Documentation

References (by topic)

General information:

Preprocessing functional and anatomical data:

  • Nieto-Castanon, A. (2020). FMRI minimal preprocessing pipeline. In Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN (pp. 3–16). Hilbert Press. doi:10.56441/hilbertpress.2207.6599

Denoising functional data:

    • Chai, X. J., Nieto-Castanon, A., Ă–ngĂĽr, D., & Whitfield-Gabrieli, S. (2012). Anticorrelations in resting state networks without global signal regression. Neuroimage, 59(2), 1420-1428.

  • Nieto-Castanon, A. (2020). FMRI denoising pipeline. In Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN (pp. 17–25). Hilbert Press. doi:10.56441/hilbertpress.2207.6600

First-level analysis connectivity measures and methods in CONN:

  • Nieto-Castanon, A. (2020). Functional Connectivity measures. In Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN (pp. 26–62). Hilbert Press. doi:10.56441/hilbertpress.2207.6601

Group-level analyses and inferences in CONN:

  • Nieto-Castanon, A. (2020). General Linear Model. In Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN (pp. 63–82). Hilbert Press. doi:10.56441/hilbertpress.2207.6602

  • Nieto-Castanon, A. (2020). Cluster-level inferences. In Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN (pp. 3–16). Hilbert Press. doi:10.56441/hilbertpress.2207.6603

CONN's group-MVPA analyses:

Software documentation

General software use: CONN toolbox manual

Cloud/HPC environments

Using CONN in the context of HPC (High Performance Computing) clusters or multi-processor environments: CONN grid, Cluster configuration

Using CONN to securely interact with and analyze data in the cloud or in any network-accessible host : Remote projects, CONN remotely, cheat-sheet

Scripting

Main user-facing scripting functions in CONN:

>> conn_batch

create and manage all aspects of your functional connectivity projects using scripts

>> conn_module

integrate modular CONN functions into your own preprocessing or analysis pipelines (for arbitrary / non-connectivity data)

see conn extensions for a description of all modules currently available in CONN: conn module PREP, conn module GLM, conn module EL, conn module FL

Examples of use: see all "How to..." sections in fMRI Methods book

Other useful scripting / command-line functions in CONN: (sorted by topic)

File input/output/conversion:

>> conn_surf_read / conn_surf_write

reads/writes surface- level data (FreeSurfer fsaverage level-8 163K tessellation, with 163842 vertices and 327680 faces) from NIFTI format file

type "help conn_surf_write" in Matlab for additional details

e.g. :

>> conn_surf_write('data.surf.nii', rand([163842*2, 1]) ); % creates surface NIFTI file (e.g. 2 hemispheres 1 datapoint)

>> data = conn_surf_read('data.surf.nii'); % reads surface NIFTI file


>> conn_mtx_read / conn_mtx_write

reads/writes matrix-level data (arbitrary square matrix) from NIFTI format file

type "help conn_mtx_write" in Matlab for additional details

e.g. :

>> conn_mtx_write('data.mtx.nii', rand([10, 10, 1]) ); % creates matrix NIFTI file (e.g. 10 ROIs 1 datapoint)

>> data = conn_mtx_read('data.mtx.nii'); % reads matrix NIFTI file


>> conn_vol_read / conn_vol_write

reads/writes volume-level data (arbitrary 3D/4D matrix) from NIFTI format file

type "help conn_vol_write" in Matlab for additional details

e.g. :

>> conn_vol_write('data.nii', rand([64, 64, 64, 1]) ); % creates volume NIFTI file (e.g. single 3D image)

>> data = conn_vol_read('data.nii'); % reads volume NIFTI file


>> conn_dcm2nii : converts DICOM (.dcm ) to NIFTI format (.nii)

>> conn_gz2nii : converts compressed NIFTI (.gz ) to NIFTI format (.nii)

>> conn_matc2nii : converts CONN (.matc ) to NIFTI format (.nii)

>> conn_mgh2nii : converts MGH (.mgh .mgz ) to NIFTI format (.nii)

>> conn_annot2nii : converts FREESURFER annotation (lh*.annot rh*.annot) to surface NIFTI format (.surf.nii)

>> conn_surf_curv2nii : converts FREESURFER paint (lh. rh.) to surface NIFTI format (.surf.nii)

>> conn_surf_gii2nii : converts FREESURFER surface GIFTI (lh*.gii rh*.gii) to surface NIFTI format (.surf.nii)

>> conn_surf_nii2curv : converts surface NIFTI (.surf.nii) to FREESURFER paint format (lh. rh.)

