Package 'neuroim'

Title: Data Structures and Handling for Neuroimaging Data
Description: A collection of data structures that represent volumetric brain imaging data. The focus is on basic data handling for 3D and 4D neuroimaging data. In addition, there are function to read and write NIFTI files and limited support for reading AFNI files.
Authors: Bradley R. Buchsbaum
Maintainer: Bradley Buchsbaum <[email protected]>
License: GPL (>= 2)
Version: 0.1.0
Built: 2024-11-06 05:13:51 UTC
Source: https://github.com/bbuchsbaum/neuroim

Help Index

extract labeled volume from BrainBucket

Description

extract labeled volume from BrainBucket

Usage

## S4 method for signature 'BrainBucket,index,missing,ANY'
x[i]

Arguments

x

the object
i

first index

subset an ROIVolume

Description

subset an ROIVolume

Usage

## S4 method for signature 'ROIVolume,numeric,missing,ANY'
x[i, j, drop]

## S4 method for signature 'ROIVolume,logical,missing,ANY'
x[i, j, drop]

Arguments

x

the object
i

first index
j

second index
drop

drop dimension

extractor

Description

extractor

Usage

## S4 method for signature 'SparseBrainVector,missing,missing,ANY'
x[i, j, k, m, ...,
  drop = TRUE]

Arguments

x

the object
i

first index
j

second index
k

third index
m

the fourth index
...

additional args
drop

dimension

extractor

Description

extractor

Usage

## S4 method for signature 'SparseBrainVector,missing,numeric,ANY'
x[i, j, k, m, ...,
  drop = TRUE]

Arguments

x

the object
i

first index
j

second index
k

third index
m

the fourth index
...

additional args
drop

dimension

extractor

Description

extractor

Usage

## S4 method for signature 'SparseBrainVector,numeric,missing,ANY'
x[i, j, k, m, ...,
  drop = TRUE]

Arguments

x

the object
i

first index
j

second index
k

third index
m

the fourth index
...

additional args
drop

dimension

extractor

Description

extractor

Usage

## S4 method for signature 'SparseBrainVector,numeric,numeric,ANY'
x[i, j, k, m, ...,
  drop = TRUE]

Arguments

x

the object
i

first index
j

second index
k

third index
m

the fourth index
...

additional args
drop

dimension

extractor

Description

extractor

Usage

## S4 method for signature 'SparseBrainVolume,matrix,missing,ANY'
x[i, j, k, ...,
  drop = TRUE]

Arguments

x

the object
i

first index
j

second index
k

third index
...

additional args
drop

dimension

extractor

Description

extractor

Usage

## S4 method for signature 'SparseBrainVolume,missing,missing,ANY'
x[i, j, k, ...,
  drop = TRUE]

Arguments

x

the object
i

first index
j

second index
k

third index
...

additional args
drop

dimension

extractor

Description

extractor

Usage

## S4 method for signature 'SparseBrainVolume,missing,numeric,ANY'
x[i, j, k, ...,
  drop = TRUE]

Arguments

x

the object
i

first index
j

second index
k

third index
...

additional args
drop

dimension

extractor

Description

extractor

Usage

## S4 method for signature 'SparseBrainVolume,numeric,missing,missing'
x[i, j, k, ..., drop]

Arguments

x

the object
i

first index
j

second index
k

third index
...

additional args
drop

drop dimension

extractor

Description

extractor

Usage

## S4 method for signature 'SparseBrainVolume,numeric,numeric,ANY'
x[i, j, k, ...,
  drop = TRUE]

Arguments

x

the object
i

first index
j

second index
k

third index
...

additional args
drop

dimension

extract labeled volume from BrainBucket

Description

extract labeled volume from BrainBucket

Usage

## S4 method for signature 'BrainBucket,index,missing'
x[[i]]

Arguments

x

the object
i

the first index

extractor

Description

extractor

extractor

Usage

## S4 method for signature 'SparseBrainVector,numeric,ANY'
x[[i]]

## S4 method for signature 'BrainVector,numeric,ANY'
x[[i]]

Arguments

i

the volume index
i

the volume index

Generic function to add a dimension to an object

Description

Generic function to add a dimension to an object

add dimension to BrainSpace

Usage

addDim(x, n)

## S4 method for signature 'BrainSpace,numeric'
addDim(x, n)

Arguments

x

a dimensioned object
n

the size of the dimension to add

Examples

x = BrainSpace(c(10,10,10), c(1,1,1))
x1 <- addDim(x, 10)
ndim(x1) == 4
dim(x1)[4] == 10

AFNIFileDescriptor

Description

This class supports the AFNI file format

AFNIMetaInfo

Description

Constructor for AFNIMetaInfo class

Usage

AFNIMetaInfo(descriptor, afni_header)

Arguments

descriptor

an instance of class AFNIFileDescriptor
afni_header

a list returned by readAFNIHeader

Value

an instance of class AFNIMetaInfo

conversion from DenseBrainVolume to array

Description

conversion from DenseBrainVolume to array

conversion from SparseBrainVolume to array

conversion from SparseBrainVolume to numeric

conversion from BrainVolume to LogicalBrainVolume

conversion from DenseBrainVolume to LogicalBrainVolume

conversion from ClusteredBrainVolume to LogicalBrainVolume

conversion from BrainVolume to array

convert BrainData instance to array

Description

convert BrainData instance to array

Usage

## S4 method for signature 'BrainData'
as.array(x)

Arguments

x

the object

as.list

Description

convert SparseBrainVector to list of DenseBrainVolume

Usage

## S4 method for signature 'SparseBrainVector'
as.list(x)

## S4 method for signature 'BrainVector'
as.list(x)

Arguments

x

the object

as.logical

Description

Convert BrainVolume to linkS4class{LogicalBrainVolume}

Usage

## S4 method for signature 'BrainVolume'
as.logical(x)

Arguments

x

the object

Details

the image values will be converted to using R base function as.logical and wrapped in LogicalBrainVolume

Value

an instance of linkS4class{LogicalBrainVolume}

Convert to a LogicalBrainVolume

Description

Convert to a LogicalBrainVolume

Usage

as.mask(x, indices)

## S4 method for signature 'BrainVolume,missing'
as.mask(x)

## S4 method for signature 'BrainVolume,numeric'
as.mask(x, indices)

Arguments

x

the object to binarize
indices

the indices to set to TRUE

convert BrainData instance to matrix

Description

convert BrainData instance to matrix

Usage

## S4 method for signature 'BrainData'
as.matrix(x)

Arguments

x

the object

convert a ROIVector to a matrix

Description

convert SparseBrainVector to matrix

Usage

## S4 method for signature 'ROIVector'
as.matrix(x)

## S4 method for signature 'ROIVector'
as.matrix(x)

## S4 method for signature 'SparseBrainVector'
as.matrix(x)

## S4 method for signature 'DenseBrainVector'
as.matrix(x)

Arguments

x

the object
x

the object
x

the object

Convert SparseBrainVolume to numeric

Description

Convert SparseBrainVolume to numeric

Usage

## S4 method for signature 'SparseBrainVolume'
as.numeric(x)

Arguments

x

the object to convert

Convert to from dense to sparse representation

Description

Convert to from dense to sparse representation

Usage

as.sparse(x, mask, ...)

## S4 method for signature 'DenseBrainVector,LogicalBrainVolume'
as.sparse(x, mask)

## S4 method for signature 'DenseBrainVector,numeric'
as.sparse(x, mask)

## S4 method for signature 'DenseBrainVolume,LogicalBrainVolume'
as.sparse(x, mask)

## S4 method for signature 'DenseBrainVolume,numeric'
as.sparse(x, mask)

Arguments

x

the object to make sparse, e.g. DenseBrainVolume or DenseBrainVector
mask

the elements to retain
...

additional arguments

Details

mask can be an integer vector of 1D indices or a mask volume of class LogicalBrainVolume

Examples

bvol <- BrainVolume(array(runif(24*24*24), c(24,24,24)), BrainSpace(c(24,24,24), c(1,1,1)))
indmask <- sort(sample(1:(24*24*24), 100))
svol <- as.sparse(bvol, indmask)


mask <- LogicalBrainVolume(runif(length(indmask)), space=space(bvol), indices=indmask)
sum(mask) == 100

convert BrainData instance to vector

Description

convert BrainData instance to vector

Usage

## S4 method for signature 'BrainData'
as.vector(x)

Arguments

x

the object

Generic getter function to extract image axes

Description

Generic getter function to extract image axes

Usage

axes(x)

## S4 method for signature 'BrainSpace'
axes(x)

## S4 method for signature 'BrainData'
axes(x)

Arguments

x

an object with a set of axes

Base

Description

Virtual base class representing an ordered set of named axes.

