Embedded Files
CONN_BATCH
CONN_BATCH commands can be used to script any processing or analysis step in CONN. The function conn_batch may be called using any of the following syntax formats: 1) conn_batch(BATCH); where BATCH is a structure (with fields defined in the section below) CONN_BATCH
e.g. clear BATCH; BATCH.Setup.RT=2; conn_batch(BATCH); 2) conn_batch('fieldname1',fieldvalue1,'fieldname2',fieldvalue2,...) where 'fieldname*' are individual BATCH structure fields
e.g. conn_batch('Setup.RT',2); 3) conn_batch(batchfilename) where batchfilename is a .mat file containing a batch structure or a .json file containing a batch structure or a .m file containing a Matlab script
e.g. conn_batch('mybatchfile.mat'); note1: in standalone releases use syntax (from system-prompt): conn batch batchfilename : runs a batch file (.m or .mat) conn batch "matlabcommands" : runs one or several matlab commands
note2: syntax conn_batch({BATCH1 BATCH2 ...}) processes sequentially multiple batch structures (equivalent to conn_batch(BATCH1); conn_batch(BATCH2); ... e.g. useful when defining/running multiple first- or second- level analyses) __________________________________________________________________________________________________________________ BATCH structure fields: filename : conn_*.mat project file (defaults to currently open project) subjects : Subset of subjects to run processing steps or define parameters for (defaults to all subjects) parallel : Parallelization options (defaults to local procesing / no parallelization) Setup : Information/processes regarding experiment Setup and Preprocessing Denoising : Information/processes regarding Denoising step Analysis : Information/processes regarding first-level analyses Results : Information/processes regarding second-level analyses/results QA : Information/processes regarding Quality Assurance plots
BATCH.parallel ------------ DEFINES PARALLELIZATION OPTIONS
BATCH.parallel ------------ DEFINES PARALLELIZATION OPTIONS
parallel parallel.N : Number of parallel jobs; 0 to run locally ([0])
parallel.profile : (optional) Name of parallelization profile if undefined CONN uses the default parallelization profile defined in GUI.Tools.GridSettings see "conn_jobmanager profiles" for a list of all available profiles see GUI Tools.GridSettings for additional information and to add/edit profiles use the profile name 'Null profile' to queue this job (queued/scripted jobs are prepared but not submitted; see GUI.Tools.SeePendingJobs to submit next queued job)
parallel.cmd_submitoptions : (optional) alternative value for parallelization profile 'in-line' additional submit-settings defaults to the chosen parallelization profile value for this field
parallel.cmd_submitoptions_infile : (optional) alternative value for parallelization profile 'in-file' additional submit-settings defaults to the chosen parallelization profile value for this field
parallel.cmd_rundeployed : (optional) aternative value for profile 'nodes use pre-compiled CONN only' setting defaults to the chosen parallelization profile value for this field
parallel.cmd_checkstatus_automatic : (optional) aternative value for profile 'check jobs status automatically' setting defaults to the chosen parallelization profile value for this field
parallel.immediatereturn : (optional) 1/0 : 1 returns control to Matlab without waiting for parallel job to finish ([0])
BATCH.Setup -------------- DEFINES PROJECT DATA
BATCH.Setup -------------- DEFINES PROJECT DATA
Setup Setup.isnew : 1/0 is this a new conn project [0]
Setup.done : 1/0: 0 defines fields only; 1 runs SETUP processing steps [0]
Setup.overwrite : (for done=1) 1/0 overwrites target files if they exist [1] Setup.nsubjects : Number of subjects
Setup.RT : Repetition time (seconds) [NaN: read from sidecar json file if available]
Setup.acquisitiontype : 1/0: Continuous acquisition of functional volumes [1] Setup.functionals : functionals{nsub}{nses} char array of functional volume files (dataset-0; for voxel-level analyses; see secondarydatasets below) Setup.structurals : structurals{nsub} char array of structural volume files OR structurals{nsub}{nses} char array of anatomical session-specific volume files
Setup.secondarydatasets : (for Setup.rois.dataset>0) structure array identifying one or several additional functional datasets (e.g. vdm files for susceptibility distorition, alternative functional files for ROI-level timeseries extraction, etc.) (secondary dataset-1 and above); default secondarydatasets equal to struct('functionals_type',2) Setup.secondarydatasets.functionals_label : (optional) label of secondary functional dataset Setup.secondarydatasets.functionals_type : Location of secondary functional dataset: 1: same files as Primary dataset 2: same files as Primary dataset field after removing leading 's' from filename 3: other (same as Primary dataset field but using alternative filename-change rule; see functionals_rule above and help conn_rulebasedfilename); 4: other (explicitly specify the functional volume files; see functionals_explicit above) [2] Setup.secondarydatasets.functionals_explicit : (only for functionals_type==4 only) functionals_explicit{nsub}{nses} char array of volume files Setup.secondarydatasets.functionals_rule : (only for functionals_type==3 only) regexprep(filename,functionals_rule{2},functionals_rule{3}) converts filenames in 'Setup.functionals' field to filenames that will be used when extracting BOLD signal ROI timeseries (if functionals_rule{1}==2 filename is interpreted as a full path; if functionals_rule{1}==1 filename is interpreted as only the file *name* -no file path, no file extension-)
