For more information about how the rapidtide library can be used, please see the API page. Common rapidtide workflows can also be called from the command line.

Background

Before talking about the individual programs, in the 2.0 release and going forward, I’ve tried to adhere to some common principals, across all program, to make them easier to understand and maintain, and more interoperable with other programs, and to simplify using the outputs.

BIDS Outputs

By default, all outputs are in BIDS compatible formats (this is most true for rapidtide and happy, which get the majority of the work, but the goal is to eventually make all the programs in the package conform to this). The two major ramifications of this are that I have tried to follow BIDS naming conventions for NIFTI, json, and text files containing time series. Also, all text files are by default BIDS continuous timeseries files - data is in compressed, tab separated column format (.tsv.gz), with the column names, sample rate, and start time, in the accompanying .json sidecar file.

Text Inputs

A side effect of moving to BIDS is that I’ve now made a standardized interface for reading text data into programs in the package to handle many different types of file. In general, now if you are asked for a timeseries, you can supply it in any of the following ways:

A plain text file with one or more columns.

You can specify any subset of columns in any order by adding “:colspec” to the end of the filename. “colspec” is a column specification consisting of one or more comma separated “column ranges”. A “column range” is either a single column number or a hyphen separated minimum and maximum column number. The first column in a file is column 0.

For example specifying, “mytextfile.txt:5-6,2,0,10-12”

would return an array containing all the timepoints from columns 5, 6, 2, 0, 10, 11, and 12 from mytextfile.txt, in that order. Not specifying “:colspec” returns all the columns in the file, in order.

If the program in question requires the actual sample rate, this can be specified using the --samplerate or --sampletime flags. Otherwise 1.0Hz is assumed.

A BIDS continuous file with one or more columns.

BIDS files have names for each column, so these are used in column specification. For these files, “colspec” is a comma separated list of one or more column names:

“thefile_desc-interestingtimeseries_physio.json:cardiac,respiration”

would return the two named columns “cardiac” and “respiration” from the accompanying .tsv.gz file. Not specifying “:colspec” returns all the columns in the file, in order.

Because BIDS continuous files require sample rate and start time to be specified in the sidecar file, these quantities will now already be set. Using the --samplerate, --sampletime or --starttime flags will override any header values, if specified.

Visualizing files

Any output NIFTI file can be visualized in your favorite NIFTI viewer. I like FSLeyes, part of FSL. It’s flexible and fast, and has lots of options for displaying 3 and 4D NIFTI files.

While there may be nice, general graphing tools for BIDS timeseries files, I wrote “showtc” many years ago, a matplotlib based file viewer with lots of nice tweaks to make pretty and informative graphs of various rapidtide input and output files. It’s part of rapidtide, and pretty easy to learn. Just type “showtc” with no arguments to get the options.

As an example, after running happy, if you want to see the derived cardiac waveform, you’d run:

showtc \
    happytest_desc-slicerescardfromfmri_timeseries.json:cardiacfromfmri,cardiacfromfmri_dlfiltered \
    --format separate

rapidtide

Description:

The central program in this package is rapidtide. This is the program that calculates a similarity function between a “probe” signal and every voxel of a BOLD fMRI dataset. It then determines the peak value, time delay, and wi dth of the similarity function to determine when and how strongly that probe signal appears in each voxel.

At its core, rapidtide is simply performing a full crosscorrelation between a “probe” timecourse and every voxel in an fMRI dataset (by “full” I mean over a range of time lags that account for any delays between the signals, rather than only at zero lag, as in a Pearson correlation). As with many things, however, the devil is in the details, and so rapidtide provides a number of features which make it pretty good at this particular task. A few highlights:

  • There are lots of ways to do something even as simple as a cross-correlation in a nonoptimal way (not windowing, improper normalization, doing it in the time rather than frequency domain, etc.). I’m pretty sure what rapidtide does by default is, if not the best way, at least a very good and very fast way.

  • rapidtide has been optimized and profiled to speed it up quite a bit; it has an optional dependency on numba – if it’s installed, some of the most heavily used routines will speed up significantly due to judicious use of @jit.

  • The sample rate of your probe regressor and the fMRI data do not have to match - rapidtide resamples the probe regressor to an integral multiple of the fMRI data rate automatically.

  • The probe and data can be temporally prefiltered to the LFO, respiratory, or cardiac frequency band with a command line switch, or you can specify any low, high, or bandpass range you want.

  • The data can be spatially smoothed at runtime (so you don’t have to keep smoothed versions of big datasets around). This is quite fast, so no reason not to do it this way.

  • rapidtide can generate a probe regressor from the global mean of the data itself - no externally recorded timecourse is required. Optionally you can input both a mask of regions that you want to be included in the mean, and the voxels that you want excluded from the mean (there are situations when you might want to do one or the other or both).

  • Determining the significance threshold for filtered correlations where the optimal delay has been selected is nontrivial; using the conventional formulae for the significance of a correlation leads to wildly inflated p values. rapidtide estimates the spurious correlation threshold by calculating the distribution of null correlation values obtained with a shuffling procedure at the beginning of each run (the default is to use 10000 shuffled correlations), and uses this value to mask the correlation maps it calculates. As of version 0.1.2 it will also handle two-tailed significance, which you need when using bipolar mode.

  • rapidtide can do an iterative refinement of the probe regressor by aligning the voxel timecourses in time and regenerating the test regressor.

  • rapidtide fits the peak of the correlation function, so you can make fine grained distinctions between close lag times. The resolution of the time lag discrimination is set by the length of the timecourse, not the timestep – this is a feature of correlations, not rapidtide.

  • Once the time delay in each voxel has been found, rapidtide outputs a 4D file of delayed probe regressors for using as voxel specific confound regressors or to estimate the strength of the probe regressor in each voxel. This regression is performed by default, but these outputs let you do it yourself if you are so inclined.

  • I’ve put a lot of effort into making the outputs as informative as possible - lots of useful maps, histograms, timecourses, etc.

  • There are a lot of tuning parameters you can mess with if you feel the need. I’ve tried to make intelligent defaults so things will work well out of the box, but you have the ability to set most of the interesting parameters yourself.

Inputs:

At a minimum, rapidtide needs a data file to work on (space by time), which is generally thought to be a BOLD fMRI data file. This can be Nifti1 or Nifti2 (for fMRI data, in which case it is time by up to 3 spatial dimensions) or a whitespace separated text file (for NIRS data, each column is a time course, each row a separate channel); I can currently read (probably) but not write Cifti files, so if you want to use grayordinate files you need to convert them to nifti2 in workbench, run rapidtide, then convert back. As soon as nibabel finishes their Cifti support (EDIT: and I get around to figuring it out), I’ll add that.

The file needs one time dimension and at least one spatial dimension. Internally, the array is flattened to a time by voxel array for simplicity.

The file you input here should be the result of any preprocessing you intend to do. The expectation is that rapidtide will be run as the last preprocessing step before resting state or task based analysis. So any slice time correction, motion correction, spike removal, etc. should already have been done. If you use FSL, this means that if you’ve run preprocessing, you would use the filtered_func_data.nii.gz file as input. Temporal and spatial filtering are the two (partial) exceptions here. Generally rapidtide is most useful for looking at low frequency oscillations, so when you run it, you usually use the --filterband lfo option or some other to limit the analysis to the detection and removal of low frequency systemic physiological oscillations. So rapidtide will generally apply it’s own temporal filtering on top of whatever you do in preprocessing. Also, you have the option of doing spatial smoothing in rapidtide to boost the SNR of the analysis; the hemodynamic signals rapidtide looks for are often very smooth, so you rather than smooth your functional data excessively, you can do it within rapidtide so that only the hemodynamic data is smoothed at that level.

Outputs:

Outputs are space or space by time Nifti or text files, depending on what the input data file was, and some text files containing textual information, histograms, or numbers. File formats and naming follow BIDS conventions for derivative data for fMRI input data. Output spatial dimensions and file type match the input dimensions and file type (Nifti1 in, Nifti1 out). Depending on the file type of map, there can be no time dimension, a time dimension that matches the input file, or something else, such as a time lag dimension for a correlation map.

BIDS Outputs:

Name

Extension(s)

Content

When present

XXX_maxtime_map

.nii.gz, .json

Time of offset of the maximum of the similarity function

Always

XXX_desc-maxtime_hist

.tsv, .json

Histogram of the maxtime map

Always

XXX_maxcorr_map

.nii.gz, .json

Maximum similarity function value (usually the correlation coefficient, R)

Always

XXX_desc-maxcorr_hist

.tsv, .json

Histogram of the maxcorr map

Always

XXX_maxcorrsq_map

.nii.gz, .json

Maximum similarity function value, squared

Always

XXX_desc-maxcorrsq_hist

.tsv, .json

Histogram of the maxcorrsq map

Always

XXX_maxwidth_map

.nii.gz, .json

Width of the maximum of the similarity function

Always

XXX_desc-maxwidth_hist

.tsv, .json

Histogram of the maxwidth map

Always

XXX_MTT_map

.nii.gz, .json

Mean transit time (estimated)

Always

XXX_corrfit_mask

.nii.gz

Mask showing where the similarity function fit succeeded

Always

XXX_corrfitfailreason_map

.nii.gz, .json

A numerical code giving the reason a peak could not be found (0 if fit succeeded)

Always

XXX_desc-corrfitwindow_info

.nii.gz

Values used for correlation peak fitting

Always

XXX_desc-runoptions_info

.json

A detailed dump of all internal variables in the program. Useful for debugging and data provenance

Always

XXX_desc-lfofilterCleaned_bold

.nii.gz, .json

Filtered BOLD dataset after removing moving regressor

If GLM filtering is enabled (default)

XXX_desc-lfofilterRemoved_bold

.nii.gz, .json

Scaled, voxelwise delayed moving regressor that has been removed from the dataset

If GLM filtering is enabled (default) and --nolimitoutput is selected

XXX_desc-lfofilterCoeff_map

.nii.gz

Magnitude of the delayed sLFO regressor from GLM filter

If GLM filtering is enabled (default)

XXX_desc-lfofilterMean_map

.nii.gz

Mean value over time, from GLM fit

If GLM filtering is enabled (default)

