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. Author manuscript; available in PMC: 2015 Oct 1.
Published in final edited form as: Neuroinformatics. 2014 Oct;12(4):513–534. doi: 10.1007/s12021-014-9226-5

Fig. 1.

Fig. 1

Conceptual comparison of DICCCOL-based activation detection and traditional group-wise activation detection methods. Four subjects were linearly warped into the MNI (Montreal Neurological Institute) atlas space. (a) (b) and (c) show 3 examples of corresponding DICCCOL landmarks, respectively. The 8 red bubbles are randomly selected from the DICCCOL system (Zhu et al., 2012). The vertical gray dashed lines represent image slices, which have the image grid point correspondences across subjects. The yellow curves show the actual correspondences of the DICCCOL landmarks in four different brains.