Slots

ndim

the number of axes (or dimensions)

AxisSet1D

Description

A one-dimensional axis set

Slots

i

the first axis

AxisSet2D

Description

A two-dimensional axis set

Slots

j

the second axis

AxisSet3D

Description

A three-dimensional axis set

Slots

k

the third axis

AxisSet4D

Description

A four-dimensional axis set

Slots

l

the fourth axis

AxisSet5D

Description

A five-dimensional axis set

Slots

m

the fifth axis

Convert 1-dimensional real axis coordinates along a single axis dimension to a 1D index along the same axis.

Description

Convert 1-dimensional real axis coordinates along a single axis dimension to a 1D index along the same axis.

Convert 1-dimensional index to a coordinate value along the same axis.

Usage

axisToIndex(x, real, dimNum)

indexToAxis(x, index, dimNum)

## S4 method for signature 'BrainSpace,numeric,numeric'
axisToIndex(x, real, dimNum)

## S4 method for signature 'BrainSpace,numeric,numeric'
indexToAxis(x, index, dimNum)

Arguments

x

the object
real

the axis coordinates
dimNum

the dimension number of the axis (e.g. 1, 2, 3)
index

the axis index
x

the object
dimNum

the dimension number of the axis (e.g. 1, 2, 3)

Value

a vector of axis indices

a vector of axis indices

Base

Description

Generic S4 Base class

BaseMetaInfo

Description

This is a base class to represent meta information

BaseSource

Description

This is a base class to represent a data source

Slots

metaInfo

meta information for the data source

BasisBrainVector

Description

a class that stores a represents a 4-dimensional array as a set of basis functions (dictionary) and corresponding set of coefficients.

Slots

mask

the mask defining the sparse domain

basis

the matrix of bases, were each column is a basis vector.

coeffs

the sparseMatrix of coefficients

map

instance of class IndexLookupVolume is used to map between spatial and index/row coordinates

BinaryReader

Description

Constructor for BinaryReader class

Usage

BinaryReader(input, byteOffset, dataType, bytesPerElement,
  endian = .Platform$endian)

Arguments

input

file name to read from or else a connection object
byteOffset

the number of bytes to skip at the start of input
dataType

R data type of binary elements
bytesPerElement

number of bytes in each data element (e.g. 4 or 8 for floating point numbers)
endian

endianness of binary input connection

BinaryReader

Description

This class supports reading of bulk binary data from a connection

Slots

input

the binary input connection

byteOffset

the number of bytes to skip at the start of input

dataType

the dataType of the binary Elements

bytesPerElement

number of bytes in each data element (e.g. 4 or 8 for floating point numbers)

endian

endianness of binary input connection

BinaryWriter

Description

This class supports writing of bulk binary data to a connection

Constructor for BinaryWriter class

Usage

BinaryWriter(output, byteOffset, dataType, bytesPerElement,
  endian = .Platform$endian)

Arguments

output

file name to write to or else a connection object
byteOffset

the number of bytes to skip at the start of output
dataType

R data type of binary elements
bytesPerElement

number of bytes in each data element (e.g. 4 or 8 for floating point numbers)
endian

endianness of binary output connection

Slots

output

the binary output connection

byteOffset

the number of bytes to skip at the start of input

dataType

the dataType of the binary Elements

bytesPerElement

number of bytes in each data element (e.g. 4 or 8 for floating point numbers)

endian

endianness of binary output connection

Create a spherical searchlight iterator that samples regions from within a mask.

Description

searchlight centers are sampled without replacement, but the same surround voxel can belong to multiple searchlight samples.

Usage

BootstrapSearchlight(mask, radius = 8, iter = 100)

Arguments

mask

an image volume containing valid central voxels for roving searchlight
radius

in mm of spherical searchlight (can be a vector which is randomly sampled)
iter

the total number of searchlights to sample (default is 100).

Generic function to extract the spatial bounds (origin + dim * spacing) of an image param x the object

Description

Generic function to extract the spatial bounds (origin + dim * spacing) of an image param x the object

bounds

Usage

bounds(x)

## S4 method for signature 'BrainSpace'
bounds(x)

## S4 method for signature 'BrainData'
bounds(x)

Arguments

x

the object with bounds property

Value

a matrix where each row contains the min (column 1) and max (column 2) bounds of the image dimension from 1 to ndim(image).

Examples

bspace <- BrainSpace(c(10,10,10), c(2,2,2))
b <- bounds(bspace)
nrow(b) == ndim(bspace)
ncol(b) == 2

BrainBucket

Description

a four-dimensional image that conists of a sequence of labeled image volumes backed by a list

Constructor function for BrainBucket class

Usage

BrainBucket(volumeList)

Arguments

volumeList

a named list of BrainVolume instances

Value

an instance of class BrainBucket

Slots

source

the data source for the bucket volumes

labels

the names of the sub-volumes contained in the bucket

data

a list of BrainVolume instances with names corresponding to volume labels

Examples

vol1 <- BrainVolume(rnorm(24*24*24), BrainSpace(c(24,24,24), c(1,1,1)))
vol2 <- BrainVolume(rnorm(24*24*24), BrainSpace(c(24,24,24), c(1,1,1)))
vol3 <- BrainVolume(rnorm(24*24*24), BrainSpace(c(24,24,24), c(1,1,1)))
vlist <- list(vol1,vol2,vol3)
names(vlist) <- paste0("V", 1:3)
bucket <- BrainBucket(vlist)
all.equal(dim(bucket[[1]]), dim(vol1))

BrainBucketSource

Description

A class that is used to produce a BrainBucket instance

Constructor function for BrainBucketSource class

Usage

BrainBucketSource(fileName, pattern = NULL, indices = NULL)

Arguments

fileName

the name of the bucket file
pattern

optional regular expression used to filter the sub-volumes using associated labels
indices

optional set of sub-volume indices to load

Slots

sourceList

a list of sources for the bucket sub-volumes

cache

a cache used to store data in memory

BrainData

Description

Base class for brain image data

Slots

source

an instance of class BaseSource to store the source of the data

space

an instance of class BrainSpace to represent the geometry of the data space

BrainFileDescriptor

Description

This class represents a neuroimaging file format

Slots

fileFormat

the name of the file format (e.g. NIfTI)

headerEncoding

the file encoding of the header file (e.g. 'raw' for binary, 'gzip' for gz compressed')

headerExtension

the file extension for the header file (e.g. 'nii' for NIfTI single files)

dataEncoding

the file encoding for the data file

dataExtension

the file extension for the data file (e.g. 'nii' for NIfTI single files)

BrainFileSource Base class for representing a data source for images. The purpose of this class is to provide a layer in between low level IO and image loading functionality.

Description

BrainFileSource

Base class for representing a data source for images. The purpose of this class is to provide a layer in between low level IO and image loading functionality.

Slots

metaInfo

meta information for the data source

BrainMetaInfo This class contains meta information from an image

Description

BrainMetaInfo

This class contains meta information from an image

This class contains meta information for an image

Usage

BrainMetaInfo(Dim, spacing, origin = rep(0, length(spacing)),
  dataType = "FLOAT", label = "",
  spatialAxes = OrientationList3D$AXIAL_LPI, additionalAxes = NullAxis)

Arguments

Dim

image dimensions
spacing

voxel dimensions
origin

coordinate origin
dataType

the type of the data (e.g. "FLOAT")
label

name(s) of images
spatialAxes

image axes for spatial dimensions (x,y,z)
additionalAxes

axes for dimensions > 3 (e.g. time, color band, direction)

Value

an instance of class BrainMetaInfo

Slots

dataType

the data type code, e.g. FLOAT

Dim

image dimensions

spatialAxes

image axes for spatial dimensions (x,y,z)

additionalAxes

axes for dimensions > 3 (e.g. time, color band, direction)

spacing

voxel dimensions

origin

coordinate origin

label

name(s) of images

BrainSlice constructor

Description

BrainSlice constructor

Usage

BrainSlice(data, space, indices = NULL)

Arguments

data

data vector or matrix
space

an instance of class BrainSpace
indices

linear indices corresponding used if data is a 1D vector.

Examples

bspace <- BrainSpace(c(64,64), spacing=c(1,1))
dat <- array(rnorm(64*64), c(64,64))
bslice <- BrainSlice(dat,bspace)
print(bslice)

BrainSlice

Description

Two-dimensional brain image

BrainSource

Description

Base class for representing a data source for images. The purpose of this class is to provide a layer in between low level IO and image loading functionality.

Slots

metaInfo

meta information for the data source

Constructor function for BrainSpace class

Description

Constructor function for BrainSpace class

Usage

BrainSpace(Dim, spacing = NULL, origin = NULL, axes = NULL,
  trans = NULL)

Arguments

Dim

a vector describing the dimensions of the spatial grid
spacing

the real-valued voxel dimensions (usually in millimeters)
origin

the coordinate origin of the image space
axes

the image axes ordering (default is based on the NIFTI standard, Left-Posterior-Inferior)
trans

a matrix representing the coordinate transformation associated with the image space (default is based on the NIFTI standard, Left-Posterior-Inferior)

Value

an instance of class BrainSpace

Note

one should rarely need to create a new BrainSpace instance, as it will almost always be created automatically using information stored in an image header. Also, If one already has an existing image object, its BrainSpace instance can be easily extracted with the space method.

