In this study, for each T1-weighted brain image, a validated fully-automatic segmentation algorithm, namely the multi-atlas likelihood fusion (MALF) (46) algorithm, was utilized for 3D whole-brain segmentation. The MALF algorithm depends on the information of multiple atlases each of which consists of an MR brain image and a pre-defined segmentation map (46). In this study, 45 atlases were used and each had previously been segmented into a total of 289 anatomical regions including the three sub-regions of CC (gCC, bCC, and sCC). Details of the 45 atlases and the associated 289 labels can be found elsewhere (47). Before segmentation all T1-weighted images had been rigidly aligned to the MNI space (48), and thus the segmentation results were in the MNI space as well. The entire segmentation pipeline is freely available at the MriCloud platform (www.mricloud.org). Each segmentation result was visually inspected and manually corrected in case of automated segmentation error.

After obtaining the 3D whole-brain segmentation of each T1-weighted image, we extracted the binary segmentation results of gCC, sCC, and bCC and combined them to form the 3D binary segmentation of CC. We then took out the 2D mid-sagittal slice. In the MNI space, there are a total of 181 sagittal slices and thus the 2D mid-sagittal one refers to the 91st slice.

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