Abstract
We demonstrate the advantage of larger SOMs than those typically used in the literature for clustering functional magnetic resonance images (fMRI). We also show the advantage of a connectivity similarity measure over distance measures for cluster discovery and extraction. We illustrate these points through maps generated from a multiple-subject investigation of the genesis of willed movement, where clusters of the fMRI time-courses signify functional (or anatomical) regions, and where accurate delineation of many clusters is critical for tracking the relationships of neural activities across space and time. While we do not provide an automated optimization of the SOM size it is clear that for this study increasing it up to 40 \(\times \) 40 facilitates clearer discovery of more relevant clusters than from a 10 \(\times \) 10 SOM (a size frequently used in the literature), and further increase has no benefits in our case despite using large data sets (all data from whole-brain scans). We offer insight through data characteristics and some objective justification.
This work was partially supported by the Program for Mind and Brain, Department of Neurosurgery, Houston Methodist Hospital. Figures are in color, request a color copy by email: patrick.odriscoll@rice.edu, erzsebet@rice.edu
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O’Driscoll, P., Merényi, E., Karmonik, C., Grossman, R. (2016). The Effect of SOM Size and Similarity Measure on Identification of Functional and Anatomical Regions in fMRI Data. In: Merényi, E., Mendenhall, M., O'Driscoll, P. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 428. Springer, Cham. https://doi.org/10.1007/978-3-319-28518-4_22
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