functional connectivity toolbox
Latest news :
CONN's releases over the last couple of years have brought several usability improvements, such as the ability to define group-level analyses using simple QDEC-style questions, integration with SSH-accessible resources to facilitate remote work environments, a simplified 1st-level analysis GUI, new connection-display tools, and updated BIDS and fMRIPrep import procedures, as well as many brand new analysis procedures, including Inter-Hemispheric Correlations, a novel Threshold Free Cluster Enhancement implementation, a new cluster-based inferences for the analysis of ROI-to-ROI connectivity matrices, or tools for anatomical lesion masking.
CONN is an open-source Matlab/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:
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), Multivoxel Pattern Analyses (MVPA), and dynamic connectivity analyses (dyn-ICA, sliding-window correlations)
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.
See this section for a detailed description of all processing and analysis methods available in CONN
To cite CONN in your work please include one or several of the following references:
CONN toolbox (www.nitrc.org/projects/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
For details about CONN's processing pipeline and analysis methods:
Nieto-Castanon, A. (2020). Handbook of fcMRI methods in CONN. Boston, MA: Hilbert Press