CCS: A Connectome Computation System for Discovery Sciences

The discovery science has been proposed to study human brain function based upon large-scale neuroimaging data. However, until now, there still lacks an integrated software pipe line to explore the human brain connectome based on multi-modal neuroimaging data. Here, we developed the Connectome Computation System (CCS), which integrates the functionality from AFNI, FSL, Freesurfer and extends the FCP scripts (FCON\_1000: http://www.nitrc.org/frs/downloadlink.php/2628) by utilizing the information of brain surfaces reconstructed to provide a common platform for brain connectome analysis. It can preprocess data for both anatomical and functional processing. CCS anatomical processing steps consist of: 1) removal of MR image noise using a spatially adaptive non-local means filter (Xing et al., 2011; Zuo and Xing, 2011), 2) brain surface reconstruction via recon-all command in Freesurfer (Dale et al. 1999; Ségonne et al. 2004; Fischl et al. 2001; Ségonne et al. 2007; Fischl et al. 1999a, 1999b), 3) spatial normalization from an individual functional space to MNI152 standard brain space (FLIRT+FNIRT in FSL) (Andersson et al., 2007), 4) boundary-based registration between individual structural and functional images (Greve and Fischl 2009). CCS functional preprocessing steps include: 5) discarding some first EPI volumes from each scan to allow for signal equilibration, 6) slice timing correction, 7) 3D motion correction, 8) 4D global mean-based intensity normalization, 9) band-pass temporal filtering (0.01-0.1Hz), 10) removal of linear and quadratic trends, 11) Gaussian (FWHM=6mm) spatial smoothing. CCS also provides the ability of computing of various R-fMRI metrics such as RSFC, ICA, ALFF/FALFF, ReHo, Network Centrality, VMHC in 3D volume or on 2D surface spaces. This pipeline will be made publicly available soon.