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Abstract #3568

End-to-End Spatial and Temporal Brain Region Feature Representation Learning from fMRI

Xinyu Wang1, Mengjun Liu2, Haolin Huang1, Haotian Jiang1, and Qian Wang1
1Shanghaitech University, Shanghai, China, 2Shanghai Jiao Tong University, Shanghai, China

Synopsis

Keywords: fMRI Analysis, fMRI Analysis

Motivation: Extracting features from brain regions is crucial for effective brain analysis. Recently, fMRI foundational models have introduced brain region embeddings by pre-training on large unlabeled datasets. However, many depend on specific ROI parcellation, which can lead to information loss.

Goal(s): We propose a voxel-based fMRI model that captures spatio-temporal dependencies, effectively extracting features adaptable to various datasets.。

Approach: We employ a self-supervised approach to train an encoder on large-scale unlabeled data and validate its performance on labeled data to demonstrate its superiority.

Results: Experimental results confirm that our model generates high-quality feature representations of fMRI data.

Impact: This project introduces a foundational model with voxel-level inputs and spatio-temporal attention, enhancing fMRI representation accuracy, generalization, and insights into brain networks.

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Keywords