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- W1972382521 abstract "So far, most fMRI studies that analyzed voxel activity patterns of more than two conditions transformed the multiclass problem into a series of binary problems. Furthermore, visualizations of the topology of underlying representations are usually not presented. Here, we explore the feasibility of different types of supervised self-organizing maps (SSOM) to decode and visualize voxel patterns of fMRI datasets consisting of multiple conditions. Our results suggest that - compared to commonly applied classification approaches - SSOMs are well suited when activity patterns consist of a small number of features (e.g. as in searchlight- or region of interest-based approaches). In addition, we demonstrate the utility of using SOM grids for intuitive and exploratory visualization of topological relations among classes of fMRI activity patterns." @default.
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- W1972382521 date "2012-07-01" @default.
- W1972382521 modified "2023-10-05" @default.
- W1972382521 title "Classification and Visualization of Multiclass fMRI Data Using Supervised Self-Organizing Maps" @default.
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- W1972382521 doi "https://doi.org/10.1109/prni.2012.34" @default.
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