Package 'AnalyzeFMRI'

Description

Create a NIFTI file from an Analyze file.

Usage

analyze2nifti(file.in,path.in=".",path.out=NULL,file.out=NULL,is.nii=TRUE,
qform.code=2,sform.code=2,data.type=rawToChar(raw(10)),db.name=rawToChar(raw(18)),
dim.info=rawToChar(raw(1)),dim=NULL,TR=0,slice.code=rawToChar(raw(1)),
xyzt.units=rawToChar(raw(1)),descrip=NULL,aux.file=rawToChar(raw(24)),
intent.name=rawToChar(raw(16)))

Arguments

Value

Nothing is returned. The NIFTI file is created in the specified path.out directory (default is current directory).

Examples

tmpdir <- tempdir()
analyze2nifti(path.in = system.file(package = "AnalyzeFMRI"),
              path.out = tmpdir, file.in = "example",
              file.out = "nifti-tmp", is.nii = TRUE)
unlink(tmpdir) # tidy up

Description

This function center the data in the two dimensions, the first dimension being indicated by col.first argument

Usage

centering(X,col.first=TRUE)

Arguments

Value

See Also

reduction

Examples

X <- matrix(rnorm(5 * 4), nrow = 5, ncol = 4)
Xcentred <- centering(X, col.first = TRUE)$Xcentred

Description

Calculate contiguous clusters of locations in a 3D array that are above some threshold and with some minimum size.

Usage

cluster.threshold(x, nmat = NULL, level.thr = 0.5, size.thr)

Arguments

Value

Returns an array of the same size as x with a 1 at all locations which have a value above level.thr and are in a cluster of similiar locations with size greater than size.thr.

Author(s)

J. L. Marchini

Examples

x <- array(0, dim = c(64, 64, 21))
x[10:20, 10:20, 1:5] <- 1
x[30:40, 30:40, 6:7] <- 1
x[50, 50, 8:9] <- 1

a <- cluster.threshold(x, size.thr = 400)
sum(x) ## should be 849
sum(a) ## should be 605

Description

Estimates the covariance between neighbouring voxels using a specified neighbourhood system.

Usage

cov.est(mat, mask, nmat)

Arguments

Value

The estimated covariance

Author(s)

J. L. Marchini

Examples

ksize <- 9
d <- c(64, 64, 21)
FWHM <- 9
sigma <- diag(FWHM^2, 3) / (8 * log(2))
voxdim <- c(2, 2, 4)

filtermat <- GaussSmoothKernel(voxdim, ksize, sigma)

mask <- array(1, dim = d)
num.vox <- sum(mask)

mat <- Sim.3D.GRF(d = d, voxdim = voxdim, sigma = sigma,
                  ksize = ksize, mask = mask, type = "field")$mat

nmat <- expand.grid(-1:1, -1:1, -1:1)
nmat4 <- nmat[c(11, 13, 15, 17), ]

cov <- cov.est(mat, mask, nmat4)

Description

Extract freq.dim, phase.dim and slice.dim fields from the one byte dim.info field of a NIFTI header file.

Usage

diminfo2fps(dim.info)

Arguments

Value

A list containing freq.dim, phase.dim and slice.dim fields.

These are provided to store some extra information that is sometimes important when storing the image data from an FMRI time series experiment. (After processing such data into statistical images, these fields are not likely to be useful.) These fields encode which spatial dimension (1,2, or 3) corresponds to which acquisition dimension for MRI data.

Examples:
Rectangular scan multi-slice EPI:
freq_dim = 1 phase_dim = 2 slice_dim = 3 (or some permutation)
Spiral scan multi-slice EPI:
freq_dim = phase_dim = 0 slice_dim = 3 since the concepts of frequency- and phase-encoding directions don't apply to spiral scan.

The fields freq.dim, phase.dim, slice.dim are all squished into the single byte field dim.info (2 bits each, since the values for each field are limited to the range 0..3). This unpleasantness is due to lack of space in the 348 byte allowance.

See Also

fps2diminfo

Examples

dim.info <- f.read.header(system.file("example-nifti.hdr", package="AnalyzeFMRI"))$dim.info
diminfo2fps(dim.info)

Description

Calculates the Expected Euler Characteristic for a 3D Random Field thesholded a level u.

Usage

EC.3D(u, sigma, voxdim = c(1, 1, 1), num.vox, type = c("Normal", "t"), df = NULL)

Arguments

Details

The Euler Characteristic $\chi_u$ (Adler, 1981) is a topological measure that essentially counts the number of isolated regions of the random field above the threshold $u$ minus the number of 'holes'. As $u$ increases the holes disappear and $\chi_u$ counts the number of local maxima. So when $u$ becomes close to the maximum of the random field $Z_{\textrm{max}}$ we have that

$P( \textrm{reject} H_0 | H_0 \textrm{true}) = P(Z_{\textrm{max}}) = P(\chi_u > 0) \approx E(\chi_u)$

where $H_0$ is the null hypothesis that there is no signicant positive actiavtion/signal present in the field. Thus the Type I error of the test can be controlled through knowledge of the Expected Euler characteristic.

Value

The value of the expected Euler Characteristic.

Author(s)

J. L. Marchini

References

Adler, R. (1981) The Geometry of Random Fields.. New York: Wiley. Worlsey, K. J. (1994) Local maxima and the expected euler characteristic of excursion sets of $\chi^2$, $f$ and $t$ fields. Advances in Applied Probability, 26, 13-42.

See Also

Threshold.RF

Examples

EC.3D(4.6, sigma = diag(1, 3), voxdim = c(1, 1, 1), num.vox = 10000)

EC.3D(4.6, sigma = diag(1, 3), voxdim = c(1, 1, 1), num.vox = 10000, type = "t", df = 100)

Description

This function computes the eigenvalues of a centered and reduced data matrix

Usage

eigenvalues(X, draw = FALSE)

Arguments

Value

A list containing

Examples

X <- matrix(rnorm(5 * 4), nrow = 5, ncol = 4)
Xc <- centering(X, col.first = TRUE)$Xcentred
Xcr <- reduction(Xc, row.red = FALSE)$Xred
eigencr <- eigenvalues(Xcr, draw = FALSE)$eigenvalues

Description

Prints a summary of the contents of an ANALYZE .img file using the associated .hdr header file.

Usage

f.analyze.file.summary(file)

Arguments

Value

A print out containing information about the .img file. This includes File name, Data Dimension, X dimension, Y dimension, Z dimension, Time dimension, Voxel dimensions, Data type

See Also

f.read.analyze.header, f.read.analyze.slice, f.read.analyze.slice.at.all.timepoints, f.read.analyze.ts, f.write.analyze, f.read.analyze.volume, f.spectral.summary, f.write.array.to.img.2bytes, f.write.array.to.img.float, f.write.list.to.hdr, f.basic.hdr.list.create

Examples

f.analyze.file.summary(system.file("example.img", package = "AnalyzeFMRI"))

Description

Starts an R/tk interfaced GUI that allows the user to explore an fMRI dataset stored in an ANALYZE format file using the functions of the AnalyzeFMRI package.

