TReND: Transformer derived features and Regularized NMF for Delineation of Functional Networks

Brisbane Convention & Exhibition Centre 

Neuroscientists have long attempted to subdivide the human brain into a mesh of anatomically and functionally distinct, contiguous regions (Huang, 2005, Yeo, 2011). This challenge become particularly complex in the neonatal brain, where functional organization differs markedly from that of adults (Peng, 2020). During the third trimester, the neonatal brain undergoes a critical phase of enhanced functional segregation, primarily driven by the rapid development of functional connectivity and the formation of hubs in primary regions (Cao, 2017). However, achieving accurate and reliable parcellation of specific functional networks (FNs) in newborns presents unique challenges. The combined effects of rapid functional segregation, low imaging quality, and the absence of established functional atlases complicate this process. In this study, we developed a four-stage bottom-up approach to parcellate neonatal brain FNs, providing a foundational neonatal functional brain atlas. This approach integrates a transformer-based autoencoder architecture for extracting novel features, coupled with innovative regularized NMF clustering algorithm.