Examples

bspace <- BrainSpace(c(64,64,64), origin=c(0,0,0), spacing=c(2,2,2))
print(bspace)
origin(bspace)
axes(bspace)
trans(bspace)

BrainSpace

Description

This class represents the geometry of a brain image

Slots

Dim

the grid dimensions of the image

origin

the coordinates of the spatial origin

spacing

the dimensions (in mm) of the grid units (voxels)

axes

the set of named spatial axes in the untransformed native grid space.

trans

an affine transformation matrix that moves from grid -> real world coordinates

inverseTrans

an inverse matrix that moves from real world -> grid coordinates

BrainVector

Description

Four-dimensional brain image

constructor function for virtual class BrainVector

Usage

BrainVector(data, space = NULL, mask = NULL, source = NULL, label = "")

Arguments

data

the image data which can be a matrix, a 4d array, or a list of BrainVolumes. If the latter, the geometric space of the data BrainSpace will be inferred from the constituent volumes, which must all be identical.
space

a BrainSpace object. Does not ned to be included if data argument is a list of BrainVolumes
mask

an optional array of type logical
source

an optional BrainSource object
label

a label of type character

Value

a concrete instance of BrainVector class. If mask is provided then SparseBrainVector, otherwise DenseBrainVector

BrainVectorSource

Description

Construct a BrainVectorSource object

Usage

BrainVectorSource(fileName, indices = NULL, mask = NULL)

Arguments

fileName

name of the 4-dimensional image file
indices

the subset of integer volume indices to load – if NULL then all volumes will be loaded
mask

image volume indicating the subset of voxels that will be loaded. If provided, function returns SparseBrainVectorSource

Details

If a mask is supplied then it should be a LogicalBrainVolume or BrainVolume instance. If the latter, then the mask will be defined by nonzero elements of the volume.

Value

a instance deriving from BrainVectorSource

BrainVectorSource

Description

A class that is used to produce a BrainVectorSource instance

Slots

indices

the index vector of the volumes to be loaded

BrainVolume

Description

Construct a BrainVolume instance, using default (dense) implementation

Usage

BrainVolume(data, space, source = NULL, label = "", indices = NULL)

Arguments

data

a three-dimensional array
space

an instance of class BrainSpace
source

an instance of class BrainSource
label

a character string to identify volume
indices

an 1D vector that gives the linear indices of the associated data vector

Value

a DenseBrainVolume instance

Examples

bspace <- BrainSpace(c(64,64,64), spacing=c(1,1,1))
dat <- array(rnorm(64*64*64), c(64,64,64))
bvol <- BrainVolume(dat,bspace, label="test")
print(bvol)

BrainVolume Base class for image representing 3D volumetric data.

Description

BrainVolume Base class for image representing 3D volumetric data.

BrainVolumeSource A class is used to produce a BrainVolume instance

Description

BrainVolumeSource

A class is used to produce a BrainVolume instance

Constructor for BrainVolumeSource

Usage

BrainVolumeSource(input, index = 1)

Arguments

input

the input file name
index

the image subvolume index

Slots

index

the index of the volume to be read – must be of length 1.

close

Description

close

Usage

## S4 method for signature 'BinaryReader'
close(con)

## S4 method for signature 'BinaryWriter'
close(con)

Arguments

con

the object to close

ClusteredBrainVolume

Description

Three-dimensional brain image that is divided into N disjoint partitions

Construct a ClusteredBrainVolume instance

Usage

ClusteredBrainVolume(mask, clusters, labelMap = NULL, source = NULL,
  label = "")

Arguments

mask

an instance of class LogicalBrainVolume
clusters

a vector of clusters ids with length equal to number of nonzero voxels in mask mask
labelMap

an optional list that maps from cluster id to a cluster label, e.g. (1 -> "FFA", 2 -> "PPA")
source

an optional instance of class BrainSource
label

an optional character string used to label of the volume

Value

ClusteredBrainVolume instance

Examples

bspace <- BrainSpace(c(16,16,16), spacing=c(1,1,1))
grid <- indexToGrid(bspace, 1:(16*16*16))
kres <- kmeans(grid, centers=10)
mask <- BrainVolume(rep(1, 16^3),bspace)
clusvol <- ClusteredBrainVolume(mask, kres$cluster)

Create a clustered Searchlight iterator

Description

Create a clustered Searchlight iterator

Usage

ClusteredSearchlight(mask, csize)

Arguments

mask

an image volume containing valid central voxels for roving searchlight
csize

the number of clusters

Value

an iter class

ColumnReader

Description

Constructor for ColumnReader class

Usage

ColumnReader(nrow, ncol, reader)

Arguments

nrow

the number of rows
ncol

the number of columns
reader

a function that takes a set of column indices and returns a matrix

ColumnReader

Description

This class supports reading of data froma matrix-like stroage format

Slots

nrow

the number of rows

ncol

the number of columns

reader

a function that takes a set of column indices and returns a matrix

Concatenate two objects

Description

Concatenate two objects

Usage

concat(x, y, ...)

## S4 method for signature 'SparseBrainVector,missing'
concat(x, y, ...)

## S4 method for signature 'SparseBrainVector,SparseBrainVector'
concat(x, y, ...)

## S4 method for signature 'BrainVector,BrainVolume'
concat(x, y, ...)

## S4 method for signature 'BrainVolume,BrainVector'
concat(x, y, ...)

## S4 method for signature 'BrainVector,BrainVector'
concat(x, y, ...)

## S4 method for signature 'DenseBrainVolume,missing'
concat(x, y, ...)

## S4 method for signature 'DenseBrainVolume,DenseBrainVolume'
concat(x, y, ...)

Arguments

x

the first object, typically BrainVolume or BrainVector
y

the second object, typically BrainVolume or BrainVector
...

additional objects

Details

The x and y images must have compatible dimensions. a BrainVolume can be concatenated to BrainVector, and vice versa. See examples.

Note

dimensions of x and y must be equal

Examples

bv1 <- BrainVolume(rep(1,1000), BrainSpace(c(10,10,10), c(1,1,1)))
bv2 <- BrainVolume(rep(2,1000), BrainSpace(c(10,10,10), c(1,1,1)))
bv3 <- concat(bv1,bv2)
inherits(bv3, "BrainVector")

bv4 <- concat(bv3, bv1)
dim(bv4)[4] == 3
bv5 <- concat(bv1, bv3)
dim(bv4)[4] == 3

bv6 <- concat(bv4,bv5)
dim(bv6)[4] == 6

Find connected components

Description

Find connected components

find connected components in BrainVolume

Usage

connComp(x, ...)

## S4 method for signature 'BrainVolume'
connComp(x, threshold = 0, clusterTable = TRUE,
  localMaxima = TRUE, localMaximaDistance = 15)

Arguments

x

the image object
...

additonal arguments
threshold

threshold defining lower intensity bound for image mask
clusterTable

return clusterTable
localMaxima

return table of local maxima
localMaximaDistance

the distance used to define minum distance between local maxima

Extract connected components from a 3D mask

Description

Extract connected components from a 3D mask

Usage

connComp3D(mask, connect = c("6-connect", "18-connect", "26-connect"))

Arguments

mask

a 3D binary array
connect

the connectiivty constraint: "6-connect", "18-connect", or "26-connect"

Value

a two-element list of the connected components (cluster index and cluster size) The first element index is a 3D array containing the cluster index of the connected component for each voxel. The second element size is a 3D array consisting of the size of the connected component inhabited by each voxel.

Examples

dat <- array(as.logical(rnorm(10*10*10)>.5), c(10, 10, 10))
res1 <- connComp3D(dat, connect="6-connect")
res2 <- connComp3D(dat, connect="18-connect")
res3 <- connComp3D(dat, connect="26-connect")

Extract coordinates

Description

Extract coordinates

coords

Usage

coords(x, ...)

## S4 method for signature 'ROIVolume'
coords(x, real = FALSE)

## S4 method for signature 'SparseBrainVector'
coords(x, i)

## S4 method for signature 'IndexLookupVolume'
coords(x, i)

Arguments

x

the object to extract coordinates from
...

additional arguments
real

if TRUE, return coordinates in real world units
i

the index in to the lookup volume

Generic function to convert N-dimensional real world coordinates to grid coordinates

Description

Generic function to convert N-dimensional real world coordinates to grid coordinates

Usage

coordToGrid(x, coords)

## S4 method for signature 'BrainSpace,matrix'
coordToGrid(x, coords)

## S4 method for signature 'BrainSpace,numeric'
coordToGrid(x, coords)

## S4 method for signature 'BrainVolume,matrix'
coordToGrid(x, coords)

Arguments

x

the object
coords

a matrix of real world coordinates

Value

a matrix of grid coordinates

Generic function to convert N-dimensional real world coordinates to 1D indices

Description

Generic function to convert N-dimensional real world coordinates to 1D indices

Usage

coordToIndex(x, coords)

## S4 method for signature 'BrainSpace,matrix'
coordToIndex(x, coords)

## S4 method for signature 'BrainSpace,numeric'
coordToIndex(x, coords)

## S4 method for signature 'BrainVolume,matrix'
coordToIndex(x, coords)

Arguments

x

the object
coords

a matrix of real world coordinates

Value

a vector of indices

Generic function to get the name of the data file, given a file name and a BrainFileDescriptor instance.

