The MRI acquisition protocol is described in the supplementary material (Tables S3 and S4). Briefly, the resting‐state fMRI scans were preprocessed using the Brainnetome Toolkit (http://brant.brainnetome.org) (Xu, Liu, Zhan, Ren, & Jiang, 2018), which included the following steps: (1) slice timing correction; (2) realignment to the first volume; (3) spatial normalization to Montreal Neurological Institute (MNI) space at 2 mm × 2 mm × 2 mm; (4) regression of nuisance signals, including linear trends, six motion parameters and their first‐order differences, and signals representing white matter and cerebrospinal fluid; (5) temporal bandpass filtering (0.01–0.08 Hz) to reduce high‐frequency noise. Subsequently, any voxel where the mean absolute deviation in the fMRI signal was less than 0.05 and any area that did not have fMRI signal recorded from one or more participants was excluded (Liu et al., 2014, 2016; Zhan et al., 2016). The cortex and subcortex were parcellated based on the Brainnetome Atlas (Fan et al., 2016). The above preprocessing steps resulted in a set of 263 regional areas of the Brainnetome Atlas, which were used in all further analyses. The 263 regions comprising the parcellation atlas based on the overlapping regions of all the individuals are listed in the supplementary material (Table S7). We derived a regional fMRI signal for each region by averaging the fMRI signal across all voxels included in the region. This process was repeated for all individuals and regions. Additionally, the florbetapir (F18‐AV‐45) PET scans of 625 subjects (291 patients with AD and a well‐matched [age and gender] group of 334 healthy comparison individuals) were collected from ADNI for subsequent correlation analysis. The downloaded F18‐AV‐45 PET images were already preprocessed, including computation of Standardized Uptake Value Ratio (SUVR) and smoothing. A detailed description of PET protocols and acquisition procedures can be found at (http://adni.loni.usc.edu/methods/pet-analysis-method/pet-analysis/). The PET images were rigidly co‐registered to the corresponding T1 images and then nonlinearly co‐registered to the standard MNI space at 2 mm × 2 mm × 2 mm by SPM12 (Statistical Parametric Mapping) software.
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Jin D., Wang P., Zalesky A., Liu B., Song C., Wang D., Xu K., Yang H., Zhang Z., Yao H., Zhou B., Han T., Zuo N., Han Y., Lu J., Wang Q., Yu C., Zhang X., Zhang X., Jiang T., Zhou Y, & Liu Y. (2020). Grab‐AD: Generalizability and reproducibility of altered brain activity and diagnostic classification in Alzheimer's Disease. Human Brain Mapping, 41(12), 3379-3391.
Corresponding Organization : Center for Excellence in Brain Science and Intelligence Technology
Other organizations :
Melbourne Health, University of Melbourne, Qilu Hospital of Shandong University, Capital Medical University, Chinese PLA General Hospital, National Clinical Research Center for Digestive Diseases, Chinese Institute for Brain Research, Beijing Geriatric Hospital, Tianjin Medical University General Hospital
Regional fMRI signal for each of the 263 regions in the Brainnetome Atlas parcellation
Standardized Uptake Value Ratio (SUVR) from the florbetapir (F18‐AV‐45) PET scans
control variables
Slice timing correction
Realignment to the first volume
Spatial normalization to MNI space at 2 mm × 2 mm × 2 mm
Regression of nuisance signals, including linear trends, six motion parameters and their first‐order differences, and signals representing white matter and cerebrospinal fluid
Temporal bandpass filtering (0.01–0.08 Hz)
Exclusion of voxels where the mean absolute deviation in the fMRI signal was less than 0.05 and any area that did not have fMRI signal recorded from one or more participants
Rigid co‐registration of PET images to corresponding T1 images and then nonlinear co‐registration to the standard MNI space at 2 mm × 2 mm × 2 mm by SPM12 software
positive controls
Not explicitly mentioned
negative controls
Not explicitly mentioned
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