Introduction

The Human Connectome Project (HCP) Young Adult study is a comprehensive dataset of neural connections in the human brain. The data provided here are derived from the minimally-preprocessed diffusion MRI (dMRI) data from the WU-Minn HCP Consortium. The data have been converted to DSI Studio SRC files format, which stores the minimum information needed for dMRI processing, including the diffusion-weighted imaging (DWI) data, image resolution, and the b-table. The SRC files can be converted back to 4D NIFTI in DSI Studio.

The HCP-YA study is a subset of the larger HCP project. It focuses on mapping the brain connections in young adults, aged 22-35 years. The study aims to:

• Map brain connections : Create a detailed map of the neural connections in the brains of young adults.

• Understand brain development : Study how brain connections change and mature during young adulthood.

• Identify biomarkers: Discover potential biomarkers for neurological and psychiatric disorders that often emerge during young adulthood.

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.

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.

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.

Download

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). The analysis was conducted using the resource allocation (TG-CIS200026) at Extreme Science and Engineering Discovery Environment (XSEDE) resources (Towns, J. et al. Computing in science & engineering 16, 62-74 2014).

[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](http://dx.doi.org/10.1371/journal.pcbi.1005203)

MGH-USC

This data set was originally from the connectome db website (https://db.humanconnectome.org/). The MGH HCP team has released diffusion imaging and structural imaging data acquired from 35 young adults using the customized MGH Siemens 3T Connectome scanner, which has 300 mT/m maximum gradient strength for diffusion imaging.

Methods

A multishell diffusion scheme was used, and the b-values were 1000 ,3000 ,5000 and 10000 s/mm2. The number of diffusion sampling directions were 64, 64, 128, and 256. The in-plane resolution was 1.5 mm. The slice thickness was 1.5 mm.

Lifespan Human Connectome Project Development (HCP-D) Study

The HCP-D Study planned to enroll 1,300+ healthy children, adolescents, and young adults (ages 5-21). The purpose is to discover how different parts of a child's brain are connected and how these connections (the "connectome") change as the brain develops.

License

The NDA agreement prohibits me from sharing raw MRI data (e.g. T1W, DWI, and SRC.GZ files). After discussion with the NDA program lead, I am allowed to share the "derived" data, including the anisotropy, diffusivities, local fiber orientations, and tractography. This includes the FIB file and the tractography. For other data, you may request access at NDA.

The FIB files and tractography files are shared using Creative Commons Attribution-ShareAlike 4.0 International License. For those using FIB and tractography files, I would appreciate your mentioning of the contribution of XSEDE resources: TG-CIS200026.

Demographics

• Demographics
• Behavioral data "Crosswalk" Data Dictionary: excel spreadsheet to translate NDA data structures/elements to HCP behavioral variables and instruments
• Behavioral & Clinical Instrument Details

Methods

A multishell diffusion scheme was used, and the b-values were 1500 and 3000 s/mm². The number of diffusion sampling directions was 93 and 92, respectively. The in-plane resolution was 1.5 mm. The slice thickness was 1.5 mm. The susceptibility artifact was estimated using reversed phase-encoding b0 by TOPUP from the Tiny FSL package (http://github.com/frankyeh/TinyFSL), a re-compilied version of FSL TOPUP (FMRIB, Oxford) with multi-thread support. The correction was conducted through the integrated interface in DSI Studio's ("Chen" release). The diffusion MRI data were rotated to align with the AC-PC line. The accuracy of b-table orientation was examined by comparing fiber orientations with those of a population-averaged template (Yeh et al. Neuroimage, 2018). The restricted diffusion was quantified using restricted diffusion imaging (Yeh et al., MRM, 77:603--612 (2017)). The diffusion data were reconstructed using generalized q-sampling imaging (Yeh et al., IEEE TMI, ;29(9):1626-35, 2010) with a diffusion sampling length ratio of 1.25. The tensor metrics were calculated. The analysis was conducted using the resource allocation (TG-CIS200026) at Extreme Science and Engineering Discovery Environment (XSEDE) resources (Towns, J. et al. Computing in science & engineering 16, 62-74 2014).

