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Proceedings Paper

Clustered cNMF for fMRI data analysis
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Paper 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.

Paper Details

Date Published: 14 April 2005
PDF: 8 pages
Proc. SPIE 5746, Medical Imaging 2005: Physiology, Function, and Structure from Medical Images, (14 April 2005); doi: 10.1117/12.596023
Show Author Affiliations
Xiaoxiang Wang, Institute of Automation/CAS (China)
Jie Tian, Institute of Automation/CAS (China)
Lei Yang, Institute of Automation/CAS (China)
Jin Hu, Institute of Automation/CAS (China)

Published in SPIE Proceedings Vol. 5746:
Medical Imaging 2005: Physiology, Function, and Structure from Medical Images
Amir A. Amini; Armando Manduca, Editor(s)

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