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CCS: A Connectome Computation System for Discovery Sciences

Filed under:

Xi-Nian Zuo (Institute of Psychology, Chinese Academy of Sciences), Lili Jiang (Institute of Psychology, Chinese Academy of Sciences), Zhi Yang (Institute of Psychology, Chinese Academy of Sciences), Ting Xu (Institute of Psychology, Chinese Academy of Sciences), Zhe Zhang (Institute of Psychology, Chinese Academy of Sciences), Feng-Mei Fan (Institute of Psychology, Chinese Academy of Sciences), Xiao-Yan Cao (Institute of Psychology, Chinese Academy of Sciences), Hui-Jie Li (Institute of Psychology, Chinese Academy of Sciences), Gao-Xia Wei (Institute of Psychology, Chinese Academy of 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: 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.
Preferred presentation format: Poster
Topic: Neuroimaging

Filed under:
Jean-Baptiste Poline
Jean-Baptiste Poline says:
May 07, 2012 10:24 AM
I wish they had made their pipeline available and compare it with others