Description

Generic function to get the name of the data file, given a file name and a BrainFileDescriptor instance.

Usage

dataFile(x, fileName)

## S4 method for signature 'BrainFileDescriptor,character'
dataFile(x, fileName)

Arguments

x

descriptor instance
fileName

file name to be stripped of its extension

Value

the correct header name

Generic function to test whether a file name conforms to the given a BrainFileDescriptor instance. Will test for match to data file only

Description

Generic function to test whether a file name conforms to the given a BrainFileDescriptor instance. Will test for match to data file only

Usage

dataFileMatches(x, fileName)

## S4 method for signature 'BrainFileDescriptor,character'
dataFileMatches(x, fileName)

Arguments

x

object for which the file name is to matched to
fileName

file name to be matched

Value

TRUE for match, FALSE otherwise

Generic function to create data reader

Description

Generic function to create data reader

Usage

dataReader(x, offset)

## S4 method for signature 'NIfTIMetaInfo'
dataReader(x, offset = 0)

## S4 method for signature 'AFNIMetaInfo'
dataReader(x, offset = 0)

Arguments

x

an object specifying the infromation required to produce the reader
offset

the byte offset (number of bytes to skip before reading)

DenseBrainVector

Description

Four-dimensional brain image, backed by an array

constructor function for class DenseBrainVector

Usage

DenseBrainVector(data, space, source = NULL, label = "")

Arguments

data

a 4-dimensional array or a 2-dimension matrix that is either nvoxels by ntime-points or ntime-points by nvoxels
space

a BrainSpace object
source

an optional BrainSource object
label

a label of type character

Value

DenseBrainVector instance

DenseBrainVolume

Description

Three-dimensional brain image, backed by an array

Construct a DenseBrainVolume instance

Usage

DenseBrainVolume(data, space, source = NULL, label = "", indices = NULL)

Arguments

data

a three-dimensional array
space

an instance of class BrainSpace
source

an instance of class BrainSource
label

a character string
indices

an optional 1-d index vector

Value

DenseBrainVolume instance

dim of BrainData object

Description

dim of BrainData object

Usage

## S4 method for signature 'BrainData'
dim(x)

Arguments

x

the object

dim

Description

dim

Usage

## S4 method for signature 'BrainSpace'
dim(x)

Arguments

x

the object

dim of FileMetaInfo

Description

dim of FileMetaInfo

Usage

## S4 method for signature 'FileMetaInfo'
dim(x)

Arguments

x

the object

Generic function to drop a dimension from an object

Description

Generic function to drop a dimension from an object

Usage

dropDim(x, dimnum)

## S4 method for signature 'AxisSet2D,numeric'
dropDim(x, dimnum)

## S4 method for signature 'AxisSet2D,missing'
dropDim(x, dimnum)

## S4 method for signature 'AxisSet3D,numeric'
dropDim(x, dimnum)

## S4 method for signature 'AxisSet3D,missing'
dropDim(x, dimnum)

## S4 method for signature 'BrainSpace,numeric'
dropDim(x, dimnum)

## S4 method for signature 'BrainSpace,missing'
dropDim(x)

Arguments

x

a dimensioned object
dimnum

the index of the dimension to drop

Examples

x = BrainSpace(c(10,10,10), c(1,1,1))
x1 <- dropDim(x)
ndim(x1) == 2
dim(x1)[2] == 10

Generic functions to apply a function to each series of a 4D image That is, if the 4th dimension is 'time' each series is a 1D time series.

Description

Generic functions to apply a function to each series of a 4D image That is, if the 4th dimension is 'time' each series is a 1D time series.

Usage

eachSeries(x, FUN, withIndex, ...)

## S4 method for signature 'SparseBrainVector,'function',logical'
eachSeries(x, FUN,
  withIndex = FALSE, ...)

## S4 method for signature 'DenseBrainVector,'function',missing'
eachSeries(x, FUN,
  withIndex = FALSE, ...)

## S4 method for signature 'BrainVector,'function',missing'
eachSeries(x, FUN,
  withIndex = FALSE, ...)

Arguments

x

a four dimensional image
FUN

a function taking one or two arguments (depending on the value of withIndex
withIndex

whether the index of the series is supplied as the second argument to the function
...

additional arguments

Details

when x is a SparseBrainVector eachSeries only iterates over nonzero series.

Examples

bvec <- BrainVector(array(rnorm(24*24*24*24), c(24,24,24,24)), BrainSpace(c(24,24,24,24), c(1,1,1)))
res <- eachSeries(bvec, mean)
length(res) == 24*24*24

Generic functions to apply a function to each (2D) slice of an image

Description

Generic functions to apply a function to each (2D) slice of an image

Usage

eachSlice(x, FUN, withIndex, ...)

## S4 method for signature 'BrainVolume,'function',missing'
eachSlice(x, FUN)

## S4 method for signature 'BrainVolume,'function',logical'
eachSlice(x, FUN, withIndex)

Arguments

x

the object
FUN

a function taking one or two arguments (depending on the value of withIndex
withIndex

whether the index of the slice is supplied as the second argument to the function
...

additional arguments

Generic function to apply a function to each volume of a four-dimensional image

Description

Generic function to apply a function to each volume of a four-dimensional image

Usage

eachVolume(x, FUN, withIndex, mask, ...)

## S4 method for signature 'SparseBrainVector,'function',logical,missing'
eachVolume(x, FUN,
  withIndex = FALSE, mask, ...)

## S4 method for signature 'SparseBrainVector,'function',missing,missing'
eachVolume(x, FUN,
  withIndex, mask, ...)


  ## S4 method for signature 
## 'SparseBrainVector,‘function',missing,LogicalBrainVolume’
eachVolume(x,
  FUN, withIndex, mask, ...)

## S4 method for signature 'BrainVector,'function',missing,missing'
eachVolume(x, FUN, withIndex,
  mask, ...)

## S4 method for signature 'BrainVector,'function',missing,BrainVolume'
eachVolume(x, FUN,
  withIndex, mask, ...)

## S4 method for signature 'BrainVector,'function',missing,missing'
eachVolume(x, FUN, withIndex,
  mask, ...)

## S4 method for signature 'BrainBucket,'function',missing,missing'
eachVolume(x, FUN, withIndex,
  mask, ...)

## S4 method for signature 'BrainBucket,'function',logical,ANY'
eachVolume(x, FUN, withIndex,
  mask, ...)

## S4 method for signature 'BrainVector,'function',logical,ANY'
eachVolume(x, FUN, withIndex,
  mask, ...)

Arguments

x

four-dimensional image, e.g. of class BrainVector
FUN

a function taking one or two arguments (depending on the value of withIndex)
withIndex

whether the index of the volume supplied as the second argument to the function
mask

an image mask indicating subset of volume elements to apply function over
...

additional arguments

Value

a list of results of apply FUN to each volume.

Examples

bvec <- BrainVector(array(rnorm(24*24*24*24), c(24,24,24,24)), BrainSpace(c(24,24,24,24), c(1,1,1)))
res <- eachVolume(bvec, mean)

res <- eachVolume(bvec, function(x,i) median(x), withIndex=TRUE)

Generic function to test whether a file name conforms to the given BrainFileDescriptor instance. Will test for match to either header file or data file

Description

Generic function to test whether a file name conforms to the given BrainFileDescriptor instance. Will test for match to either header file or data file

Usage

fileMatches(x, fileName)

## S4 method for signature 'BrainFileDescriptor,character'
fileMatches(x, fileName)

Arguments

x

object for which the file name is to matched to
fileName

file name to be matched

Value

TRUE for match, FALSE otherwise

FileMetaInfo

Description

This class contains meta information from an image data file

This class contains meta information for a NIfTI image file

This class contains meta information for a AFNI image file

Slots

headerFile

name of the file containing meta information

dataFile

name of the file containing data

fileDescriptor

descriptor of image file format

endian

byte order of data ('little' or 'big')

dataOffset

the number of bytes preceding the start of image data in data file

bytesPerElement

number of bytes per element

intercept

constant value added to image – multiple values allowed (must equal numer of sub-images)

slope

image multiplier – multiple values allowed (must equal numer of sub-images)

header

a list of format specific attributes

nifti_header

a list of attributes specific to the NIfTI file format

afni_header

a list of attributes specific to the AFNI file format

afni_header

a list of attributes specific to the AFNI file format

Generic function to map values from one set to another using a user-supplied lookup table

Description

Generic function to map values from one set to another using a user-supplied lookup table

Usage

fill(x, lookup)

## S4 method for signature 'BrainVolume,list'
fill(x, lookup)

## S4 method for signature 'BrainVolume,matrix'
fill(x, lookup)

Arguments

x

the object to map values from
lookup

the lookup table. The first column is the "key" the second column is the "value".