Usage

f.analyzeFMRI.gui()

Value

No value is returned

Description

Creates a basic list that can be used to write a .hdr file

Usage

f.basic.hdr.list.create(X, file.hdr)

Arguments

Value

Returns a list of all the fields needed to create a .hdr file (see the functions code for details).

See Also

f.write.list.to.hdr, f.analyze.file.summary

Examples

a <- array(rnorm(20 * 30 * 40 * 3), dim = c(20, 30, 40, 3))
file <- "temp.hdr"
hdr.list <- f.basic.hdr.list.create(a, file)

Description

Creates a basic list that can be used to write a .hdr file or the header part of a .nii file

Usage

f.basic.hdr.nifti.list.create(dim.mat, file)

Arguments

Value

Returns a list of all the fields needed to create a .hdr file (see the function code for details).

See Also

f.write.list.to.hdr.nifti, f.nifti.file.summary

Examples

dim.mat <- c(20, 30, 40, 3)
file <- "temp.hdr"
hdr.nifti <- f.basic.hdr.nifti.list.create(dim.mat, file)

Description

Creates a complete list that can be used to write a .hdr file or the header part of a .nii file

Usage

f.complete.hdr.nifti.list.create(file,dim.info=character(1),dim,
intent.p1=single(1),intent.p2=single(1),intent.p3=single(1),intent.code=integer(1),
datatype=integer(1),bitpix=integer(1),slice.start=integer(1),pixdim=single(8),
scl.slope=single(1),scl.inter=single(1),slice.end=integer(1),slice.code=character(1),
xyzt.units=character(1),cal.max=single(1),cal.min=single(1),slice.duration=single(1),
toffset=single(1),descrip=paste(rep(" ", 80), sep = "", collapse = ""),
aux.file=paste(rep(" ", 24), sep = "", collapse =""),qform.code=integer(1),
sform.code=integer(1),quatern.b=single(1),quatern.c=single(1),quatern.d=single(1),
qoffset.x=single(1),qoffset.y=single(1),qoffset.z=single(1),srow.x=single(4),
srow.y=single(4),srow.z=single(4),
intent.name=paste(rep(" ", 16), sep = "", collapse = ""))

Arguments

Value

Returns a list of all the fields needed to create a .hdr file (see the function code for details).

See Also

f.basic.hdr.nifti.list.create, f.write.list.to.hdr.nifti, f.nifti.file.summary

Examples

dim.mat <- c(20, 30, 40, 3)
dim <- c(length(dim.mat), dim.mat, rep(0, 7 - length(dim.mat)))
filename <- "temp.hdr"
hdr.nifti <- f.complete.hdr.nifti.list.create(file = filename, dim = dim)

Description

Decomposes an fMRI dataset into a specified number of Spatially Independent Components maps and associated time-courses using the FastICA algorithm

Usage

f.ica.fmri(file.name, n.comp, norm.col=TRUE, fun="logcosh", maxit=1000,
alg.type="parallel", alpha=1, tol=1e-04, mask.file.name=NULL, slices=NULL)

Arguments

Details

The fMRI dataset is rearranged into a 2-dimensional data matrix X, where the column vectors are voxel time-series. A mask is used to specify which voxels are included. If this is not supplied by the user then a mask is constructed automatically using a 10% intensity threshold.

The data matrix is considered to be a linear combination of non-Gaussian (independent) components i.e. X = AS where rows of S contain the independent components and A is a linear mixing matrix. In short ICA attempts to 'un-mix' the data by estimating an un-mixing matrix U where UX = S.

Under this generative model the measured 'signals' in X will tend to be 'more Gaussian' than the source components (in S) due to the Central Limit Theorem. Thus, in order to extract the independent components/sources we search for an un-mixing matrix U that maximizes the non-gaussianity of the sources.

In FastICA, non-gaussianity is measured using approximations to negentropy (J) which are more robust than kurtosis based measures and fast to compute.

The approximation takes the form

$J(y)=[E{G(y)}-E{G(v)}]^2$ where $v$ is a N(0,1) r.v

The following choices of G are included as options $G(u)=\frac{1}{\alpha} \log \cosh (\alpha u)$ and $G(u)=-\exp(\frac{-u^2}{2})$

The FastICA algorithm is used to 'un-mix' the data and recover estimates of the mixing matrix A and the source matrix S. Rows of the source matrix S represent spatially independent components of the dataset (these are arranged spatially in the output). Columns of A contain the associated time-courses of the independent components.

Pre-processing involves removing the mean of each row of the data matrix and (optionally) standardizing the columns of the data matrix to have zero mean and unit variance.

All computations are done using C code. This avoids reading the entire dataset into R and thus saves memory space.

Value

A list containing the following components

Author(s)

J L Marchini <[email protected]> and C Heaton <[email protected]>

References

A. Hyvarinen and E. Oja (2000) Independent Component Analysis: Algorithms and Applications, Neural Networks, 13(4-5):411-430

Beckmann C. (2000) Independent Component Analysis for fMRI. First Year D.Phil Report, Dept. of Engineering Science, University of Oxford.

See Also

f.ica.fmri.gui,f.plot.ica.fmri

Description

The GUI provides a quick and easy to use interface for applying spatial ICA to fMRI datasets. Computations are done in C for speed and low memory usage.

Usage

f.ica.fmri.gui()

Details

The user is required to enter the location of the fMRI dataset (stored in the ANALYZE format) and (optionally) a mask for the dataset. If no mask is supplied then an option to create mask is available. There is option to normalize the columns of the data matrix and to exclude the top and bottom slices (which are sometimes affected by the registration procedures).

Once completed, the user has the option of saving the results to an R object or viewing the estimated components. The slices of each component map are plotted sequentially in a grid followed by the components associated time-course and that time-courses periodogram/power spectrum.

Value

Author(s)

J L Marchini <[email protected]> and C Heaton <[email protected]>

See Also

f.ica.fmri,f.plot.ica.fmri

Description

Decomposes an fMRI dataset into a specified number of Spatially or Temporally Independent Components maps and associated time-courses using the FastICA algorithm

Usage

f.icast.fmri(foncfile, maskfile, is.spatial, n.comp.compute = TRUE,
             n.comp = 0, hp.filter = TRUE, verbose = TRUE, path.out = "")

Arguments

Details

TODO!!! The fMRI dataset is rearranged into a 2-dimensional data matrix X, where the column vectors are voxel time-series. A mask is used to specify which voxels are included. If this is not supplied by the user then a mask is constructed automatically using a 10% intensity threshold.

The data matrix is considered to be a linear combination of non-Gaussian (independent) components i.e. X = AS where rows of S contain the independent components and A is a linear mixing matrix. In short ICA attempts to 'un-mix' the data by estimating an un-mixing matrix U where UX = S.