>> conn_surf_nii2gii : converts surface NIFTI (.surf.nii) to FREESURFER surface GIFTI format (lh*.gii rh*.gii)

>> conn_cat2mgh : converts CAT12 output files to FREESURFER format

>> conn_surf_surf2vol : projects surface NIFTI (.surf.nii) to volume NIFTI (.nii) format (in MNI-space)

>> conn_surf_vol2surf : projects volume NIFTI (.nii) (in MNI-space) to surface NIFTI format (.surf.nii)


>> conn_cache pull/push

manages local cache (for fast repeated access to files from slow or remote drives)

type "help conn_cache" in Matlab for additional details

e.g. :

>> file = conn_cache('pull','/Volumes/ext/data.txt'); % creates local copy of remote/network file

>> edit(file); % change local copy

>> conn_cache('push','/Volumes/ext/data.txt'); % updates remote/network file with changes if necessary


Display functions:

>> conn_display <SPM.mat>

displays group-level analysis results (including analyses of standard 3D volume-level data, as well as surface- and matrix-level analyses)

type "help conn_display" in Matlab for additional details

e.g. :

>> hf = conn_display('/results/analysis01/SPM.mat'); % displays 2nd-level analysis results

>> conn_display(hf, 'export_mask', 'mask.nii'); % exports supra-threshold mask


>> conn_mesh_display <file.nii>

displays MNI-space 3D volume or surface data projected onto a 3D cortical reference surface (Freesurfer tessellation of ICBM 2009c Nonlinear Asymmetric template)

type "help conn_mesh_display" in Matlab for additional details

e.g. :

>> fh = conn_mesh_display('/results/analysis01/spmT_0001.nii'); % displays T-stat map projected on cortical surface

>> fh('print', 4, 'figure01.jpg', '-nogui'); % prints 4-view mosaic


>> conn_slice_display <file.nii>

displays MNI-space 3D volume data overlaid on individual slices of a reference structural image (ICBM 2009c Nonlinear Asymmetric template)

type "help conn_slice_display" in Matlab for additional details

e.g. :

>> fh = conn_slice_display('/results/analysis01/spmT_0001.nii'); % displays T-stat map on individual slices

>> fh('pointer_mm',[0 0 10]); % sets display center coordinates to [0 0 10] mm

>> fh('multisliceset',true,16,8); % sets multi-slice display to 16 slices and 8mm interslice

>> fh('togglegui',true); % hide GUI buttons

>> fh('print', 'figure01.jpg', '-nogui'); % prints figure


>> conn_mtx_display <file.nii>

displays matrix-level data

type "help conn_mtx_display" in Matlab for additional details

e.g. :

>> fh = conn_mtx_display('/results/analysis02/spmT_0001.nii'); % displays T-stat map from matrix group-analysis

>> fh('print', 'figure01.jpg', '-nogui'); % prints figure


Using CONN to process/analyze arbitrary -non-connectivity- data:

>> conn module preprocessing <filename.json>

preprocesses and/or denoises arbitrary functional data. Processing methods include all those described in the Preprocessing pipeline and Denoising pipeline sections

type "help conn_module" in Matlab for additional details

e.g. :

>> conn_module( 'preprocessing', 'PREPROC01.json'); % runs default preprocessing pipeline on raw functional/structural data

example PREPROC01.json: (preprocessing only)

{

"structurals": [ "/data/anat.nii" ],

"functionals": [ [

"/data/run1/func.nii",

"/data/run2/func.nii"

] ],

"steps": "default_mni",

"sliceorder": "interleaved (Siemens)"

}


example PREPROC02.json: (preprocessing + denoising)

{

"structurals": [ "/data/anat.nii" ],

"functionals": [ [

"/data/run1/func.nii",

"/data/run2/func.nii"

] ],

"steps": ["default_mni", "functional_regression", "functional_bandpass" ],

"sliceorder": "interleaved (Siemens)",

"reg_names": ["realignment", "scrubbing", "White Matter", "CSF"],

"reg_dimensions": [Inf,Inf,5,5],

"reg_deriv": [1,0,0,0],

"bp_filter": [0.008,Inf]

