Computational protocol: Cognitive and anatomical data in a healthy cohort of adults

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Protocol publication

[…] Automated brain tissue segmentation and reconstruction of the T1-weighted structural MRI images were performed using the standard recon-all processing pipeline in FreeSurfer, version 5.2.0 (Released May, 2013; http://surfer-nmr.mgh.harvard.edu/). This produced estimates of 1) cortical thickness, 2) cortical volumes, 3) sub-cortical volumes, 4) ventricles, and 5) corpus callosum , , , , , . Segmentations and tractography were manually checked for errors. Estimates in the left and right hemispheres were summed to produce bilateral estimates, and all values were converted to z-scores to control for differences in scale. A complete list of estimated structures appears in . FreeSurfer produced automated segmentation that closely approximates hand tracing, but like all segmentation procedures may introduce systematic bias.The diffusion tensor imaging estimates for fractional anisotropy (FA) and radial diffusivity (RD) data was analyzed using tract-based spatial statistics in FSL , , . This pipeline involves fitting a tensor model to the raw diffusion data using fMRIDB׳s diffusion toolbox, and non-brain tissues were removed using FSL׳s brain extraction tool. All subjects׳ FA data were then aligned into a common space using the nonlinear registration tool FNIRT , . Next, the mean FA image was created and thinned to create a mean FA skeleton that represents the centers of all tracts common to the group. Each subject׳s aligned FA data was then projected onto this skeleton to create an estimate of the subject-level value associated with each tract. […]

Pipeline specifications

Software tools FreeSurfer, BET
Application Magnetic resonance imaging