Under this generative model the measured 'signals' in X will tend to be 'more Gaussian' than the source components (in S) due to the Central Limit Theorem. Thus, in order to extract the independent components/sources we search for an un-mixing matrix U that maximizes the non-gaussianity of the sources.

In FastICA, non-gaussianity is measured using approximations to negentropy (J) which are more robust than kurtosis based measures and fast to compute.

The approximation takes the form

$J(y)=[E{G(y)}-E{G(v)}]^2$ where $v$ is a N(0,1) r.v

The following choices of G are included as options $G(u)=\frac{1}{\alpha} \log \cosh (\alpha u)$ and $G(u)=-\exp(\frac{-u^2}{2})$

The FastICA algorithm is used to 'un-mix' the data and recover estimates of the mixing matrix A and the source matrix S. Rows of the source matrix S represent spatially independent components of the dataset (these are arranged spatially in the output). Columns of A contain the associated time-courses of the independent components.

Pre-processing involves removing the mean of each row of the data matrix and (optionally) standardizing the columns of the data matrix to have zero mean and unit variance.

All computations are done using C code. This avoids reading the entire dataset into R and thus saves memory space.

Value

Nothing for the moment ... TODO!! The spatial and temporal components are written on disk

Author(s)

P Lafaye de Micheaux <[email protected]>

References

A. Hyvarinen and E. Oja (2000) Independent Component Analysis: Algorithms and Applications, Neural Networks, 13(4-5):411-430

Beckmann C. (2000) Independent Component Analysis for fMRI. First Year D.Phil Report, Dept. of Engineering Science, University of Oxford.

See Also

f.icast.fmri.gui

Description

The GUI provides a quick and easy to use interface for applying spatial or temporal ICA to fMRI NIFTI datasets. Computations WILL BE (NOT YET IMPLEMENTED) done in C for speed and low memory usage.

Usage

f.icast.fmri.gui()

Details

The user is required to enter the location of the fMRI dataset (stored in the NIFTI format) and (optionally) a mask for the dataset. If no mask is supplied then an option to create mask is available. TODO!!

Once completed, the user has the option of saving the results to an R object or viewing the estimated components. The slices of each component map are plotted sequentially in a grid followed by the components associated time-course and that time-courses periodogram/power spectrum.TODO!!

Value

Author(s)

P Lafaye de Micheaux <[email protected]>

See Also

f.icast.fmri,f.ica.fmri.gui

Description

Prints a summary of the contents of a NIFTI .img file using the associated .hdr header file.

Usage

f.nifti.file.summary(file)

Arguments

Value

A print out containing information about the .img file. This includes File name, Data Dimension, X dimension, Y dimension, Z dimension, Time dimension, Voxel dimensions, Data type

See Also

f.read.nifti.header, f.read.nifti.slice, f.read.nifti.slice.at.all.timepoints, f.read.nifti.ts, f.write.nifti, f.read.nifti.volume, f.spectral.summary.nifti, f.write.array.to.img.2bytes, f.write.array.to.img.float, f.write.list.to.hdr.nifti, f.basic.hdr.nifti.list.create

Examples

f.nifti.file.summary(system.file("example-nifti.img", package = "AnalyzeFMRI"))

Description

Plots a specified component from the output of f.ica.fmri

Usage

f.plot.ica.fmri(obj.ica, comp, cols)

Arguments

Details

The slices of the specified component map are plotted sequentially in a grid followed by the components associated time-course and that time-courses periodogram/power spectrum

Value

No return value, just plot graphs

Author(s)

J L Marchini <[email protected]> and C Heaton <[email protected]>

See Also

f.ica.fmri,f.ica.fmri.gui

Description

This function allows the compact graphical storage of the output of a spatial ICA decomposition of an fMRI dataset. each component is plotted to a jpeg.

Usage

f.plot.ica.fmri.jpg(ica.obj, file="", cols=heat.colors(100), width=700, height=700)

Arguments

Value

No return value, just write in jpeg files

Author(s)

J L Marchini

See Also

f.ica.fmri, jpeg

Description

tcltk GUI to display FMRI or MRI images. This GUI is very usefull, for example, for investigating the results of an ICA performed with f.icast.fmri.gui(). But it can also be used to display an MRI or an FMRI image

Usage

f.plot.volume.gui(array.fonc = NULL, hdr.fonc = NULL)

Arguments

Details

One has the possibility to enter either a filename (with its path) or directly an R object in the file field. This function will only work if package 'tkrplot' is installed. It seems this package is not available for arm64 Macs.

Value

Nothing. Opens our Graphical User Interface

Author(s)

P Lafaye de Micheaux <[email protected]>

See Also

f.icast.fmri.gui

Examples

if (interactive()) f.plot.volume.gui()

Description

Reads the ANALYZE image format .hdr header file into a list.

Usage

f.read.analyze.header(file)

Arguments

Value

A list containing the information in the fields of the .hdr file.

See Also

f.analyze.file.summary

Examples

f.read.analyze.header(system.file("example.hdr", package="AnalyzeFMRI"))

Description

Reads in a specific slice from an ANALYZE .img image format file into an array.

Usage

f.read.analyze.slice(file, slice, tpt)

Arguments

Details

The entire dataset is assumed to be 4D and a slice is extracted that is referenced by specifying the last two dimensions of the dataset i.e.slice and tpt.

Value

An array containing the slice

See Also

f.read.analyze.slice.at.all.timepoints, f.read.analyze.ts, f.read.analyze.volume

Examples

a <- f.read.analyze.slice(system.file("example.img", package = "AnalyzeFMRI"), 10, 1)
dim(a)

Description

Reads in a slice of a .img file at all time points into an array

Usage

f.read.analyze.slice.at.all.timepoints(file, slice)

Arguments

Value

An array containing the slice at all time points

See Also

f.read.analyze.slice, f.read.analyze.ts, f.read.analyze.volume

Examples

a <- f.read.analyze.slice.at.all.timepoints(system.file("example.img", package = "AnalyzeFMRI"),10)
dim(a)

Description

Given a 4D ANALYZE .img/.hdr image pair this function can read in the 3D volume of measurements at a specific time point.

Usage

f.read.analyze.tpt(file, tpt)

Arguments

Details

Given a 4D ANALYZE .img/.hdr image pair this function can read in the 3D volume of measurements at a specific time point.

Value

A 3D array containing the volume.

See Also

f.read.analyze.slice, f.read.analyze.slice.at.all.timepoints, f.write.analyze,

Examples

a <- f.read.analyze.tpt(system.file("example.img", package = "AnalyzeFMRI"), 1)

Description

Given a 4D ANALYZE .img/.hdr image pair this function can read in the time series from a specified position in 3D into a vector.

Usage

f.read.analyze.ts(file, x, y, z)

Arguments

Details

Given a 4D ANALYZE .img/.hdr image pair this function can read in the time series from a specified position in 3D into a vector.

