2024/05/24 by Youzhi Qu, Junfeng Xia, Qu, Youzhi +13 · 1 citation
Computer Science · Medicine · Neuroscience · #Artificial Intelligence (cs.AI) #FOS: Biological sciences #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Medical Imaging Techniques and Applications #Neural Networks and Applications #Neurons and Cognition (q-bio.NC)
paper · pdf · doi:10.48550/arxiv.2407.00033
openalex publication_date 2024/05/24 · openalex created_date 2024/07/03 · openalex updated_date 2026/07/28
Data reconstruction is a widely used pre-training task to learn the generalized features for many downstream tasks. Although reconstruction tasks have been applied to neural signal completion and denoising, neural signal reconstruction is less studied. Here, we employ the masked autoencoder (MAE) model to reconstruct functional magnetic resonance imaging (fMRI) data, and utilize a transfer learning framework to obtain the cognitive taskonomy, a matrix to quantify the similarity between cognitive tasks. Our experimental results demonstrate that the MAE model effectively captures the temporal dynamics patterns and interactions within the brain regions, enabling robust cross-subject fMRI signal reconstruction. The cognitive taskonomy derived from the transfer learning framework reveals the relationships among cognitive tasks, highlighting subtask correlations within motor tasks and similarities between emotion, social, and gambling tasks. Our study suggests that the fMRI reconstruction with MAE model can uncover the latent representation and the obtained taskonomy offers guidance for selecting source tasks in neural decoding tasks for improving the decoding performance on target tasks.