Processing Scripts

1. generate SRC files from NIFTI files

First, copy all NIFTI, bval, bvec files in the same folder and run this script to generate SRC files (one for AP, one for PA)

#!/bin/bash
for sub in $(ls HCD8*98_AP.nii.gz)
do    
    dsi_studio --action=src --source=${sub}--other_source=${sub:0:25}99_AP.nii.gz
    dsi_studio --action=src --source=${sub:0:25}98_PA.nii.gz --other_source=${sub:0:25}99_PA.nii.gz
done

2. TOPUP/EDDY and save corrected SRC files

Correct artifacts using AP and PA SRC files and generate corrected AP-PA combined SRC files.
The script needs a number input that allows for running the task using clusters job arrary

#!/bin/bash
subs_ap=$(ls -Lr *AP.nii.gz.src.gz)
subs_ap=(${subs_ap// /})
subs_pa="${subs_ap[$1]:0:28}PA.nii.gz.src.gz"
if [ ! -e $sub_pa ]; then
     echo "." > ${sub_pa}_not_found.txt
else    
     echo "processing ${subs_ap[$1]}"
     dsi_studio --action=rec --source=${subs_ap[$1]} --rev_pe=${subs_pa} --cmd="[Step T2][File][Save Src File]=${subs_pa:0:10}.src.gz"
fi

The following is the job array to run the above script using sbatch. The script needs an the file name of the script to run the job array.

#!/bin/bash
#SBATCH -t 24:00:00
#SBATCH -p RM-shared
#SBATCH -N 1
#SBATCH --ntasks-per-node 8
#SBATCH --mem=15GB
#SBATCH --array=0-999
set -x
sh $1 $SLURM_ARRAY_TASK_ID

3. reconstruction

The following command generate native space FIB files for automatic fiber tracking

dsi_studio --action=rec --source=*.src.gz

The following command generate template space FIB files for correlational tractography

dsi_studio --action=rec --source=*.src.gz --method=7 --template=0 --record_odf=1 --dti_no_high_b=1

4. automatic fiber tracking

The following script for a job arrary to runs fiber tracking on all native-space FIB file.

#!/bin/bash
subs=$(ls -L *.fib.gz)
subs=(${subs// /})
echo "processing ${subs[$1]}"
dsi_studio --action=atk --export_template_trk=1 --source=${subs[$1]}

5. create connectometry database for correlation tractography

The following command create connectometry database from template-space FIB file

dsi_studio --action=atl --source=. --cmd=db --template=../../HCP1065.1mm.fib.gz 

Lifespan Human Connectome Project Aging (HCP-A) Study

The HCP-A Study planned to enroll 1,500+ healthy adults ages 36-100+. The purpose is to discover how individual experiences affect the ways in which different parts of the brain are connected and how these connections (the “connectome”) change across healthy adulthood.

License

The NDA agreement prohibits me from sharing raw MRI data (e.g. T1W, DWI, and SRC.GZ files). After discussion with the NDA program lead, I am allowed to share the "derived" data, including the anisotropy, diffusivities, local fiber orientations, and tractography. This includes the FIB file and the tractography. For other data, you may request access at NDA.

The FIB files and tractography files are shared using Creative Commons Attribution-ShareAlike 4.0 International License. For those using FIB and tractography files, I would appreciate your mentioning of the contribution of ACCESS resources: CIS200026.

Demographics

• Demographics
• Behavioral data "Crosswalk" Data Dictionary: Excel spreadsheet to translate NDA data structures/elements to HCP behavioral variables and instruments
• Behavioral & Clinical Instrument Details

