Analysis of functional magnetic resonance imaging data using self-organizing mapping with spatial connectivity.
Commonly used methods in analyzing functional magnetic resonance imaging (fMRI) data, such as the Student's t-test and cross-correlation analysis, are model-based approaches. Although these methods are easy to implement and are effective in analyzing data obtained with simple paradigms, they are not applicable in situations in which patterns of neuronal response are complicated and when fMRI response is unknown. In this work, Kohonen's self-organizing mapping (SOM), which is a model-free approach, is adapted for analyzing fMRI data. Because spatial connectivity is an important function in the identification of activation sites in functional brain imaging, it is incorporated into the SOM algorithm. Receiver operating characteristic analysis on simulated data shows that the new algorithm achieves measurable improvement over the standard algorithm. The applicability of the new algorithm is demonstrated on experimental data.
Ngan
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SC
Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, USA.
Hu
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D000465:Algorithms; D001921:Brain / Q000378:metabolism; D003198:Computer Simulation; D017352:Echo-Planar Imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D012372:ROC Curve