title: Combining self-organizing mapping and supervised affinity propagation clustering approach to investigate functional brain networks involved in motor imagery and execution with fMRI measurements.
abstract: Clustering analysis methods have been widely applied to identifying the functional brain networks of a multitask paradigm. However, the previously used clustering analysis techniques are computationally expensive and thus impractical for clinical applications. In this study a novel method, called SOM-SAPC that combines self-organizing mapping (SOM) and supervised affinity propagation clustering (SAPC), is proposed and implemented to identify the motor execution (ME) and motor imagery (MI) networks. In SOM-SAPC, SOM was first performed to process fMRI data and SAPC is further utilized for clustering the patterns of functional networks. As a result, SOM-SAPC is able to significantly reduce the computational cost for brain network analysis. Simulation and clinical tests involving ME and MI were conducted based on SOM-SAPC, and the analysis results indicated that functional brain networks were clearly identified with different response patterns and reduced computational cost. In particular, three activation clusters were clearly revealed, which include parts of the visual, ME and MI functional networks. These findings validated that SOM-SAPC is an effective and robust method to analyze the fMRI data with multitasks.
authors.affiliation: Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University Chengdu, China.
authors.forename: Jiang
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University Chengdu, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Liu
authors.affiliation: School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China.
authors.forename: Huafu
authors.initials: H
authors.lastname: Chen
authors.affiliation: Bioimaging Core, Faculty of Health Sciences, University of Macau Macau, China.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Yuan
authors.affiliation: School of Foreign Studies, University of Electronic Science and Technology of China Chengdu, China.
authors.forename: Jin
authors.initials: J
authors.lastname: Huang
authors.affiliation: Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University Chengdu, China.
authors.forename: Lihua
authors.initials: L
authors.lastname: Deng
authors.affiliation: Bioimaging Core, Faculty of Health Sciences, University of Macau Macau, China.
authors.forename: Fengmei
authors.initials: F
authors.lastname: Lu
authors.affiliation: Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University Chengdu, China.
authors.forename: Junpeng
authors.initials: J
authors.lastname: Zhang
authors.affiliation: CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety National Center for Nanoscience and Technology of China Beijing, China.
authors.forename: Yuqing
authors.initials: Y
authors.lastname: Wang
authors.affiliation: School of Mathematics, Southwest Jiaotong University Chengdu, China.
authors.forename: Mingwen
authors.initials: M
authors.lastname: Wang
authors.affiliation: School of Computer Science, Sichuan University Chengdu, China.
authors.forename: Liangyin
authors.initials: L
authors.lastname: Chen
keywords: affinity propagation clustering; functional magnetic resonance imaging; motor execution; motor imagery; self-organizing mapping