XXX_desc-lfofilterNorm_map

.nii.gz

GLM filter coefficient, divided by the voxel mean over time

If GLM filtering is enabled (default)

XXX_desc-lfofilterR2_map

.nii.gz

R value for the GLM fit in the voxel, squared

If GLM filtering is enabled (default)

XXX_desc-lfofilterR_map

.nii.gz

R value for the GLM fit in the voxel

If GLM filtering is enabled (default)

XXX_desc-processed_mask

.nii.gz

Mask of all voxels in which the similarity function is calculated

Always

XXX_desc-globalmean_mask

.nii.gz

Mask of voxels used to calculate the global mean signal

This file will exist if no external regressor is specified

XXX_desc-refine_mask

.nii.gz

Mask of voxels used in the last estimate a refined version of the probe regressor

Present if passes > 1

XXX_desc-despeckle_mask

.nii.gz

Mask of the last set of voxels that had their time delays adjusted due to autocorrelations in the probe regressor

Present if despecklepasses > 0

XXX_desc-corrout_info

.nii.gz

Full similarity function over the search range

Always

XXX_desc-gaussout_info

.nii.gz

Gaussian fit to similarity function peak over the search range

Always

XXX_desc-autocorr_timeseries

.tsv, .json

Autocorrelation of the probe regressor for each pass

Always

XXX_desc-corrdistdata_info

.tsv, .json

Null correlations from the significance estimation for each pass

Present if --numnull > 0

XXX_desc-nullsimfunc_hist

.tsv, .json

Histogram of the distribution of null correlation values for each pass

Present if --numnull > 0

XXX_desc-plt0p050_mask

.nii.gz

Voxels where the maxcorr value exceeds the p < 0.05 significance level

Present if --numnull > 0

XXX_desc-plt0p010_mask

.nii.gz

Voxels where the maxcorr value exceeds the p < 0.01 significance level

Present if --numnull > 0

XXX_desc-plt0p005_mask

.nii.gz

Voxels where the maxcorr value exceeds the p < 0.005 significance level

Present if --numnull > 0

XXX_desc-plt0p001_mask

.nii.gz

Voxels where the maxcorr value exceeds the p < 0.001 significance level

Present if --numnull > 0

XXX_desc-globallag_hist

.tsv, .json

Histogram of peak correlation times between probe and all voxels, over all time lags, for each pass

Always

XXX_desc-initialmovingregressor_timeseries

.tsv, .json

The raw and filtered initial probe regressor, at the original sampling resolution

Always

XXX_desc-movingregressor_timeseries

.tsv, .json

The probe regressor used in each pass, at the time resolution of the data

Always

XXX_desc-oversampledmovingregressor_timeseries

.tsv, .json

The probe regressor used in each pass, at the time resolution used for calculating the similarity function

Always

XXX_desc-refinedmovingregressor_timeseries

.tsv, .json

The raw and filtered probe regressor produced by the refinement procedure, at the time resolution of the data

Present if passes > 1

Usage:

Perform a RIPTiDe time delay analysis on a dataset.

usage: rapidtide [-h] [--denoising | --delaymapping] [--venousrefine | --nirs]
                 [--datatstep TSTEP | --datafreq FREQ] [--noantialias]
                 [--invert] [--interptype {univariate,cubic,quadratic}]
                 [--offsettime OFFSETTIME] [--autosync]
                 [--filterband {None,vlf,lfo,resp,cardiac,lfo_legacy}]
                 [--filterfreqs LOWERPASS UPPERPASS]
                 [--filterstopfreqs LOWERSTOP UPPERSTOP]
                 [--filtertype {trapezoidal,brickwall,butterworth}]
                 [--butterorder ORDER] [--padseconds SECONDS]
                 [--permutationmethod {shuffle,phaserandom}] [--numnull NREPS]
                 [--skipsighistfit]
                 [--windowfunc {hamming,hann,blackmanharris,None}]
                 [--nowindow] [--zeropadding PADVAL] [--detrendorder ORDER]
                 [--spatialfilt GAUSSSIGMA] [--globalmean]
                 [--globalmaskmethod {mean,variance}] [--globalpreselect]
                 [--globalmeaninclude MASK[:VALSPEC]]
                 [--globalmeanexclude MASK[:VALSPEC]] [--motionfile MOTFILE]
                 [--motpos] [--motderiv] [--motdelayderiv]
                 [--globalsignalmethod {sum,meanscale,pca}]
                 [--globalpcacomponents VALUE] [--slicetimes FILE]
                 [--numskip SKIP] [--timerange START END] [--nothresh]
                 [--oversampfac OVERSAMPFAC] [--regressor FILE]
                 [--regressorfreq FREQ | --regressortstep TSTEP]
                 [--regressorstart START]
                 [--corrweighting {None,phat,liang,eckart}]
                 [--corrmaskthresh PCT | --corrmask MASK[:VALSPEC]]
                 [--similaritymetric {correlation,mutualinfo,hybrid}]
                 [--mutualinfosmoothingtime TAU]
                 [--fixdelay DELAYTIME | --searchrange LAGMIN LAGMAX]
                 [--sigmalimit SIGMALIMIT] [--bipolar] [--nofitfilt]
                 [--peakfittype {gauss,fastgauss,quad,fastquad,COM,None}]
                 [--despecklepasses PASSES] [--despecklethresh VAL]
                 [--refineprenorm {None,mean,var,std,invlag}]
                 [--refineweighting {None,NIRS,R,R2}] [--passes PASSES]
                 [--refineinclude MASK[:VALSPEC]]
                 [--refineexclude MASK[:VALSPEC]] [--norefinedespeckled]
                 [--lagminthresh MIN] [--lagmaxthresh MAX] [--ampthresh AMP]
                 [--sigmathresh SIGMA] [--norefineoffset] [--psdfilter]
                 [--pickleft] [--pickleftthresh THRESH]
                 [--refineupperlag | --refinelowerlag]
                 [--refinetype {pca,ica,weighted_average,unweighted_average}]
                 [--pcacomponents VALUE] [--convergencethresh THRESH]
                 [--maxpasses MAXPASSES] [--nolimitoutput] [--savelags]
                 [--histlen HISTLEN] [--glmsourcefile FILE] [--noglm]
                 [--preservefiltering] [--saveintermediatemaps]
                 [--legacyoutput] [--calccoherence] [--noprogressbar]
                 [--checkpoint] [--wiener] [--spcalculation] [--dpoutput]
                 [--cifti] [--simulate] [--displayplots] [--nonumba]
                 [--nosharedmem] [--memprofile] [--mklthreads MKLTHREADS]
                 [--nprocs NPROCS] [--version] [--echocancel] [--respdelete]
                 [--negativegradient] [--cleanrefined] [--dispersioncalc]
                 [--acfix] [--tmask FILE] [--debug] [--verbose]
                 [--alwaysmultiproc] [--singleproc_getNullDist]
                 [--singleproc_calcsimilarity] [--singleproc_peakeval]
                 [--singleproc_fitcorr] [--singleproc_glm]
                 in_file outputname

Positional Arguments

in_file

The input data file (BOLD fMRI file or NIRS text file).

outputname

The root name for the output files. For BIDS compliance, this can only contain valid BIDS entities from the source data.

Named Arguments

--permutationmethod

Possible choices: shuffle, phaserandom

Permutation method for significance testing. Default is “shuffle”.

Default: “shuffle”

--numnull

Estimate significance threshold by running NREPS null correlations (default is 10000, set to 0 to disable).

Default: 10000

--skipsighistfit

Do not fit significance histogram with a Johnson SB function.

Default: True

Analysis type

Single arguments that change default values for many arguments. Any parameter set by an analysis type can be overridden by setting that parameter explicitly. Analysis types are mutually exclusive with one another.

--denoising

Preset for hemodynamic denoising - this is a macro that sets lagmin=-10.0, lagmax=10.0, passes=3, despeckle_passes=4, refineoffset=True, peakfittype=gauss, doglmfilt=True. Any of these options can be overridden with the appropriate additional arguments.

Default: False

--delaymapping

Preset for delay mapping analysis - this is a macro that sets lagmin=-10.0, lagmax=30.0, passes=3, despeckle_passes=4, refineoffset=True, pickleft=True, limitoutput=True, doglmfilt=False. Any of these options can be overridden with the appropriate additional arguments.

Default: False

Macros

Single arguments that change default values for many arguments. Macros override individually set parameters. Macros are mutually exclusive with one another.

--venousrefine

This is a macro that sets –lagminthresh=2.5, –lagmaxthresh=6.0, –ampthresh=0.5, and –refineupperlag to bias refinement towards voxels in the draining vasculature for an fMRI scan.

Default: False

--nirs

This is a NIRS analysis - this is a macro that sets –nothresh, –preservefiltering, –refineprenorm=var, –ampthresh=0.7, and –lagminthresh=0.1.

Default: False

Preprocessing options

--datatstep

Set the timestep of the data file to TSTEP. This will override the TR in an fMRI file. NOTE: if using data from a text file, for example with NIRS data, using one of these options is mandatory.

Default: auto

--datafreq

Set the timestep of the data file to 1/FREQ. This will override the TR in an fMRI file. NOTE: if using data from a text file, for example with NIRS data, using one of these options is mandatory.

Default: auto

--noantialias

Disable antialiasing filter.

Default: True

--invert

Invert the sign of the regressor before processing.

Default: False

--interptype

Possible choices: univariate, cubic, quadratic

Use specified interpolation type. Options are “cubic”, “quadratic”, and “univariate”. Default is univariate.

Default: “univariate”

--offsettime

Apply offset OFFSETTIME to the lag regressors.

Default: 0.0

--autosync

Estimate and apply the initial offsettime of an external regressor using the global crosscorrelation. Overrides offsettime if present.

Default: False

--detrendorder

Set order of trend removal (0 to disable). Default is 3.

Default: 3

--spatialfilt

Spatially filter fMRI data prior to analysis using GAUSSSIGMA in mm. Set GAUSSSIGMA negative to have rapidtide set it to half the mean voxel dimension (a rule of thumb for a good value).

Default: 0.0

--globalmean

Generate a global mean regressor and use that as the reference regressor. If no external regressor is specified, this is enabled by default.