Value

a new object where the original values have been filled in with the values in the lookup table

Examples

x <- BrainSpace(c(10,10,10), c(1,1,1))
vol <- BrainVolume(sample(1:10, 10*10*10, replace=TRUE), x)

## lookup table is list
lookup <- lapply(1:10, function(i) i*10)
ovol <- fill(vol, lookup)

## lookup table is matrix. First column is key, second column is value
names(lookup) <- 1:length(lookup)
lookup.mat <- cbind(as.numeric(names(lookup)), unlist(lookup))
ovol2 <- fill(vol, lookup.mat)
all.equal(as.vector(ovol2), as.vector(ovol))

Generic function to convert N-dimensional grid coordinate coordinates to real world coordinates

Description

Generic function to convert N-dimensional grid coordinate coordinates to real world coordinates

Usage

gridToCoord(x, coords)

## S4 method for signature 'BrainSpace,matrix'
gridToCoord(x, coords)

## S4 method for signature 'BrainSpace,numeric'
gridToCoord(x, coords)

## S4 method for signature 'BrainVolume,matrix'
gridToCoord(x, coords)

Arguments

x

the object
coords

a matrix of grid coordinates

Value

a matrix of real coordinates

Generic function to convert voxel coordinates in the reference space (LPI) to native array space.

Description

Generic function to convert voxel coordinates in the reference space (LPI) to native array space.

Usage

gridToGrid(x, vox)

## S4 method for signature 'BrainSpace,matrix'
gridToGrid(x, vox)

## S4 method for signature 'matrix,matrix'
gridToGrid(x, vox)

Arguments

x

the object
coords

a matrix of LPI voxel coordinates

Value

a matrix of native voxel coordinates

Generic function to convert N-dimensional grid coordinate to 1D indices

Description

Generic function to convert N-dimensional grid coordinate to 1D indices

Usage

gridToIndex(x, coords)

## S4 method for signature 'BrainSlice,matrix'
gridToIndex(x, coords)

## S4 method for signature 'BrainSpace,matrix'
gridToIndex(x, coords)

## S4 method for signature 'BrainSpace,numeric'
gridToIndex(x, coords)

## S4 method for signature 'BrainVolume,matrix'
gridToIndex(x, coords)

## S4 method for signature 'BrainVolume,numeric'
gridToIndex(x, coords)

Arguments

x

the object, typically a BrainVolume or BrainSpace instance.
coords

a matrix where each row is a coordinate or a vector of length equal to ndim(x)

Value

a vector of indices

Generic function to get the name of the header file, given a file name and a BrainFileDescriptor instance.

Description

Generic function to get the name of the header file, given a file name and a BrainFileDescriptor instance.

Usage

headerFile(x, fileName)

## S4 method for signature 'BrainFileDescriptor,character'
headerFile(x, fileName)

Arguments

x

descriptor instance
fileName

file name to be stripped of its extension

Value

the correct header name

Generic function to test whether a file name conforms to the given BrainFileDescriptor instance. Will test for match to header file only

Description

Generic function to test whether a file name conforms to the given BrainFileDescriptor instance. Will test for match to header file only

Usage

headerFileMatches(x, fileName)

## S4 method for signature 'BrainFileDescriptor,character'
headerFileMatches(x, fileName)

Arguments

x

object for which the file name is to matched to
fileName

file name to be matched

Value

TRUE for match, FALSE otherwise

image

Description

image

Usage

## S4 method for signature 'BrainVolume'
image(x, slice = dim(vol)[3]/2,
  col = gray((0:255)/255, alpha = 1), zero_col = "#000000", axis = 3, ...)

Arguments

slice

the voxel index of the slice to display
col

a color map
zero_col

the color to use when the value is 0 (e.g background color)
...

extra arguments to passed to grid.raster

IndexLookupVolume

Description

Three-dimensional brain image that can be used as a map between 1D grid indices and a table of values Currently used in the SparseBrainVector class.

Usage

IndexLookupVolume(space, indices)

Arguments

space

a BrainSpace object
indices

the set of 1-d indices defining the lookup map

Generic function to convert 1D indices to N-dimensional real world coordinates

Description

Generic function to convert 1D indices to N-dimensional real world coordinates

Usage

indexToCoord(x, idx)

## S4 method for signature 'BrainSpace,numeric'
indexToCoord(x, idx)

## S4 method for signature 'BrainVolume,index'
indexToCoord(x, idx)

Arguments

x

the object
idx

the 1D indices

Value

a matrix of real coordinates

Examples

bvol <- BrainVolume(array(0, c(10,10,10)), BrainSpace(c(10,10,10), c(1,1,1)))
idx <- 1:10
g <- indexToCoord(bvol, idx)
idx2 <- coordToIndex(bvol, g)
all.equal(idx, idx2)

Generic function to convert 1D indices to N-dimensional grid coordinates

Description

Generic function to convert 1D indices to N-dimensional grid coordinates

Usage

indexToGrid(x, idx)

## S4 method for signature 'BrainSlice,index'
indexToGrid(x, idx)

## S4 method for signature 'BrainSpace,numeric'
indexToGrid(x, idx)

## S4 method for signature 'BrainVector,index'
indexToGrid(x, idx)

## S4 method for signature 'BrainVector,integer'
indexToGrid(x, idx)

## S4 method for signature 'BrainVolume,index'
indexToGrid(x, idx)

## S4 method for signature 'BrainVolume,integer'
indexToGrid(x, idx)

Arguments

x

the object
idx

the 1D vector of indices

Value

a matrix of grid coordinates

Examples

bvol <- BrainVolume(array(0, c(10,10,10)), BrainSpace(c(10,10,10), c(1,1,1)))
 idx <- 1:10
 g <- indexToGrid(bvol, idx)
 bvol[g]

Extract indices

Description

Extract indices

indices

Usage

indices(x)

## S4 method for signature 'ROIVolume'
indices(x)

## S4 method for signature 'ROIVector'
indices(x)

## S4 method for signature 'SparseBrainVector'
indices(x)

## S4 method for signature 'IndexLookupVolume'
indices(x)

Arguments

x

the object to extract indices

Generic getter to extract inverse image coordinate transformation

Description

Generic getter to extract inverse image coordinate transformation

Usage

inverseTrans(x)

## S4 method for signature 'BrainSpace'
inverseTrans(x)

## S4 method for signature 'BrainData'
inverseTrans(x)

Arguments

x

an object

Examples

bspace <- BrainSpace(c(10,10,10), c(2,2,2))
itrans <- inverseTrans(bspace)
identical(trans(bspace) %*% inverseTrans(bspace), diag(4))

Create a Kernel object from a function of distance from kernel center

Description

Create a Kernel object from a function of distance from kernel center

Usage

Kernel(kerndim, vdim, FUN = dnorm, ...)

Arguments

kerndim

the dimensions in voxels of the kernel
vdim

the dimensions of the voxels in real units
FUN

the kernel function taking as its first argument representing the distance from the center of the kernel
...

additional parameters to the kernel FUN

Kernel

Description

A class representing an image kernel

Slots

width

the width in voxels of the kernel

weights

the kernel weights

voxels

the relative voxel coordinates of the kernel

coords

the relative real coordinates of the kernel

Get length of BrainVector. This is the numbe rof volumes in the volume vector (e.g. the 4th image dimension)

Description

Get length of BrainVector. This is the numbe rof volumes in the volume vector (e.g. the 4th image dimension)

Usage

## S4 method for signature 'ROIVolume'
length(x)

## S4 method for signature 'BrainVector'
length(x)

loadBucket

Description

load a BrainBucket object from file

Usage

loadBucket(fileName, pattern = NULL, indices = NULL)

Arguments

fileName

the name of the file to load
pattern

optional regular expression used to filter the sub-volumes using associated labels
indices

optional set of sub-volume indices to load

Generic function to load data from a data source

Description

Generic function to load data from a data source

Load data from a BrainBucketSource

loadData

load a BrainVolume

Usage

loadData(x, ...)