Value

A vector containing the time series

See Also

f.read.analyze.slice, f.read.analyze.slice.at.all.timepoints, f.write.analyze,

Examples

a <- f.read.analyze.ts(system.file("example.img", package = "AnalyzeFMRI"), 30, 30, 10)

Description

Reads the ANALYZE image format .img file into an array.

Usage

f.read.analyze.volume(file)

Arguments

Value

An array with the appropriate dimensions containing the image volume. A print out of the file information is also given. The function assumes that the corresponding .hdr file is in the same directory as the .img file.

See Also

f.read.analyze.slice, f.read.analyze.slice.at.all.timepoints, f.read.analyze.ts

Examples

a <- f.read.analyze.volume(system.file("example.img", package = "AnalyzeFMRI"))
dim(a)

Description

Reads the ANALYZE or NIFTI image format .hdr (or .nii) header file into a list. The format type is determined by first reading the magic field.

Usage

f.read.header(file)

Arguments

Value

A list containing the information in the fields of the .hdr (.nii) file. See f.read.analyze.header of f.read.nifti.header to have the list of values.

See Also

f.read.analyze.header f.read.nifti.header

Examples

f.read.header(system.file("example.hdr", package = "AnalyzeFMRI"))
f.read.header(system.file("example-nifti.hdr", package = "AnalyzeFMRI"))

Description

Reads the NIFTI image format .hdr (or .nii) header file into a list.

Usage

f.read.nifti.header(file)

Arguments

Value

A list containing the information in the fields of the .hdr (.nii) file.

Examples

f.read.nifti.header(system.file("example-nifti.hdr", package = "AnalyzeFMRI"))

Description

Reads in a specific slice from a NIFTI .img or .nii image format file into an array.

Usage

f.read.nifti.slice(file, slice, tpt)

Arguments

Details

The entire dataset is assumed to be 4D and a slice is extracted that is referenced by specifying the last two dimensions of the dataset i.e.slice and tpt.

Value

An array containing the slice

See Also

f.read.nifti.slice.at.all.timepoints, f.read.nifti.ts, f.read.nifti.volume

Examples

a <- f.read.nifti.slice(system.file("example-nifti.img", package = "AnalyzeFMRI"), 10, 1)
dim(a)

Description

Reads in a slice of a .img or .nii file at all time points into an array

Usage

f.read.nifti.slice.at.all.timepoints(file, slice)

Arguments

Value

An array containing the slice at all time points

See Also

f.read.nifti.slice, f.read.nifti.ts, f.read.nifti.volume

Examples

a <- f.read.nifti.slice.at.all.timepoints(system.file("example-nifti.img",
                                             package = "AnalyzeFMRI"), 10)
dim(a)

Description

Given a 4D NIFTI .img/.hdr image pair or a .nii file this function can read in the 3D volume of measurements at a specific time point.

Usage

f.read.nifti.tpt(file, tpt)

Arguments

Details

Given a 4D NIFTI .img/.hdr image pair or a .nii file this function can read in the 3D volume of measurements at a specific time point.

Value

A 3D array containing the volume.

See Also

f.read.nifti.slice, f.read.nifti.slice.at.all.timepoints, f.write.nifti,

Examples

f.read.nifti.tpt(system.file("example-nifti.img", package = "AnalyzeFMRI"), 1)

Description

Given a 4D NIFTI .img/.hdr image pair or a .nii file this function can read in the time series from a specified position in 3D into a vector.

Usage

f.read.nifti.ts(file, x, y, z)

Arguments

Details

Given a 4D NIFTI .img/.hdr image pair or a .nii file this function can read in the time series from a specified position in 3D into a vector.

Value

A vector containing the time series

See Also

f.read.nifti.slice, f.read.nifti.slice.at.all.timepoints, f.write.nifti,

Examples

f.read.nifti.ts(system.file("example-nifti.img", package = "AnalyzeFMRI"), 30, 30, 10)

Description

Reads the NIFTI image file into an array.

Usage

f.read.nifti.volume(file)

Arguments

Value

An array with the appropriate dimensions containing the image volume. A print out of the file information is also given. The function assumes that the corresponding .hdr file is in the same directory as the .img file (but if a .nii file is provided).

See Also

f.read.nifti.slice, f.read.nifti.slice.at.all.timepoints, f.read.nifti.ts

Examples

a <- f.read.nifti.volume(system.file("example-nifti.img", package = "AnalyzeFMRI"))
dim(a)

Description

Reads the ANALYZE or NIFTI image format image file into an array. Autodetects format type.

Usage

f.read.volume(file)

Arguments

Value

An array with the appropriate dimensions containing the image volume. A print out of the file information is also given. The function assumes that the corresponding .hdr file is in the same directory as the .img file. (but if it is a .nii file)

See Also

f.read.nifti.slice, f.read.nifti.slice.at.all.timepoints, f.read.nifti.ts

Examples

a <- f.read.volume(system.file("example-nifti.img", package = "AnalyzeFMRI"))
dim(a)

Description

For an analyze .img file the periodogram of the time series are divided by a flat spectral estimate using the median periodogram ordinate. The resulting values are then combined within each Fourier frequency and quantiles are plotted against frequency. This provides a fast look at a fMRI dataset to identify any artifacts that reside at single frequencies.

Usage

f.spectral.summary(file, mask.file, ret.flag=FALSE)

Arguments

Value

If ret.flag = TRUE the an array of quantiles at each frequency is returned

See Also

f.analyze.file.summary

Description

For a NIFTI .img file the periodogram of the time series are divided by a flat spectral estimate using the median periodogram ordinate. The resulting values are then combined within each Fourier frequency and quantiles are plotted against frequency. This provides a fast look at a fMRI dataset to identify any artifacts that reside at single frequencies.

Usage

f.spectral.summary.nifti(file, mask.file, ret.flag = FALSE, verbose = TRUE)

Arguments

Value

If ret.flag = TRUE the an array of quantiles at each frequency is returned

See Also

f.nifti.file.summary

Description

Creates a .img and .hdr pair of files from a given array

Usage

f.write.analyze(mat,file,size,pixdim,vox.units,cal.units,originator,path.out=NULL)

Arguments

Value

Nothing is returned

See Also

f.write.array.to.img.8bit, f.write.array.to.img.2bytes, f.write.array.to.img.float

Examples

a <- array(rnorm(20 * 30 * 40 * 3), dim = c(20, 30, 40, 3))
file <- "temp"
tmpdir <- tempdir()
f.write.analyze(a, file, size = "float", path.out = tmpdir)
unlink(tmpdir) # tidy up

Description

Writes an array to a .img file of 2 byte integers

Usage

f.write.array.to.img.2bytes(mat,file,path.out=NULL)

Arguments

Value

Nothing is returned

See Also

f.write.analyze f.write.array.to.img.float

Description

Writes an array to a .img file of 1 byte integers

Usage

f.write.array.to.img.8bit(mat,file,path.out=NULL)

