Computational protocol: A Hybrid CPU GPU Accelerated Framework for Fast Mapping of High Resolution Human Brain Connectome

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Protocol publication

[…] The dataset was downloaded from the 1000 Functional Connectomes Project (www.nitrc.org/projects/fcon_1000/), which is a worldwide multi-site project with fMRI data sharing for the imaging community. The dataset we used was from Dr. Yu-Feng Zang, Beijing. The resting-state images were acquired from 198 healthy right-handed volunteers, comprising 76 males and 122 females, age 21.2±3.3 years (ranging from 18 to 26 years old). We excluded one subject’s data because of an orienting error during scanning. Each participant signed a written informed consent before the scanning. The study was approved by the Institutional Review Board of the Beijing Normal University Imaging Center for Brain Research.The acquisition was performed on a Siemens 3 T scanner. For each participant, functional images were scanned using the following parameters: time points = 225, repetition time = 2000 ms, echo time = 30 ms, in-plane resolution = 3.125 mm×3.125 mm, slice thickness = 3 mm, number of slices = 33, section gap = 0.6 mm, flip angle = 90°, and field of view = 200 mm×200 mm. The participants were instructed to close their eyes and stay awake during the scanning.All of the image preprocessing was conducted using DPARSF and SPM5 (www.fil.ion.ucl.ac.uk/spm/). The first 10 volumes on each participant were removed because of signal equilibrium and to allow the participants’ adaptation to the scanning noise. The following preprocessing steps included slice timing, realignment, normalization into standard MNI space with EPI as a template and resampled to voxel size 3 mm×3 mm×3 mm, detrend, and a band-pass filtering from 0.01 to 0.08 Hz. Furthermore, several frequently used noise reduction strategies were utilized, including the regression of white matter (WM), cerebrospinal fluid (CSF), global mean signal time courses, and head-motion profiles. To restrict subsequent functional analysis within gray matter tissues, we generated a gray matter mask as follows. First, we resampled the gray matter tissue probability map provided by SPM5 into 3 mm×3 mm×3 mm resolution. Then we binarized the resampled probability map by a threshold of 0.2, which resulted in a gray matter mask of 58523 voxels. […]

Pipeline specifications

Software tools DPABI, SPM
Application Functional magnetic resonance imaging
Organisms Homo sapiens