## S4 method for signature 'BrainBucketSource'
loadData(x, key)

## S4 method for signature 'SparseBrainVectorSource'
loadData(x, mmap = FALSE)

## S4 method for signature 'BrainVectorSource'
loadData(x, mmap = FALSE)

## S4 method for signature 'BrainVolumeSource'
loadData(x)

Arguments

x

a data source
...

additional arguments
key

the name or index of the bucket to load
mmap

use memory-mapped file

Value

an instance of class BrainVolume

an instance of class BrainVector

loadVector

Description

load an image volume from a file

Usage

loadVector(fileName, indices = NULL, mask = NULL, mmap = FALSE)

Arguments

fileName

the name of the file to load
indices

the indices of the sub-volumes to load (e.g. if the file is 4-dimensional)
mask

a mask defining the spatial elements to load
mmap

memory mapping if possible

Value

an BrainVector object

Load an image volume from a file

Description

Load an image volume from a file

Usage

loadVolume(fileName, index = 1)

Arguments

fileName

the name of the file to load
index

the index of the volume (e.g. if the file is 4-dimensional)

Value

an instance of the class DenseBrainVolume

Examples

fname <- system.file("extdata", "global_mask.nii", package="neuroim")
x <- loadVolume(fname)
print(dim(x))
space(x)

loadVolumeList

Description

load a list of image volumes and return a BrainVector instance

Usage

loadVolumeList(fileNames, mask = NULL)

Arguments

fileNames

a list of files to load
mask

an optional mask indicating subset of voxels to load

Value

an instance of class BrainVector

LogicalBrainVolume

Description

Three-dimensional brain image where all values are either TRUE or FALSE

Construct a LogicalBrainVolume instance

Usage

LogicalBrainVolume(data, space, source = NULL, label = "", indices = NULL)

Arguments

data

a three-dimensional array, a 1D vector with length equal to prod(dim(space)), or a set of indices where elements are TRUE
space

an instance of class BrainSpace
source

an instance of class BrainSource
label

a character string
indices

an optional 1-d index vector

Value

LogicalBrainVolume instance

Index Lookup operation

Description

Index Lookup operation

lookup

Usage

lookup(x, i, ...)

## S4 method for signature 'SparseBrainVector,numeric'
lookup(x, i)

## S4 method for signature 'IndexLookupVolume,numeric'
lookup(x, i)

Arguments

x

the object to query
i

the index to lookup
...

additional arguments

makeVector

Description

Construct a BrainVector instance, using default (dense) implementation

Usage

makeVector(data, refdata, source = NULL, label = "")

Arguments

data

a four-dimensional array
refdata

an instance of class BrainVector or BrainVolume containing the reference space for the new vector.
source

an instance of class BrainSource
label

a character string

Value

DenseBrainVector instance

makeVolume

Description

Construct a BrainVolume instance, using default (dense) implementation

Usage

makeVolume(data = NULL, refvol, source = NULL, label = "",
  indices = NULL)

Arguments

data

an optional one- or three-dimensional vector or array
refvol

an instance of class BrainVolume containing the reference space for the new volume.
source

an optional instance of class BrainSource
label

an optional character string
indices

an optional 1d vector of indices in to the 3d space

Value

DenseBrainVolume instance

Examples

bspace <- BrainSpace(c(64,64,64), spacing=c(1,1,1))
dat <- array(rnorm(64*64*64), c(64,64,64))
bvol <- BrainVolume(dat,bspace, label="test")
bvol2 <- makeVolume(dat, bvol)
all.equal(as.array(bvol),as.array(bvol2))
data <- 1:10
indices = seq(1,1000, length.out=10)
bvol3 <- makeVolume(data,bvol,indices=indices)
sum(bvol3) == sum(data)

Generic function to apply a function to an object

Description

Generic function to apply a function to an object

apply a kernel function to a BrainVolume

Usage

map(x, m, ...)

## S4 method for signature 'BrainVolume,Kernel'
map(x, m, mask = NULL)

Arguments

x

the object that is mapped
m

the mapping object
...

additional arguments
mask

restrict application of kernel to masked area

mapToColors

Description

map an matrix of intensity values to a matrix of color values.

Usage

mapToColors(imslice, col = heat.colors(128, alpha = 1),
  zero_col = "#00000000", alpha = 1, irange = range(imslice),
  threshold = c(0, 0))

Arguments

imslice

vector or matrix of intensity values
col

a color map
zero_col

the background color.
alpha

transparency multiplier

given two named axes return AxisSet2D singleton

Description

given two named axes return AxisSet2D singleton

Usage

matchAnatomy2D(axis1, axis2)

Arguments

axis1

the first axis
axis2

the second axis

given three named axes return AxisSet3D singleton

Description

given three named axes return AxisSet3D singleton

Usage

matchAnatomy3D(axis1, axis2, axis3)

Arguments

axis1

the first axis
axis2

the second axis
axis3

the third axis

matrixToVolumeList converts a matrix to a list of BrainVolumes with values filled at grid coordinates determined by the vox argument.

Description

matrixToVolumeList converts a matrix to a list of BrainVolumes with values filled at grid coordinates determined by the vox argument.

Usage

matrixToVolumeList(voxmat, mat, mask, default = NA)

Arguments

voxmat

an N by 3 matrix of voxel coordinates
mat

an N by M matrix of values where M is the number of volumes to create (e.g. one volume per column in mat)
mask

a reference volume defining the geometry of the output volumes. This can either be of type BrainSpace or BrainVolume
default

the value that will be used for voxels not contained within voxmat (defualt is NA)

Value

a list of BrainVolume instances, one for each column of mat

MNI SPACE 1MM

Description

This is a BrainSpace object encoding the geometry of the MNI_1MM template.

Usage

data(MNI_SPACE_1MM)

Format

a BrainSpace instance

NamedAxis

Description

This class represents an axis with a name attribute

Slots

axis

the name of the axis

direction

of axis (-1,+1)

names

Description

names

Usage

## S4 method for signature 'BrainBucketSource'
names(x)

## S4 method for signature 'BrainBucket'
names(x)

Arguments

x

the object to get names of

Generic function to extract the number of dimensions of an object

Description

Generic function to extract the number of dimensions of an object

Usage

ndim(x, ...)

## S4 method for signature 'AxisSet'
ndim(x, ...)

## S4 method for signature 'BrainData'
ndim(x)

## S4 method for signature 'BrainSpace'
ndim(x)

Arguments

x

n-dimensional object
...

additional arguments

Examples

x = BrainSpace(c(10,10,10), c(1,1,1))
ndim(x) == 3
x = BrainSpace(c(10,10,10,3), c(1,1,1,1))
ndim(x) == 4

neuroim

Description

Data structures for analysis of neuroimaging data.

Details

none

NIfTIFileDescriptor

Description

This class supports the NIfTI file format

Constructor for NIfTIMetaInfo class

Description

Constructor for NIfTIMetaInfo class

Usage

NIfTIMetaInfo(descriptor, nifti_header)

Arguments

descriptor

an instance of class NIfTIFileDescriptor
nifti_header

a list returned by readNIftiHeader

Value

an instance of class NIfTIMetaInfo

NullMetaInfo

Description

This is class is used to denote the absense of meta information

numClusters

Description

numClusters

get number of clusters in a ClusteredBrainVolume

Usage

numClusters(x)

## S4 method for signature 'ClusteredBrainVolume'
numClusters(x)

Arguments

x

the object to extract number of clusters

Generic getter to extract image origin

Description

Generic getter to extract image origin

Usage

origin(x)

## S4 method for signature 'BrainSpace'
origin(x)

## S4 method for signature 'BrainData'
origin(x)

Arguments

x

an object with an origin

Examples

bspace <- BrainSpace(c(10,10,10), c(2,2,2))
origin(bspace)

overlay two objects

Description

overlay two objects

Usage

overlay(x, y, ...)

Arguments

x

the underlay object
y

the overlay object
...

additional arguments for class-specific implementations

patchSet

Description

patchSet

Usage

patchSet(x, dims, mask, ...)

Arguments

x

the object to extract patches from
dims

a vector indicating the dimensions of the patches
mask

mask indicating the valid patch area

Extract permutation matrix

Description

Extract permutation matrix

permMat

permMat

permMat

Usage

permMat(x, ...)

## S4 method for signature 'AxisSet2D'
permMat(x, ...)

## S4 method for signature 'AxisSet3D'
permMat(x, ...)

## S4 method for signature 'BrainSpace'
permMat(x, ...)

Arguments

x

the object
...

additional arguments

Generic function to print an object

Description

Generic function to print an object

Usage

print(x, ...)

Arguments

x

the object to print
...

additional arguments

print a AxisSet2D instance

Description

print a AxisSet2D instance

Usage

## S4 method for signature 'AxisSet2D'
print(x, ...)

Arguments

x

the object
...

extra args

print a AxisSet3D instance

Description

print a AxisSet3D instance

Usage

## S4 method for signature 'AxisSet3D'
print(x, ...)

Arguments

x

the object
...

extra args

print a NamedAxis

Description

print a NamedAxis

Usage

## S4 method for signature 'NamedAxis'
print(x, ...)

Arguments

x

the object
...

extra arguments

Create an spherical random searchlight iterator

Description

Create an spherical random searchlight iterator

Usage

RandomSearchlight(mask, radius)

Arguments

mask

an volumetric image mask of type BrainVolume containing valid searchlight voxel set.
radius

width in mm of spherical searchlight

readAFNIHeader

Description

readAFNIHeader

Usage

readAFNIHeader(fileName)

Arguments

fileName

the name of the AFNI header file (ending in .HEAD)

Generic function to read a set of column vector from an input source (e.g. ColumnReader)

Description

Generic function to read a set of column vector from an input source (e.g. ColumnReader)

Usage

readColumns(x, columnIndices)

## S4 method for signature 'ColumnReader,numeric'
readColumns(x, columnIndices)

Arguments

x

the input channel
columnIndices

the column indices

Value

a matrix consisting of the requested column vectors

Generic function to read a sequence of elements from an input source

Description

Generic function to read a sequence of elements from an input source

readElements

Usage

readElements(x, numElements)

## S4 method for signature 'BinaryReader,numeric'
readElements(x, numElements)

Arguments

x

the input channel
numElements

the number of elements to read

Value

the elements as a vector

read header information of an image file

Description

read header information of an image file

Usage

readHeader(fileName)

Arguments

fileName

the name of the file to read

Value

an instance of class FileMetaInfo

Generic function to read image meta info given a file and a BrainFileDescriptor instance.