Default: False

--globalmaskmethod

Possible choices: mean, variance

Select whether to use timecourse mean or variance to mask voxels prior to generating global mean. Default is “mean”.

Default: “mean”

--globalpreselect

Treat this run as an initial pass to locate good candidate voxels for global mean regressor generation.

Default: False

--globalmeaninclude

Only use voxels in mask file NAME for global regressor generation (if VALSPEC is given, only voxels with integral values listed in VALSPEC are used).

--globalmeanexclude

Do not use voxels in mask file NAME for global regressor generation (if VALSPEC is given, only voxels with integral values listed in VALSPEC are excluded).

--motionfile

Read 6 columns of motion regressors out of MOTFILE file (.par or BIDS .json) (with timepoints rows) and regress their derivatives and delayed derivatives out of the data prior to analysis.

--motpos

Toggle whether displacement regressors will be used in motion regression. Default is False.

Default: False

--motderiv

Toggle whether derivatives will be used in motion regression. Default is True.

Default: True

--motdelayderiv

Toggle whether delayed derivative regressors will be used in motion regression. Default is False.

Default: False

--globalsignalmethod

Possible choices: sum, meanscale, pca

The method for constructing the initial global signal regressor - straight summation, mean scaling each voxel prior to summation, or MLE PCA of the voxels in the global signal mask. Default is “sum.”

Default: “sum”

--globalpcacomponents

Number of PCA components used for estimating the global signal. If VALUE >= 1, will retain thismany components. If 0.0 < VALUE < 1.0, enough components will be retained to explain the fraction VALUE of the total variance. If VALUE is negative, the number of components will be to retain will be selected automatically using the MLE method. Default is 0.8.

Default: 0.8

--slicetimes

Apply offset times from FILE to each slice in the dataset.

--numskip

SKIP TRs were previously deleted during preprocessing (e.g. if you have done your preprocessing in FSL and set dummypoints to a nonzero value.) Default is 0.

Default: 0

--timerange

Limit analysis to data between timepoints START and END in the fmri file. If END is set to -1, analysis will go to the last timepoint. Negative values of START will be set to 0. Default is to use all timepoints.

Default: (-1, -1)

--nothresh

Disable voxel intensity threshold (especially useful for NIRS data).

Default: False

Filtering options

--filterband

Possible choices: None, vlf, lfo, resp, cardiac, lfo_legacy

Filter data and regressors to specific band. Use “None” to disable filtering. Default is “lfo”.

Default: “lfo”

--filterfreqs

Filter data and regressors to retain LOWERPASS to UPPERPASS. If –filterstopfreqs is not also specified, LOWERSTOP and UPPERSTOP will be calculated automatically.

--filterstopfreqs

Filter data and regressors to with stop frequencies LOWERSTOP and UPPERSTOP. LOWERSTOP must be <= LOWERPASS, UPPERSTOP must be >= UPPERPASS. Using this argument requires the use of –filterfreqs.

--filtertype

Possible choices: trapezoidal, brickwall, butterworth

Filter data and regressors using a trapezoidal FFT, brickwall FFT, or butterworth bandpass filter. Default is “trapezoidal”.

Default: “trapezoidal”

--butterorder

Set order of butterworth filter (if used). Default is 6.

Default: 6

--padseconds

The number of seconds of padding to add to each end of a filtered timecourse to reduce end effects. Default is 30.0.

Default: 30.0

Windowing options

--windowfunc

Possible choices: hamming, hann, blackmanharris, None

Window function to use prior to correlation. Options are hamming, hann, blackmanharris, and None. Default is hamming

Default: “hamming”

--nowindow

Disable precorrelation windowing.

Default: “hamming”

--zeropadding

Pad input functions to correlation with PADVAL zeros on each side. A PADVAL of 0 does circular correlations, positive values reduce edge artifacts. Set PADVAL < 0 to set automatically. Default is 0.

Default: 0

Correlation options

--oversampfac

Oversample the fMRI data by the following integral factor. Set to -1 for automatic selection (default).

Default: -1

--regressor

Read the initial probe regressor from file FILE (if not specified, generate and use the global regressor).

--regressorfreq

Probe regressor in file has sample frequency FREQ (default is 1/tr) NB: –regressorfreq and –regressortstep) are two ways to specify the same thing.

Default: auto

--regressortstep

Probe regressor in file has sample frequency FREQ (default is 1/tr) NB: –regressorfreq and –regressortstep) are two ways to specify the same thing.

Default: auto

--regressorstart

The time delay in seconds into the regressor file, corresponding in the first TR of the fMRI file (default is 0.0).

Default: 0.0

--corrweighting

Possible choices: None, phat, liang, eckart

Method to use for cross-correlation weighting. Default is “None”.

Default: “None”

--corrmaskthresh

Do correlations in voxels where the mean exceeds this percentage of the robust max. Default is 1.0.

Default: 1.0

--corrmask

Only do correlations in nonzero voxels in NAME (if VALSPEC is given, only voxels with integral values listed in VALSPEC are used).

--similaritymetric

Possible choices: correlation, mutualinfo, hybrid

Similarity metric for finding delay values. Choices are “correlation”, “mutualinfo”, and “hybrid”. Default is correlation.

Default: “correlation”

--mutualinfosmoothingtime

Time constant of a temporal smoothing function to apply to the mutual information function. Default is 3.0 seconds. TAU <=0.0 disables smoothing.

Default: 3.0

Correlation fitting options

--fixdelay

Don’t fit the delay time - set it to DELAYTIME seconds for all voxels.

--searchrange

Limit fit to a range of lags from LAGMIN to LAGMAX. Default is -30.0 to 30.0 seconds.

Default: (-30.0, 30.0)

--sigmalimit

Reject lag fits with linewidth wider than SIGMALIMIT Hz. Default is 1000.0 Hz.

Default: 1000.0

--bipolar

Bipolar mode - match peak correlation ignoring sign.

Default: False

--nofitfilt

Do not zero out peak fit values if fit fails.

Default: True

--peakfittype

Possible choices: gauss, fastgauss, quad, fastquad, COM, None

Method for fitting the peak of the similarity function “gauss” performs a Gaussian fit, and is most accurate. “quad” and “fastquad” use a quadratic fit, which is faster, but not as well tested. Default is “gauss”.

Default: “gauss”

--despecklepasses

Detect and refit suspect correlations to disambiguate peak locations in PASSES passes. Default is to perform 4 passes. Set to 0 to disable.

Default: 4

--despecklethresh

Refit correlation if median discontinuity magnitude exceeds VAL. Default is 5.0 seconds.

Default: 5.0

Regressor refinement options

--refineprenorm

Possible choices: None, mean, var, std, invlag

Apply TYPE prenormalization to each timecourse prior to refinement. Default is “mean”.

Default: “mean”

--refineweighting

Possible choices: None, NIRS, R, R2

Apply TYPE weighting to each timecourse prior to refinement. Default is “R2”.

Default: “R2”

--passes

Set the number of processing passes to PASSES. Default is 3.

Default: 3

--refineinclude

Only use voxels in file MASK for regressor refinement (if VALSPEC is given, only voxels with integral values listed in VALSPEC are used).

--refineexclude

Do not use voxels in file MASK for regressor refinement (if VALSPEC is given, voxels with integral values listed in VALSPEC are excluded).

--norefinedespeckled

Do not use despeckled pixels in calculating the refined regressor.

Default: True

--lagminthresh

For refinement, exclude voxels with delays less than MIN. Default is 0.5 seconds.

Default: 0.5

--lagmaxthresh

For refinement, exclude voxels with delays greater than MAX. Default is 5.0 seconds.

Default: 5.0

--ampthresh

For refinement, exclude voxels with correlation coefficients less than AMP (default is 0.3). NOTE: ampthresh will automatically be set to the p<0.05 significance level determined by the –numnull option if NREPS is set greater than 0 and this is not manually specified.

Default: -1.0

--sigmathresh

For refinement, exclude voxels with widths greater than SIGMA seconds. Default is 100.0 seconds.

Default: 100.0

--norefineoffset

Disable realigning refined regressor to zero lag.

Default: True

--psdfilter

Apply a PSD weighted Wiener filter to shifted timecourses prior to refinement.

Default: False

--pickleft

Will select the leftmost delay peak when setting the refine offset.

Default: False

--pickleftthresh

Threshhold value (fraction of maximum) in a histogram to be considered the start of a peak. Default is 0.33.

Default: 0.33

--refineupperlag

Only use positive lags for regressor refinement.

Default: “both”

--refinelowerlag

Only use negative lags for regressor refinement.

Default: “both”

--refinetype

Possible choices: pca, ica, weighted_average, unweighted_average

Method with which to derive refined regressor. Default is “pca”.

Default: “pca”

--pcacomponents

Number of PCA components used for refinement. If VALUE >= 1, will retain this many components. If 0.0 < VALUE < 1.0, enough components will be retained to explain the fraction VALUE of the total variance. If VALUE is negative, the number of components will be to retain will be selected automatically using the MLE method. Default is 0.8.

Default: 0.8

--convergencethresh

Continue refinement until the MSE between regressors becomes <= THRESH. By default, this is not set, so refinement will run for the specified number of passes.

--maxpasses

Terminate refinement after MAXPASSES passes, whether or not convergence has occured. Default is 15.

Default: 15

Output options

--nolimitoutput

Save some of the large and rarely used files.

Default: True

--savelags

Save a table of lagtimes used.

Default: False

--histlen

Change the histogram length to HISTLEN. Default is 101.

Default: 101

--glmsourcefile

Regress delayed regressors out of FILE instead of the initial fmri file used to estimate delays.

--noglm

Turn off GLM filtering to remove delayed regressor from each voxel (disables output of fitNorm).

Default: True

--preservefiltering

Don’t reread data prior to performing GLM.

Default: False

--saveintermediatemaps

Save lag times, strengths, widths, and mask for each pass.

Default: False

--legacyoutput

Use legacy file naming and formats rather than BIDS naming and format conventions for output files.

Default: True

--calccoherence

Calculate and save the coherence between the final regressor and the data.

Default: False

Miscellaneous options

--noprogressbar

Will disable showing progress bars (helpful if stdout is going to a file).