Setup.add : 1/0; use 0 (default) to define the full set of subjects in your experiment; use 1 to define an additional set of subjects (to be added to any already-existing subjects in your project) [0] When using Setup.add=1, the following fields are expected to contain the information for the new /added subjects *only*: Setup.functionals, Setup.structurals, Setup.functionals_explicit, Setup.vdm_functionals, Setup.fmap_functionals, Setup.coregsource_functionals, Setup.spmfiles, Setup.masks.Grey/White/CSF, Setup.rois.files, Setup.conditions.onsets/durations, Setup.covariates.files When using Setup.add=1 in combination with Setup.done, Setup.preprocessing, Denoising.done, and/or Analysis.done only the new/added subjects will be processed When using Setup.add=1 the BATCH.subjects field is disregarded/overwritten to point to the new/added subjects only note: Setup.add cannot be used in combination with any of the Setup.rois.add, Setup.conditions.add, or Setup.covariates.add options within the same batch structure Setup.masks Setup.masks,.Grey : masks.Grey{nsub} char array of grey matter mask volume file [defaults to Grey mask from structural] Setup.masks,.White : masks.White{nsub} char array of white matter mask volume file [defaults to White mask from structural] Setup.masks,.CSF : masks.CSF{nsub} char array of CSF mask volume file [defaults to CSF mask from structural] : each of these fields can also be defined as a double cell array for session-specific files (e.g. mask.Grey{nsub}{nses} grey matter file for subject nsub and session nses) : each of these fields can also be defined as a structure with fields files/dimensions/etc. (same as 'Setup.rois' below)
Setup.rois Setup.rois.names : rois.names{nroi} char array of ROI name [defaults to ROI filename] Setup.rois.files : rois.files{nroi}{nsub}{nses} char array of roi file (rois.files{nroi}{nsub} char array of roi file, to use the same roi for all sessions; or rois.files{nroi} char array of roi file, to use the same roi for all subjects) Setup.rois.dimensions : rois.dimensions{nroi} number of ROI dimensions - # temporal components to extract from ROI [1] (set to 1 to extract the average timeseries within ROI voxels; set to a number greater than 1 to extract additional PCA timeseries within ROI voxels Setup.rois.weighted : rois.weighted(nroi) 1/0 to use weighted average/PCA computation when extracting temporal components from each ROI (BOLD signals are weighted by the ROI mask value at each voxel) Setup.rois.multiplelabels: rois.multiplelabels(nroi) 1/0 to indicate roi file contains multiple labels/ROIs (default: set to 1 if there exist an associated .txt or .xls file with the same filename and in the same folder as the roi file) Setup.rois.mask : rois.mask(nroi) 1/0 to mask with grey matter voxels [0] Setup.rois.regresscovariates: rois.regresscovariates(nroi) 1/0 to regress known first-level covariates before computing PCA decomposition of BOLD signal within ROI [1 if dimensions>1; 0 otherwise] Setup.rois.dataset : rois.dataset(nroi) index n to Secondary Dataset #n identifying the version of functional data coregistered to this ROI to extract BOLD timeseries from [1] (set to 0 to extract BOLD signal from Primary Dataset instead; secondary datasets may be identified by their index or by their label -see 'functional_label' preprocessing step) Setup.rois.add : 1/0; use 0 (default) to define the full set of ROIs to be used in your analyses; use 1 to define an additional set of ROIs (to be added to any already-existing ROIs in your project) [0] Setup.conditions Setup.conditions.names : conditions.names{ncondition} char array of condition name Setup.conditions.onsets : conditions.onsets{ncondition}{nsub}{nses} vector of condition onsets (in seconds) Setup.conditions.durations : conditions.durations{ncondition}{nsub}{nses} vector of condition durations (in seconds) Setup.conditions.param : conditions.param(ncondition) temporal modulation (0 for no temporal modulation; positive index to first-level covariate for other temporal interactions) Setup.conditions.filter : conditions.filter{ncondition} temporal/frequency decomposition ([] for no decomposition; [low high] for fixed band-pass frequency filter; [N] for filter bank decompositoin with N frequency filters; [Duration Onsets] in seconds for sliding-window decomposition where Duration is a scalar and Onsets is a vector of two or more sliding-window onset values) Setup.conditions.missingdata : 1/0 Allow subjects with missing condition data (empty onset/duration fields in *all* of the sessions) [0] Setup.conditions.model : (optional) conditions.model{ncondition} cell array characterizing definition of secondary conditions, as a function of other regular conditions. Format {@fun, condition_name1, condition_name2, ...} where @fun is a function handle characterizing a function fun(x) with x a matrix of functional connectivity values for one or multiple conditions (rows) and one or multiple datapoints (columns) Example {@(x)mean(x,1),'Time1','Time2','Time3'} will compute the mean of the three Time# conditions Alternative forms of conditions.model{ncondition}{1} values (functional definition) 'avg','std','min','max' : to compute average/standard-deviation/minimum/maximum across selected conditions 'lin',G : to compute regressor coefficients in linear model X=G*B fitting all selected conditions where G is a [conditions x regressors] design matrix (note: for subject-specific values define G as a char array using Matlab notation and enter second-level covariate names as values). When G contains multiple columns the first column defines the effect of interest (the rest are estimated but simply disregarded; not taken into second-level analyses) @fun : (standard single argument function) to compute an arbitrary function fun(X) of condition values X (conditions x datapoints matrix) for each subject @fun : (two or three arguments function for subject-specific functions) to compute an arbitrary function fun(X,C,Cnames) as a function of not only the condition values X but also of second-level covariate values C (1 x covariates vector) for all second-level covariates in your project (covariate names will be listed as the third argument of fun for reference) Setup.conditions.importfile : (optional) Alternatively, importfile is a char or cell array pointing to a '*.txt','*.csv', or BIDS- '*.tsv' file containing conditions names/onsets/durations information (see help conn_importcondition) Setup.conditions.importfile_options: (for conditions.importfile procedure only) cell