Description

Generic function to read image meta info given a file and a BrainFileDescriptor instance.

Usage

readMetaInfo(x, fileName)

## S4 method for signature 'NIfTIFileDescriptor'
readMetaInfo(x, fileName)

## S4 method for signature 'AFNIFileDescriptor'
readMetaInfo(x, fileName)

Arguments

x

descriptor instance
fileName

file name contianing meta information

Create A Cuboid Region of Interest

Description

Create A Cuboid Region of Interest

Usage

RegionCube(bvol, centroid, surround, fill = NULL, nonzero = FALSE)

Arguments

bvol

an BrainVolume or BrainSpace instance
centroid

the center of the cube in voxel coordinates
surround

the number of voxels on either side of the central voxel. A vector of length 3.
fill

optional value(s) to assign to data slot.
nonzero

keep only nonzero elements from bvol. If bvol is A BrainSpace then this argument is ignored.

Value

an instance of class ROIVolume

Examples

sp1 <- BrainSpace(c(10,10,10), c(1,1,1))
 cube <- RegionCube(sp1, c(5,5,5), 3)
 vox <- coords(cube)
 cube2 <- RegionCube(sp1, c(5,5,5), 3, fill=5)

Create a Spherical Region of Interest

Description

Creates a Spherical ROI based on a Centroid.

Usage

RegionSphere(bvol, centroid, radius, fill = NULL, nonzero = FALSE)

Arguments

bvol

an BrainVolume or BrainSpace instance
centroid

the center of the sphere in voxel space
radius

the radius in real units (e.g. millimeters) of the spherical ROI
fill

optional value(s) to store as data
nonzero

if TRUE, keep only nonzero elements from bvol

Value

an instance of class ROIVolume

Examples

sp1 <- BrainSpace(c(10,10,10), c(1,2,3))
 cube <- RegionSphere(sp1, c(5,5,5), 3.5)
 vox <- coords(cube)
 cds <- coords(cube, real=TRUE)
 ## fill in ROI with value of 6
 cube1 <- RegionSphere(sp1, c(5,5,5), 3.5, fill=6)
 all(cube1@data == 6)

Create a square region of interest where the z-dimension is fixed at one voxel coordinate.

Description

Create a square region of interest where the z-dimension is fixed at one voxel coordinate.

Usage

RegionSquare(bvol, centroid, surround, fill = NULL, nonzero = FALSE,
  fixdim = 3)

Arguments

bvol

an BrainVolume or BrainSpace instance.
centroid

the center of the cube in voxel coordinates.
surround

the number of voxels on either side of the central voxel.
fill

optional value(s) to assign to data slot.
nonzero

keep only nonzero elements from bvol. If bvol is A BrainSpace then this argument is ignored.
fixdim

the fixed dimension is the third, or z, dimension.

Value

an instance of class ROIVolume.

Examples

sp1 <- BrainSpace(c(10,10,10), c(1,1,1))
 square <- RegionSquare(sp1, c(5,5,5), 1)
 vox <- coords(square)
 ## a 3 X 3 X 1 grid
 nrow(vox) == 9

Render an image to create a drawable image.

Description

Render an image to create a drawable image.

Usage

render(x, width, height, colmap, ...)

## S4 method for signature 'BrainSlice,numeric,numeric,character'
render(x, width, height,
  colmap, zero_col = "#00000000", alpha = 1, units = "points",
  irange = range(x), threshold = c(0, 0))

Arguments

x

the object, e.g. an instance of type BrainSlice
width

width of the rendered image
height

height of the rendered image
colmap

the colors used to map from values to RGBA colors.
...

additional arguments
zero_col

color used when background intensity is 0.
alpha

transparency multiplier
units

grid unit type, e.g. "points", "mm", "inches", "npc"

Render a slice at z coordinate

Description

Render a slice at z coordinate

Usage

renderSlice(x, zpos, width, height, colmap, ...)

Arguments

x

the object, e.g. an instance of type Layer or Overlay
zpos

the z coordinate to slice through.
width

width of the rendered image
height

height of the rendered image
colmap

the colors used to map from values to RGBA colors.
...

additional arguments

Generic function to remap the grid-to-world coordinates mapping of an image.

Description

Generic function to remap the grid-to-world coordinates mapping of an image.

Usage

reorient(x, orient)

## S4 method for signature 'BrainSpace,character'
reorient(x, orient)

Arguments

x

the object
orient

the orientation code indcating the "remapped" axes.

Value

a reoriented space

ROI

Description

Base marker class for a region of interest (ROI)

Create an instance of class ROIVector

Description

Create an instance of class ROIVector

Usage

ROIVector(vspace, coords, data = rep(nrow(coords), 1))

Arguments

vspace

an instance of class BrainSpace
coords

matrix of voxel coordinates
data

the matrix of data values

Value

an instance of class ROIVector

ROIVector

Description

A class that represents a vector-valued volumetric region of interest

Slots

data

the matrix data stored in ROI

coords

the voxel coordinates of the ROI

Create an instance of class ROIVolume

Description

Create an instance of class ROIVolume

Usage

ROIVolume(vspace, coords, data = rep(nrow(coords), 1))

Arguments

coords

matrix of voxel coordinates
data

the data values, numeric vector
space

an instance of class BrainSpace

Value

an instance of class ROIVolume

ROIVolume

Description

A class that represents a volumetric region of interest

Slots

data

the numeric data stored in ROI

coords

the voxel coordinates of the ROI

Generic functions to scale (center and/or normalize by standard deviation) each series of a 4D image That is, if the 4th dimension is 'time' each series is a 1D time series.

Description

Generic functions to scale (center and/or normalize by standard deviation) each series of a 4D image That is, if the 4th dimension is 'time' each series is a 1D time series.

Usage

scaleSeries(x, center, scale)

## S4 method for signature 'BrainVector,logical,logical'
scaleSeries(x, center, scale)

## S4 method for signature 'BrainVector,missing,logical'
scaleSeries(x, center, scale)

## S4 method for signature 'BrainVector,missing,missing'
scaleSeries(x, center, scale)

## S4 method for signature 'BrainVector,logical,missing'
scaleSeries(x, center, scale)

Arguments

x

a four dimensional image
center

a logical value indicating whether series should be centered
scale

a logical value indicating whether series should be divided by standard deviation

Examples

bvec <- BrainVector(array(rnorm(24*24*24*24), c(24,24,24,24)), BrainSpace(c(24,24,24,24), c(1,1,1)))
res <- scaleSeries(bvec, TRUE, TRUE)

Create an exhaustive searchlight iterator

Description

Create an exhaustive searchlight iterator

Usage

Searchlight(mask, radius, eager = FALSE)

Arguments

mask

an image volume containing valid central voxels for roving searchlight
radius

in mm of spherical searchlight

Value

an iter class

Extract one or more series from object and return as ROI object

Description

Extract one or more series from object and return as ROI object

Extract one or more series from object

Usage

series_roi(x, i, ...)

series(x, i, ...)

## S4 method for signature 'BasisBrainVector,matrix'
series(x, i)

## S4 method for signature 'BasisBrainVector,numeric'
series(x, i, j, k)

## S4 method for signature 'SparseBrainVector,matrix'
series(x, i)

## S4 method for signature 'SparseBrainVector,numeric'
series(x, i, j, k)

## S4 method for signature 'BrainVector,matrix'
series(x, i)

## S4 method for signature 'BrainVector,matrix'
series_roi(x, i)

## S4 method for signature 'BrainVector,ROIVolume'
series(x, i)

## S4 method for signature 'BrainVector,ROIVolume'
series_roi(x, i)

## S4 method for signature 'BrainVector,LogicalBrainVolume'
series(x, i)

## S4 method for signature 'BrainVector,LogicalBrainVolume'
series_roi(x, i)

## S4 method for signature 'BrainVector,numeric'
series(x, i, j, k)

## S4 method for signature 'BrainVector,numeric'
series_roi(x, i, j, k)

Arguments

x

the object
i

the series indices
...

additional arguments
j

index for 2nd dimension
k

index for 3rd dimension
x

the object
i

the series indices
...

additional arguments
j

index for 2nd dimension
k

index for 3rd dimension

seriesIter

Description

Construct a series iterator

Usage

seriesIter(x)

## S4 method for signature 'SparseBrainVector'
seriesIter(x)

## S4 method for signature 'BrainVector'
seriesIter(x)

Arguments

x

the object to be iterated over. This is typically an instance of class BrainVector

Value

an iter object from the iterators package.

