CONN_fMRI_Functional_connectivity_toolbox_manual_v17.pdf

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Gabrieli Lab. McGovern Institute for Brain Research Massachusetts Institute of Technology http://www.nitrc.org/projects/conn Susan Whitfield-Gabrieli Alfonso Nieto-Castanon

(2) CONN - functional connectivity toolbox v17 Overview ..................................................................................................................................... 3 General ........................................................................................................................................ 4 Step one: Setup (Defines experiment information, file sources for functional data, structural data, regions of interest, and other covariates) ............................ 5 Basic experimental info Setup: ........................................................................................................ 6 Structural files Setup: ...................................................................................................................... 7 Functional files Setup: ..................................................................................................................... 8 Preprocessing functional & structural data: .................................................................................... 9 ROI files Setup: .............................................................................................................................. 11 Rest or task conditions Setup: ....................................................................................................... 13 First-level (within-subjects) covariates Setup: .............................................................................. 14 Second-level (between-subjects) covariates Setup: ...................................................................... 15 Options Setup: ............................................................................................................................... 17 Step two: Denoising (Define, explore, and remove possible confounds) .......... 18 Step three: first-level Analyses (Define and explore functional connectivity measures for each subject) ............................................................................................... 20 Seed-to-voxel and ROI-to-ROI analyses: ........................................................................................ 20 Voxel-to-voxel & ICA analyses: ...................................................................................................... 22 Dynamic ICA analyses: ................................................................................................................... 24 Step four: second-level Results (Define and explore contrasts of interest and second-level results) ........................................................................................................... 25 ROI-to-ROI analyses (ROI-to-ROI functional connectivity matrices): ............................................ 27 Seed-to-Voxel analyses (functional connectivity maps): ............................................................... 30 Voxel-to-Voxel analyses (MVPA and other connectome-level measures): ................................... 32 ICA analyses: .................................................................................................................................. 33 Dynamic ICA analyses: ................................................................................................................... 36

(3) CONN - functional connectivity toolbox v17 Installing the toolbox: Updating to latest version: On the CONN gui click on Help->Update Requirements: To start the toolbox: SPM8 or above Matlab R2008b or above (no additional toolboxes required) download and unzip conn*.zip, and add the resulting ./conn/ directory to the matlab path (in Matlab’s File-Set path) Overview CONN is a Matlab-based cross-platform software for the computation, display, and analysis of functional connectivity in fMRI (fcMRI). Connectivity measures include seed-to-voxel connectivity maps, ROI-to- ROI connectivity matrices, graph properties of connectivity networks, generalized psychophysiological interaction models (gPPI), intrinsic connectivity, local correlation and other voxel-to-voxel measures, independent component analyses (ICA), and dynamic component analyses (dyn-ICA). CONN is available for resting state data (rsfMRI) as well as task-related designs. It covers the entire pipeline from raw fMRI data to hypothesis testing, including spatial coregistration, ART-based scrubbing, aCompCor strategy for control of physiological and movement confounds, first-level connectivity estimation, and second-level random-effect analyses and hypothesis testing. Note: a standalone version of CONN for linux64 systems (precompiled including both SPM and CONN) that does not from www.nitrc.projects/conn. OS-specific notes: On Mac OS/X use “ctrl-click” instead of “right-click” to bring contextual menus in the CONN gui; If the default GUI display fonts are too small click on Tools->GUI settings (top-right corner) to change the GUI font size (your choice of font sizes will be kept across CONN sessions and across toolbox updates); When using VNC to connect remotely to Linux machines type in Matlab’s command window opengl software right after starting Matlab if you experience VNC crashes when displaying 3d renderings or when printing; On the Matlab command window, type : conn (make sure your matlab path includes the path to the connectivity toolbox) require Matlab installed or a Matlab license can be downloaded

(4) CONN - functional connectivity toolbox v17 General In order to perform connectivity analyses using this toolbox you will need: Functional data. Either resting-state or task designs can be analyzed. Structural data. At least one anatomical volume for each subject (this is used mostly for plotting purposes but also to derive the gray/white/CSF masks used in the aCompCor confound removal method) ROI definitions. A series of files defining seeds of interest. ROIs can be defined from mask images, text files defining a list of MNI positions, or multiple-label images. The toolbox also provides a series of default pre-defined regions of interest that that will be loaded automatically. These include a series of seed areas useful for investigating resting state connectivity –regions characterizing DMN, dorsal attention network, executive control network, etc.-, as well as a complete brain parcellation including 91 cortical areas and 15 subcortical areas from the FSL Harvard-Oxford Atlas as well as 26 cerebellar areas from the AAL atlas. See the conn/utils/otherrois/ folder for additional/optional ROI files, including Brodmann areas, large- voxel parcellations, etc. tip: To try out the toolbox in the absence of any data, select Help. Sample Data in the CONN gui in order to automatically download and process the NYU test-retest dataset The toolbox operation is divided in four sequential steps: Setup: Defines basic experiment information, data locations, regions of interest (seeds), temporal covariates, and second-level models. Optionally, perform functional and anatomical preprocessing steps if necessary, including realignment, slice-timing correction, coregistration/normalization, segmentation, outlier identification, and smoothing. Denoising: Define, explore, and remove possible confounds in the BOLD signal, including motion, physiological and other noise sources. Analyses: Perform first-level analyses. Define the seeds of interest and explore the functional connectivity of different sources separately for each subject. Define ICA, voxel-to-voxel analyses, dynamic analyses, etc. Results: Perform second-level analyses. Define group analyses and perform population-level inferences from the resulting connectivity measures of each first-level analysis. Each of these steps can be defined interactively using the toolbox GUI or programmatically using scripts and conn_batch functionality. In addition, and both when using the GUI or batch processes, all of the analyses can be performed locally on a single computer, or they can be distributed among multiple computers in a cluster environment. Currently CONN supports Sun Grid Engine (SGE), Open Grid Scheduler (OGS/GE) and other Grid Engine implementations, PBS/Torque, LSF, and Slurm batch queuing systems. The following sections describe the experiment definition and analysis steps when using the GUI on a local computer in more detail. See Help.Documentation.Info:batch processing for additional information on batch scripts, or see Help.Documentation.Info:grid computing as well as Tools.Grid settings for additional information on cluster environments. General help resources In the CONN GUI select Help.Search to easily search through a database of user questions/answers, select Help.Updates to check and update CONN to the latest available release, select Help.Documentation for general documentation, and Help.Web for additional resources, including access to the CONN forum.