array containing additional options to pass to conn_importcondition when importing condition info (see help conn_importcondition) Setup.conditions.add : 1/0; use 0 (default) to define the full set of conditions to be used in your analyses; use 1 to define an additional set of conditions (to be added to any already-existing conditions in your project) [0] Setup.covariates Setup.covariates.names : covariates.names{ncovariate} char array of first-level covariate name Setup.covariates.files : covariates.files{ncovariate}{nsub}{nses} char array of covariate file Setup.covariates.add : 1/0; use 0 (default) to define the full set of covariates to be used in your analyses; use 1 to define an additional set of covariates (to be added to any already-existing covariates in your project) [0] Setup.subjects Setup.subjects.effect_names : subjects.effect_names{neffect} char array of second-level covariate name Setup.subjects.effects : subjects.effects{neffect} vector of size [nsubjects,1] defining second-level effects Setup.subjects.descrip : (optional) subjects.descrip{neffect} char array of effect description (long name; for display purposes only) Setup.subjects.add : 1/0; use 0 (default) to define the full set of covariates to be used in your analyses; use 1 to define an additional set of covariates (to be added to any already-existing covariates in your project) [0] Setup.subjects Setup.subjects.group_names : subjects.group_names{ngroup} char array of second-level group name Setup.subjects.groups : subjects.group vector of size [nsubjects,1] (with values from 1 to ngroup) defining subject groups Setup.subjects.descrip : (optional) subjects.descrip{neffect} char array of group description (long name; for display purposes only) Setup.subjects.add : 1/0; use 0 (default) to define the full set of covariates to be used in your analyses; use 1 to define an additional set of covariates (to be added to any already-existing covariates in your project) [0] Setup.analyses : Vector of index to analysis types (1: ROI-to-ROI; 2: Seed-to-voxel; 3: Voxel-to-voxel; 4: Dynamic FC) Defaults to vector [1,2,3,4] (all analyses)
Setup.voxelmask : Analysis mask (voxel-level analyses): 1: Explicit mask (brainmask.nii); 2: Implicit mask (subject-specific) [1]
Setup.voxelmaskfile : Explicit mask file (only when voxelmask=1) [fullfile(fileparts(which('spm')),'apriori', 'brainmask.nii')]
Setup.voxelresolution : Analysis space (voxel-level analyses): 1: Volume-based template (SPM; default 2mm isotropic or same as explicit mask if specified); 2: Same as structurals; 3: Same as functionals; 4: Surface-based template (Freesurfer) [1]
Setup.analysisunits : BOLD signal units: 1: PSC units (percent signal change); 2: raw units [1]
Setup.outputfiles : Optional output files (outputfiles(1): 1/0 creates confound beta-maps; outputfiles(2): 1/0 creates confound-corrected timeseries; outputfiles(3): 1/0 creates seed-to-voxel r-maps) ;outputfiles(4): 1/0 creates seed-to-voxel p-maps) ;outputfiles(5): 1/0 creates seed-to-voxel FDR-p-maps); outputfiles(6): 1/0 creates ROI-extraction REX files; [0,0,0,0,0,0]
Setup.spmfiles : Optionally, spmfiles{nsub} is a char array pointing to the 'SPM.mat' source file to extract Setup information from for each subject (use alternatively spmfiles{nsub}{nses} for session-specific SPM.mat files)
Setup.spmfiles_options: (for Setup.spmfiles procedure) Cell array containing additional options to pass to conn_importspm when importing experiment info from spmfiles (see help conn_importspm)
Setup.vdm_functionals : (for Setup.preprocessing.steps=='realign&unwarp&fieldmap') vdm_functionals{nsub}{nses} char array of voxel-displacement volumes (vdm* file; explicitly entering these volumes here superceeds CONN's default option to search for/use vdm* files in same directory as functional data)
Setup.fmap_functionals: (for Setup.preprocessing.steps=='vdm_create') fmap_functionals{nsub}{nses} char array of fieldmap sequence files (magnitude1+phasediff or real1+imag1+real2+imag2 or fieldmap (Hz) volumes)
Setup.coregsource_functionals: (for Setup.preprocessing.steps=='functional_coregister/segment/normalize') coregsource_functionals{nsub} char array of source volume for coregistration/normalization/ segmentation (used only when preprocessing "coregtomean" field is set to 2, user-defined source volumes are used in this case instead of either the first functional volume (coregtomean=0) or the mean functional volume (coregtomean=1) for coregistration/normalization/segmentation)
Setup.localcopy : (for Setup.structural, Setup.functional, Setup.secondarydatasets, and Setup.rois) 1/0 : copies structural/ functional files into conn_*/data/BIDS folder before importing into CONN [0]
Setup.binary_threshold: (for BOLD extraction from Grey/White/CSF ROIs) Threshold value # for binarizing Grey/White/CSF masks [.5 .5 .5]
Setup.binary_threshold_type: (for BOLD extraction from Grey/White/CSF ROIs) 1: absolute threshold (keep voxels with values above x); 2: percentile threshold (keep x% of voxels with the highest values) [1 1 1]
Setup.exclude_grey_matter : (for BOLD extration from White/CSF ROIs) threhsold for excluding Grey matter voxels (nan for no threshold) [nan nan nan]
Setup.erosion_steps : (for BOLD extraction from Grey/White/CSF ROIs) integer numbers are interpreted as erosion kernel size for Grey/White/CSF mask erosion after binarization; non-integer numbers are interpreted as percentile voxels kept after erosion [0 1 1]
Setup.erosion_neighb : (for BOLD extraction from Grey/White/CSF ROIs; only when using integer erosion_steps/ kernel sizes, this field is disregarded otherwise) Neighborhood size for Grey/White/CSF mask erosion after binarization (a voxel is eroded if there are more than masks_erosion_neighb zeros within the (2*masks_erosionsteps+1)^3-neighborhood of each voxel) [1 1 1]
BATCH.Setup.preprocessing - PERFORMS DATA PREPROCESSING STEPS (realignment/slicetiming/coregistration/segmentation/normalization/etc.)
BATCH.Setup.preprocessing - PERFORMS DATA PREPROCESSING STEPS (realignment/slicetiming/coregistration/segmentation/normalization/etc.)