Methods

A multishell diffusion scheme was used, and the b-values were 1500 and 3000 s/mm². The number of diffusion sampling directions was 93 and 92, respectively. The in-plane resolution was 1.5 mm. The slice thickness was 1.5 mm. The susceptibility artifact was estimated using reversed phase-encoding b0 by TOPUP from the Tiny FSL package (http://github.com/frankyeh/TinyFSL), a re-compilied version of FSL TOPUP (FMRIB, Oxford) with multi-thread support. The correction was conducted through the integrated interface in DSI Studio's ("Chen" release). The diffusion MRI data were rotated to align with the AC-PC line. The accuracy of b-table orientation was examined by comparing fiber orientations with those of a population-averaged template (Yeh et al. Neuroimage, 2018). The restricted diffusion was quantified using restricted diffusion imaging (Yeh et al., MRM, 77:603--612 (2017)). The diffusion data were reconstructed using generalized q-sampling imaging (Yeh et al., IEEE TMI, ;29(9):1626-35, 2010) with a diffusion sampling length ratio of 1.25. The tensor metrics were calculated. The analysis was conducted using the resource allocation (TG-CIS200026) at Extreme Science and Engineering Discovery Environment (XSEDE) resources (Towns, J. et al. Computing in science & engineering 16, 62-74 2014).

Processing Scripts

1. generate SRC files from NIFTI files

First, copy all NIFTI, bval, bvec files in the same folder and run this script to generate SRC files (one for AP, one for PA)

#!/bin/bash
for sub in $(ls HCA8*98_AP.nii.gz)
do    
    dsi_studio --action=src --source=${sub} --other_source=${sub:0:25}99_AP.nii.gz
    dsi_studio --action=src --source=${sub:0:25}98_PA.nii.gz --other_source=${sub:0:25}99_PA.nii.gz
done

2. TOPUP/EDDY and save corrected SRC files

Correct artifacts using AP and PA SRC files and generate corrected AP-PA combined SRC files.
The script needs a number input that allows for running the task using clusters job arrary

#!/bin/bash
subs_ap=$(ls -Lr *AP.nii.gz.src.gz)
subs_ap=(${subs_ap// /})
subs_pa="${subs_ap[$1]:0:28}PA.nii.gz.src.gz"
if [ ! -e $sub_pa ]; then
     echo "." > ${sub_pa}_not_found.txt
else    
     echo "processing ${subs_ap[$1]}"
     dsi_studio --action=rec --source=${subs_ap[$1]} --rev_pe=${subs_pa} --cmd="[Step T2][File][Save Src File]=${subs_pa:0:10}.src.gz"
fi

The following is the job array to run the above script using sbatch. The script needs an the file name of the script to run the job array.

#!/bin/bash
#SBATCH -t 24:00:00
#SBATCH -p RM-shared
#SBATCH -N 1
#SBATCH --ntasks-per-node 8
#SBATCH --mem=15GB
#SBATCH --array=0-999
set -x
sh $1 $SLURM_ARRAY_TASK_ID

3. reconstruction

The following command generate native space FIB files for automatic fiber tracking

dsi_studio --action=rec --source=*.src.gz

The following command generate template space FIB files for correlational tractography

dsi_studio --action=rec --source=*.src.gz --method=7 --template=0 --record_odf=1 --dti_no_high_b=1

4. automatic fiber tracking

The following script for a job arrary to runs fiber tracking on all native-space FIB file.

#!/bin/bash
subs=$(ls -L *.fib.gz)
subs=(${subs// /})
echo "processing ${subs[$1]}"
dsi_studio --action=atk --export_template_trk=1 --source=${subs[$1]}

5. create connectometry database for correlation tractography

The following command create connectometry databases from template-space FIB file

dsi_studio --action=atl --source=. --cmd=db --template=../../HCP1065.1mm.fib.gz 

The dHCP study planned to enroll 1500 Subjects at age 20-44 weeks post-conception. The purpose is to link together imaging, clinical, behavioural, and genetic information..

License

The derived files below are shared under the dHCP data sharing agreement. The source of the data are from the 3rd release.

Users using the files should follow agreement and cite/acknowledge the source.

The dHCP study planned to enroll 1500 Subjects at age 20-44 weeks post-conception. The purpose is to link together imaging, clinical, behavioural, and genetic information..

License

The derived files below are shared under the dHCP data sharing agreement. The source of the data are from the 3rd release.

Users using the files should follow agreement and cite/acknowledge the source.