Default: True

--checkpoint

Enable run checkpoints.

Default: False

--wiener

Do Wiener deconvolution to find voxel transfer function.

Default: False

--spcalculation

Use single precision for internal calculations (may be useful when RAM is limited).

Default: “double”

--dpoutput

Use double precision for output files.

Default: “single”

--cifti

Data file is a converted CIFTI.

Default: False

--simulate

Simulate a run - just report command line options.

Default: False

--displayplots

Display plots of interesting timecourses.

Default: False

--nonumba

Disable jit compilation with numba.

Default: False

--nosharedmem

Disable use of shared memory for large array storage.

Default: True

--memprofile

Enable memory profiling - warning: this slows things down a lot.

Default: False

--mklthreads

Use no more than MKLTHREADS worker threads in accelerated numpy calls.

Default: 1

--nprocs

Use NPROCS worker processes for multiprocessing. Setting NPROCS to less than 1 sets the number of worker processes to n_cpus - 1.

Default: 1

--version

Print version information and exit.

Default: False

Experimental options (not fully tested, may not work)

--echocancel

Attempt to perform echo cancellation.

Default: False

--respdelete

Attempt to detect and remove respiratory signal that strays into the LFO band.

Default: False

--negativegradient

Calculate the negative gradient of the fmri data after spectral filtering so you can look for CSF flow à la https://www.biorxiv.org/content/10.1101/2021.03.29.437406v1.full.

Default: False

--cleanrefined

Perform additional processing on refined regressor to remove spurious components.

Default: False

--dispersioncalc

Generate extra data during refinement to allow calculation of dispersion.

Default: False

--acfix

Perform a secondary correlation to disambiguate peak location. Experimental.

Default: False

--tmask

Only correlate during epochs specified in MASKFILE (NB: each line of FILE contains the time and duration of an epoch to include.

Debugging options. You probably don’t want to use any of these unless I ask you to to help diagnose a problem

--debug

Enable additional debugging output.

Default: False

--verbose

Enable additional runtime information output.

Default: False

--alwaysmultiproc

Use the multiprocessing code path even when nprocs=1.

Default: False

--singleproc_getNullDist

Force single proc path for getNullDist.

Default: False

--singleproc_calcsimilarity

Force single proc path for calcsimilarity.

Default: False

--singleproc_peakeval

Force single proc path for peakeval.

Default: False

--singleproc_fitcorr

Force single proc path for fitcorr.

Default: False

--singleproc_glm

Force single proc path for glm.

Default: False

Legacy interface:

For compatibility with old workflows, rapidtide can be called using legacy syntax by using “rapidtide2x_legacy”. Although the underlying code is the same, not all options are settable from the legacy interface. This interface is deprecated and will be removed in a future version of rapidtide, so please convert existing workflows.

usage:  rapidtide2x_legacy  datafilename outputname
[-r LAGMIN,LAGMAX] [-s SIGMALIMIT] [-a] [--nowindow] [--phat] [--liang] [--eckart] [-f GAUSSSIGMA] [-O oversampfac] [-t TSTEP] [--datatstep=TSTEP] [--datafreq=FREQ] [-d] [-b] [-V] [-L] [-R] [-C] [-F LOWERFREQ,UPPERFREQ[,LOWERSTOP,UPPERSTOP]] [-o OFFSETTIME] [--autosync] [-T] [-p] [-P] [-B] [-h HISTLEN] [-i INTERPTYPE] [-I] [-Z DELAYTIME] [--nofitfilt] [--searchfrac=SEARCHFRAC] [-N NREPS] [--motionfile=MOTFILE] [--pickleft] [--numskip=SKIP] [--refineweighting=TYPE] [--refineprenorm=TYPE] [--passes=PASSES] [--refinepasses=PASSES] [--excluderefine=MASK] [--includerefine=MASK] [--includemean=MASK] [--excludemean=MASK][--lagminthresh=MIN] [--lagmaxthresh=MAX] [--ampthresh=AMP] [--sigmathresh=SIGMA] [--corrmask=MASK] [--corrmaskthresh=PCT] [--refineoffset] [--pca] [--ica] [--weightedavg] [--avg] [--psdfilter] [--noprogressbar] [--despecklethresh=VAL] [--despecklepasses=PASSES] [--dispersioncalc] [--refineupperlag] [--refinelowerlag] [--nosharedmem] [--tmask=MASKFILE] [--limitoutput] [--motionfile=FILENAME[:COLSPEC] [--softlimit] [--timerange=START,END] [--skipsighistfit] [--accheck] [--acfix][--numskip=SKIP] [--slicetimes=FILE] [--glmsourcefile=FILE] [--regressorfreq=FREQ] [--regressortstep=TSTEP][--regressor=FILENAME] [--regressorstart=STARTTIME] [--usesp] [--peakfittype=FITTYPE] [--mklthreads=NTHREADS] [--nprocs=NPROCS] [--nirs] [--venousrefine]

Required arguments:
    datafilename               - The input data file (BOLD fmri file or NIRS)
    outputname                 - The root name for the output files

Optional arguments:
    Arguments are processed in order of appearance.  Later options can override ones earlier on
    the command line

Macros:
    --venousrefine                 - This is a macro that sets --lagminthresh=2.5, --lagmaxthresh=6.0,
                                     --ampthresh=0.5, and --refineupperlag to bias refinement towards
                                     voxels in the draining vasculature for an fMRI scan.
    --nirs                         - This is a NIRS analysis - this is a macro that sets --nothresh,
                                     --preservefiltering, --refinenorm=var, --ampthresh=0.7,
                                     and --lagminthresh=0.1.

Preprocessing options:
    -t TSTEP,                      - Set the timestep of the data file to TSTEP (or 1/FREQ)
      --datatstep=TSTEP,             This will override the TR in an fMRI file.
      --datafreq=FREQ                NOTE: if using data from a text file, for example with
                                     NIRS data, using one of these options is mandatory.
    -a                             - Disable antialiasing filter
    --detrendorder=ORDER           - Set order of trend removal (0 to disable, default is 1 - linear)
    -I                             - Invert the sign of the regressor before processing
    -i                             - Use specified interpolation type (options are 'cubic',
                                     'quadratic', and 'univariate (default)')
    -o                             - Apply an offset OFFSETTIME to the lag regressors
    --autosync                     - Calculate and apply offset time of an external regressor from
                                     the global crosscorrelation.  Overrides offsettime if specified.
    -b                             - Use butterworth filter for band splitting instead of
                                     trapezoidal FFT filter
    -F  LOWERFREQ,UPPERFREQ[,LOWERSTOP,UPPERSTOP]
                                   - Filter data and regressors from LOWERFREQ to UPPERFREQ.
                                     LOWERSTOP and UPPERSTOP can be specified, or will be
                                     calculated automatically
    -V                             - Filter data and regressors to VLF band
    -L                             - Filter data and regressors to LFO band
    -R                             - Filter data and regressors to respiratory band
    -C                             - Filter data and regressors to cardiac band
    --padseconds=SECONDS           - Set the filter pad time to SECONDS seconds.  Default
                                     is 30.0
    -N NREPS                       - Estimate significance threshold by running NREPS null
                                     correlations (default is 10000, set to 0 to disable).  If you are
                                     running multiple passes, 'ampthresh' will be set to the 0.05 significance.
                                     level unless it is manually specified (see below).
    --permutationmethod=METHOD     - Method for permuting the regressor for significance estimation.  Default
                                     is shuffle
    --skipsighistfit               - Do not fit significance histogram with a Johnson SB function
    --windowfunc=FUNC              - Use FUNC window funcion prior to correlation.  Options are
                                     hamming (default), hann, blackmanharris, and None
    --nowindow                     - Disable precorrelation windowing
    -f GAUSSSIGMA                  - Spatially filter fMRI data prior to analysis using
                                     GAUSSSIGMA in mm
    -M                             - Generate a global mean regressor and use that as the
                                     reference regressor
    --globalmeaninclude=MASK[:VALSPEC]
                                   - Only use voxels in NAME for global regressor generation (if VALSPEC is
                                     given, only voxels with integral values listed in VALSPEC are used.)
    --globalmeanexclude=MASK[:VALSPEC]
                                   - Do not use voxels in NAME for global regressor generation (if VALSPEC is
                                     given, only voxels with integral values listed in VALSPEC are used.)
    -m                             - Mean scale regressors during global mean estimation
    --slicetimes=FILE              - Apply offset times from FILE to each slice in the dataset
    --numskip=SKIP                 - SKIP tr's were previously deleted during preprocessing (e.g. if you
                                     have done your preprocessing in FSL and set dummypoints to a
                                     nonzero value.) Default is 0.
    --timerange=START,END          - Limit analysis to data between timepoints START
                                     and END in the fmri file. If END is set to -1,
                                     analysis will go to the last timepoint.  Negative values
                                     of START will be set to 0. Default is to use all timepoints.
    --nothresh                     - Disable voxel intensity threshold (especially useful
                                     for NIRS data)
    --motionfile=MOTFILE[:COLSPEC] - Read 6 columns of motion regressors out of MOTFILE text file.
                                     (with timepoints rows) and regress their derivatives
                                     and delayed derivatives out of the data prior to analysis.
                                     If COLSPEC is present, use the comma separated list of ranges to
                                     specify X, Y, Z, RotX, RotY, and RotZ, in that order.  For
                                     example, :3-5,7,0,9 would use columns 3, 4, 5, 7, 0 and 9
                                     for X, Y, Z, RotX, RotY, RotZ, respectively
    --motpos                       - Toggle whether displacement regressors will be used in motion regression.
                                     Default is False.
    --motderiv                     - Toggle whether derivatives will be used in motion regression.
                                     Default is True.
    --motdelayderiv                - Toggle whether delayed derivative  regressors will be used in motion regression.
                                     Default is False.