Arguments

Value

Nothing is returned

See Also

f.write.analyze, f.write.array.to.img.float, f.write.array.to.img.2bytes

Description

Writes an array to a .img file of 4 byte floats

Usage

f.write.array.to.img.float(mat,file,path.out=NULL)

Arguments

Value

Nothing is returned

See Also

f.write.analyze, f.write.array.to.img.2bytes , f.write.array.to.img.8bit

Description

Writes a list of attributes to a .hdr file

Usage

f.write.list.to.hdr(L,file,path.out=NULL)

Arguments

Value

Nothing is returned

See Also

f.basic.hdr.list.create

Examples

a <- array(rnorm(20 * 30 * 40 * 3), dim = c(20, 30, 40, 3))
file <- "temp.hdr"
b <- f.basic.hdr.list.create(a, file)
tmpdir <- tempdir()
f.write.list.to.hdr(b, file, path.out = tmpdir)
unlink(tmpdir) # tidy up

Description

Writes a list of attributes to a .hdr file

Usage

f.write.list.to.hdr.nifti(L,file, path.out=NULL)

Arguments

Value

Nothing is returned

See Also

f.basic.hdr.nifti.list.create

Examples

a <- array(rnorm(20 * 30 * 40 * 3), dim = c(20, 30, 40, 3))
file <- "temp.hdr"
b <- f.basic.hdr.nifti.list.create(dim(a), file)
tmpdir <- tempdir()
f.write.list.to.hdr.nifti(b, file, path.out = tmpdir)
unlink(tmpdir) # tidy up

Description

Creates a .img/.hdr pair of files or a .nii file from a given array

Usage

f.write.nifti(mat,file,size,L,nii,path.out=NULL)

Arguments

Value

Nothing is returned

See Also

f.write.array.to.img.8bit, f.write.array.to.img.2bytes, f.write.array.to.img.float f.write.nii.array.to.img.8bit, f.write.nii.array.to.img.2bytes, f.write.nii.array.to.img.float

Examples

a<-array(rnorm(20*30*40*3),dim=c(20,30,40,3))
file<-"temp"
tmpdir <- tempdir()
f.write.nifti(a,file,size="float",nii=TRUE, path.out = tmpdir)
unlink(tmpdir) # tidy up

Description

Writes an array to a .img file of 2 byte integers and add at the begining of the file the NIFTI header part

Usage

f.write.nii.array.to.img.2bytes(mat,L,file,path.out=NULL)

Arguments

Value

Nothing is returned

See Also

f.write.nifti f.write.nii.array.to.img.float

Description

Writes an array to a .img file of 1 byte integers and add at the begining of the file the NIFTI header part

Usage

f.write.nii.array.to.img.8bit(mat,L,file,path.out)

Arguments

Value

Nothing is returned

See Also

f.write.nifti, f.write.nii.array.to.img.float, f.write.nii.array.to.img.2bytes

Description

Writes an array to a .img file of 4 byte floats and add at the begining of the file the NIFTI header part

Usage

f.write.nii.array.to.img.float(mat,L,file,path.out=NULL)

Arguments

Value

Nothing is returned

See Also

f.write.nifti, f.write.nii.array.to.img.2bytes , f.write.nii.array.to.img.8bit

Description

This function transforms a 4D image array into a 2D image matrix by unrolling space. This is usefull to perform a subsequent ICA.

Usage

fourDto2D(volume.4d, tm)

Arguments

Value

a matrix of size tm x vm which contains the tm images

See Also

threeDto4D twoDto4D

Examples

volume.4d <- array(rnorm(96), dim = c(2, 4, 4, 3)) # a fake 4D image array
x.2d <- fourDto2D(volume.4d, tm = 3)

Description

Encode freq.dim, phase.dim and slice.dim fields into the one byte dim.info field of a NIFTI header file.

Usage

fps2diminfo(freq.dim,phase.dim,slice.dim)

Arguments

Value

A list containing dim.info field.

See Value Section of the help file of function diminfo2fps().

See Also

diminfo2fps

Examples

dim.info <- f.read.header(system.file("example-nifti.hdr", package="AnalyzeFMRI"))$dim.info
mylist <- diminfo2fps(dim.info)
fps2diminfo(mylist$freq.dim,mylist$phase.dim,mylist$slice.dim)

Description

Applies a stationary Gaussian spatial smoothing kernel to a 3D or 4D array.

Usage

GaussSmoothArray(x, voxdim=c(1, 1, 1), ksize=5, sigma=diag(3, 3),
                 mask=NULL, var.norm=FALSE)

Arguments

Value

The smoothed array is returned.

Author(s)

J. L. Msrchini

See Also

GaussSmoothKernel

Examples

d <- c(10, 10, 10, 20)
mat <- array(rnorm(cumprod(d)[length(d)]), dim = d)
mat[, , 6:10, ] <- mat[, , 6:10, ] + 3
mask <- array(0, dim = d[1:3])
mask[3:8, 3:8, 3:8] <- 1
b <- GaussSmoothArray(mat, mask = mask, voxdim = c(1, 1, 1), ksize = 5, sigma = diag(1, 3))

Description

Calculates a simple, discrete Gaussian smoothing kernel of a specfic size given the covariance matrix of the Gaussian.

Usage

GaussSmoothKernel(voxdim=c(1, 1, 1), ksize=5, sigma=diag(3, 3))

Arguments

Value

An array of dimension (ksize,ksize,ksize) containing the smoothing kernel.

Author(s)

J. L. Marchini

Examples

a <- GaussSmoothKernel(voxdim=c(1,1,1), ksize=5, sigma=diag(1,3))

Description

This function performs a spatial ICA

Usage

ICAspat(X,n.comp,alg.typ="parallel",centering=TRUE,hp.filter=TRUE)

Arguments

Value

A list containing

See Also

ICAtemp

Description

This function performs a temporal ICA

Usage

ICAtemp(X,n.comp,alg.typ="parallel",centering=TRUE,hp.filter=TRUE)

Arguments

Value

A list containing

See Also

ICAspat

Description

This function maps from data coordinates (e.g. column i, row j, slice k), into some real world (x,y,z) positions in space. These positions could relate to Talairach-Tournoux (T&T) space, MNI space, or patient-based scanner coordinates.

Usage

ijk2xyz(ijk=c(1,1,1),method=2,L)

Arguments

Details

The NIfTI format allows storage on disk to be in either a left- or right-handed coordinate system. However, the format includes an implicit spatial transformation into a RIGHT-HANDED coordinate system. This transform maps from data coordinates (e.g. column $i$, row $j$, slice $k$), into some real world $(x,y,z)$ positions in space. These positions could relate to Talairach-Tournoux (T&T) space, MNI space, or patient-based scanner coordinates. For T&T, and MNI coordinates, $x$ increases from left to right, $y$ increases from posterior to anterior, and $z$ increases in the inferior to superior direction. Directions in the scanner coordinate system are similar. MRI data is usually exported as DICOM format, which encodes the positions and orientations of the slices. When data are converted from DICOM to NIfTI-1 format, the relevant information can be determined from the Pixel Spacing, Image Orientation (Patient) and Image Position (Patient) fields of the DICOM files. NIfTI-1 also allows the space of one image to be mapped to that of another (via a rigid or affine transform). This is to enable on-the-fly resampling of registered images. This would allow intra-subject images, collected with lots of different orientations or resolutions, to be treated as if they are all in register.

