AUTHOR=Zhang Jiang , Liu Qi , Chen Huafu , Yuan Zhen , Huang Jin , Lu Fengmei , Deng Lihua , Wang Yuqing , Zhang Junpeng , Wang Mingwen , Chen Liangyin 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 JOURNAL=Frontiers in Human Neuroscience VOLUME=9 YEAR=2015 URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2015.00400 DOI=10.3389/fnhum.2015.00400 ISSN=1662-5161 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.