Setup.preprocessing
Setup.preprocessing.steps : List of data preprocessing steps (cell array containing a subset of the following step names, in the desired order; e.g. {'functional_realign','functional_art'}):
PIPELINES: 'default_mni' : default MNI-space preprocessing pipeline 'default_mnifield' : same as default_mni but with vdm/fieldmap information and indirect normalization 'default_mnidirectfield' : same as default_mni but with vdm/fieldmap information and direct normalization 'default_ss' : default subject-space preprocessing pipeline 'default_ssfield' : same as default_ss but with vdm/fieldmap information 'default_ssnl' : same as default_ss but with non-linear coregistration
INDIVIDUAL STRUCTURAL STEPS: 'structural_center' : centers structural data to origin (0,0,0) coordinates 'structural_manualorient' : applies user-defined affine transformation to structural data 'structural_manualspatialdef' : applies user-defined spatial deformation to structural data 'structural_mask' : masks structural data using inclusive or exclusive mask 'structural_segment&normalize' : structural unified normalization and segmentation 'structural_segment&normalize&lesion' : structural unified normalization and segmentation with lesion mask (normalizes structural data and creates a modified TPM for functional normalization that includes the lesion as an added tissue class) 'structural_normalize' : structural normalization to MNI space (without segmentation) 'structural_normalize_preservemasks' : structural normalization to MNI space with user-defined Grey/White/CSF masks (normalizes structural data and applies same transformation to user-defined Grey/White/CSF mask ROIs) 'structural_segment' : structural segmentation (Grey/White/CSF tissue classes)
INDIVIDUAL FUNCTIONAL (or combined functional/structural) STEPS: 'functional_art' : functional identification of outlier scans (from motion displacement and global signal changes) 'functional_bandpass' : functional band-pass filtering 'functional_center' : centers functional data to origin (0,0,0) coordinates 'functional_coregister_affine' : functional affine coregistration to structural volumes 'functional_coregister_nonlinear' : functional non-linear coregistration to structural volumes 'functional_label' : labels current functional files (to list of Secondary Datasets) 'functional_load' : assigns current functional files (from list of Secondary Datasets) 'functional_manualorient' : applies user-defined affine transformation to functional data 'functional_manualspatialdef' : applies user-defined spatial deformation to functional data 'functional_mask' : masks functional data using inclusive or exclusive mask 'functional_motionmask' : creates functional motion masks (mean BOLD signal spatial derivatives wrt motion parameters) 'functional_normalize_direct' : functional direct normalization 'functional_normalize_indirect' : functional indirect normalization (coregister to structural; normalize structural; apply same transform to functionals) 'functional_normalize_indirect_preservemasks': functional indirect normalization with user-defined Grey/White/CSF masks (coregister to structural; normalize structural; apply same transformation to functionals as well as to Grey/White/CSF masks) 'functional_realign' : functional realignment 'functional_realign_noreslice' : functional realignment without reslicing (applies transform to source header files) 'functional_realign&unwarp' : functional_realignment + unwarp (removes motion-by-inhomogeneity interactions) 'functional_realign&unwarp&fieldmap' : functional_realignemnt&unwarp + distortion correction (corrects static inhomogeneity distortions) 'functional_regression' : removal of user-defined temporal components from BOLD timeseries (keeps residuals of linear regression model) 'functional_removescans' : removes user-defined number of initial scans from functional 'functional_roiextract' : extraction of ROI timeseries (compute BOLD timeseres within ROI) 'functional_segment' : functional segmentation (Grey/White/CSF tissue classes) 'functional_segment&normalize_direct' : functional direct unified normalization and segmentation 'functional_segment&normalize_indirect' : functional indirect unified normalization and segmentation (coregister to structural; normalize and segment structural; apply same transformation to functionals) 'functional_sliceintensity' : functional slice-timing correction (e.g. for sparse acquisition sequences) 'functional_slicetime' : functional slice-timing correction (for continuous acquisition sequences) 'functional_smooth' : functional spatial smoothing 'functional_smooth_masked' : functional spatial masked-smoothing (spatial convolution with Gaussian kernel restricted to voxels within custom functional mask 'functional_vdm_create' : creation of vdm (voxel-displacement-map) from fieldmap dataset (reads 'fmap' secondary functional dataset containing magnitude and phasediff images and creates 'vdm' secondary functional dataset containing voxel-displacement map)
SURFACE FUNCTIONAL STEPS: 'functional_surface_resample' : resample functional data at the location of FreeSurfer subject-specific structural cortical surface 'functional_surface_smooth' : functional spatial diffusion of surface data 'functional_surface_coreg&resample' : coregister&resample functional data at the location of FreeSurfer subject-specific structural cortical surface
If steps is left empty or unset a gui will prompt the user to specify the desired preprocessing pipeline If steps points to an existing preprocessing-pipeline file (e.g. saved from GUI) the corresponding preprocessing-pipeline will be run Setup.preprocessing.affreg : (normalization) affine registration before normalization ['mni']
