HCP data (WU-Minn)

Introduction

The data are originally from the minimally-preprocessed dMRI data from WU-Minn HCP Consortium and converted to DSI Studio SRC files format. The SRC file stores the minimum information needed for diffusion MRI processing, including the DWI data, image resolution, and the b-table. The SRC file can be converted back to 4D NIFTI in DSI Studio.

Data Sharing License

The data are shared under the WU-Minn HCP open access data use term (4) at https://www.humanconnectome.org/study/hcp-young-adult/document/wu-minn-hcp-consortium-open-access-data-use-terms

Please acknowledge the source to the WU-Minn HCP.

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Methods:The minimally-preprocessed data (Glasser et al., 2013) from Human Connectome Projects (Q1-Q4 release, 2015) were acquired by Washington University in Saint Louis and University of Minnesota (Van Essen et al., 2012). The diffusion MRI scan was conducted on a Siemens 3T Skyra scanner using a 2D spin-echo single-shot multiband EPI sequence with a multi-band factor of 3 and monopolar gradient pulse. The spatial resolution was 1.25 mm isotropic. TR = 5500 ms, TE = 89.50 ms. The b-values were 1000, 2000, and 3000 s/mm2. The total number of diffusion sampling directions was 90, 90, and 90 for each of the shells in addition to 6 b0 images. The preprocessed data were corrected for eddy current and susceptibility artifact.

The original NIFTI files can be downloaded from the HCP's connectome db website. 

File List:

LCF of all 930 subjects: HCP930_fp.mat
ID of the subjects: HCP930.QSDR25.db.fib.gz.name.txt
Squared difference of 930 subjects: HCP930.QSDR25.db.fib.gz.vec.dif.mat

The link provides MATLAB MAT files storing the LCF of the HCP 930 subjects. These MAT files store LCF vectors of the HCP subjects. The dimension matrix gives the dimension of the standard space. The MNI matrix stores the MNI coordinate of each LCF entry. The fiber orientation matrix stores the local axonal direction of each LCF entry. The subject ID of these 1065 subjects are stored in the subject id text file.

Local connectome fingerprints (LCF)[1] provide a unified quantification of brain connections in a standard space at the voxel level. The concept is similar to "DNA profiling", which gives a unique pattern for individuals' genomic characteristics without sequencing the entire genome. LCF does not map the entire connectome either. The local connectome is the brain connection (the global connectome) breaking down into small elements at the voxel level, allowing us to study their connectivity from a piece-wise perspective. The connectivity of the local axonal connection can be quantified by their density of diffusing water calculated from the diffusion MRI signals. The quantification of LCF does not rely on fiber tracking or tracing. This is very different from commonly used connectomic approach, which maps region-to-region connectivity as the basic unit of the brain connectivity, and fiber tracking or fiber tracing play important roles.

Why LCF?
Fiber tracking methods or axonal tracing techniques have limitation in resolution and accuracy. Diffusion MRI fiber tracking is known to be sensitive to parameters and tends to give a substantial amount of false connections. The error accumulates along with the tracking and tracing process, and often time the results can be highly sensitive to both systematic and experimental error. Any quantitative measurement based on fiber tracking or tracing thus inherits the variability and the test-retest reliability of the results are usually not very high. In comparison, DNA profiling has a high test-retest reliability with a miss identification rate of 1 over billions. The uniqueness achieved by fMRI or dMRI connectome connectivity is around 1%~10% shown from the connectome fingerprint studies [1][2].

LCF aims to bypass this limitation by directly quantifying the connectivity using the density of diffusing water measured from the diffusion MRI signals. Our study showed that LCF has a uniqueness at 10-6 using 2.5 mm dMRI data sets [1].

Methods:
A group average template was constructed from a total of 930 subjects. A multishell diffusion scheme was used, and the b-values were 1000, 2000, and 3000 s/mm2. The number of diffusion sampling directions were 90, 90, and 90, respectively. The in-plane resolution was 1.25 mm. The slice thickness was 1.25 mm. The diffusion data were reconstructed in the MNI space using q-space diffeomorphic reconstruction (Yeh et al., Neuroimage, 58(1):91-9, 2011) to obtain the spin distribution function (Yeh et al., IEEE TMI, ;29(9):1626-35, 2010). A diffusion sampling length ratio of 2.5 was used, and the output resolution was 1 mm. The analysis was conducted using DSI Studio (http://dsi-studio.labsolver.org).

Citations
[1] Yeh F-C, Vettel JM, Singh A, Poczos B, Grafton ST, Erickson KI, et al. (2016) Quantifying Differences and Similarities in Whole-Brain White Matter Architecture Using Local Connectome Fingerprints. PLoS Comput Biol 12(11): e1005203. https://doi.org/10.1371/journal.pcbi.1005203
[2] Finn, Emily S., Xilin Shen, Dustin Scheinost, Monica D. Rosenberg, Jessica Huang, Marvin M. Chun, Xenophon Papademetris, and R. Todd Constable. "Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity." Nature neuroscience 18, no. 11 (2015): 1664-1671.


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