Correlation options:
    -O OVERSAMPFAC                 - Oversample the fMRI data by the following integral
                                     factor.  Setting to -1 chooses the factor automatically (default)
    --regressor=FILENAME           - Read probe regressor from file FILENAME (if none
                                     specified, generate and use global regressor)
    --regressorfreq=FREQ           - Probe regressor in file has sample frequency FREQ
                                     (default is 1/tr) NB: --regressorfreq and --regressortstep
                                     are two ways to specify the same thing
    --regressortstep=TSTEP         - Probe regressor in file has sample time step TSTEP
                                     (default is tr) NB: --regressorfreq and --regressortstep
                                     are two ways to specify the same thing
    --regressorstart=START         - The time delay in seconds into the regressor file, corresponding
                                     in the first TR of the fmri file (default is 0.0)
    --phat                         - Use generalized cross-correlation with phase alignment
                                     transform (PHAT) instead of correlation
    --liang                        - Use generalized cross-correlation with Liang weighting function
                                     (Liang, et al, doi:10.1109/IMCCC.2015.283)
    --eckart                       - Use generalized cross-correlation with Eckart weighting function
    --corrmaskthresh=PCT           - Do correlations in voxels where the mean exceeeds this
                                     percentage of the robust max (default is 1.0)
    --corrmask=MASK                - Only do correlations in voxels in MASK (if set, corrmaskthresh
                                     is ignored).
    --accheck                      - Check for periodic components that corrupt the autocorrelation

Correlation fitting options:
    -Z DELAYTIME                   - Don't fit the delay time - set it to DELAYTIME seconds
                                     for all voxels
    -r LAGMIN,LAGMAX               - Limit fit to a range of lags from LAGMIN to LAGMAX
    -s SIGMALIMIT                  - Reject lag fits with linewidth wider than SIGMALIMIT
    -B                             - Bipolar mode - match peak correlation ignoring sign
    --nofitfilt                    - Do not zero out peak fit values if fit fails
    --searchfrac=FRAC              - When peak fitting, include points with amplitude > FRAC * the
                                     maximum amplitude.
                                     (default value is 0.5)
    --peakfittype=FITTYPE          - Method for fitting the peak of the similarity function
                                     (default is 'gauss'). 'quad' uses a quadratic fit. Other options are
                                     'fastgauss' which is faster but not as well tested, and 'None'.
    --despecklepasses=PASSES       - detect and refit suspect correlations to disambiguate peak
                                     locations in PASSES passes
    --despecklethresh=VAL          - refit correlation if median discontinuity magnitude exceeds
                                     VAL (default is 5s)
    --softlimit                    - Allow peaks outside of range if the maximum correlation is
                                     at an edge of the range.

Regressor refinement options:
    --refineprenorm=TYPE           - Apply TYPE prenormalization to each timecourse prior
                                     to refinement (valid weightings are 'None',
                                     'mean' (default), 'var', and 'std'
    --refineweighting=TYPE         - Apply TYPE weighting to each timecourse prior
                                     to refinement (valid weightings are 'None',
                                     'R', 'R2' (default)
    --passes=PASSES,               - Set the number of processing passes to PASSES
     --refinepasses=PASSES           (default is 1 pass - no refinement).
                                     NB: refinepasses is the wrong name for this option -
                                     --refinepasses is deprecated, use --passes from now on.
    --refineinclude=MASK[:VALSPEC] - Only use nonzero voxels in MASK for regressor refinement (if VALSPEC is
                                     given, only voxels with integral values listed in VALSPEC are used.)
    --refineexclude=MASK[:VALSPEC] - Do not use nonzero voxels in MASK for regressor refinement (if VALSPEC is
                                     given, only voxels with integral values listed in VALSPEC are used.)
    --lagminthresh=MIN             - For refinement, exclude voxels with delays less
                                     than MIN (default is 0.5s)
    --lagmaxthresh=MAX             - For refinement, exclude voxels with delays greater
                                     than MAX (default is 5s)
    --ampthresh=AMP                - For refinement, exclude voxels with correlation
                                     coefficients less than AMP (default is 0.3).  NOTE: ampthresh will
                                     automatically be set to the p<0.05 significance level determined by
                                     the -N option if -N is set greater than 0 and this is not
                                     manually specified.
    --sigmathresh=SIGMA            - For refinement, exclude voxels with widths greater
                                     than SIGMA (default is 100s)
    --refineoffset                 - Adjust offset time during refinement to bring peak
                                     delay to zero
    --pickleft                     - When setting refineoffset, always select the leftmost histogram peak
    --pickleftthresh=THRESH        - Set the threshold value (fraction of maximum) to decide something is a
                                     peak in a histogram.  Default is 0.33.
    --refineupperlag               - Only use positive lags for regressor refinement
    --refinelowerlag               - Only use negative lags for regressor refinement
    --pca                          - Use pca to derive refined regressor (default is
                                     unweighted averaging)
    --ica                          - Use ica to derive refined regressor (default is
                                     unweighted averaging)
    --weightedavg                  - Use weighted average to derive refined regressor
                                     (default is unweighted averaging)
    --avg                          - Use unweighted average to derive refined regressor
                                     (default)
    --psdfilter                    - Apply a PSD weighted Wiener filter to shifted
                                     timecourses prior to refinement

Output options:
    --limitoutput                  - Don't save some of the large and rarely used files
    -T                             - Save a table of lagtimes used
    -h HISTLEN                     - Change the histogram length to HISTLEN (default is
                                     100)
    --glmsourcefile=FILE           - Regress delayed regressors out of FILE instead of the
                                     initial fmri file used to estimate delays
    --noglm                        - Turn off GLM filtering to remove delayed regressor
                                     from each voxel (disables output of fitNorm)
    --preservefiltering            - don't reread data prior to GLM

Miscellaneous options:
    --noprogressbar                - Disable progress bars - useful if saving output to files
    --wiener                       - Perform Wiener deconvolution to get voxel transfer functions
    --usesp                        - Use single precision for internal calculations (may
                                     be useful when RAM is limited)
    -c                             - Data file is a converted CIFTI
    -S                             - Simulate a run - just report command line options
    -d                             - Display plots of interesting timecourses
    --nonumba                      - Disable jit compilation with numba
    --nosharedmem                  - Disable use of shared memory for large array storage
    --memprofile                   - Enable memory profiling for debugging - warning:
                                     this slows things down a lot.
    --multiproc                    - Enable multiprocessing versions of key subroutines.  This
                                     speeds things up dramatically.  Almost certainly will NOT
                                     work on Windows (due to different forking behavior).
    --mklthreads=NTHREADS          - Use no more than NTHREADS worker threads in accelerated numpy calls.
    --nprocs=NPROCS                - Use NPROCS worker processes for multiprocessing.  Setting NPROCS
                                     less than 1 sets the number of worker processes to
                                     n_cpus - 1 (default).  Setting NPROCS enables --multiproc.
    --debug                        - Enable additional information output
    --saveoptionsasjson            - Save the options file in json format rather than text.  Will eventually
                                     become the default, but for now I'm just trying it out.

Experimental options (not fully tested, may not work):
    --cleanrefined                 - perform additional processing on refined regressor to remove spurious
                                     components.
    --dispersioncalc               - Generate extra data during refinement to allow calculation of
                                     dispersion.
    --acfix                        - Perform a secondary correlation to disambiguate peak location
                                     (enables --accheck).  Experimental.
    --tmask=MASKFILE               - Only correlate during epochs specified in
                                     MASKFILE (NB: if file has one colum, the length needs to match
                                     the number of TRs used.  TRs with nonzero values will be used
                                     in analysis.  If there are 2 or more columns, each line of MASKFILE
                                     contains the time (first column) and duration (second column) of an
                                     epoch to include.)

These options are somewhat self-explanatory. I will be expanding this section of the manual going forward, but I want to put something here to get this out here.

When using the legacy interface, file names will be output using the old, non-BIDS names and formats. rapidtide can be forced to use the old style outputs with the --legacyoutput flag.

Equivalence between BIDS and legacy outputs:

BIDS style name

Legacy name

XXX_maxtime_map(.nii.gz, .json)

XXX_lagtimes.nii.gz

XXX_desc-maxtime_hist(.tsv, .json)

XXX_laghist.txt

XXX_maxcorr_map(.nii.gz, .json)

XXX_lagstrengths.nii.gz

XXX_desc-maxcorr_hist(.tsv, .json)

XXX_strengthhist.txt

XXX_maxcorrsq_map(.nii.gz, .json)

XXX_R2.nii.gz

XXX_desc-maxcorrsq_hist(.tsv, .json)

XXX_R2hist.txt

XXX_maxwidth_map(.nii.gz, .json)

XXX_lagsigma.nii.gz

XXX_desc-maxwidth_hist(.tsv, .json)

XXX_widthhist.txt

XXX_MTT_map(.nii.gz, .json)

XXX_MTT.nii.gz

XXX_corrfit_mask.nii.gz

XXX_fitmask.nii.gz

XXX_corrfitfailreason_map(.nii.gz, .json)

XXX_failreason.nii.gz

XXX_desc-corrfitwindow_info.nii.gz

XXX_windowout.nii.gz

XXX_desc-runoptions_info.json

XXX_options.json

XXX_desc-lfofilterCleaned_bold(.nii.gz, .json)

XXX_filtereddata.nii.gz

XXX_desc-lfofilterRemoved_bold(.nii.gz, .json)

XXX_datatoremove.nii.gz

XXX_desc-lfofilterCoeff_map.nii.gz

XXX_fitcoeff.nii.gz

XXX_desc-lfofilterMean_map.nii.gz

XXX_meanvalue.nii.gz

XXX_desc-lfofilterNorm_map.nii.gz

XXX_fitNorm.nii.gz

XXX_desc-lfofilterR2_map.nii.gz

XXX_r2value.nii.gz

XXX_desc-lfofilterR_map.nii.gz

XXX_rvalue.nii.gz

XXX_desc-processed_mask.nii.gz

XXX_corrmask.nii.gz

XXX_desc-globalmean_mask.nii.gz

XXX_meanmask.nii.gz

XXX_desc-refine_mask.nii.gz

XXX_refinemask.nii.gz

XXX_desc-despeckle_mask.nii.gz

XXX_despecklemask.nii.gz

XXX_desc-corrout_info.nii.gz

XXX_corrout.nii.gz

XXX_desc-gaussout_info.nii.gz

XXX_gaussout.nii.gz

XXX_desc-autocorr_timeseries(.tsv, .json)