Setup.preprocessing.art_thresholds : (functional_art) ART thresholds for identifying outlier scans art_thresholds(1): threshold value for global-signal (z-value; default 5) art_thresholds(2): threshold value for subject-motion (mm; default .9) additional options: art_thresholds(3): 1/0 global-signal threshold based on scan-to-scan changes in global-BOLD measure (default 1) art_thresholds(4): 1/0 subject-motion threshold based on scan-to-scan changes in subject-motion measure (default 1) art_thresholds(5): 1/0 subject-motion threhsold based on composite-movement measure (default 1) art_thresholds(6): 1/0 force interactive mode (ART gui) (default 0) art_thresholds(7): [only when art_threshold(5)=0] subject-motion threshold based on rotation measure art_thresholds(8): N number of initial scans to be flagged for removal (default 0) note: when art_threshold(5)=0, art_threshold(2) defines the threshold based on the translation measure, and art_threhsold(7) defines the threshold based on the rotation measure; otherwise art_threshold(2) defines the (single) threshold based on the composite-motion measure note: the default art_thresholds(1:2) [5 .9] values correspond to the "intermediate" (97th percentile) settings; to use the "conservative" (95th percentile) settings use [3 .5]; to use the "liberal" (99th percentile) settings use [9 2] values instead note: art needs subject-motion files to estimate possible outliers. If a 'realignment' first-level covariate exists it will load the subject-motion parameters from that first- level covariate; otherwise it will look for a rp_*.txt file (SPM format) in the same folder as the functional data note: subject-motion files can be in any of the following formats: a) *.txt file (SPM format; three translation parameters in mm followed by pitch/roll/yaw in radians); b) *.par (FSL format; three Euler angles in radians followed by translation parameters in mm); c) *.siemens.txt (Siemens MotionDetectionParameter.txt format); d) *.deg.txt (same as SPM format but rotations in degrees instead of radians)
Setup.preprocessing.boundingbox : (normalization) target bounding box for resliced volumes (mm) [-90,-126,-72;90,90,108]
Setup.preprocessing.bp_filter : (functional_bandpass, functional_regression) Low- and High- frequency thresholds (in Hz)
Setup.preprocessing.bp_keep0 : (functional_bandpass) 0: removes average BOLD signal (freq=0Hz component); 1: keeps average BOLD signal in output independent of band-pass filter values; [1]
Setup.preprocessing.coregtomean : (functional_coregister/segment/normalize) 0: use first volume; 1: use mean volume (computed during realignment); 2: use user-defined source volume (see Setup.coregsource_functionals field) [1]
Setup.preprocessing.diffusionsteps : (surface_smooth) number of diffusion steps
Setup.preprocessing.fwhm : (functional_smooth) Smoothing factor (mm) [8]
Setup.preprocessing.interp : (normalization) target voxel interpolation method (0:nearest neighbor; 1:trilinear; 2 or higher:n-order spline) [4]
Setup.preprocessing.label : (functional_label) label of secondary dataset (note: the following functional step names do not require an explicit label field: 'functional_label_as_original', 'functional_label_as_subjectspace', 'functional_label_as_mnispace', 'functional_label_as_surfacespace', 'functional_label_as_smoothed')
Setup.preprocessing.load_label : (functional_load) label of secondary dataset (note: the following functional step names do not require an explicit continue field: 'functional_load_from_original', 'functional_load_from_subjectspace', 'functional_load_from_mnispace', 'functional_load_from_surfacespace', 'functional_load_from_smoothed')
Setup.preprocessing.mask_names_anat : (structural_mask) list of ROI names (if multiple ROIs, the intersection of all ROIs will be used as mask)
Setup.preprocessing.mask_inclusive_anat : (structural_mask) 1: inclusive ROI mask (keep voxels inside ROI); 0: exclusive ROI mask (keep voxels outside ROI) [1]
Setup.preprocessing.mask_names_func : (functional_mask) list of ROI names (if multiple ROIs, the intersection of all ROIs will be used as mask)
Setup.preprocessing.mask_inclusive_func : (functional_mask) 1: inclusive ROI mask (keep voxels inside ROI); 0: exclusive ROI mask (keep voxels outside ROI) [1]
Setup.preprocessing.reg_names : (functional_regression) list of first-level covariates to use as model regressors / design matrix (valid entries are first-level covariate names or ROI names)
Setup.preprocessing.reg_dimensions : (functional_regression) list of maximum number of dimensions (one value for each model regressor in reg_names)
Setup.preprocessing.reg_deriv : (functional_regression) list of 0/1/2 values (one value for each model regressor in reg_names): add first- or second- order derivatives to each model regressor
Setup.preprocessing.reg_filter : (functional_regression) list of 0/1 values (one value for each model regressor in reg_names): band-pass filter individual model regressors (filter specified in bp_filter field)
Setup.preprocessing.reg_detrend : (functional_regression) 1: adds a linear/detrending term to model regressors [1]
Setup.preprocessing.reg_skip : (functional_regression) 1: does not create output functional files, only creates session-specific dp_*.txt files with covariate timeseries to be included later in an arbitrary first-level model [0]
Setup.preprocessing.removescans : (functional_removescans) number of initial scans to remove
Setup.preprocessing.reorient : (functional/structural_manualorient) 3x3 or 4x4 transformation matrix or filename containing corresponding matrix
Setup.preprocessing.respatialdef : (functional/structural_manualspatialdef) nifti deformation file (e.g. y_*.nii or *seg_sn.mat files)
Setup.preprocessing.roi_names : (functional_roiextract) list of ROI names additional 1st-level covariate names may be included (to be regressed-out from ROI timeseries)
Setup.preprocessing.roi_dimensions : (functional_roiextract) list of maximum number of dimensions (one value for each entry in roi_names)
