Template-guided Functional Network Identification via Supervised Dictionary Learning

• Published in: Proceedings (International Symposium on Biomedical Imaging) pp. 72 - 76
• Main Authors: Yu Zhao, Xiang Li, Makkie, Milad, Quinn, Shannon, Binbin Lin, Jieping Ye, Tianming Liu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2017
• Subjects: Algorithm design and analysis; Autism; autism spectrum disorder(ASD); Brain modeling; Data models; Dictionaries; functional network identification; Matrix decomposition; network based diagnosis; supervised learning
• ISSN: 1945-8452
• DOI: 10.1109/ISBI.2017.7950471

Functional network analysis based on matrix decomposition/factorization methods including ICA and dictionary learning models have become a popular approach in fMRI study. Yet it is still a challenging issue in interpreting the result networks because of the inter-subject variability and image noises, thus in many cases, manual inspection on the obtained networks is needed. Aiming to provide a fast and reliable functional network identification tool for both normal and diseased brain fMRI data analysis, in this work, we propose a novel supervised dictionary learning model based on rank-1 matrix decomposition algorithm (S-r1DL) with sparseness constraint. Application on the Autism Brain Imaging Data Exchange (ABIDE) database showed that S-r1DL can fast and accurately identify the functional networks based on the given templates, comparing to unsupervised learning method.