimates between task runs.
