Title :
Analysis of fMRI Data Using Improved Self-Organizing Mapping and Spatio-Temporal Metric Hierarchical Clustering
Author :
Liao, Wei ; Chen, Huafu ; Yang, Qin ; Lei, Xu
Author_Institution :
Sch. of Life Sci. & Technol., Univ. of Electron. Sci. & Technol. of China, Chengdu
Abstract :
The self-organizing mapping (SOM) and hierarchical clustering (HC) methods are integrated to detect brain functional activation; functional magnetic resonance imaging (fMRI) data are first processed by SOM to obtain a primary merged neural nodes image, and then by HC to obtain further brain activation patterns. The conventional Euclidean distance metric was replaced by the correlation distance metric in SOM to improve clustering and merging of neural nodes. To improve the use of spatial and temporal information in fMRI data, a new spatial distance (node coordinates in the 2-D lattice) and temporal correlation (correlation degree of each time course in the exemplar matrix) are introduced in HC to merge the primary SOM results. Two simulation studies and two in vivo fMRI data that both contained block-design and event-related experiments revealed that brain functional activation can be effectively detected and that different response patterns can be distinguished using these methods. Our results demonstrate that the improved SOM and HC methods are clearly superior to the statistical parametric mapping (SPM), independent component analysis (ICA), and conventional SOM methods in the block-design, especially in the event-related experiment, as revealed by their performance measured by receiver operating characteristic (ROC) analysis. Our results also suggest that the proposed new integrated approach could be useful in detecting block-design and event-related fMRI data.
Keywords :
biomedical MRI; brain; independent component analysis; self-organising feature maps; brain functional activation; fMRI data; functional magnetic resonance imaging; independent component analysis; receiver operating characteristic; self-organizing mapping; spatiotemporal metric hierarchical clustering; statistical parametric mapping; Brain modeling; Data analysis; Euclidean distance; Image analysis; Independent component analysis; Lattices; Magnetic analysis; Magnetic resonance imaging; Merging; Pattern analysis; Correlation distance metric; correlation distance metric; fMRI; functional magnetic resonance imaging (fMRI); hierarchical clustering analysis; self-organizing maps; spatio-temporal measure; Adult; Algorithms; Brain; Brain Mapping; Cluster Analysis; Evoked Potentials, Motor; Female; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Magnetic Resonance Imaging; Male; Reproducibility of Results; Sensitivity and Specificity;
Journal_Title :
Medical Imaging, IEEE Transactions on
DOI :
10.1109/TMI.2008.923987