Neurological and radiological conventions only relate to visualization of axial images. They are unrelated to how the data are stored on disk, or even how the real-world coordinates are represented. It is more appropriate to consider whether the real-world coordinate system is left- or right-handed (see below). Talairach and Tournoux use a right-handed system, whereas the storage convention of ANALYZE files is usually considered as left-handed. These coordinate systems are mirror images of each other (if you are a psychologist, try explaining why mirror images appear to be left-right flipped, rather than flipped up-down, or back-front). Transforming between left- and right-handed coordinate systems involves flipping, and can not be done by rotations alone.

$x$=thumb, $y$=index finger (forefinger), $z$=left (resp. right) hand's middle finger for left-handed persons (resp. right-handed persons).

Volume orientation is given by a transformation that maps voxel indices $(i,j,k)$ to spatial coordinates $(x,y,z)$, typically anatomical coordinates assigned by the scanner. This transformation (Method 2 in the ‘nifti1.h’ documentation) is generated using the voxel dimensions, a quaternion encoding a rotation matrix, and a 3D shift, all stored in the NIfTI-1 header; details can be found in the ‘nifti1.h’ comments. The NIfTI-1 header also provides for a general affine transformation, separate from that described by Method 2. This transformation (Method 3) also maps voxel indices $(i,j,k)$ to $(x,y,z)$, which in this case are typically coordinates in a standard space such as the Talairach space. The elements of this transformation matrix are stored in the NIfTI-1 header. For example, the Method 2 transformation can be constructed from the attributes from a set of DICOM files; the Method 3 transform can be computed offline and inserted into the header later. The exact "meaning" of the coordinates given by the Method 2 and Method 3 transformations is recorded in header fields qform.code and sform.code, respectively. Code values can indicate if the $(x,y,z)$ axes are

• Anatomical coordinates from the scanner (e.g., the DICOM header)

• Aligned to some anatomical "truth" or standard

• Aligned and warped to Talairach-Tournoux coordinates

• Aligned and warped to MNI-152 coordinates

It is possible that neither transformation is specified (i.e., qform.code=sform.code=0), in which case we are left with the voxel size in pixdim[], and no orientation is given or assumed. This use (Method 1) is discouraged.

The basic idea behind having two coordinate systems is to allow the image to store information about (1) the scanner coordinate system used in the acquisition of the volume (in the qform) and (2) the relationship to a standard coordinate system - e.g. MNI coordinates (in the sform). The qform allows orientation information to be kept for alignment purposes without losing volumetric information, since the qform only stores a rigid-body transformation which preserves volume. On the other hand, the sform stores a general affine transformation which can map the image coordinates into a standard coordinate system, like Talairach or MNI, without the need to resample the image. By having both coordinate systems, it is possible to keep the original data (without resampling), along with information on how it was acquired (qform) and how it relates to other images via a standard space (sform). This ability is advantageous for many analysis pipelines, and has previously required storing additional files along with the image files. By using NIfTI-1 this extra information can be kept in the image files themselves.
Note: the qform and sform also store information on whether the coordinate system is left-handed or right-handed (see Q15) and so when both are set they must be consistent, otherwise the handedness of the coordinate system (often used to distinguish left-right order) is unknown and the results of applying operations to such an image are unspecified.

There are 3 different methods by which continuous coordinates can be attached to voxels. The discussion below emphasizes 3D volumes, and the continuous coordinates are referred to as $(x,y,z)$. The voxel index coordinates (i.e., the array indexes) are referred to as $(i,j,k)$, with valid ranges:

• $i = 0,\ldots,$dim[1]-1
  

• $j = 0,\ldots,$dim[2]-1 (if dim[0] $>= 2$)
  

• $k = 0,\ldots,$dim[3]-1 (if dim[0] $>= 3$)
  

The $(x,y,z)$ coordinates refer to the CENTER of a voxel. In methods 2 and 3, the (x,y,z) axes refer to a subject-based coordinate system, with

$+x$ = Right $+y$ = Anterior $+z$ = Superior.

This is a right-handed coordinate system. However, the exact direction these axes point with respect to the subject depends on qform.code (Method 2) and sform.code (Method 3).

N.B.: The $i$ index varies most rapidly, $j$ index next, $k$ index slowest. Thus, voxel $(i,j,k)$ is stored starting at location

$(i + j*\code{dim[1]} + k*\code{dim[1]}*\code{dim[2]}) * (\code{bitpix}/8)$

into the dataset array.

N.B.: The ANALYZE 7.5 coordinate system is

$+x$ = Left $+y$ = Anterior $+z$ = Superior

which is a left-handed coordinate system. This backwardness is too difficult to tolerate, so this NIFTI-1 standard specifies the coordinate order which is most common in functional neuroimaging.

N.B.: The 3 methods below all give the locations of the voxel centers in the $(x,y,z)$ coordinate system. In many cases, programs will wish to display image data on some other grid. In such a case, the program will need to convert its desired $(x,y,z)$ values into $(i,j,k)$ values in order to extract (or interpolate) the image data. This operation would be done with the inverse transformation to those described below.

N.B.: Method 2 uses a factor qfac which is either -1 or 1; qfac is stored in the otherwise unused pixdim[0]. If pixdim[0]=0.0 (which should not occur), we take qfac=1. Of course, pixdim[0] is only used when reading a NIFTI-1 header, not when reading an ANALYZE 7.5 header.

N.B.: The units of $(x,y,z)$ can be specified using the xyzt.units field.

• METHOD 1 (the "old" way, used only when qform.code = 0):

  The coordinate mapping from $(i,j,k)$ to $(x,y,z)$ is the ANALYZE 7.5 way. This is a simple scaling relationship:

  x = pixdim[1] * i
y = pixdim[2] * j
z = pixdim[3] * k

  No particular spatial orientation is attached to these $(x,y,z)$ coordinates. (NIFTI-1 does not have the ANALYZE 7.5 orient field, which is not general and is often not set properly.) This method is not recommended, and is present mainly for compatibility with ANALYZE 7.5 files.

