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Multi-head Attention-Based Masked Sequence Model for Mapping Functional Brain Networks by Mengshen He; Xiangyu Hou; Zhenwei Wang; Zili Kang; Xin Zhang; Ning Qiang; Bao Ge is a book available to read on EtoBox.

What is Multi-head Attention-Based Masked Sequence Model for Mapping Functional Brain Networks about?

It has been of great interest in the neuroimaging community to discover brain functional networks (FBNs) based on task functional magnetic resonance imaging (tfMRI). A variety of methods have been used to model tfMRI sequences so far, such as recurrent neural network (RNN) and Autoencoder. However, these models are not designed to incorporate the characteristics of tfMRI sequences, and the same signal values at different time points in a fMRI time series may rep-resent different states and meanings. Inspired by cloze learning methods and the human ability to judge polysemous words based on context, we proposed a self-supervised a Multi-head Attention-based Masked Sequence Model (MAMSM), as BERT model uses (Masked Language Modeling) MLM and multi-head attention to learn the different meanings of the same word in different sentences. MAMSM masks and encodes tfMRI time series, uses multi-head attention to calculate different meanings corresponding to the same signal value in fMRI sequence, and obtains context information through MSM pre-training. Furthermore this work redefined a new loss function to extract FBNs according to the task de-sign information of tfMRI data. The model has b

Author

Mengshen He; Xiangyu Hou; Zhenwei Wang; Zili Kang; Xin Zhang; Ning Qiang; Bao Ge

Publisher

Springer International Publishing AG

Published

2022

Language

EN

ISBN

9783031164460

Subjects

Computer Science, Mathematics, Engineering