International Neuroimaging Data-sharing Initiative (INDI)
The International Neuroimaging Data-sharing Initiative (INDI) aims to advance discovery science in human brain function by promoting large-scale, open sharing of resting-state fMRI (R-fMRI) and phenotypic data. INDI builds on the success of the 1000 Functional Connectomes Project (FCP), which demonstrated the feasibility and impact of aggregating and openly releasing multi-site imaging datasets.
Background
The 1000 Functional Connectomes Project launched on December 11, 2009, aggregating and releasing over 1200 R-fMRI datasets collected from 33 sites worldwide. Released through NITRC, the dataset generated immediate and widespread engagement, including:
- 9000+ downloads within 6 months
- Access from 1,223 cities in 78 countries
- Coverage in Nature Medicine, Nature Methods, and the NIMH Director’s blog
- The landmark feasibility paper (Biswal et al., 2010, PNAS), downloaded 1000+ times in two weeks
The scientific impact was further demonstrated by rapid independent publications using the shared data (e.g., Tomasi & Volkow, 2010).
The response highlighted a clear need: the neuroimaging community requires large, openly accessible datasets to overcome barriers to statistical power, reproducibility, and cross-disciplinary involvement.
All raw imaging data remain hosted by INDI / NITRC and are subject to the INDI / CoRR Data Use Agreement.
XHCUMS — 6-Month Test–Retest Dataset
Dataset: Xuanwu Hospital, Capital Medical University (XHCUMS) – 6-Month Test–Retest Dataset
DOI: 10.15387/fcp_indi.corr.xhcums1
Site: Xuanwu Hospital, Capital Medical University, Beijing, China
Sample: 24 healthy adults, 5 sessions per subject, 6-month intervals
Modalities: T1-weighted anatomical, resting-state fMRI (EPI), diffusion MRI (DTI)
Condition: Rest, eyes closed, blank screen, no task
This dataset was designed to evaluate long-term test–retest reliability of structural, functional, and diffusion MRI measures. Each participant was scanned five times over ~2 years, providing dense longitudinal data for:
- Morphometry and cortical thickness
- Intrinsic functional connectivity
- Diffusion-based white-matter mapping
Typical assets in this repo include multi-modal group derivatives such as corr-xhcums.dti.dz, corr-xhcums.gqi.dz, participants.tsv, qc.tsv, and subject-level *_dwi.*.fz files.
Download (Linux / macOS — bash)
curl -s https://api.github.com/repos/data-indi/corr/releases/tags/xhcums | jq -r '.assets[].browser_download_url' | xargs -n1 -P4 curl -LO
Download (Windows PowerShell 5.x)
(Invoke-RestMethod "https://api.github.com/repos/data-indi/corr/releases/tags/xhcums").assets | ForEach-Object { Invoke-WebRequest $_.browser_download_url -OutFile (Split-Path $_.browser_download_url -Leaf) }
SWU1 — Training Effects for Attention Blink Task
Dataset: Southwest University (SWU1) – Training Effects for Attention Blink Task
DOI: 10.15387/fcp_indi.corr.swu1
Site: Faculty of Psychology, Southwest University, Chongqing, China
Sample: 20 healthy adults
Design: 2 sessions — pre-training and post-training on an attention blink paradigm
Modalities: T1-weighted structural MRI, resting-state fMRI (two sessions)
Condition: Rest, eyes closed, no task
Participants completed an attention blink training paradigm between scans, enabling:
- Pre/post comparison of resting-state networks
- Evaluation of training-induced plasticity
- Test–retest reliability analysis in a short longitudinal design
Assets in this repo typically include subject-level *_T1w.nii.gz, resting-state fMRI derivatives, tractography-ready .fz files, and QC tables.
