CONN

functional connectivity toolbox

80,000 lines of code

50,000 downloads

yearly software releases since 2012

@ nitrc.org/projects/conn


4,000 articles published

using CONN for functional connectivity analyses

@ google scholar

5,000 users

12,000 messages

support forum for CONN users

@ nitrc.org/projects/conn

Latest news :

November 2022. Check out some of the latest functional connectivity analysis methods available in CONN :

Nieto-Castanon, A. (2022). Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc- MVPA). PLoS Comput Biol 18(11): e1010634

Fc-MVPA analyses allow researchers to test hypotheses about the entire functional connectome (all voxel-to-voxel functional connections across the entire brain), without having to limit the analyses to a single or a few seed areas (e.g. SBC analyses) or a specific parcellation of the brain into ROIs (e.g. RRC analyses). For example, questions like "is functional connectivity different between patients and control subjects?", "does functional connectivity change with an intervention?", or "what aspects of functional connectivity covary with symptom severity?" can be asked using fc-MVPA examining the entire set of (billions of) functional connections between all pairs of individual voxels in the fMRI images.

Software description

CONN is an open-source SPM-based cross-platform software for the computation, display, and analysis of functional connectivity Magnetic Resonance Imaging (fcMRI). CONN is used to analyze resting state data (rsfMRI) as well as task-related designs. Processing and analysis steps in CONN include:

    • Importing DICOM, ANALYZE, and NIfTI functional and anatomical files, either raw or partially/fully preprocessed volumes. Automatic import tools for BIDS datasets and fMRIPrep outputs

    • Standardized preprocessing pipelines of functional and anatomical volumes powered by SPM12 (including susceptibility distortion correction, motion correction / realignment, slice-timing correction, outlier identification, coregistration, tissue-class segmentation, MNI-normalization, and smoothing)

    • Control of residual physiological and motion artifacts (e.g. scrubbing, aCompCor, ICA-based denoising, Global Regression, band-pass filtering)

    • Integrated quality control procedures and measures (e.g. FC histogram plots, BOLD signal carpetplots, Framewise Displacement, GCOR measures, FC-QC correlations)

    • Multiple connectivity analyses and measures, including Seed-Based Correlations (SBC), ROI-to-ROI analyses, complex-network analyses, generalized Psycho-Physiological Interaction models (gPPI), Independent Component Analyses (group-ICA), masked ICA, Amplitude of Low-Frequency Fluctuations (ALFF & fALFF), Intrinsic Connectivity (ICC), Local Homogeneity (LCOR), Global Correlations (GCOR), Inter-hemispheric correlations (IHC), functional connectivity Multivoxel Pattern Analyses (fc-MVPA), and dynamic connectivity analyses (dyn-ICA, and sliding-window analyses)

    • Group- and population-level inferences and models, including ANOVA, regression, longitudinal, experimental, and mixed within- and between-subject designs. Univariate and Multivariate statistics. Control for multiple comparisons using parametric (e.g. Random Field Theory), and non-parametric permutation/randomization techniques (e.g. Threshold Free Cluster Enhancement)

When used within a distributed cluster or multi-processor environment CONN can automatically parallelize most time consuming steps in the fcMRI processing pipeline, allowing the analyses of hundreds of subjects in minimal time. CONN can be entirely controlled through a user-friendly GUI, or through batch scripts/commands if preferred.

The toolbox is developed in Matlab, and it is distributed both as Matlab source code and as a pre-compiled executable file (standalone release, no Matlab installations or licenses required)

See this section for a detailed description of all processing and analysis methods available in CONN

Citing CONN

To cite CONN in your work, please include CONN's RRID and at least one of the following general references:

e.g. we really like/hate CONN (RRID:SCR_009550) [1]

  • [1] Whitfield-Gabrieli, S., & Nieto-Castanon, A. (2012). Conn: A functional connectivity toolbox for correlated and anticorrelated brain networks. Brain connectivity, 2(3), 125-141

  • [1] Nieto-Castanon, A. (2020). Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN. Boston, MA: Hilbert Press


To cite one specific version of CONN in your work, please include the specific DOI of your CONN release/version:

e.g. we used CONN version 21a [2] for the analysis of fMRI data.

  • [2] Nieto-Castanon, A., & Whitfield-Gabrieli, S. (2021). CONN functional connectivity toolbox (RRID:SCR_009550) version 21. doi:10.56441/hilbertpress.2161.7292


(note: doi's for older versions of CONN)

[2] Nieto-Castanon, A., & Whitfield-Gabrieli, S. (2020). CONN functional connectivity toolbox (RRID:SCR_009550) version 20. doi:10.56441/hilbertpress.2048.3738

[2] Nieto-Castanon, A., & Whitfield-Gabrieli, S. (2019). CONN functional connectivity toolbox (RRID:SCR_009550) version 19. doi:10.56441/hilbertpress.1927.9364

[2] Nieto-Castanon, A., & Whitfield-Gabrieli, S. (2018). CONN functional connectivity toolbox (RRID:SCR_009550) version 18. doi:10.56441/hilbertpress.1818.9585

[2] Nieto-Castanon, A., & Whitfield-Gabrieli, S. (2017). CONN functional connectivity toolbox (RRID:SCR_009550) version 17. doi:10.56441/hilbertpress.1744.6736

[2] Nieto-Castanon, A., & Whitfield-Gabrieli, S. (2012). CONN functional connectivity toolbox (RRID:SCR_009550) version 12. doi:10.56441/hilbertpress.1243.7679