  Given sufficient pairs of resting-state and task-evoked fMRI scans from
subjects, it is possible to train ML models to predict subject-specific
task-evoked activity using resting-state functional MRI (rsfMRI) scans.
However, while rsfMRI scans are relatively easy to collect, obtaining
sufficient task fMRI scans is much harder as it involves more complex
experimental designs and procedures. Thus, the reliance on scarce paired data
limits the application of current techniques to only tasks seen during
training. We show that this reliance can be reduced by leveraging group-average
contrasts, enabling zero-shot predictions for novel tasks. Our approach, named
OPIC (short for Omni-Task Prediction of Individual Contrasts), takes as input a
subject's rsfMRI-derived connectome and a group-average contrast, to produce a
prediction of the subject-specific contrast. Similar to zero-shot learning in
large language models using special inputs to obtain answers for novel natural
language processing tasks, inputting group-average contrasts guides the OPIC
model to generalize to novel tasks unseen in training. Experimental results
show that OPIC's predictions for novel tasks are not only better than simple
group-averages, but are also competitive with a state-of-the-art model's
in-domain predictions that was trained using in-domain tasks' data.