Download (Linux / macOS — bash)
curl -s https://api.github.com/repos/data-indi/corr/releases/tags/swu | jq -r '.assets[].browser_download_url' | xargs -n1 -P4 curl -LO
Download (Windows PowerShell 5.x)
(Invoke-RestMethod "https://api.github.com/repos/data-indi/corr/releases/tags/swu").assets | ForEach-Object { Invoke-WebRequest $_.browser_download_url -OutFile (Split-Path $_.browser_download_url -Leaf) }
NKI — Test–Retest Multiband fMRI and DTI
Dataset: Nathan Kline Institute – Test–Retest Multiband fMRI and DTI Dataset
DOI: 10.15387/fcp_indi.corr.nki1
Site: Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
Sample: 24 participants, multi-session test–retest
Modalities: Multiband resting-state fMRI (TR = 645 ms, 1400 ms, 2500 ms), DTI (137 directions)
Condition: Resting-state, eyes open, fixation
This dataset served as a pilot for the Enhanced NKI–Rockland Sample (NKI–RS), enabling:
- Test–retest reliability analysis across multiple TRs
- Multimodal comparisons between diffusion and functional measures
- Methods development for multiband fMRI and dynamic connectivity
This repo includes tractography-ready .dti.dz, .gqi.dz, subject-level .fz files, and QC information.
Download (Linux / macOS — bash)
curl -s https://api.github.com/repos/data-indi/corr/releases/tags/nki | jq -r '.assets[].browser_download_url' | xargs -n1 -P4 curl -LO
Download (Windows PowerShell 5.x)
(Invoke-RestMethod "https://api.github.com/repos/data-indi/corr/releases/tags/nki").assets | ForEach-Object { Invoke-WebRequest $_.browser_download_url -OutFile (Split-Path $_.browser_download_url -Leaf) }
MRN — Repeat Resting Sample
Dataset: Mind Research Network (MRN) – Repeat Resting Sample
DOI: 10.15387/fcp_indi.corr.mrn1
Site: Mind Research Network, Albuquerque, NM, USA
Sample: 56 healthy adults, 2 sessions, ~4 months apart
Modalities: T1-weighted anatomical MRI, resting-state fMRI, DTI
Condition: Rest, eyes open, fixation cross
Participants were scanned twice with a ~four-month interval, providing:
- Reproducibility benchmarks for resting-state networks
- Stability measures for diffusion-based connectivity
- Data to evaluate intra-individual variability over longer intervals
Data here include group .dti.dz / .gqi.dz files, subject-level tractography-ready .fz data, and QC tables.
Download (Linux / macOS — bash)
curl -s https://api.github.com/repos/data-indi/corr/releases/tags/mrn | jq -r '.assets[].browser_download_url' | xargs -n1 -P4 curl -LO
Download (Windows PowerShell 5.x)
(Invoke-RestMethod "https://api.github.com/repos/data-indi/corr/releases/tags/mrn").assets | ForEach-Object { Invoke-WebRequest $_.browser_download_url -OutFile (Split-Path $_.browser_download_url -Leaf) }
IPCAS1 — Time Perception and Estimation Sample
Dataset: Institute of Psychology, Chinese Academy of Sciences (IPCAS 1) – Time Perception and Estimation Sample
DOI: 10.15387/fcp_indi.corr.ipcas1
Site: Institute of Psychology, Chinese Academy of Sciences, Beijing, China
Sample: 30 healthy adults, 2 sessions one week apart
Modalities: Resting-state fMRI, T1-weighted anatomical MRI, DTI
Condition: Rest, eyes open, fixation cross
This dataset supports work on:
- Neural mechanisms of time perception and estimation
- Short-term test–retest reliability (1-week interval)
- Structure–function relationships involving the amygdala and other regions
The repo includes multi-modal group .dz files, participants.tsv, qc.tsv, and subject-level .fz derivatives.
Download (Linux / macOS — bash)
curl -s https://api.github.com/repos/data-indi/corr/releases/tags/ipcas | jq -r '.assets[].browser_download_url' | xargs -n1 -P4 curl -LO
Download (Windows PowerShell 5.x)
(Invoke-RestMethod "https://api.github.com/repos/data-indi/corr/releases/tags/ipcas").assets | ForEach-Object { Invoke-WebRequest $_.browser_download_url -OutFile (Split-Path $_.browser_download_url -Leaf) }
HNU1 — One-Month Test–Retest & Dynamical Resting-State
Dataset: Hangzhou Normal University (HNU) – One-Month Test–Retest Reliability and Dynamical Resting-State Study
DOI: 10.15387/fcp_indi.corr.hnu1
Site: Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China
Sample: 30 healthy adults
Design: 10 sessions per participant, spaced 3 days apart (~1 month)
Modalities per session: EPI (resting-state fMRI), ASL, T1-weighted, DTI, T2-weighted
Condition: Rest, eyes open, fixation cross, no intentional task
HNU1 is one of the most comprehensive CoRR resources for:
- Dynamic functional connectivity
- Intra-subject variability across many sessions
- Short-term reliability across multiple MRI modalities
This repo provides group .dti.dz / .gqi.dz, session-level .fz files, and QC/participants tables to facilitate longitudinal analyses.
