Zero-shot Learning of Individualized Task Contrast Prediction from Resting-state Functional Connectomes

• URL: http://arxiv.org/abs/2310.14105v1
• Date: Sat, 21 Oct 2023 20:12:22 GMT
• Title: Zero-shot Learning of Individualized Task Contrast Prediction from Resting-state Functional Connectomes
• Authors: Minh Nguyen, Gia H. Ngo, Mert R. Sabuncu
• Abstract summary: It is possible to train ML models to predict subject-specific task-evoked activity using resting-state functional MRI (rsfMRI) scans. 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. We show that this reliance can be reduced by leveraging group-average contrasts, enabling zero-shot predictions for novel tasks.
• Score: 9.78824332635036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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