Prompt Your Brain: Scaffold Prompt Tuning for Efficient Adaptation of fMRI Pre-trained Model

TL;DR

ScaPT is a prompt-based framework that efficiently adapts large pre-trained fMRI models to new tasks using minimal parameters, outperforming traditional fine-tuning especially in low-resource scenarios.

Contribution

The paper introduces a hierarchical prompt structure with a DIP module for efficient, low-parameter adaptation of pre-trained fMRI models to downstream tasks.

Findings

Outperforms fine-tuning and baselines in neurodegenerative disease diagnosis.

Requires updating only 2% of parameters for adaptation.

Effective in low-resource settings with fewer than 20 participants.

Abstract

We introduce Scaffold Prompt Tuning (ScaPT), a novel prompt-based framework for adapting large-scale functional magnetic resonance imaging (fMRI) pre-trained models to downstream tasks, with high parameter efficiency and improved performance compared to fine-tuning and baselines for prompt tuning. The full fine-tuning updates all pre-trained parameters, which may distort the learned feature space and lead to overfitting with limited training data which is common in fMRI fields. In contrast, we design a hierarchical prompt structure that transfers the knowledge learned from high-resource tasks to low-resource ones. This structure, equipped with a Deeply-conditioned Input-Prompt (DIP) mapping module, allows for efficient adaptation by updating only 2% of the trainable parameters. The framework enhances semantic interpretability through attention mechanisms between inputs and prompts, and it clusters prompts in the latent space in alignment with prior knowledge. Experiments on public resting state fMRI datasets reveal ScaPT outperforms fine-tuning and multitask-based prompt tuning in neurodegenerative diseases diagnosis/prognosis and personality trait prediction, even with fewer than 20 participants. It highlights ScaPT's efficiency in adapting pre-trained fMRI models to low-resource tasks.