XXX_referenceautocorr_passN.txt

XXX_desc-corrdistdata_info(.tsv, .json)

XXX_corrdistdata_passN.txt

XXX_desc-nullsimfunc_hist(.tsv, .json)

XXX_nullsimfunchist_passN.txt

XXX_desc-plt0p050_mask.nii.gz

XXX_p_lt_0p050_mask.nii.gz

XXX_desc-plt0p010_mask.nii.gz

XXX_p_lt_0p010_mask.nii.gz

XXX_desc-plt0p005_mask.nii.gz

XXX_p_lt_0p005_mask.nii.gz

XXX_desc-plt0p001_mask.nii.gz

XXX_p_lt_0p001_mask.nii.gz

XXX_desc-globallag_hist(.tsv, .json)

XXX_globallaghist_passN.txt

XXX_desc-initialmovingregressor_timeseries(.tsv, .json)

XXX_reference_origres.txt, XXX_reference_origres_prefilt.txt

XXX_desc-movingregressor_timeseries(.tsv, .json)

XXX_reference_fmrires_passN.txt

XXX_desc-oversampledmovingregressor_timeseries(.tsv, .json)

XXX_reference_resampres_passN.txt

XXX_desc-refinedmovingregressor_timeseries(.tsv, .json)

XXX_unfilteredrefinedregressor_passN.txt, XXX_refinedregressor_passN.txt

XXX_commandline.txt

XXX_commandline.txt

XXX_formattedcommandline.txt

XXX_formattedcommandline.txt

XXX_memusage.csv

XXX_memusage.csv

XXX_runtimings.txt

XXX_runtimings.txt

Examples:

Rapidtide can do many things - as I’ve found more interesting things to do with time delay processing, it’s gained new functions and options to support these new applications. As a result, it can be a little hard to know what to use for a new experiment. To help with that, I’ve decided to add this section to the manual to get you started. It’s broken up by type of data/analysis you might want to do.

Removing low frequency physiological noise from resting state data

This is what I thought most people would use rapidtide for - finding and removing the low frequency (LFO) signal from an existing dataset. This presupposes you have not made a simultaneous physiological recording (well, you may have, but it assumes you aren’t using it). For this, you can use a minimal set of options, since the defaults are mostly right.

The base command you’d use would be:

rapidtide inputfmrifile outputname --frequencyband lfo --passes 3

This will do a fairly simple analysis. First, the -L option means that rapidtide will prefilter the data to the LFO band (0.009-0.15Hz). It will then construct a regressor from the global mean of the signal in inputfmrifile (default behavior if no regressor is specified), and then use crosscorrelation to determine the time delay in each voxel. The --passes=3 option directs rapidtide to to perform the delay analysis 3 times, each time generating a new estimate of the global noise signal by aligning all of the timecourses in the data to bring the global signal in phase prior to averaging. The --refineoffset flag recenters the peak of the delay distribution on zero during the refinement process, which should make datasets easier to compare. After the three passes are complete, it will then use a GLM filter to remove a lagged copy of the final mean regressor that from the data - this denoised data will be in the file “outputname_filtereddata.nii.gz”. There will also a number of maps output with the prefix “outputname_” of delay, correlation strength and so on.

Mapping long time delays in response to a gas challenge experiment:

Processing this sort of data requires a very different set of options from the previous case. Instead of the distribution of delays you expect in healthy controls (a slightly skewed, somewhat normal distribution with a tail on the positive side, ranging from about -5 to 5 seconds), in this case, the maximum delay can be extremely long (100-120 seconds is not uncommon in stroke, moyamoya disesase, and atherosclerosis). To do this, you need to radically change what options you use, not just the delay range, but a number of other options having to do with refinement and statistical measures.

For this type of analysis, a good place to start is the following:

rapidtide inputfmrifile outputname --numnull 0 --searchrange -10 140 --filterfreqs 0.0 0.1 --ampthresh 0.2 --noglm --nofitfilt

The first option (--numnull 0), shuts off the calculation of the null correlation distribution. This is used to determine the significance threshold, but the method currently implemented in rapidtide is a bit simplistic - it assumes that all the time points in the data are exchangable. This is certainly true for resting state data (see above), but it is very much NOT true for block paradigm gas challenges. To properly analyze those, I need to consider what time points are ‘equivalent’, and up to now, I don’t, so setting the number of iterations in the Monte Carlo analysis to zero omits this step.

The second option (--searchrange -10 140) is fairly obvious - this extends the detectable delay range out to 140 seconds. Note that this is somewhat larger than the maximum delays we frequently see, but to find the correlation peak with maximum precision, you need sufficient additional delay values so that the correlation can come to a peak and then come down enough that you can properly fit it.

Setting --filterfreqs 0.0 0.1 is VERY important. By default, rapidtide assumes you are looking at endogenous low frequency oscillations, which typically between 0.09 and 0.15 Hz. However, gas challenge paradigms are usually MUCH lower frequency (90 seconds off, 90 seconds on corresponds to 1/180s = ~0.006Hz). So if you use the default frequency settings, you will completely filter out your stimulus, and presumably, your response. If you are processing one of these experiments and get no results whatsoever, this is almost certainly the problem.

The --noglm option disables data filtering. If you are using rapidtide to estimate and remove low frequency noise from resting state or task fMRI data, the last step is to use a glm filter to remove this circulatory signal, leaving “pure” neuronal signal, which you’ll use in further analyses. That’s not relevant here - the signal you’d be removing is the one you care about. So this option skips that step to save time and disk space.

--nofitfilt skips a step after peak estimation. Estimating the delay and correlation amplitude in each voxel is a two step process. First you make a quick estimate (where is the maximum point of the correlation function, and what is its amplitude?), then you refine it by fitting a Gaussian function to the peak to improve the estimate. If this step fails, which it can if the peak is too close to the end of the lag range, or strangely shaped, the default behavior is to mark the point as bad and zero out the parameters for the voxel. The nofitfilt option means that if the fit fails, output the initial estimates rather than all zeros. This means that you get some information, even if it’s not fully refined. In my experience it does tend to make the maps for the gas challenge experiments a lot cleaner to use this option since the correlation function is pretty well behaved.

Denoising NIRS data (NEW)

When we started this whole research effort, I waw originally planning to denoise NIRS data, not fMRI data. But one thing led to another, and the NIRS got derailed for the fMRI effort. Now that we have some time to catch our breaths, and more importantly, we have access to some much higher quality NIRS data, this moved back to the front burner. The majority of the work was already done, I just needed to account for a few qualities that make NIRS data different from fMRI data:

  • NIRS data is not generally stored in NIFTI files. There is not as yet a standard NIRS format. In the absence of one, you could do worse than a multicolumn text file, with one column per data channel. That’s what I did here - if the file has a ‘.txt’ extension rather than ‘.nii.’, ‘.nii.gz’, or no extension, it will assume all I/O should be done on multicolumn text files.

  • NIRS data is often zero mean. This turned out to mess with a lot of my assumptions about which voxels have significant data, and mask construction. This has led to some new options for specifying mask threshholds and data averaging.

  • NIRS data is in some sense “calibrated” as relative micromolar changes in oxy-, deoxy-, and total hemoglobin concentration, so mean and/or variance normalizing the timecourses may not be right thing to do. I’ve added in some new options to mess with normalizations.

happy

Description:

happy is a new addition to the rapidtide suite. It’s complementary to rapidtide - it’s focussed on fast, cardiac signals in fMRI, rather than the slow, LFO signals we are usually looking at. It’s sort of a Frankenprogram - it has three distinct jobs, which are related, but are very distinct.

The first thing happy does is try to extract a cardiac waveform from the fMRI data. This is something I’ve been thinking about for a long time. Words go here

The second task is to take this raw estimate of the cardiac waveform, and clean it up using a deep learning filter. The original signal is useful, but pretty gross, but I figured you should be able to exploit the pseudoperiodic nature of the signal to greatly improve it. This is also a testbed to work on using neural nets to process time domain signals. It seemed like a worthwhile project, so it got grafted in.

The final task (which was actually the initial task, and the reason I wrote happy to begin with) is to implement Henning Voss’ totally cool hypersampling with analytic phase projection (guess where the name “happy” comes from). This is fairly straightforward, as Voss describes his method very clearly. But I have lots of data with no simultaneously recorded cardiac signals, and I was too lazy to go find datasets with pleth data to play with, so that’s why I did the cardiac waveform extraction part.

Inputs:

Happy needs a 4D BOLD fMRI data file (space by time) as input. This can be Nifti1 or Nifti2. If you have a simultaneously recorded cardiac waveform, it will happily use it, otherwise it will try to construct (and refine) one. NOTE: the 4D input dataset needs to be completely unpreprocessed - gradient distortion correction and motion correction can destroy the relationship between slice number and actual acquisition time, and slice time correction does not behave as expected for aliased signals (which the cardiac component in fMRI most certainly is), and in any case we need the slice time offsets to construct our waveform.

Outputs:

Outputs are space or space by time Nifti or text files, depending on what the input data file was, and some text files containing textual information, histograms, or numbers. File formats and naming follow BIDS conventions for derivative data for fMRI input data. Output spatial dimensions and file type match the input dimensions and file type (Nifti1 in, Nifti1 out). Depending on the file type of map, there can be no time dimension, a time dimension that matches the input file, or something else, such as a time lag dimension for a correlation map.