Download

Methods

A multishell diffusion scheme was used, and the b-values were 400 ,1000 and 2600 s/mm². The number of diffusion sampling directions were 64, 88, and 128, respectively. The in-plane resolution was 1.5 mm. The slice thickness was 1.5 mm. The images were denoised and corrected for Gibbs ringing, motion, eddy current, and susceptibility artifact using the diffusion SHARD pipeline. A quality check was conducted using neighboring DWI correcltion (NDC) (Yeh, Neuroimage. 2019 Nov 15;202:116131). 34 out of 758 scans (including repeated scans) were excluded due to their low NDC values identified by a median value based outlier detector. The accuracy of b-table orientation was examined by comparing fiber orientations with those of a population-averaged template (Yeh et al. Neuroimage, 2018). The restricted diffusion was quantified using restricted diffusion imaging (Yeh et al., MRM, 77:603--612 (2017)). The diffusion data were reconstructed using generalized q-sampling imaging (Yeh et al., IEEE TMI, ;29(9):1626-35, 2010) with a diffusion sampling length ratio of 1.25. The tensor metrics were calculated. The analysis was conducted using the resource allocation (TG-CIS200026) at Extreme Science and Engineering Discovery Environment (XSEDE) resources (Towns, J. et al. Computing in science & engineering 16, 62-74 2014).

• 642 FIB files (Ready-to-track using DSI Studio)
• 642 SRC files (none repeated scans, including twins)
• 164 Scan-Rescan SRC files (n=82) Repeated scan of the same subjects. All subjects are also included in the 642 SRC files above. These SRC files are preprocessed results from the diffusion SHARD pipeline. The background signals are zeroed and image dimension cropped. The data are largely reduced and CANNOT be reversed back to the original data distribution.
• Connectometry DB for correlational or differential tractography
• Demographics

Processing Steps

1. generate SRC files from NIFTI files

Copy all NIFTI (DWI and mask), bval, bvec files to the same folder and use DSI Studio's GUI Batch function [Batch Processing[Step B2b: NIFTI to SRC (Single Folder)]

2. reconstruction

This was done using DSI Studio GUI. Click on [Step T2 Reconstruction] and select all SRC files.

1. [Step T2a][Edit][Open] to load mask
2. [Edit][Crop Background]
3. [Run Reconstruction]

3. fiber Tracking

The following script for a job arrary to runs fiber tracking on all FIB file.

#!/bin/bash
subs=$(ls -L *.fib.gz)
subs=(${subs// /})
echo "processing ${subs[$1]}"
singularity exec dsistudio_latest.sif dsi_studio --action=atk --export_template_trk=1 --source=${subs[$1]}

The following is the job array to run the above script using sbatch. The script needs an the file name of the script to run the job array.

#!/bin/bash
#SBATCH -t 24:00:00
#SBATCH -p RM-shared
#SBATCH -N 1
#SBATCH --ntasks-per-node 8
#SBATCH --mem=15GB
#SBATCH --array=0-999
set -x
sh $1 $SLURM_ARRAY_TASK_ID

The LifeSpan Baby Connectome Project (BCP) aims to investigate human brain development from birth to early childhood, with a focus on factors that contribute to healthy brain development.

#Study Design
The study will recruit 500 typically developing children between birth and five years of age, across two data collection sites, using a sequential cohort, accelerated longitudinal study design. The participants will be divided into two main groups:
Longitudinal Group (n=285): This group will be followed over time to capture detailed changes in brain development.
Cross-Sectional Group (n=215): This group will provide a snapshot of brain development at specific ages.
This hybrid design allows for a comprehensive characterization of early brain development from both structural/functional and behavioral aspects. It also balances the advantages of a longitudinal design with the need to accommodate a relatively short funding duration.

#Participant Enrollment

The study will enroll an equal proportion of males and females, and the racial/ethnic diversity of the sample will reflect US Census data.
Imaging Analysis Tools. Our team has developed novel imaging analysis tools that provide quantitative measures of early brain development. These tools will be integrated into the Human Connectome Project (HCP) pipelines, enabling the analysis of large-scale brain imaging data.