functional connectivity toolbox
peer-reviewed publications using CONN for functional connectivity analyses
@ google scholar
This release brings many exciting updates, including new options for fc-MVPA analyses and an interactive GUI exploring the entire brain-wide functional connectome (similar to AFNI InstaCorr but across all subjects in a study; see 2nd-level fc-MVPA summary tab), new automated "Methods" descriptions to help describe CONN's processing and analysis steps in your manuscripts, and several new displays options (e.g. 'connections display' and 'glass display' in ROI-to-ROI, or 'network display' in voxel-level results explorer windows), among many others. See the release changes log and the updated manual for details.
February 2023. CONN can now help you write the Methods section of your manuscript by creating automatically boilerplate text describing the specific procedures and methods used by CONN in your project.
Many replicability efforts stumble at the initial steps by a lack of rigorous reporting of the specific choices used by a team when processing and analyzing their data. To help change this, CONN can now create boilerplate descriptions of the specific preprocessing, denoising, and analysis choices that were used in your CONN project. These automatic descriptions are distributed under a public domain license and can be used, copied, modified and redistributed freely as part of the Methods section of your manuscript, or anywhere else. See this section for additional details.
This is a work in progress and we welcome your feedback to help us make these descriptions as informative and useful as possible!
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.
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 the fMRI methods section for a detailed description of all processing and analysis methods available in CONN.
See the section citing CONN for details about the automatically-generated boilerplate text describing the procedures and methods used by CONN on your specific project, including proper attribution to the different software and methods used.
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) 
 Whitfield-Gabrieli, S., & Nieto-Castanon, A. (2012). Conn: A functional connectivity toolbox for correlated and anticorrelated brain networks. Brain connectivity, 2(3), 125-141
 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 22a  for the analysis of fMRI data.
 Nieto-Castanon, A. & Whitfield-Gabrieli, S. (2022). CONN functional connectivity toolbox: RRID SCR_009550, release 22. doi:10.56441/hilbertpress.2246.5840
(note: doi's for older versions of CONN)
 Nieto-Castanon, A., & Whitfield-Gabrieli, S. (2021). CONN functional connectivity toolbox: RRID SCR_009550, release 21. Boston, MA. doi:10.56441/hilbertpress.2161.7292
 Nieto-Castanon, A., & Whitfield-Gabrieli, S. (2020). CONN functional connectivity toolbox: RRID SCR_009550, release 20. Boston, MA. doi:10.56441/hilbertpress.2048.3738
 Nieto-Castanon, A., & Whitfield-Gabrieli, S. (2019). CONN functional connectivity toolbox: RRID SCR_009550, release 19. Boston, MA. doi:10.56441/hilbertpress.1927.9364
 Nieto-Castanon, A., & Whitfield-Gabrieli, S. (2018). CONN functional connectivity toolbox: RRID SCR_009550, release 18. Boston, MA. doi:10.56441/hilbertpress.1818.9585
 Nieto-Castanon, A., & Whitfield-Gabrieli, S. (2017). CONN functional connectivity toolbox: RRID SCR_009550, release 17. Boston, MA. doi:10.56441/hilbertpress.1744.6736
 Nieto-Castanon, A., & Whitfield-Gabrieli, S. (2012). CONN functional connectivity toolbox: RRID SCR_009550, release 12. Boston, MA. doi:10.56441/hilbertpress.1243.7679
 Nieto-Castanon, A. & Whitfield-Gabrieli, S. (2009). CONN functional connectivity toolbox: RRID SCR_009550, release 9. Boston, MA. doi:10.56441/hilbertpress.0984.0411