BIDS Outputs:

Name

Extension(s)

Content

When present

XXX_commandline

.txt

The command line used to run happy

Always

XXX_formattedcommandline

.txt

The command line used to run happy, attractively formatted

Always

XXX_desc-rawapp_info

.nii.gz

The analytic phase projection map of the cardiac waveform

Always

XXX_desc-app_info

.nii.gz

The analytic phase projection map of the cardiac waveform, voxelwise minimum subtracted

Always

XXX_desc-normapp_info

.nii.gz

The analytic phase projection map of the cardiac waveform, voxelwise minimum subtracted and normalized

Always

XXX_desc-apppeaks_hist

.tsv.gz, .json

Not sure

Always

XXX_desc-apppeaks_hist_centerofmass

.txt

Not sure

Always

XXX_desc-apppeaks_hist_peak

.txt

Not sure

Always

XXX_desc-slicerescardfromfmri_timeseries

.tsv.gz, .json

Cardiac timeseries at the time resolution of slice acquisition ((1/TR * number of slices / multiband factor

Always

XXX_desc-stdrescardfromfmri_timeseries

.tsv.gz, .json

Cardiac timeseries at standard time resolution (25.O Hz)

Always

XXX_desc-cardpulsefromfmri_timeseries

.tsv.gz, .json

The average (over time from minimum) of the cardiac waveform over all voxels

Always

XXX_desc-cardiaccyclefromfmri_timeseries

.tsv.gz, .json

The average (over a single cardiac cycle) of the cardiac waveform over all voxels

Always

XXX_desc-cine_info

.nii.gz

Average image of the fMRI data over a single cardiac cycle

Always

XXX_desc-cycleaverage_timeseries

.tsv.gz, .json

Not sure

Always

XXX_desc-maxphase_map

.nii.gz

Map of the average phase where the maximum amplitude occurs for each voxel

Always

XXX_desc-minphase_map

.nii.gz

Map of the average phase where the minimum amplitude occurs for each voxel

Always

XXX_desc-processvoxels_mask

.nii.gz

Map of all voxels used for analytic phase projection

Always

XXX_desc-vessels_map

.nii.gz

Amplitude of variance over a cardiac cycle (large values are assumed to be vessels)

Always

XXX_desc-vessels_mask

.nii.gz

Locations of voxels with variance over a cardiac cycle that exceeds a threshold (assumed to be vessels)

Always

XXX_desc-arteries_map

.nii.gz

High variance vessels with early maximum values within the cardiac cycle

Always

XXX_desc-veins_map

.nii.gz

High variance vessels with late maximum values within the cardiac cycle

Always

XXX_info

.json

Run parameters and derived values found during the run (quality metrics, derived thresholds, etc.)

Always

XXX_memusage

.csv

Memory statistics at multiple checkpoints over the course of the run

Always

XXX_runtimings

.txt

Detailed timing information

Always

Usage:

Example:

Extract the cardiac waveform and generate phase projections

Case 1: When you don’t have a pleth recording

There are substantial improvements to the latest versions of happy. In the old versions, you actually had to run happy twice - the first time to estimate the vessel locations, and the second to actually derive the waveform. Happy now combines these operations interpolation a single run with multiple passes - the first pass locates voxels with high variance, labels them as vessels, then reruns the derivation, restricting the cardiac estimation to these high variance voxels. This gives substantially better results.

Using the example data in the example directory, try the following:

happy \
    rapidtide/data/examples/src/sub-HAPPYTEST.nii.gz \
    rapidtide/data/examples/src/sub-HAPPYTEST.json \
    rapidtide/data/examples/dst/happytest

This will perform a happy analysis on the example dataset. To see the extracted cardiac waveform (original and filtered), you can use showtc (also part of them rapidtide package):

showtc \
    rapidtide/data/examples/src/happytest_desc-slicerescardfromfmri_timeseries.json:cardiacfromfmri,cardiacfromfmri_dlfiltered \
    --format separate

rapidtide2std

Description:

This is a utility for registering rapidtide output maps to standard coordinates. It’s usually much faster to run rapidtide in native space then transform afterwards to MNI152 space. NB: this will only work if you have a working FSL installation.

Inputs:

Outputs:

New versions of the rapidtide output maps, registered to either MNI152 space or to the hires anatomic images for the subject. All maps are named with the specified root name with ‘_std’ appended.

Usage:

usage: rapidtide2std INPUTFILEROOT OUTPUTDIR FEATDIRECTORY [--all] [--hires]

required arguments:
    INPUTFILEROOT      - The base name of the rapidtide maps up to but not including the underscore
    OUTPUTDIR          - The location for the output files
    FEADDIRECTORY      - A feat directory (x.feat) where registration to standard space has been performed

optional arguments:
    --all              - also transform the corrout file (warning - file may be huge)
    --hires            - transform to match the high resolution anatomic image rather than the standard
    --linear           - only do linear transformation, even if warpfile exists

showxcorr_legacy

Description:

Like rapidtide, but for single time courses. Takes two text files as input, calculates and displays the time lagged crosscorrelation between them, fits the maximum time lag, and estimates the significance of the correlation. It has a range of filtering, windowing, and correlation options. This is the old interface - for new analyses you should use showxcorrx.

Inputs:

showxcorr requires two text files containing timecourses with the same sample rate, one timepoint per line, which are to be correlated, and the sample rate.

Outputs:

showxcorr outputs everything to standard out, including the Pearson correlation, the maximum cross correlation, the time of maximum cross correlation, and estimates of the significance levels (if specified). There are no output files.

Usage:

usage: showxcorr timecourse1 timecourse2 samplerate [-l LABEL] [-s STARTTIME] [-D DURATION] [-d] [-F LOWERFREQ,UPPERFREQ[,LOWERSTOP,UPPERSTOP]] [-V] [-L] [-R] [-C] [-t] [-w] [-f] [-z FILENAME] [-N TRIALS]

required arguments:
        timcoursefile1: text file containing a timeseries
        timcoursefile2: text file containing a timeseries
        samplerate:     the sample rate of the timecourses, in Hz

optional arguments:
    -t            - detrend the data
    -w            - prewindow the data
    -l LABEL      - label for the delay value
    -s STARTTIME  - time of first datapoint to use in seconds in the first file
    -D DURATION   - amount of data to use in seconds
    -r RANGE      - restrict peak search range to +/- RANGE seconds (default is
                    +/-15)
    -d            - turns off display of graph
    -F            - filter data and regressors from LOWERFREQ to UPPERFREQ.
                    LOWERSTOP and UPPERSTOP can be specified, or will be
                    calculated automatically
    -V            - filter data and regressors to VLF band
    -L            - filter data and regressors to LFO band
    -R            - filter data and regressors to respiratory band
    -C            - filter data and regressors to cardiac band
    -T            - trim data to match
    -A            - print data on a single summary line
    -a            - if summary mode is on, add a header line showing what values
                    mean
    -f            - negate (flip) second regressor
    -z FILENAME   - use the columns of FILENAME as controlling variables and
                    return the partial correlation
    -N TRIALS     - estimate significance thresholds by Monte Carlo with TRIALS
                    repetition

showxcorrx

Description:

This is the newest, most avant-garde version of showxcorr. Because it’s an x file, it’s more fluid and I don’t guarantee that it will keep a stable interface (or even work at any given time). But every time I add something new, it goes here. The goal is eventually to make this the “real” version. Unlike rapidtide, however, I’ve let it drift quite a bit without syncing it because some people here actually use showxcorr and I don’t want to disrupt workflows…

Inputs:

showxcorrx requires two text files containing timecourses with the same sample rate, one timepoint per line, which are to be correlated, and the sample rate.

Outputs:

showxcorrx outputs everything to standard out, including the Pearson correlation, the maximum cross correlation, the time of maximum cross correlation, and estimates of the significance levels (if specified). There are no output files.

Usage:

showtc

Description:

A very simple command line utility that takes a text file and plots the data in it in a matplotlib window. That’s it. A good tool for quickly seeing what’s in a file. Has some options to make the plot prettier.

Inputs:

Text files containing time series data

Outputs:

None

Usage:

Plots the data in text files.

usage: showtc texfilename[:col1,col2...,coln] [textfilename]... [options]

Positional Arguments

textfilenames

One or more input files, with optional column specifications

Named Arguments

--samplerate

Set the sample rate of the data file to FREQ. If neither samplerate or sampletime is specified, sample rate is 1.0.

Default: auto

--sampletime

Set the sample rate of the data file to 1.0/TSTEP. If neither samplerate or sampletime is specified, sample rate is 1.0.

Default: auto

--displaytype

Possible choices: time, power, phase

Display data as time series (default), power spectrum, or phase spectrum.

Default: “time”

--format

Possible choices: overlaid, separate, separatelinked

Display data overlaid (default), in individually scaled windows, or in separate windows with linked scaling.

Default: “overlaid”

--waterfall

Display multiple timecourses in a waterfall plot.

Default: False

--voffset

Plot multiple timecourses with OFFSET between them (use negative OFFSET to set automatically).

Default: 0.0

--transpose

Swap rows and columns in the input files.

Default: False

--starttime

Start plotting at START seconds (default is the start of the data).

--endtime

Finish plotting at END seconds (default is the end of the data).

--numskip

Skip NUM lines at the beginning of each file (to get past header lines).

Default: 0

--debug

Output additional debugging information.

Default: False

General plot appearance options

--title

Use TITLE as the overall title of the graph.

Default: “”

--xlabel

Label for the plot x axis.

Default: “”

--ylabel

Label for the plot y axis.

Default: “”

--legends

Comma separated list of legends for each timecourse.

--legendloc

Integer from 0 to 10 inclusive specifying legend location. Legal values are: 0: best, 1: upper right, 2: upper left, 3: lower left, 4: lower right, 5: right, 6: center left, 7: center right, 8: lower center, 9: upper center, 10: center. Default is 2.

Default: 2

--colors

Comma separated list of colors for each timecourse.

--nolegend

Turn off legend label.

Default: True

--noxax

Do not show x axis.

Default: True

--noyax

Do not show y axis.

Default: True

--linewidth

A comma separated list of linewidths (in points) for plots. Default is 1.

--tofile

Write figure to file FILENAME instead of displaying on the screen.

--fontscalefac

Scaling factor for annotation fonts (default is 1.0).

Default: 1.0

--saveres

Write figure to file at DPI dots per inch (default is 1000).

Default: 1000

glmfilt

Description:

Uses a GLM filter to remove timecourses (1D text files or 4D NIFTI files) from 4D NIFTI files.

Inputs:

Outputs:

Usage:

Fits and removes the effect of voxel specific and/or global regressors.

usage: glmfilt inputfile numskip outputroot --evfile FILE [FILE [FILE...]]

Positional Arguments

inputfile

The name of the 3 or 4 dimensional nifti file to fit.

numskip

The number of points to skip at the beginning of the timecourse when fitting.

outputroot

The root name for all output files.

