🧠 INDI Dataset · Resting-State fMRI

1000 Functional
Connectomes Project

The grassroots open-science initiative that released 1,414 resting-state fMRI datasets from 35 international sites — launching the era of large-scale human connectome discovery. Freely available via NITRC and AWS S3.

Access on NITRC → AWS S3 Download View All Sites
1,414
Participants
35
International Sites
Dec 2009
Release Date
9,000+
Downloads in 6 mo.
78
Countries Accessed

Overview

The 1000 Functional Connectomes Project (1000FCP) was launched on December 11, 2009, when a consortium of investigators publicly released over 1,200 resting-state fMRI (R-fMRI) datasets collected independently at 35 international centers — without restriction. It was the first large-scale, multi-site, open-access sharing of R-fMRI data in the history of neuroscience.

Resting-state fMRI captures spontaneous low-frequency (<0.1 Hz) BOLD signal fluctuations while participants rest in the scanner without performing any task. These fluctuations are temporally correlated across functionally related brain areas — a phenomenon called functional connectivity — yielding detailed maps of an individual's functional connectome.

The landmark feasibility paper (Biswal et al., 2010, PNAS) demonstrated a universal architecture of positive and negative functional connections across all 35 sites, with consistent loci of inter-individual variability. Age and sex emerged as significant determinants. The dataset was immediately downloaded by researchers in 1,223 cities across 78 countries within its first 6 months.

Dataset Highlights

  • 1,414 R-fMRI datasets across 35 sites
  • Standardized NIfTI format (RPI orientation)
  • Age and sex information per subject
  • Anatomical images (face-scrambled for privacy)
  • No task — eyes open or closed per site
  • Unrestricted open-access (non-commercial)
  • HIPAA-compliant, fully anonymized
  • Multiple TR, slice, and timepoint configurations

Origin & Impact

💡

The Question That Started It All

In 2008, Bharat Biswal and Michael Milham posed the question at the 1st Biennial Conference on Resting State Brain Connectivity: "How reproducible is resting-state fMRI across imaging centers?" The response was overwhelming — sites from around the world offered to contribute data.

🌍

Grassroots International Consortium

No funding agency mandated it. No grant required it. The FCP was built entirely on voluntary data donations from 35 principal investigators across North America, Europe, and Asia — a genuine grassroots movement for open science.

📈

Immediate Global Impact

9,000+ 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 Biswal et al. 2010 PNAS paper was downloaded 1,000+ times in two weeks.

🔗

Successor: INDI

The FCP's success led directly to the International Neuroimaging Data-sharing Initiative (INDI) — a formal, sustained program for prospective data sharing with richer phenotypic data, and the parent initiative of all subsequent NITRC-hosted fMRI datasets.

Contributing Sites

A selection of the 35 international sites. Each row shows the site identifier used in the data archive, lead PI, and scan parameters.

Site Token Lead PI n Ages TR (s) Slices Timepoints
Baltimore Pekar / Mostofsky 23 20–40 2.5 47 123
Bangor Colcombe 20 19–38 2 34 265
Beijing_Zang Zang, Y-F. 198 18–26 2 33 225
Berlin_Margulies Margulies 26 23–44 2.3 34 195
Cambridge Buckner 198 18–30 3 47 119
Cleveland Lowe 25 21–52 2 34 200
Dallas Rypma 22 20–35 1.9 34 130
Detroit Bhaumik 30 18–40 2 36 150
Leiden_2180 Van Buchem / Rombouts 14 19–25 2.18 38 215
Leipzig Villringer 39 20–42 2.3 34 195
Milwaukee_a McLaren / Bhaumik 25 21–39 2 36 150
Montreal_BI Bhaumik 32 18–43 2 36 150
Munchen Hemmer / Spoormaker 24 21–42 2.5 34 105
NewYork_a Milham 25 18–43 2 39 192
Oulu Tervonen / Nikkinen 103 20–23 1.8 40 245
Oxford Mackay / Smith 22 22–35 2 33 175
Taipei_a Biswal 22 20–29 2 34 175
Utah Anderson 26 21–46 2 34 200
Full site listing and download links available at fcon_1000.projects.nitrc.org

Data Access

NITRC

Via NITRC Portal

  1. Create a free account at nitrc.org
  2. Request to join the 1000 Functional Connectomes Project group
  3. Once approved, access individual site downloads
  4. Right to unrestricted usage for non-commercial purposes
Register on NITRC →
AWS S3

Via Amazon S3

No AWS account required. Use the --no-sign-request flag. The 1000FCP classic data is nested under the INDI prefix.

aws s3 ls --no-sign-request s3://fcp-indi/
S3 Download Docs →

AWS S3 Download Examples

The entire FCP/INDI archive is hosted in the fcp-indi S3 bucket in us-east-1. No AWS account is needed for public datasets.