}


>> conn module glm <filename.json>

performs group-level analyses of arbitrary data (including analyses of standard 3D volume-level data, as well as surface- and matrix-level analyses). Analyses methods include all those described in the Cluster-level inferences section (e.g. RFT, randomization/permutation, TFCE, NBS, etc.)

type "help conn_module" in Matlab for additional details

e.g. :

>> conn_module( 'glm', 'ANALYSIS01.json'); % runs 2nd-level analysis of arbitrary input images

>> conn_display( '/results/analysis01' ); % displays results

example ANALYSIS01.json (defining a two-sample t-test with N=10):

{

"data": [

"/data/file01.nii",

"/data/file02.nii",

"/data/file03.nii",

"/data/file04.nii",

"/data/file05.nii",

"/data/file06.nii",

"/data/file07.nii",

"/data/file08.nii",

"/data/file09.nii",

"/data/file10.nii"

],

"design_matrix": [[1,0],[1,0],[1,0],[1,0],[1,0],[0,1],[0,1],[0,1],[0,1],[0,1]],

"contrast_between": [-1,1],

"contrast_within": 1,

"folder": "/results/analysis01"

}


Managing/submitting jobs in HPC/cluster environments:

>> conn_batch(..., 'parallel.N', N)

submits a request for N nodes in your cloud or HPC environment to execute the steps specified in a conn_batch command (each node will process/analyze a different subset of subjects)

type "help conn_batch" in Matlab for additional details

e.g.:

>> conn_batch('Denoising.done', true, 'parallel.profile', 'slurm', 'parallel.N', 10);

% runs CONN denoising step on your current CONN project using 10 simultaneous

% nodes/jobs, and using the Slurm protocol to submit these jobs


>> conn jobmanager

manages jobs submitted to cloud or HPC environments

type "help conn_jobmanager" in Matlab for additional details

e.g.:

>> conn_jobmanager profiles % displays a list of all available HPC profiles

>> conn_jobmanager % launches gui displaying the status of any pending or running jobs

>> conn_jobmanager restartif error % restarts any nodes/jobs which have stopped with errors


>> conn('submit', fcn_name [, arg1, arg2, ... ] )

submits a request for a single node in your cloud or HPC environment to run the Matlab function or script fcn_name with (optional) input arguments arg1, arg2., etc.

e.g.:

>> conn submit myscript01; % run myscript01 remotely and wait until job finishes

>> job = conn('submit', 'myscript01'); % run myscript01 remotely and return immediately (before job starts)

>> conn_jobmanager('statusjob', job, [], true, true); % checks the status of a submitted job


>> conn submit_spmbatch <matlabbatch>

submits arbitrary SPM batch <matlabbatch> (.m or .mat file, or matlabbatch structure) as a single job to your cloud or HPC environment

e.g.:

>> matlabbatch{1}.spm... = ... % define matlabbatch structure

>> save batchfile.mat matlabbatch; % saves matlabbatch information in .mat file

>> conn submit_spmbatch batchfile.mat; % submit SPM batch file and wait until job finishes


Working with CONN remotely (command-line options)

>> conn remotely on

starts SSH-based encrypted/secure connection with remote server (this is not needed if you are already connected to a remote server in CONN's GUI)

e.g.:

>> conn remotely on

Server address []: yourinstitution.edu

Username []:

...


>> conn_server cmd

runs interactive Matlab commands in remote CONN server

e.g.:

>> conn_server cmd % interactive command-line prompt (commands executed in CONN server)

yourinstitution.edu >> vpa(pi,100) % run vpa function in remote CONN server

ans =

3.141592653589793238462643383279502884197169399375105820974944592307816406286208998628034825342117068

yourinstitution.edu >> quit % exits interactive commands


>> [out1, out2, ...] = conn_server('run', fcn_name, arg1, arg2, ... )

runs in remote CONN server the Matlab function or script fcn_name with (optional) input arguments arg1, arg2., etc.

e.g.:

>> out = conn_server('run', 'vpa', pi, 1000); run vpa function in remote CONN server and imports output variable

see "help conn_remotely" and command-line remote connection guide for additional details