Named Arguments

--evfile

One or more files (text timecourse or 4D NIFTI) containing signals to regress out.

temporaldecomp

Description:

Inputs:

Outputs:

Usage:

spatialdecomp

Description:

Inputs:

Outputs:

Usage:

polyfitim

Description:

Inputs:

Outputs:

Usage:

Fit a spatial template to a 3D or 4D NIFTI file.

usage: polyfitim datafile datamask templatefile templatmask outputroot [options]

Positional Arguments

datafile

The name of the 3 or 4 dimensional nifti file to fit.

datamask

The name of the 3 or 4 dimensional nifti file valid voxel mask (must match datafile).

templatefile

The name of the 3D nifti template file (must match datafile).

templatemask

The name of the 3D nifti template mask (must match datafile).

outputroot

The root name for all output files.

Named Arguments

--regionatlas

Do individual fits to every region in ATLASFILE (3D NIFTI file).

--order

Perform fit to ORDERth order (default (and minimum) is 1)

Default: 1

histnifti

Description:

A command line tool to generate a histogram for a nifti file

Inputs:

A nifti file

Outputs:

A text file containing the histogram information

None

Usage:

usage: histnifti inputfile outputroot

required arguments:
        inputfile       - the name of the input nifti file
        outputroot      - the root of the output nifti names

showhist

Description:

Another simple command line utility that displays the histograms generated by rapidtide.

Inputs:

A textfile generated by rapidtide containing histogram information

Outputs:

None

Usage:

usage: showhist textfilename
        plots xy histogram data in text file

required arguments:
        textfilename    - a text file containing one timepoint per line

resamp1tc

Description:

This takes an input text file at some sample rate and outputs a text file resampled to the specified sample rate.

Inputs:

Outputs:

Usage:

resamp1tc - resample a timeseries file

usage: resamp1tc infilename insamplerate outputfile outsamplerate [-s]

required arguments:
        inputfile        - the name of the input text file
        insamplerate     - the sample rate of the input file in Hz
        outputfile       - the name of the output text file
        outsamplerate    - the sample rate of the output file in Hz

 options:
        -s               - split output data into physiological bands (LFO, respiratory, cardiac)

resamplenifti

Description:

This takes an input nifti file at some TR and outputs a nifti file resampled to the specified TR.

Inputs:

Outputs:

Usage:

usage: resamplenifti inputfile inputtr outputname outputtr [-a]

required arguments:
        inputfile       - the name of the input nifti file
        inputtr         - the tr of the input file in seconds
        outputfile      - the name of the output nifti file
        outputtr        - the tr of the output file in seconds

options:
        -a              - disable antialiasing filter (only relevant if you are downsampling in time)

tcfrom3col

Description:

A simple command line that takes an FSL style 3 column regressor file and generates a time course (waveform) file. FSL 3 column files are text files containing one row per “event”. Each row has three columns: start time in seconds, duration in seconds, and waveform value. The output waveform is zero everywhere that is not covered by an “event” in the file.

Inputs:

A three column text file

Outputs:

A single column text file containing the waveform

Usage:

tcfrom3col - convert a 3 column fsl style regressor into a one column timecourse

usage: tcfrom3col infile timestep numpoints outfile

required arguments:
        infile:      a text file containing triplets of start time, duration, and value
        timestep:    the time step of the output time coures in seconds
        numpoints:   the number of output time points
        outfile:     the name of the output time course file

pixelcomp

Description:

A program to compare voxel values in two 3D NIFTI files. You give pixelcomp two files, each with their own mask. Any voxel that has a nonzero mask in both files gets added to a list of xy pairs, with the value from the first file being x, and the value from the second file being y. Pixelcomp then: 1) Makes and displays a 2D histogram of all the xy values. 2) Does a linear fit to x and y, and outputs the coefficients (slope and offset) to a XXX_linfit.txt file. 3) Writes all the xy pairs to a tab separated text file, and 4) Makes a Bland-Altman plot of x vs y

Inputs:

Two 3D NIFTI image files, the accompanying mask files, and the root name for the output files.

Outputs:

None

Usage:

showtc - plots the data in text files

usage: showtc texfilename[:col1,col2...,coln] [textfilename]... [--nolegend] [--pspec] [--phase] [--samplerate=Fs] [--sampletime=Ts]

required arguments:
    textfilename        - a text file containing whitespace separated timecourses, one timepoint per line
                       A list of comma separated numbers following the filename and preceded by a colon is used to select columns to plot

optional arguments:
    --nolegend               - turn off legend label
    --pspec                  - show the power spectra magnitudes of the input data instead of the timecourses
    --phase                  - show the power spectra phases of the input data instead of the timecourses
    --transpose              - swap rows and columns in the input files
    --waterfall              - plot multiple timecourses as a waterfall
    --voffset=VOFFSET        - plot multiple timecourses as with VOFFSET between them (use negative VOFFSET to set automatically)
    --samplerate=Fs          - the sample rate of the input data is Fs Hz (default is 1Hz)
    --sampletime=Ts          - the sample time (1/samplerate) of the input data is Ts seconds (default is 1s)
    --colorlist=C1,C2,..     - cycle through the list of colors specified by CN
    --linewidth=LW           - set linewidth to LW points (default is 1)
    --fontscalefac=FAC       - scale all font sizes by FAC (default is 1.0)
    --legendlist=L1,L2,..    - cycle through the list of legends specified by LN
    --tofile=FILENAME        - write figure to file FILENAME instead of displaying on the screen
    --title=TITLE            - use TITLE as the overall title of the graph
    --separate               - use a separate subplot for each timecourse
    --separatelinked         - use a separate subplot for each timecourse, but use a common y scaling
    --noxax                  - don't show x axis
    --noyax                  - don't show y axis
    --starttime=START        - start plot at START seconds
    --endtime=END            - end plot at END seconds
    --legendloc=LOC          - Integer from 0 to 10 inclusive specifying legend location.  Legal values are:
                               0: best, 1: upper right, 2: upper left, 3: lower left, 4: lower right,
                               5: right, 6: center left, 7: center right, 8: lower center, 9: upper center,
                               10: center.  Default is 2.
    --debug                  - print debugging information

glmfilt

Description:

Uses a GLM filter to remove timecourses (1D text files or 4D NIFTI files) from 4D NIFTI files.

Inputs:

Outputs:

Usage:

usage: glmfilt datafile numskip outputroot evfile [evfile_2...evfile_n]
    Fits and removes the effect of voxel specific and/or global regressors

ccorrica

Description:

Find temporal crosscorrelations between all the columns in a text file (for example the timecourse files output by MELODIC.)

Inputs:

Outputs:

Usage:

ccorrica - find temporal crosscorrelations between ICA components

        usage: ccorrica timecoursefile TR
                timcoursefile:  text file containing multiple timeseries, one per column, whitespace separated
                TR:             the sample period of the timecourse, in seconds

showstxcorr

Description:

Calculate and display the short term crosscorrelation between two timeseries (useful for dynamic correlation).

Inputs:

Outputs:

Usage:

showstxcorr - calculate and display the short term crosscorrelation between two timeseries

usage: showstxcorr -i timecoursefile1 [-i timecoursefile2] --samplefreq=FREQ -o outputfile [-l LABEL] [-s STARTTIME] [-D DURATION] [-d] [-F LOWERFREQ,UPPERFREQ[,LOWERSTOP,UPPERSTOP]] [-V] [-L] [-R] [-C] [--nodetrend] [-nowindow] [-f] [--phat] [--liang] [--eckart] [-z FILENAME]

required arguments:
    -i, --infile= timcoursefile1     - text file containing one or more timeseries
    [-i, --infile= timcoursefile2]   - text file containing a timeseries
                                       NB: if one timecourse file is specified, each column
                                       is considered a timecourse, and there must be at least
                                       2 columns in the file.  If two filenames are given, each
                                       file must have only one column of data.

    -o, --outfile=OUTNAME:           - the root name of the output files

    --samplefreq=FREQ                - sample frequency of all timecourses is FREQ
           or
    --sampletime=TSTEP               - time step of all timecourses is TSTEP
                                       NB: --samplefreq and --sampletime are two ways to specify
                                       the same thing.

optional arguments:
    --nodetrend   - do not detrend the data before correlation
    --nowindow    - do not prewindow data before corrlation
    --phat        - perform phase alignment transform (PHAT) rather than
                    standard crosscorrelation
    --liang       - perform phase alignment transform with Liang weighting function rather than
                    standard crosscorrelation
    --eckart      - perform phase alignment transform with Eckart weighting function rather than
                    standard crosscorrelation
    -s STARTTIME  - time of first datapoint to use in seconds in the first file
    -D DURATION   - amount of data to use in seconds
    -d            - turns off display of graph
    -F            - filter data and regressors from LOWERFREQ to UPPERFREQ.
                    LOWERSTOP and UPPERSTOP can be specified, or will be calculated automatically
    -V            - filter data and regressors to VLF band
    -L            - filter data and regressors to LFO band
    -R            - filter data and regressors to respiratory band
    -C            - filter data and regressors to cardiac band
    -W WINDOWLEN  - use a window length of WINDOWLEN seconds (default is 50.0s)
    -S STEPSIZE   - timestep between subsequent measurements (default is 25.0s).  Will be rounded to the nearest sample time
    -f            - negate second regressor

tidepool

Description:

This is a very experimental tool for displaying all of the various maps generated by rapidtide in one place, overlayed on an anatomic image. This makes it a bit easier to see how all the maps are related to one another. To use it, launch tidepool from the command line, and then select a lag time map - tidpool will figure out the root name and pull in all of the other associated maps. Works in native or standard space.

Inputs:

Outputs:

Usage:

usage: tidepool [-h] [-o OFFSETTIME] [-r] [-n] [-t TRVAL] [-d DATAFILEROOT]
                        [-a ANATNAME] [-m GEOMASKNAME]

A program to display the results of a time delay analysis

optional arguments:
  -h, --help       show this help message and exit
  -o OFFSETTIME    Set lag offset
  -r               enable risetime display
  -n               enable movie mode
  -t TRVAL         Set correlation TR
  -d DATAFILEROOT  Use this dataset (skip initial selection step)
  -a ANATNAME      Set anatomic mask image
  -m GEOMASKNAME   Set geometric mask image