Computational Brain Imaging Core

The mission of the Computational Brain Imaging Core is to provide a high-throughput, functional MRI (fMRI) preprocessing service to the MUSC research community and beyond. Our specialized computational core, consisting of a 4-node 192 processing unit computing cluster with 450 TB storage capacity, leverages the high-performance Palmetto computing resource at Clemson University to serve the needs of researchers in the field of functional neuroimaging by offering a fast, reliable high-quality service to preprocess fMRI data for further analysis. Additionally, our Core service employs advanced state-of-the-art imaging techniques for localizing functional networks and extracting functional regions at the individual-level, allowing for individualized analyses of brain connectivity and functional topology. Our Core service also offers an option for storing data in a central place where it can be retrieved in a timely fashion upon request.

Core Services

  • Preprocessing of functional and structural fMRI data to prepare data for subsequent analyses, includes:
    • (i) Slice timing correction (SPM2; Wellcome Department of Cognitive Neurology, London, UK)
    • (ii) Rigid body correction for head motion with the FSL package
    • (iii) Normalization for global mean signal intensity across runs
    • (iv) Bandpass temporal filtering, head-motion regression, whole-brain signal regression, and ventricular and white-matter signal regression
  • Individual- and group-level resting-state and/or task-based fMRI data analyses:
    • (i) 3D reconstruction of structural images
    • (ii) Extracting anatomical features from the data, including cortical thickness and sulcal depth for each individual
    • (iii) Surface and volumetric data of functional (resting-state and or task) scans
      • individualized mapping of functional networks and regions (including maps and data for connectivity analyses)
      • advanced analysis of imaging features (individualized network and region size, connectivity analysis between functional regions for each subject)
      • visualization of advanced analysis results
  • Storage of processed data and results in users’ allocated storage space.

Typical output for resting-state fMRI data analyses:

  • 3-D reconstruction of the brain anatomy using FreeSurfer
  • Processing resting-state fMRI data both on cortical surface, as well as in volumetric space for subsequent connectivity analysis (i.e., vertex and voxel)
  • Mapping 7 canonical functional networks, 18 fine-grained functional networks for each individual subject:
    • extracting ~200 cortical functional ROI from each individual subject
    • extracting time courses from each ROI for subsequent connectivity analyses
    • extracting imaging features for advanced analysis

Typical output for task-based fMRI data analyses:

  • Task activation map of each individual subject using cutting-edge denoising techniques
  • Group-level task activation maps