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Abstract #4134

TReND: Transformer derived features and regularized NMF for neonatal functional network delineation

Sovesh Mohapatra1,2, Minhui Ouyang1,3, Lianglong Sun4,5, Yong He4,5, and Hao Huang1,3
1Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, United States, 2Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States, 3Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States, 4State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, 5Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China

Synopsis

Keywords: Analysis/Processing, Brain Connectivity, fMRI (resting state), fMRI Analysis, Machine Learning/Artificial Intelligence

Motivation: Neonatal brain functional organization, characterized by extensive immature networks, remains poorly understood. Few methods were established to accurately parcellate neonate functional networks.

Goal(s): We aim to develop novel DL approach to extract reliable features, combined with regularized clustering algorithm for robust neonatal functional network delineation.

Approach: We developed a novel computational framework, including transformer-based autoencoder to extract feature from BOLD signals, coupled with regularized NMF clustering algorithm to parcellate simulated and real-world neonatal fMRI.

Results: TReND surpasses competing feature extraction techniques like PCA, UMAP, and TD, and outperforms clustering methods: K-PCA, ICA, and NMF, demonstrating high stability and robustness in neonate brain parcellation.

Impact: We established TReND, a novel and robust framework, for neonatal functional network delineation. TReND-derived neonatal functional networks could serve as a neonatal functional atlas for perinatal populations in health and disease.

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