# List the top-level bucket aws s3 ls --no-sign-request s3://fcp-indi/ # Browse available projects aws s3 ls --no-sign-request s3://fcp-indi/data/Projects/ # List INDI sub-datasets aws s3 ls --no-sign-request s3://fcp-indi/data/Projects/INDI/ # Sync an entire site's data locally (e.g. Beijing_Zang) aws s3 sync --no-sign-request \ s3://fcp-indi/data/Projects/INDI/Beijing_Zang \ ./fcp_data/Beijing_Zang # Download CoRR (Consortium for Reliability and Reproducibility) aws s3 sync --no-sign-request \ s3://fcp-indi/data/Projects/CORR \ ./corr_data # Python: list bucket contents with boto3 (no credentials needed) import boto3 from botocore import UNSIGNED from botocore.config import Config s3 = boto3.client('s3', config=Config(signature_version=UNSIGNED)) result = s3.list_objects_v2(Bucket='fcp-indi', Prefix='data/Projects/', Delimiter='/') for p in result.get('CommonPrefixes', []): print(p['Prefix'])

INDI Dataset Ecosystem

The 1000FCP launched a family of successor datasets, all hosted in the fcp-indi S3 bucket:

Addiction Connectome Preprocessed Initiative (ACPI) RAW PREPROCESSED

Substance use disorder resting-state and structural MRI. Preprocessed with C-PAC.

s3://fcp-indi/data/Projects/ACPI
ADHD-200 RAW PREPROCESSED

ADHD diagnosis dataset, 8 sites, ~900 participants. Includes phenotypic data (diagnosis, IQ, medication).

s3://fcp-indi/data/Projects/ADHD200
Brain Genomics Superstruct Project (BGSP) RAW

Harvard/MGH dataset linking resting-state fMRI with genomic data.

s3://fcp-indi/data/Projects/BGSP
Consortium for Reliability & Reproducibility (CoRR) RAW PREPROCESSED

Test-retest reliability dataset. Multiple sessions per subject across >30 sites.

s3://fcp-indi/data/Projects/CORR
Healthy Brain Network (HBN) RAW PREPROCESSED

Pediatric/adolescent dataset with psychiatric diagnoses. 10,000 participant target.

s3://fcp-indi/data/Projects/HBN
Enhanced NKI Rockland Sample RAW

Lifespan dataset (ages 6–85) with 30+ behavioral measures, R-fMRI, DTI.

s3://fcp-indi/data/Projects/RocklandSample
SLIM Brain Data Repository RAW

Southwest University longitudinal imaging multimodal dataset.

s3://fcp-indi/data/Projects/INDI/SLIM
SALD — Southwest University Adult Lifespan RAW

Adult lifespan dataset with multiple MRI modalities.

s3://fcp-indi/data/Projects/INDI/SALD

Key Publication

B. B. Biswal, M. Mennes, X-N. Zuo, S. Gohel, C. Kelly, S. M. Smith, C. F. Beckmann, J. S. Adelstein, R. L. Buckner, S. Colcombe, A-M. Dogonowski, M. Ernst, D. Fair, M. Hampson, M. J. Hoptman, J. S. Hyde, V. J. Kiviniemi, R. Kötter, S-J. Li, C-P. Lin, F. G. Lowe, C. Mackay, D. S. Madden, K. Madsen, D. S. Margulies, H. S. Mayberg, K. McMahon, C. S. Monk, S. H. Mostofsky, B. J. Nagel, J. J. Pekar, S. J. Peltier, S. E. Petersen, V. Riedl, S. A. R. B. Rombouts, B. Rypma, B. L. Schlaggar, S. Schmidt, R. D. Seidler, G. J. Siegle, C. Sorg, G. J. Teng, J. Veijola, A. Villringer, M. Walter, L. Wang, X-C. Weng, S. Whitfield-Gabrieli, P. Williamson, C. Windischberger, Y-F. Zang, H-Y. Zhang, F. X. Castellanos, and M. P. Milham. Toward discovery science of human brain function. Proc. Natl. Acad. Sci. USA, 107(10):4734–4739, March 2010.

PNAS · 2010 · DOI: 10.1073/pnas.0911855107 · PMID: 20176931

K. R. A. Van Dijk, T. Hedden, A. Venkataraman, K. C. Evans, S. W. Lazar, and R. L. Buckner. Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. J. Neurophysiol., 103(1):297–321, January 2010.

J. Neurophysiology · 2010

E. Milham, M. Milham, and the ABIDE Consortium. Making data sharing work: The FCP/INDI experience. NeuroImage, 82:683–691, November 2013.

NeuroImage · 2013 · PMID: 23631928