Setup.preprocessing.roi_deriv : (functional_roiextract) list of 0/1/2 values (one value for each entry in roi_names): adds first- or second- order derivatives to each extracted timeseries
Setup.preprocessing.roi_filter : (functional_roiextract) list of 0/1 values (one value for each entry in roi_names): band-pass filter individual timeseries (filter specified in bp_filter field)
Setup.preprocessing.roi_detrend : (functional_roiextract) 1: detrends extracted BOLD timeseries [0]
Setup.preprocessing.roi_scale : (functional_roiextract) 1: scales extracted BOLD timeseries to PSC units (within each ROI) [1]
Setup.preprocessing.rtm : (functional_realign) 0: use first volume; 1: use mean volume [0]
Setup.preprocessing.rmask : (functional_realign) 1: applies implicit masking (voxels outside of field of view in >=1 image are set to NaN); [1]
Setup.preprocessing.sliceorder : (functional_slicetime) acquisition order (vector of indexes; 1=first slice in image; note: use cell array for subject-specific vectors) alternatively sliceorder may also be defined as one of the following strings: 'ascending', 'descending','interleaved (middle-top)','interleaved (bottom-up)','interleaved (top-down)', 'interleaved (Siemens)','interleaved (Philips)','BIDS' (this option reads slice timing information from .json files) alternatively sliceorder may also be defined as a vector containing the acquisition time in milliseconds for each slice (e.g. for multi-band sequences)
Setup.preprocessing.ta : (functional_slicetime) acquisition time (TA) in seconds (used to determine slice times when sliceorder is defined by a vector of slice indexes; note: use vector for subject-specific values). Defaults to (1-1/nslices)*TR where nslices is the number of slices
Setup.preprocessing.template_structural: (structural_normalize SPM8 only) anatomical template file for approximate coregistration [spm/template/T1.nii]
Setup.preprocessing.template_functional: (functional_normalize SPM8 only) functional template file for normalization [spm/template/EPI.nii]
Setup.preprocessing.tpm_template : (structural_segment, structural_segment&normalize in SPM8, and any segment/normalize option in SPM12) tissue probability map [spm/tpm/TPM.nii]
Setup.preprocessing.tpm_ngaus : (structural_segment, structural_segment&normalize in SPM8&SPM12) number of gaussians for each tissue probability map
Setup.preprocessing.tpm_structlesion : (structural_segment&normalize&lesion) name of ROI containing a structural-lesion mask (the lesion mask is expected to be coregistered with the structural, as part of structural normalization a new TPM template will be created with the lesion as an added tissue class)
Setup.preprocessing.vdm_et1 : (functional_vdm_create) ET1 (Echo Time first echo in fieldmap sequence) (default [] : read from .json file / BIDS)
Setup.preprocessing.vdm_et2 : (functional_vdm_create) ET2 (Echo Time second echo in fieldmap sequence) (default [] : read from .json file / BIDS)
Setup.preprocessing.vdm_ert : (functional_vdm_create) ERT (Effective Readout Time in funcional data) (default [] : read from .json file / BIDS)
Setup.preprocessing.vdm_blip : (functional_vdm_create) k-space traversal blip direction along the y-axis following SPM convention (i.e. positive for P>A, negative for A>P) use +1 or -1 to specify this value explicitly leave empty to read from .json file /BIDS PhaseEncodingDirection field and use the formula BLIP = sign([0 1 0 0]*vol.mat*[i j k 0]'; e.g. PhaseEncodingDirection='j+', mat=[-1 0 0;0 1 0;0 0 1] => BLIP=sign([0 1 0 0]*mat*[0 1 0 0])=+1 use 0 to reverse the sign from the above formula
Setup.preprocessing.vdm_type : (functional_vdm_create only) type of fieldmap sequence files ([]: automatically detect; 1: magnitude+phasediff (or magnitude1+magnitude2+phasediff); 2: real1+imag1+real2+imag2; 3: fieldmapHz)
Setup.preprocessing.vdm_fmap : (functional_vdm_create only) location of fieldmap sequence files (secondary functional dataset number or label containing fieldmap sequence files) ['fmap']
Setup.preprocessing.voxelsize_anat : (structural normalization) target voxel size for resliced volumes (mm) [2]
Setup.preprocessing.voxelsize_func : (functional normalization) target voxel size for resliced volumes (mm) [2]
Setup.preprocessing.sessions : defines functional sessions to preprocess [1:max # of sessions]
Setup.preprocessing.sets : defines functional dataset to preprocess (0 for Primary Dataset; [1-N] or labels for Secondary Datasets) [0]
BATCH.Denoising --------- PERFORMS DENOISING STEPS (confound removal & filtering)
BATCH.Denoising --------- PERFORMS DENOISING STEPS (confound removal & filtering)
Denoising Denoising.done : 1/0: 0 defines fields only; 1 runs DENOISING processing steps [0]
Denoising.overwrite : (for done=1) 1/0: overwrites target files if they exist [1]
Denoising.filter : vector with two elements specifying band pass filter: low-frequency & high-frequency cutoffs (Hz)
Denoising.detrending : 0/1/2/3: BOLD times-series polynomial detrending order (0: no detrending; 1: linear detrending; ... 3: cubic detrending)
Denoising.despiking : 0/1/2: temporal despiking with a hyperbolic tangent squashing function (1:before regression; 2:after regression) [0]
Denoising.regbp : 1/2: order of band-pass filtering step (1 = RegBP: regression followed by band-pass; 2 = Simult: simultaneous regression&band-pass) [1]
Denoising.confounds : Cell array of confound names (alternatively see below) Denoising.confounds : alternatively confounds can be a structure with fields Denoising.confounds.names : confounds.names{nconfound} char array of confound name (confound names can be: 'Grey Matter', 'White Matter','CSF',any ROI name, any covariate name, or 'Effect of *' where * represents any condition name]) note: use confounds.names={} to specify CONN's default set of confounds, or confounds.names={''} to indicate no confounds at all Denoising.confounds.dimensions : confounds.dimensions{nconfound} number of confound dimensions [defaults to using all dimensions available for each confound variable] Denoising.confounds.deriv : confounds.deriv{nconfound} include temporal derivatives up to n-th order of each effect (0 for raw timeseries, 1 for raw+firstderivative timeseries, etc.) [0|1] Denoising.confounds.power : confounds.power{nconfound} include powers up to n-th order of each effect (1 for linear effects, 2 for linear+quadratic effect, etc.) [1] Denoising.confounds.filter : (for regbp==1) confounds.filter{nconfound} band-pass filter confound regressors before entering in regression equation [0]