• METHOD 2 (used when qform.code > 0, which should be the "normal" case):

  The $(x,y,z)$ coordinates are given by the pixdim[] scales, a rotation matrix, and a shift. This method is intended to represent "scanner-anatomical" coordinates, which are often embedded in the image header (e.g., DICOM fields (0020,0032), (0020,0037), (0028,0030), and (0018,0050)), and represent the nominal orientation and location of the data. This method can also be used to represent "aligned" coordinates, which would typically result from some post-acquisition alignment of the volume to a standard orientation (e.g., the same subject on another day, or a rigid rotation to true anatomical orientation from the tilted position of the subject in the scanner). The formula for $(x,y,z)$ in terms of header parameters and $(i,j,k)$ is:

  [ x ]		[ R11 R12 R13 ] [	pixdim[1] * i ]		[ qoffset.x ]
  [ y ]	=	[ R21 R22 R23 ] [	pixdim[2] * j ]	+	[ qoffset.y ]
  [ z ]		[ R31 R32 R33 ] [	qfac * pixdim[3] * k ]		[ qoffset.z ]

  The qoffset.* shifts are in the NIFTI-1 header. Note that the center of the $(i,j,k)=(0,0,0)$ voxel (first value in the dataset array) is just $(x,y,z)=(\code{qoffset.x},\code{qoffset.y},\code{qoffset.z})$.

  The rotation matrix $R$ is calculated from the quatern.* parameters. This calculation is described below.

  The scaling factor qfac is either 1 or -1. The rotation matrix $R$ defined by the quaternion parameters is "proper" (has determinant 1). This may not fit the needs of the data; for example, if the image grid is
  $i$ increases from Left-to-Right
  $j$ increases from Anterior-to-Posterior
  $k$ increases from Inferior-to-Superior
  Then $(i,j,k)$ is a left-handed triple. In this example, if qfac=1, the $R$ matrix would have to be

  [ 1 0 0 ]
  [ 0 -1 0 ] which is "improper" (determinant = -1).
  [ 0 0 1 ]

  If we set qfac=-1, then the $R$ matrix would be

  [ 1 0 0 ]
  [ 0 -1 0 ] which is proper.
  [ 0 0 -1 ]

  This $R$ matrix is represented by quaternion $[a,b,c,d] = [0,1,0,0]$ (which encodes a 180 degree rotation about the $x$-axis).

• METHOD 3 (used when sform.code > 0):

  The $(x,y,z)$ coordinates are given by a general affine transformation of the $(i,j,k)$ indexes:

  x = srow.x[0] * i + srow.x[1] * j + srow.x[2] * k + srow.x[3]

  y = srow.y[0] * i + srow.y[1] * j + srow.y[2] * k + srow.y[3]

  z = srow.z[0] * i + srow.z[1] * j + srow.z[2] * k + srow.z[3]

  The srow.* vectors are in the NIFTI.1 header. Note that no use is made of pixdim[] in this method.

• WHY 3 METHODS?

  Method 1 is provided only for backwards compatibility. The intention is that Method 2 (qform.code > 0) represents the nominal voxel locations as reported by the scanner, or as rotated to some fiducial orientation and location. Method 3, if present (sform.code > 0), is to be used to give the location of the voxels in some standard space. The sform.code indicates which standard space is present. Both methods 2 and 3 can be present, and be useful in different contexts (method 2 for displaying the data on its original grid; method 3 for displaying it on a standard grid).

  In this scheme, a dataset would originally be set up so that the Method 2 coordinates represent what the scanner reported. Later, a registration to some standard space can be computed and inserted in the header. Image display software can use either transform, depending on its purposes and needs.

  In Method 2, the origin of coordinates would generally be whatever the scanner origin is; for example, in MRI, (0,0,0) is the center of the gradient coil.

  In Method 3, the origin of coordinates would depend on the value of sform.code; for example, for the Talairach coordinate system, (0,0,0) corresponds to the Anterior Commissure.

• QUATERNION REPRESENTATION OF ROTATION MATRIX (METHOD 2)
  

  The orientation of the $(x,y,z)$ axes relative to the $(i,j,k)$ axes in 3D space is specified using a unit quaternion $[a,b,c,d]$, where $a*a+b*b+c*c+d*d=1$. The $(b,c,d)$ values are all that is needed, since we require that $a = \sqrt(1.0-(b*b+c*c+d*d))$ be nonnegative. The $(b,c,d)$ values are stored in the (quatern.b,quatern.c,quatern.d) fields.

  The quaternion representation is chosen for its compactness in representing rotations. The (proper) 3x3 rotation matrix that corresponds to $[a,b,c,d]$ is

  		[	a*a+b*b-c*c-d*d	2*b*c-2*a*d	2*b*d+2*a*c	]
  R	=	[	2*b*c+2*a*d	a*a+c*c-b*b-d*d	2*c*d-2*a*b	]
  		[	2*b*d-2*a*c	2*c*d+2*a*b	a*a+d*d-c*c-b*b	]

  		[ R11	R12	R13	]
  	=	[ R21	R22	R23	]
  		[ R31	R32	R33	]

  If $(p,q,r)$ is a unit 3-vector, then rotation of angle $h$ about that direction is represented by the quaternion

  $[a,b,c,d] = [cos(h/2), p*sin(h/2), q*sin(h/2), r*sin(h/2)]$.

  Requiring $a >= 0$ is equivalent to requiring $-Pi <= h <= Pi$. (Note that $[-a,-b,-c,-d]$ represents the same rotation as $[a,b,c,d]$; there are 2 quaternions that can be used to represent a given rotation matrix $R$.) To rotate a 3-vector $(x,y,z)$ using quaternions, we compute the quaternion product

  $[0,x',y',z'] = [a,b,c,d] * [0,x,y,z] * [a,-b,-c,-d]$

  which is equivalent to the matrix-vector multiply

  [ x' ]		[ x ]
  [ y' ]	= R	[ y ] (equivalence depends on a*a+b*b+c*c+d*d=1)
  [ z' ]		[ z ]

  Multiplication of 2 quaternions is defined by the following:

  $[a,b,c,d] = a*1 + b*I + c*J + d*K$

  where
  $I*I = J*J = K*K = -1$ ($I,J,K$ are square roots of -1)
  $I*J = K$ , $J*K = I$ , $K*I = J$
$J*I = -K$ , $K*J = -I$ , $I*K = -J$ (not commutative!).

  For example
  

  $[a,b,0,0] * [0,0,0,1] = [0,0,-b,a]$

  since this expands to
  

  $(a+b*I)*(K) = (a*K+b*I*K) = (a*K-b*J).$

  The above formula shows how to go from quaternion $(b,c,d)$ to rotation matrix and direction cosines. Conversely, given $R$, we can compute the fields for the NIFTI-1 header by

  $a = 0.5 * \sqrt(1+R11+R22+R33)$ (not stored)
  $b = 0.25 * (R32-R23) / a$ => quatern.b
$c = 0.25 * (R13-R31) / a$ => quatern.c
$d = 0.25 * (R21-R12) / a$ => quatern.d

  If a=0 (a 180 degree rotation), alternative formulas are needed. See the ‘nifti1.io.c’ function mat44.to.quatern() for an implementation of the various cases in converting $R$ to $[a,b,c,d]$.

  Note that $R$-transpose (= $R$-inverse) would lead to the quaternion $[a,-b,-c,-d]$.

