  Prompt learning has demonstrated impressive efficacy in the fine-tuning of
multimodal large models to a wide range of downstream tasks. Nonetheless,
applying existing prompt learning methods for the diagnosis of neurological
disorder still suffers from two issues: (i) existing methods typically treat
all patches equally, despite the fact that only a small number of patches in
neuroimaging are relevant to the disease, and (ii) they ignore the structural
information inherent in the brain connection network which is crucial for
understanding and diagnosing neurological disorders. To tackle these issues, we
introduce a novel prompt learning model by learning graph prompts during the
fine-tuning process of multimodal large models for diagnosing neurological
disorders. Specifically, we first leverage GPT-4 to obtain relevant disease
concepts and compute semantic similarity between these concepts and all
patches. Secondly, we reduce the weight of irrelevant patches according to the
semantic similarity between each patch and disease-related concepts. Moreover,
we construct a graph among tokens based on these concepts and employ a graph
convolutional network layer to extract the structural information of the graph,
which is used to prompt the pre-trained multimodal large models for diagnosing
neurological disorders. Extensive experiments demonstrate that our method
achieves superior performance for neurological disorder diagnosis compared with
state-of-the-art methods and validated by clinicians.
