  We present TFF, which is a Transformer framework for the analysis of
functional Magnetic Resonance Imaging (fMRI) data. TFF employs a two-phase
training approach. First, self-supervised training is applied to a collection
of fMRI scans, where the model is trained to reconstruct 3D volume data.
Second, the pre-trained model is fine-tuned on specific tasks, utilizing ground
truth labels. Our results show state-of-the-art performance on a variety of
fMRI tasks, including age and gender prediction, as well as schizophrenia
recognition. Our code for the training, network architecture, and results is
attached as supplementary material.