Download (Linux / macOS — bash)
curl -s https://api.github.com/repos/data-indi/corr/releases/tags/hnu | jq -r '.assets[].browser_download_url' | xargs -n1 -P4 curl -LO
Download (Windows PowerShell 5.x)
(Invoke-RestMethod "https://api.github.com/repos/data-indi/corr/releases/tags/hnu").assets | ForEach-Object { Invoke-WebRequest $_.browser_download_url -OutFile (Split-Path $_.browser_download_url -Leaf) }
BNU1 — Connectivity-Based Brain Imaging Research Database (C-BIRD)
Dataset: Beijing Normal University (BNU) – Connectivity-Based Brain Imaging Research Database (C-BIRD)
DOI: 10.15387/fcp_indi.corr.bnu1
Site: State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
Sample: 57 healthy young adults (30 male / 27 female), ages 19–30
Design: 2 sessions ~6 weeks apart (40.9 ± 4.5 days)
Modalities: Resting-state fMRI, T1-weighted, T2-weighted, DTI
Condition: Rest, eyes closed, no task
BNU1 supports:
- Reliability and temporal stability analyses of functional and structural connectivity
- Graph-theoretic connectivity and network topology studies
- Cross-session comparisons of resting-state networks and DTI-based connectomes
This repo includes BNU1 .dti.dz / .gqi.dz, participants.tsv, qc.tsv, and subject-level tractography-ready .fz files.
Download (Linux / macOS — bash)
curl -s https://api.github.com/repos/data-indi/corr/releases/tags/bnu | jq -r '.assets[].browser_download_url' | xargs -n1 -P4 curl -LO
Download (Windows PowerShell 5.x)
(Invoke-RestMethod "https://api.github.com/repos/data-indi/corr/releases/tags/bnu").assets | ForEach-Object { Invoke-WebRequest $_.browser_download_url -OutFile (Split-Path $_.browser_download_url -Leaf) }
How to access original data
To obtain original imaging and phenotypic data from CoRR / INDI (beyond the derivatives mirrored here):
- Create an account on NITRC.
- Request to join the INDI / 1000 Functional Connectomes Project group.
- Navigate to the corresponding CoRR dataset page (e.g., XHCUMS, SWU1, NKI, MRN, IPCAS1, HNU1, BNU1).
- Download imaging and phenotypic data from NITRC once access is approved.
The derivatives in this repository are intended to complement the official CoRR distributions and to facilitate diffusion/tractography and connectomics analyses.
Licensing and data use
Use of CoRR datasets is governed by the INDI / CoRR Data Use Agreement:
http://fcon_1000.projects.nitrc.org/indi/corr/html/data_citation.html
In general:
- Datasets are for research use only.
- Users must not attempt re-identification of participants.
- Users must follow any site-specific restrictions and citation requirements listed on the dataset page.
Check each project page for precise license terms (including acknowledgements and additional conditions).
How to cite
Always:
- Cite the original dataset / DOI and associated publications (e.g., site-specific reliability papers).
- Acknowledge CoRR, INDI, and the contributing site(s) as specified on the CoRR citation page.
- When using derivatives or helper files from this repository, please add:
“Processed artifacts and curation were prepared by the Pittsburgh Fiber Data Hub (
data-indi/corr) based on INDI/CoRR datasets.”
Disclaimer
- All data originate from independent sites and are shared as-is.
- Sequence parameters, scanner models, and acquisition instructions (eyes open/closed, fixation, etc.) differ across datasets.
-
Users are responsible for:
- Performing appropriate quality control (e.g., motion, artifacts).
- Choosing analysis pipelines that account for site differences, TR differences, and multiband acquisition when applicable.