  Despite the impressive advancements achieved using deep-learning for
functional brain activity analysis, the heterogeneity of functional patterns
and scarcity of imaging data still pose challenges in tasks such as prediction
of future onset of Post-Traumatic Epilepsy (PTE) from data acquired shortly
after traumatic brain injury (TBI). Foundation models pre-trained on separate
large-scale datasets can improve the performance from scarce and heterogeneous
datasets. For functional Magnetic Resonance Imaging (fMRI), while data may be
abundantly available from healthy controls, clinical data is often scarce,
limiting the ability of foundation models to identify clinically-relevant
features. We overcome this limitation by introducing a novel training strategy
for our foundation model by integrating meta-learning with self-supervised
learning to improve the generalization from normal to clinical features. In
this way we enable generalization to other downstream clinical tasks, in our
case prediction of PTE. To achieve this, we perform self-supervised training on
the control dataset to focus on inherent features that are not limited to a
particular supervised task while applying meta-learning, which strongly
improves the model's generalizability using bi-level optimization. Through
experiments on neurological disorder classification tasks, we demonstrate that
the proposed strategy significantly improves task performance on small-scale
clinical datasets. To explore the generalizability of the foundation model in
downstream applications, we then apply the model to an unseen TBI dataset for
prediction of PTE using zero-shot learning. Results further demonstrated the
enhanced generalizability of our foundation model.
