Analysis of functional networks involved in motor execution and motor imagery using combined hierarchical clustering analysis and independent component analysis

• Published in: Magnetic resonance imaging Vol. 28; no. 5; pp. 653 - 660
• Main Authors: Wang, Yuqing, Chen, Huafu, Gong, Qiyong, Shen, Shan, Gao, Qing
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.06.2010
• Subjects: Adult; Algorithms; Brain Mapping - methods; Cluster Analysis; Evoked Potentials, Motor - physiology; Female; Hierarchical clustering analysis independent component analysis; Humans; Imagination - physiology; Magnetic Resonance Imaging - methods; Male; Motor Cortex - physiology; Motor execution; Motor imagery; Movement - physiology; Pattern Recognition, Automated - methods; Principal Component Analysis; Radiology; Task Performance and Analysis; Young Adult; Motor execution; Hierarchical clustering analysis independent component analysis; Motor imagery
• ISSN: 0730-725X; 1873-5894; 1873-5894
• DOI: 10.1016/j.mri.2010.02.008

Cognitive experiments involving motor execution (ME) and motor imagery (MI) have been intensively studied using functional magnetic resonance imaging (fMRI). However, the functional networks of a multitask paradigm which include ME and MI were not widely explored. In this article, we aimed to investigate the functional networks involved in MI and ME using a method combining the hierarchical clustering analysis (HCA) and the independent component analysis (ICA). Ten right-handed subjects were recruited to participate a multitask experiment with conditions such as visual cue, MI, ME and rest. The results showed that four activation clusters were found including parts of the visual network, ME network, the MI network and parts of the resting state network. Furthermore, the integration among these functional networks was also revealed. The findings further demonstrated that the combined HCA with ICA approach was an effective method to analyze the fMRI data of multitasks.