DOI: 10.6084/m9.figshare.7965065
title: CMU SC and BOLD fMRI

description: <b>Participants</b>Sixty participants (28 male, 32 female) were recruited locally from the Pittsburgh, Pennsylvania area as well as the U.S. Army Research Laboratory in Aberdeen, Maryland. Participants were neurologically healthy adults with no history of head trauma, neurological pathology, or psychological pathology. Participant ages ranged from 18 to 45 years old (mean age, 26.5 years). The study protocol for acquiring the human subjects data was reviewed and approved by the IRB at Carnegie Mellon University and written informed consent was obtained for all participants. As the present work uses de-identified human data from the original CMU study, the Penn IRB deemed this study exempt from the requirement for ethical review.<b>MRI acquisition</b>All 60 participants were scanned at the Scientific Imaging and Brain Research Center at Carnegie Mellon University on a Siemens Verio 3T magnet fitted with a 32-channel head coil. An MPRAGE sequence was used to acquire a high-resolution (1 mm<sup>3</sup> isotropic voxels, 176 slices) T1-weighted brain image for all participants. DSI data was acquired following fMRI sequences using a 50 min, 257-direction, twice-refocused spin-echo EPI sequence with multiple q values (TR =11,400 ms, TE =128 ms, voxel size 2.4 mm<sup>3</sup>, field of view 231x 231 mm, <i>b</i>-max 5000 s/mm<sup>2</sup>, 51 slices). Resting state fMRI (rsfMRI) data consisting of 210 T2*-weighted volumes were collected for each participant (56 participants) with a BOLD contrast with echo planar imaging (EPI) sequence (TR 2000 ms, TE 29 ms, voxel size 3.5 mm<sup>3</sup>, field of view 224 x 224 mm, flip angle 79 degrees).Head motion is a major source of artifact in resting state fMRI data (rsfMRI). Although recently developed motion correction algorithms are far more effective than typical procedures, head motion was additionally minimized during image acquisition with a custom foam padding setup designed to minimize the variance of head motion along pitch and yaw directions. The setup also included a chin restraint that held the participant's head to the receiving coil itself. Preliminary inspection of EPI images at the imaging center showed that the setup minimized resting head motion to 1 mm maximum deviation for most subjects. Only 3 out of 56 subject were excluded from the final analysis because they moved more than 2 voxels multiple times throughout the imaging session.<b>Diffusion MRI reconstruction</b>DSI Studio (http://dsi-studio.labsolver.org) was used to process all DSI images using a <i>q</i>-space diffeomorphic reconstruction method (yeh et al 2011). A nonlinear spatial normalization approach (Ashburner et al 1999) was implemented through 16 iterations to obtain the spatial mapping function of quantitative anisotropy (QA) values from individual subject diffusion space tothe FMRIB 1 mm fractional anisotropy (FA) atlas template. QA is an orientation distribution function (ODF) based index that is scaled with spin density information that permits the removal of isotropic diffusion components from the ODF to filter false peaks, facilitating the resolution of fiber tracts using deterministic fiber tracking algorithms. For a detailed description and comparison of QA with standard FA techniques, see (Yeh et al 2013). The ODFs were reconstructed to a spatial resolution of 2 mm<sup>3</sup> with a diffusion sampling length ratio of 1.25. Whole-brain ODF maps of all 60 subjects were averaged together to generate a template image of the average tractography space.<b>Fiber tractography analysis</b>Fiber tractography was performed using an ODF-streamline version of the FACT algorithm (Yeh et al 2013) in DSI Studio, using the builds from September 23, 2013 and August 29, 2014. All fiber tractography was initiated from seed positions with random locations within the whole-brain seed mask with random initial fiber orientations. Using a step size of 1 mm, the directional estimates of fiber progression within each voxel were weighted by 80% of the incoming fiber direction and 20% of the previous fiber direction. A streamline was terminated when the QA index fell below 0.05 or had a turning angle greater than 75 degrees. We performed a region-based tractography to isolate streamlines between pairs of regional masks. All cortical masks were selected from an upsampled version of the original Automated Anatomical Labeling Atlas (AAL) (Tzourio et al 2002, Desikan et al 2006) containing 90 cortical and subcortical regions of interest but not containing cerebellar structures or the brainstem. This resampled version contains 600 regions and is created via a series of upsampling steps in which any given region is bisected perpendicular to its principal spatial axis in order to create 2 equally sized sub-regions (Hermundstad et al 2014). The final atlas contained regions of an average size of 268 voxels, with a standard deviation of 35 voxels. Diffusion-based tractography has been shown to exhibit a strong medial bias (Croxson et al 2005) due to partial volume effects and poor resolution of complex fiber crossings (jones et al 2010). To counter the bias away from more lateral cortical regions, tractography was generated for each cortical surface mask separately.<b>Resting state fMRI preprocessing</b>SPM8 (Wellcome Department of Imaging Neuroscience, London) was used to preprocess all rsfMRI collected from 53 of the 60 participants with DSI data. To estimate the normalization transformation for each EPI image, the mean EPI image was first selected as a source image and weighted by its mean across all volumes. Then, an MNI-space EPI template supplied with SPM was selected as the target image for normalization. The source image smoothing kernel was set to a FWHM of 4 mm, and all other estimation options were kept at the SPM8 defaults to generate a transformation matrix that was applied to each volume of the individual source images for further analyses. The time-series was up-sampled to a 1Hz TR using a cubic-spline interpolation. Regions from the AAL600 atlas were used as seed points for the functional connectivity analysis (Hermundstad et al 2014). A series of custom MATLAB functions were used to extract the voxel time series of activity for each region, and to remove estimated noise from the time series by selecting the first five principal components from the white matter and CSF masks.<b>Dataset</b>Here we provided the streamline count matrices of all 60 participants in CMU_SC.zip, which contains individual .mat files per participant. We also provided the average ROI BOLD fMRI time series of 53 participants with low head motion in CMU_BOLD.mat file. <br><br>