Clustered cNMF for fMRI data analysis

Xiaoxiang Wang; Jie Tian; Lei Yang; Jin Hu

doi:10.1117/12.596023

14 April 2005 Clustered cNMF for fMRI data analysis

Xiaoxiang Wang, Jie Tian, Lei Yang, Jin Hu

Proceedings Volume 5746, Medical Imaging 2005: Physiology, Function, and Structure from Medical Images; (2005) https://doi.org/10.1117/12.596023
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

This paper introduces a framework for the application of constrained non-negative matrix factorization (cNMF) to estimate the statistically distinct neural responses in a sequence of functional magnetic resonance images (fMRI). While an improved objective function has been defined to make the representation suitable for task-related brain activation detection, in this paper we explore various methods for better detection and efficient computation, placing particular emphasis on the initialization of the constrained NMF algorithm. The K-means algorithm performs this structured initialization and the information theoretic criterion of minimum description length (MDL) is used to estimate the number of clusters. We illustrate the method by a set of functional neuroimages from a motor activation study.

Citation Download Citation

Xiaoxiang Wang, Jie Tian, Lei Yang, and Jin Hu "Clustered cNMF for fMRI data analysis", Proc. SPIE 5746, Medical Imaging 2005: Physiology, Function, and Structure from Medical Images, (14 April 2005); https://doi.org/10.1117/12.596023

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