BATCH.Analysis ----------- PERFORMS FIRST-LEVEL ANALYSES (SBC/RRC/gPPI/temporal modulation)
BATCH.Analysis ----------- PERFORMS FIRST-LEVEL ANALYSES (SBC/RRC/gPPI/temporal modulation)
Analysis Analysis.done : 1/0: 0 defines fields only; 1 runs ANALYSIS processing steps [0]
Analysis.overwrite : (for done=1) 1/0: overwrites target files if they exist [1]
Analysis.name : analysis name (identifying each set of independent analysis) (alternatively sequential index identifying each set of independent analyses [1]) Analysis.measure : connectivity measure used, 1 = 'correlation (bivariate)', 2 = 'correlation (semipartial)', 3 = 'regression (bivariate)', 4 = 'regression (multivariate)'; [1]
Analysis.weight : within-condition weight, 1 = 'none', 2 = 'hrf', 3 = 'hanning'; [2]
Analysis.modulation : temporal modulation, 0 = standard weighted GLM analyses; 1 = gPPI analyses of condition-specific temporal modulation factor, or a string for PPI analyses of other temporal modulation factor (same for all conditions; valid strings are ROI names and 1st-level covariate names)'; [0]
Analysis.conditions : (for modulation==1 only) list of task condition names to be simultaneously entered in gPPI model (leave empty for default 'all existing conditions') []
Analysis.type : analysis type, 1 = 'ROI-to-ROI', 2 = 'Seed-to-Voxel', 3 = 'all'; [3]
Analysis.sources : Cell array of sources names (seeds) (source names can be: any ROI name) (if this variable does not exist the toolbox will perform the analyses for all of the existing ROIs which are not defined as confounds in the Denoising step) note: partial ROI name matches are accepted (e.g. using 'networks.Language' will match all ROIs starting with that token) Analysis.sources : alternatively sources can be a structure with fields Analysis.sources.names : sources.names{nsource} char array of source names (seeds) Analysis.sources.dimensions : sources.dimensions{nsource} number of source dimensions [1] Analysis.sources.deriv : sources.deriv{nsource} number of derivatives for each dimension [0] Analysis.sources.fbands : sources.fbands{nsource} number of frequency bands for each dimension [1]
BATCH.vvAnalysis -------- PERFORMS FIRST-LEVEL ANALYSES (ICA/fc-MVPA/PCA/IC/LCOR/GCOR/IHC/RCC/RSC/ALFF)
BATCH.vvAnalysis -------- PERFORMS FIRST-LEVEL ANALYSES (ICA/fc-MVPA/PCA/IC/LCOR/GCOR/IHC/RCC/RSC/ALFF)
vvAnalysis vvAnalysis.done : 1/0: 0 defines fields only; 1 runs ANALYSIS processing steps [0]
vvAnalysis.overwrite : (for done=1) 1/0: overwrites target files if they exist [1]
vvAnalysis.name : analysis name (identifying each set of independent analysis) (alternatively sequential index identifying each set of independent analyses [1]) vvAnalysis.measures : voxel-to-voxel measure name (type 'conn_v2v measurenames' for a list of default measures) (if this variable does not exist the toolbox will perform the analyses for all of the default voxel-to-voxel measures) 'group-PCA' : Principal Component Analysis of BOLD timeseries 'group-ICA' : Independent Component Analysis of BOLD timeseries 'group-MVPA'/'MCOR' : MultiVoxel Pattern Analysis of connectivity patterns (MCOR) 'IntrinsicConnectivity' : Intrinsic Connectivity Contrast (ICC) 'LocalCorrelation' : Integrated Local Correlation (ILC,LCOR) 'InterHemisphericCorrelation' : Inter-hemispheric Correlation (IHC) 'GlobalCorrelation' : Integrated Global Correlation (IGC,GCOR) 'RadialCorrelation' : Radial Correlation Contrast (RCC) 'RadialSimilarity' : Radial Similarity Contrast (RSC) 'ALFF' : Amplitude of Low Frequency Fluctuations 'fALFF' : fractional ALFF vvAnalysis.measures : alternatively voxel-to-voxel measures can be a structure with fields vvAnalysis.measures.names : measures.names voxel-to-voxel measure name (see above for a list of valid measure names) vvAnalysis.measures.factors : (for group-PCA, group-ICA, group-MVPA) number of group-level components to estimate vvAnalysis.measures.kernelsupport : (for ILC, RCC) local support (FWHM mm) of smoothing kernel [8] vvAnalysis.measures.norm : (for ILC,ICC,RCC,RSC,ALFF,fALFF) 0/1 normalize values to z-scores [1] vvAnalysis.measures.mask : (for group-PCA, group-ICA, group-MVPA) optional mask for group-level component estimation (e.g. masked ICA) vvAnalysis.measures.options : (for group-ICA) optional ICA method : string containing 'GICA1' or 'GICA3' for choice of ICA back- projection method; string containing 'tanh','gauss', or 'pow3' for ICA estimation method (G1/G2/G3) vvAnalysis.measures.dimensions : number of subject-level dimensions to retain (subject-level dimensionality reduction) [64]
BATCH.dynAnalysis ------ PERFORMS FIRST-LEVEL ANALYSES (dyn-ICA)
BATCH.dynAnalysis ------ PERFORMS FIRST-LEVEL ANALYSES (dyn-ICA)
dynAnalysis dynAnalysis.done : 1/0: 0 defines fields only; 1 runs ANALYSIS processing steps [0]
dynAnalysis.overwrite : (for done=1) 1/0: overwrites target files if they exist [1]
dynAnalysis.name : analysis name (identifying each set of independent analysis) (alternatively sequential index identifying each set of independent analyses [1]) dynAnalysis.sources : Cell array of sources names (seeds) (source names can be: any ROI name) (if this variable does not exist the toolbox will perform the analyses for all of the existing ROIs which are not defined as confounds in the Denoising step)
dynAnalysis.factors : Number of group-level dynamic components to estimate [20]
