1. Multi-Modal Neuroimaging Test-retest Database

Sample Description. Data included in this sample are a subset of the Connectivity-based Brain Imaging Research Database (C-BIRD) at BNU. This sample contains data from 57 healthy young volunteers (male/female: 30/27; age: 19-30 years) who completed two MRI scan sessions within an interval of approximate 6-weeks (40.94±4.51 days). All participants were right-handed and had no history of neurological and psychiatric disorders. Written informed consent was obtained from each participant, and this study was approved by the Institutional Review Board of the State Key Laboratory of Cognitive Neuroscience and Learning at Beijing Normal University.

The first session included two resting-state fMRI scans, T1, T2 and DTI. The two resting-state fMRI scans were at the beginning and the end of the session (~20 minutes apart). The second scan session included resting-state fMRI, T1 and DTI. The scanning parameters were listed as follows.

(i) For the R-fMRI scans, participants were instructed to rest and relax with their eyes closed without falling asleep. Each R-fMRI scan includes 200 contiguous EPI functional volumes: time repetition (TR) = 2000 ms; time echo (TE) = 30 ms; flip angle (FA) = 90°; number of slices = 33; slice thickness = 3.5 mm; slice gap = 0.7 mm; matrix = 64×64; and field of view (FOV) = 200×200 mm².

(ii) For the structural MRI scans, a high-resolution T1-weighted magnetization prepared gradient echo (MPRAGE) sequence was also obtained: TR = 2530 ms; TE = 3.39 ms; inversion time = 1100 ms; FA = 7°; number of slices = 144; slice thickness = 1.3 mm; slice gap = 0.65 mm; matrix = 256×192; and FOV = 256×256 mm².

(iii) For the diffusion-weighted imaging (DWI) scans, a single-shot twice-refocused spin-echo diffusion EPI sequence was applied: TR = 8,000 ms; TE = 89 ms; 30 optimal diffusion-weighted directions with a b-value of 1,000 s/mm² and one image with a b-value of 0 s/mm²; data matrix = 128 × 128; FOV = 282× 282 mm²; slice thickness = 2.2 mm; 62 axial slices without interslice gap; voxel size = 2.2 × 2.2 × 2.2 mm³; number of average = 2.

Data is freely available on the NITRC website:
http://fcon_1000.projects.nitrc.org/indi/CoRR/html/bnu_1.html

Data phenotypic information and quality control results:
[BNU_DataInfo_QualityControl.zip]

Data citation
Lin Q, Dai Z, Xia M, Han Z, Huang R, Gong G, Liu C, Bi Y, He Y (2015) A connectivity-based test-retest dataset of multi-modal magnetic resonance imaging in young healthy adults. Sci. Data 2:150056. [PDF]

Principal Investigator

• Yong He, State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, China

Acknowledgements

• Qixiang Lin, State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, China
• Ruiwang Huang, State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, China
• Gaolang Gong, State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, China
• Mingrui Xia, State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, China

Funding

• National Natural Science Foundation of China (Grant No. 81030028)
• National Science Fund for Distinguished Young Scholars (Grant No. 81225012)
• CERS-China Equipment and Education Resources System (CERS-1-52)

Publications

Wang X, Lin Q, Xia M, He Y (2018) Differentially categorized structural brain hubs are involved in different microstructural, functional and cognitive characteristics and contribute to individual identification. Hum Brain Mapp 39(4):1647-1663.

Xu Y, Lin Q, Han Z, He Y, Bi Y (2016) Intrinsic functional network architecture of human semantic processing: modules and hubs. NeuroImage 132:542–555.

Xia M, Lin Q, Bi Y, He Y (2016) Connectomic insights into topologically centralized network edges and relevant motifs in the human brain. Front Hum Neurosci 10:158.

Dai Z, Yan C, Li K, Wang Z, Wang J, Cao M, Lin Q, Shu N, Xia M, Bi Y, He Y (2015) Identifying and mapping connectivity patterns of brain network hubs in Alzheimer’s disease. Cereb Cortex 25: 3723-3742.

Zhong S, He Y, Gong G (2015) Convergence and divergence across construction methods for human brain white-matter networks: an assessment based on individual differences. Hum Brain Mapp 36(5):1995-2013.

Du H, Liao X, Lin Q, Li G, Chi Y, Liu X, Yang H, Wang Y, Xia M (2015) Test–retest reliability of graph metrics in high-resolution functional connectomics: a resting-state functional MRI study. CNS Neurosci Ther 21(10):802-816.

Wang J, Wang X, Xia M, Liao X, Evans A, He Y (2015) GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front Hum Neurosci 9:386.

Lin Q, Dai Z, Xia M, Han Z, Huang R, Gong G, Liu C, Bi Y, He Y (2015) A connectivity-based test-retest dataset of multi-modal magnetic resonance imaging in young healthy adults. Scientific Data 2:150056.