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