(5) CONN - functional connectivity toolbox v17 Step one: Setup (Defines experiment information, file sources for functional data, structural data, regions of interest, and other covariates) Click on the SETUP tab Click on the Project.New (blank) button to start a new project. Note 1: Alternatively, click on Project.New (wizard) to follow an initial simple step-by-step wizard that will allow you to import your functional, anatomical data, and optionally spatially preprocess your data using standard settings (segmentation, realignment, slice timing correction, coregistration, normalization, smoothing, and outlier detection/scrubbing; the wizard will offer a choice between two preprocessing pipelines: defaultMNI for analyses in MNI-space, and defaultSS for analyses in subject-space or surface- based analyses; see “Notes on Structural, Functional, and ROI files coregistration” section below). If using the wizard, this will also initialize some of the basic setup information that is described in the following sections (e.g. you can skip the Basic, Functional and Structural steps below, as well as some of the Conditions, and First-level covariates since those will be defined automatically by the spatial preprocessing steps). Note 2: If the data has initially been defined/analyzed in SPM you can similarly skip several of the steps below by using instead the Import functionality. Right after entering the number of subjects in the Basic setup, select Project.Import and specify one SPM.mat file for each subject. The program will extract the location of the functional data, the number of conditions per subject, the onset/length of the conditions of interest, and any specified first-level covariates from these SPM.mat files.

(6) CONN - functional connectivity toolbox v17 Basic experimental info Setup: Click on the Basic button on the left side, enter experiment information (Number of subjects, TR, number of sessions per subject, and acquisition type). If the same number of sessions was acquired for every subject enter a single number in Number of sessions, otherwise enter the subject-specific values (one value per subject). If your data was acquired continuously select ‘continuous’ in Acquisition type, and if you used sparse sampling select ‘sparse’ (this will skip hrf-convolution when computing task effects)

(7) CONN - functional connectivity toolbox v17 Structural files Setup: Click on the Structural button on the left side to load the structural images. Click sequentially on each subject and select the associated anatomical image (one anatomical volume per subject). If you have multiple anatomical volumes per subject (e.g. one per session/scan), select ‘Session-specific structural data’ and then enter the corresponding session-specific anatomical volumes. Anatomical volumes should typically be coregistered to the functional and ROI volumes for each subject (e.g. if using MNI-space normalized functional volumes you should enter here the normalized anatomical volume). Alternatively you may enter the raw (subject-space) anatomical volumes and transform them to your desired target space (e.g. MNI) using ‘structural tools: individual preprocessing step’ or ‘Preprocessing’ (see “Notes on Structural, Functional, and ROI files coregistration” section below). GUI tip 1: The “Find” in the “Select functional data files” window can be used to search for all files within the target folder recursively. Change the “Filter” window to narrow the search. GUI tip 2: If multiple subjects are selected in the “Subjects” list, and the number of files selected in the “Select functional data files” window matches the number of subjects, each subject is assigned one single file from the list If your structural volumes have been preprocessed using Freesurfer (https://surfer.nmr.mgh.harvard.edu/), you may enter here the T1 or brainmask volumes in the subject-specific mri folder generated by Freesurfer. The toolbox will identify the associated surf folder containing the estimated cortical surfaces and it will display at this point a 3d-rendering of the pial surface (and this will allow you to obtain connectivity measures on the cortical surface; see Setup.Options below).

(8) CONN - functional connectivity toolbox v17 Functional files Setup: Click on Functional button on the left side, from the right side panel, select the functional images (*img, or *nii, or 4d nii). This will take a second to load, check the middle panel (Functional data setup) to make sure the correct volumes are loaded. The brain display in the “Functional data setup” window shows the first (left) and last (right) scan for the selected subject/session (as in the figure above). The functional images are expected to be already pre-processed (realigned and smoothed), as well as coregistered with the structural and ROI volumes. If they are not, you may select ‘functional tools: individual preprocessing step’ or ‘Preprocessing’ to perform the appropriate preprocessing steps (see “Notes on Structural, Functional, and ROI files coregistration” section below). Clicking on ‘functional tools: check registration’ displays the coregistration between the functional and structural volumes for the selected subjects/sessions. If you wish to use an alternative set of functional volumes specifically when extracting ROI-level BOLD signal estimates (e.g. extract ROI BOLD signals from unsmoothed functional volumes, in order to avoid “spillage” from nearby regions; or use subject-space volumes, in order to use subject-specific ROIs) you may indicate this in the ‘Secondary datasets’ menu. By default the toolbox will define an alternative ‘dataset 1’ pointing to the unsmoothed volumes (using the SPM-convention for unsmoothed volumes, same files without the initial ‘s’ in the filename) and this dataset will be the one used unless specified otherwise when extracting ROI-level BOLD signal timeseries (see Setup.ROIs section below).

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