dynAnalysis.window : Length of temporal windows (FWHM in seconds) [30]
BATCH.Results ------------ PERFORMS SECOND-LEVEL ANALYSES (SBC/RRC/gPPI/temporal modulation)
BATCH.Results ------------ PERFORMS SECOND-LEVEL ANALYSES (SBC/RRC/gPPI/temporal modulation)
Results Results.done : 1/0: 0 defines fields only; 1 runs processing steps [0]
Results.overwrite : (for done=1) 1/0: overwrites target files if they exist [1]
Results.name : analysis name (identifying each set of first-level independent analysis) (alternatively sequential index identifying each set of first-level independent analyses [1])
Results.display : 1/0 display results [1]
Results.saveas : (optional) name to save between-subjects/between_conditions contrast
Results.foldername : (optional) alternative folder name to store the results Results.between_subjects Results.between_subjects.effect_names : cell array of second-level effect names Results.between_subjects.contrast : contrast vector (same size as effect_names) Results.between_conditions [defaults to multiple analyses, one per condition] Results.between_conditions.effect_names : cell array of condition names (as in Setup.conditions.names) Results.between_conditions.contrast : contrast vector (same size as effect_names) Results.between_sources [defaults to multiple analyses, one per source] Results.between_sources.effect_names : cell array of source names (as in Analysis.regressors, typically appended with _1_1; generally they are appended with _N_M -where N is an index ranging from 1 to 1+derivative order, and M is an index ranging from 1 to the number of dimensions specified for each ROI; for example ROINAME_2_3 corresponds to the first derivative of the third PCA component extracted from the roi ROINAME) Results.between_sources.contrast : contrast vector (same size as effect_names)
BATCH.vvResults --------- PERFORMS SECOND-LEVEL ANALYSES (ICA/fc-MVPA/PCA/IC/LCOR/GCOR/IHC/RCC/RSC/ALFF)
BATCH.vvResults --------- PERFORMS SECOND-LEVEL ANALYSES (ICA/fc-MVPA/PCA/IC/LCOR/GCOR/IHC/RCC/RSC/ALFF)
vvResults vvResults.done : 1/0: 0 defines fields only; 1 performs processing steps [0]
vvResults.overwrite : (for done=1) 1/0: overwrites target files if they exist [1]
vvResults.name : analysis name (identifying each set of independent analysis) (alternatively sequential index identifying each set of independent analyses [1])
vvResults.foldername : folder to store the results
vvResults.display : 1/0 display results [1]
vvResults.saveas : optional name to save between-subjects/between_conditions contrast vvResults.between_subjects vvResults.between_subjects.effect_names : cell array of second-level effect names vvResults.between_subjects.contrast : contrast vector (same size as effect_names) vvResults.between_conditions [defaults to multiple analyses, one per condition] vvResults.between_conditions.effect_names : cell array of condition names (as in Setup.conditions.names) vvResults.between_conditions.contrast : contrast vector (same size as effect_names) vvResults.between_measures [defaults to multiple analyses, one per measure] vvResults.between_measures.effect_names : cell array of measure names (as in Analysis.measures) vvResults.between_measures.contrast : contrast vector (same size as effect_names)
BATCH.QA ----------------- PERFORMS QUALITY ASSURANCE PLOTS
BATCH.QA ----------------- PERFORMS QUALITY ASSURANCE PLOTS
QA QA.foldername : output folder where QA plots will be stored [results/qa/QA_#date#]
QA.plots : list of QA plots to create (cell array of labels below) QA_NORM structural (1) : structural data + outline of MNI TPM template QA_NORM functional (2) : mean functional data + outline of MNI TPM template QA_NORM rois (3) : ROI data + outline of MNI TPM template QA_REG functional (10): display mean functional data + structural data overlay QA_REG structural (4) : structural data + outline of ROI QA_REG functional (5) : mean functional data + outline of ROI QA_REG mni (6) : reference MNI structural template + outline of ROI QA_COREG functional (7) : display same single-slice (z=0) across multiple sessions/datasets QA_TIME functional (8) : display same single-slice (z=0) across all timepoints within each session QA_TIMEART functional (9) : display same single-slice (z=0) across all timepoints within each session together with ART timeseries (global signal changes and framewise displacement) QA_DENOISE histogram (11) : histogram of voxel-to-voxel correlation values (before and after denoising) QA_DENOISE timeseries (12) : BOLD signal traces before and after denoising QA_DENOISE FC-QC (13) : histogram of FC-QC associations; between-subject correlation between QC (Quality Control) and FC (Functional Connectivity) measures QA_DENOISE scatterplot(14) : scatterplot of FC (Functional Connectivity r coeff) vs. distance (mm) QA_SPM design (21) : SPM review design matrix (from SPM.mat files only) QA_SPM contrasts (22) : SPM review contrast specification (from SPM.mat files only) QA_SPM results (23) : SPM review contrast effect-size (from SPM.mat files only) QA_COV (31) : histogram display of second-level variables QA.rois : (only for plots==3,4,5,6) ROI numbers to include (defaults to WM -roi#2-)
QA.sets : (only for plots==2,7,8,9,10) functional dataset number (defaults to dataset-0)
QA.l2covariates : (only for plots==13,31) l2 covariate names (defaults to all QC_*)
QA.l1contrasts : (only for plots==23) l1 contrast name (defaults to first contrast)
QA.conditions : (only for plots==11,13,14,15) FC & QC-FC plots aggregate across sessions where the selected conditions are present (defaults to all sessions) __________________________________________________________________________________________________________________ See conn_batch_workshop_nyu.m conn_batch_workshop_nyu_parallel.m conn_batch_humanconnectomeproject.m for additional information and examples of use.
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