  The choice to specify the qoffset.x (etc.) values in the final coordinate system is partly to make it easy to convert DICOM images to this format. The DICOM attribute "Image Position (Patient)" (0020,0032) stores the $(Xd,Yd,Zd)$ coordinates of the center of the first voxel. Here, $(Xd,Yd,Zd)$ refer to DICOM coordinates, and $Xd=-x$, $Yd=-y$, $Zd=z$, where $(x,y,z)$ refers to the NIFTI coordinate system discussed above. (i.e., DICOM $+Xd$ is Left, $+Yd$ is Posterior, $+Zd$ is Superior,
  whereas $+x$ is Right, $+y$ is Anterior , $+z$ is Superior. )
  Thus, if the (0020,0032) DICOM attribute is extracted into $(px,py,pz)$, then
  qoffset.x = $-px$ qoffset.y = $-py$ qoffset.z = $pz$
  is a reasonable setting when qform.code=NIFTI.XFORM.SCANNER.ANAT.

  That is, DICOM's coordinate system is 180 degrees rotated about the $z$-axis from the neuroscience/NIFTI coordinate system. To transform between DICOM and NIFTI, you just have to negate the $x$- and $y$-coordinates.

  The DICOM attribute (0020,0037) "Image Orientation (Patient)" gives the orientation of the $x$- and $y$-axes of the image data in terms of 2 3-vectors. The first vector is a unit vector along the $x$-axis, and the second is along the $y$-axis. If the (0020,0037) attribute is extracted into the value ($xa,xb,xc,ya,yb,yc)$, then the first two columns of the $R$ matrix would be
  [ -xa -ya ]
  [ -xb -yb ]
  [ xc yc ]
  The negations are because DICOM's $x$- and $y$-axes are reversed relative to NIFTI's. The third column of the $R$ matrix gives the direction of displacement (relative to the subject) along the slice-wise direction. This orientation is not encoded in the DICOM standard in a simple way; DICOM is mostly concerned with 2D images. The third column of $R$ will be either the cross-product of the first 2 columns or its negative. It is possible to infer the sign of the 3rd column by examining the coordinates in DICOM attribute (0020,0032) "Image Position (Patient)" for successive slices. However, this method occasionally fails for reasons that I (RW Cox) do not understand.

Value

A list containing the matrix xyz of the positions of the points specified in ijk.

See Also

xyz2ijk Q2R R2Q

Examples

L <- f.read.header(system.file("example-nifti.hdr",
package="AnalyzeFMRI"))
ijk <- matrix(c(1,1,1,2,3,7),byrow=FALSE,nrow=3)
ijk2xyz(ijk=ijk,method=2,L)

Description

Determine the type of a file : NIFTI .nii format, NIFTI .hdr/.img pair format, ANALYZE format.

Usage

magicfield(file, verbose = TRUE)

Arguments

Value

A list containing the magic and dim fields.

Examples

magicfield(system.file("example-nifti.hdr", package="AnalyzeFMRI"))

Description

Extract in that order Translation, Rotation, Shear and Scale from a 4x4 (or 3x4) affine matrix from a NIFTI header list (srow.x, srow.y, srow.z).

Usage

mat34.to.TRSZ(M)

Arguments

Details

Decomposes M using the convention: M = translation * scale * skew * rotation. Be careful that rotation can be improper.

Value

A list containing Translation, Scale, Shear and Rotation. Rotation decomposition is also provided (rotation = RotZ*RotY*RotX*Ref where Ref is a Reflexion if the rotation is improper or is Identity if the rotation is proper).

See Also

R2Q Q2R mat34.to.TZSR

Examples

L <- f.read.nifti.header(system.file("example-nifti.hdr", package="AnalyzeFMRI"))
M <- rbind(L$srow.x,L$srow.y,L$srow.z)
mat34.to.TRSZ(M)

Description

Extract in that order Translation, Scale, Shear and Rotation from a 4x4 (or 3x4) affine matrix from a NIFTI header list (srow.x, srow.y, srow.z).

Usage

mat34.to.TZSR(M)

Arguments

Details

Decomposes M using the convention: M = translation * scale * skew * rotation. Be careful that rotation can be improper.

Value

A list containing Translation, Scale, Shear and Rotation. Rotation decomposition is also provided (rotation = RotZ*RotY*RotX*Ref where Ref is a Reflexion if the rotation is improper or is Identity if the rotation is proper).

See Also

R2Q Q2R mat34.to.TRSZ

Examples

L <- f.read.nifti.header(system.file("example-nifti.hdr", package="AnalyzeFMRI"))
M <- rbind(L$srow.x,L$srow.y,L$srow.z)
mat34.to.TZSR(M)

Description

Calculates covariance from Hartvig Model 2

Usage

model.2.cov.func(g, par)

Arguments

Value

The calculated covariance

Author(s)

J. L. Marchini

References

Hartvig, N. V. and Jensen, J. L (2000) Spatial Mixture Modelling of fMRI Data, Human Brain Mapping 11:233–248

Description

Estimate gamma for Model 2 of Hartvig and Jensen (2000)

Usage

model.2.est.gamma(cov, par)

Arguments

Value

The estimate of gamma

Author(s)

J. L. Marchini

References

Hartvig, N. V. and Jensen, J. L (2000) Spatial Mixture Modelling of fMRI Data, Human Brain Mapping 11:233–248

Description

Fits the N2G model (1 Normal and 2 Gamma's mixture model) to a dataset using Maximum Likelihhod.

Usage

N2G(data, par.start = c(4, 2, 4, 2, 0.9, 0.05))

Arguments

Details

The mixture model considered is a mixture of a standard normal distribution and two Gamma functions. This model is denoted N2G.

x ~ p1 * N(0, 1) + p2 * Gamma(a, b) + (1 - p1 - p2) * -Gamma(c, d)

Value

A list with components

Author(s)

J. L. Marchini

See Also

N2G.Class.Probability, N2G.Likelihood.Ratio, N2G.Spatial.Mixture, N2G.Density , N2G.Likelihood , N2G.Transform, N2G.Fit , N2G.Inverse , N2G.Region

Examples

par <- c(3, 2, 3, 2, .3, .4)
data <- c(rnorm(10000), rgamma(2000, 10, 1), -rgamma(1400, 10, 1))
hist(data, n = 100, freq = FALSE)

q <- N2G.Fit(data, par, maxit = 10000, method = "BFGS")
p <- seq(-50, 50, .1)
lines(p, N2G.Density(p, q), col = 2)

Description

Calculates the Posterior Probability of data points being in each class given the parameters of the N2G model.

Usage

N2G.Class.Probability(data, par)

Arguments

Value

Returns the Posterior Probability of data points being in each class given the parameters of the N2G model.

Author(s)

J. L. Marchini

See Also

N2G.Likelihood.Ratio, N2G.Spatial.Mixture, N2G.Density, N2G.Likelihood , N2G.Transform, N2G.Fit , N2G , N2G.Inverse , N2G.Region

Description

Calculates the density function for the N2G model

Usage

N2G.Density(data, par)

Arguments

Details

Calculates the density function for the N2G model

Value