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FCN-LLM: Empower LLM for Brain Functional Connectivity Network Understanding via Graph-level Multi-task Instruction Tuning
🤖AI Summary
Researchers have developed FCN-LLM, a framework that enables Large Language Models to understand brain functional connectivity networks from fMRI scans through multi-task instruction tuning. The system uses a multi-scale encoder to capture brain features and demonstrates strong zero-shot generalization across unseen datasets, outperforming conventional supervised models.
Key Takeaways
- →FCN-LLM is the first framework to align brain functional connectivity networks with text modality for LLM understanding.
- →The system uses multi-scale encoding to capture brain-region, functional subnetwork, and whole-brain features.
- →Multi-paradigm instruction tasks cover 19 subject-specific attributes across demographics, phenotypes, and psychiatric conditions.
- →The framework demonstrates strong zero-shot generalization on unseen datasets in experiments.
- →This work introduces a new paradigm for integrating neuroscience data with large language models.
#large-language-models#brain-networks#multimodal-ai#neuroscience#fmri#zero-shot-learning#instruction-tuning#medical-ai#graph-neural-networks#foundation-models
Read Original →via arXiv – CS AI
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