Methods (by class)

Examples

## create a BrainVector with 10X10X10X10, where the last dimension is 
## by convention the fourth dimension.
bvec <- BrainVector(array(rnorm(10*10*10*10), rep(10,4)), BrainSpace(rep(10,4), c(1,1,1)))
iter <- seriesIter(bvec)

## compute mean of each series
library(foreach)
library(iterators)
foreach(i=iter, .combine=c) %do% { mean(i) }
iter <- seriesIter(bvec)

## combine all series into a matrix
foreach(i=iter, .combine=rbind) %do% { i }

## scale all series, add as columns in matrix.
foreach(i=seriesIter(bvec), .combine=cbind) %do% { scale(i) }

show an AxisSet1D

Description

show an AxisSet1D

Usage

## S4 method for signature 'AxisSet1D'
show(object)

Arguments

object

the object

show an AxisSet2D

Description

show an AxisSet2D

Usage

## S4 method for signature 'AxisSet2D'
show(object)

Arguments

object

the object

show an AxisSet3D

Description

show an AxisSet3D

Usage

## S4 method for signature 'AxisSet3D'
show(object)

Arguments

object

the object

show an AxisSet4D

Description

show an AxisSet4D

Usage

## S4 method for signature 'AxisSet4D'
show(object)

Arguments

object

the object

show a BaseMetaInfo

Description

show a BaseMetaInfo

Usage

## S4 method for signature 'BaseMetaInfo'
show(object)

Arguments

object

the object

show a BrainSpace

Description

show a BrainSpace

Usage

## S4 method for signature 'BrainSpace'
show(object)

Arguments

object

the object

show a BrainVector

Description

show a BrainVector

Usage

## S4 method for signature 'BrainVector'
show(object)

Arguments

object

the object

show a BrainVectorSource

Description

show a BrainVectorSource

Usage

## S4 method for signature 'BrainVectorSource'
show(object)

Arguments

object

the object

show a BrainVolume

Description

show a BrainVolume

Usage

## S4 method for signature 'BrainVolume'
show(object)

Arguments

object

the object

show a FileMetaInfo

Description

show a FileMetaInfo

Usage

## S4 method for signature 'FileMetaInfo'
show(object)

Arguments

object

the object

show an NamedAxis

Description

show an NamedAxis

Usage

## S4 method for signature 'NamedAxis'
show(object)

Arguments

object

the object

show a NullMetaInfo

Description

show a NullMetaInfo

Usage

## S4 method for signature 'NullMetaInfo'
show(object)

Arguments

object

the object

show an ROIVolume

Description

show an ROIVolume

Usage

## S4 method for signature 'ROIVolume'
show(object)

Arguments

object

the object

show a SparseBrainVector

Description

show a SparseBrainVector

Usage

## S4 method for signature 'SparseBrainVector'
show(object)

Arguments

object

the object

Extract a 2D slice from an image volume

Description

Extract a 2D slice from an image volume

Usage

slice(x, zlevel, along, orientation, ...)

## S4 method for signature 'BrainVolume,numeric,numeric,missing'
slice(x, zlevel, along,
  orientation)

## S4 method for signature 'BrainVolume,numeric,BrainSpace,AxisSet3D'
slice(x, zlevel, along,
  orientation)

Arguments

x

the object
zlevel

coordinate (in voxel units) along the sliced axis
along

the axis along which to slice
orientation

the target orientation of the 2D slice
...

additional arguments

sliceData

Description

extract a 2D slice from a BrainVolume instance.

Usage

sliceData(vol, slice, axis = 3)

Arguments

vol

an BrainVolume instance
slice

the integer index of the slice to cut.
axis

the axis number (1, 2, 3) defining fixed axis of the 2D slice.

Generic function to extract geometric properties of an image.

Description

Generic function to extract geometric properties of an image.

Usage

space(x, ...)

## S4 method for signature 'BrainData'
space(x)

## S4 method for signature 'BrainSpace'
space(x)

## S4 method for signature 'IndexLookupVolume'
space(x)

Arguments

x

the object to query, e.g. an instance of BrainVolume or BrainVector
...

additional arguments

Value

an object representing the geometric space of the image of type BrainSpace

Examples

x = BrainSpace(c(10,10,10), c(1,1,1))
vol <- BrainVolume(rnorm(10*10*10), x)
identical(x,space(vol))

Generic function to extract the voxel dimensions of an image

Description

Generic function to extract the voxel dimensions of an image

spacing

Usage

spacing(x)

## S4 method for signature 'BrainData'
spacing(x)

## S4 method for signature 'BrainSpace'
spacing(x)

Arguments

x

the object

Value

a numeric vector

Examples

bspace <- BrainSpace(c(10,10,10), c(2,2,2))
all.equal(spacing(bspace), c(2,2,2))

SparseBrainVector

Description

a sparse four-dimensional brain image, backed by a matrix, where each column represents a vector spanning the fourth dimension (e.g. time)

constructs a SparseBrainVector object

Usage

SparseBrainVector(data, space, mask, source = NULL, label = "")

Arguments

data

an array which can be a matrix or 4-D array
space

a BrainSpace instance
mask

a 3D array of type logical
source

the data source – an instance of class BrainSource
label

associated sub-image labels

Slots

mask

the mask defining the sparse domain

data

the matrix of series, where rows span across voxel space and columns span the fourth dimensions

map

instance of class IndexLookupVolume is used to map between spatial and index/row coordinates

Examples

bspace <- BrainSpace(c(10,10,10,100), c(1,1,1))
mask <- array(rnorm(10*10*10) > .5, c(10,10,10))
mat <- matrix(rnorm(sum(mask)), 100, sum(mask))
svec <- SparseBrainVector(mat, bspace,mask)
length(indices(svec)) == sum(mask)

SparseBrainVectorSource

Description

A class that is used to produce a SparseBrainVector instance

constructs a SparseBrainVectorSource object

Usage

SparseBrainVectorSource(metaInfo, indices, mask)

Arguments

metaInfo

an object of class BrainMetaInfo
indices

a vector of 1D indices
mask

a 3D array of type logical

Slots

mask

the subset of voxels that will be stored in memory

SparseBrainVolume

Description

Three-dimensional brain image, backed by a sparseVector for Matrix package

Construct a SparseBrainVolume instance

Usage

SparseBrainVolume(data, space, indices = NULL, source = NULL, label = "")

Arguments

data

a numeric vector
space

an instance of class BrainSpace
indices

a index vector indicating the 1-d coordinates of the data values
source

an instance of class BrainSource
label

a character string

Details

Image data is backed by Matrix::sparseVector.

Value

SparseBrainVolume instance

Slots

data

a sparseVector instance

Examples

data <- 1:10
indices <- seq(1,1000, length.out=10)
bspace <- BrainSpace(c(64,64,64), spacing=c(1,1,1))
sparsevol <- SparseBrainVolume(data,bspace,indices=indices)
densevol <- BrainVolume(data,bspace,indices=indices)
sum(sparsevol) == sum(densevol)

Generic function to fill disjoint sets of values with the output of a function

Description

Generic function to fill disjoint sets of values with the output of a function

Usage

splitFill(x, fac, FUN)

## S4 method for signature 'BrainVolume,factor,'function''
splitFill(x, fac, FUN)

Arguments

x

the object to split
fac

the factor to split by
FUN

the function to summarize the the sets

Details

FUN can either return a scalar for each input vector or a vector equal to the length of the input vector. If it returns a scalar then every voxel in the set will be filled with that value in the output vector.

Value

a new object where the original values have been replaced by the function output

Examples

## summarize with mean -- FUN returns a scalar
x = BrainSpace(c(10,10,10), c(1,1,1))
vol <- BrainVolume(rnorm(10*10*10), x)
fac <- factor(rep(1:10, length.out=1000))
ovol.mean <- splitFill(vol, fac, mean)
identical(dim(ovol.mean), dim(vol))
length(unique(as.vector(ovol.mean))) == 10
## transform by reversing vector -- FUN returns a vector.
ovol2 <- splitFill(vol, fac, rev)

Generic function to summarize subsets of an object by first splitting by row and then "reducing" by a summary function

Description

Generic function to summarize subsets of an object by first splitting by row and then "reducing" by a summary function

Usage

splitReduce(x, fac, FUN)

## S4 method for signature 'matrix,integer,'function''
splitReduce(x, fac, FUN)

## S4 method for signature 'matrix,integer,missing'
splitReduce(x, fac)

## S4 method for signature 'matrix,factor,missing'
splitReduce(x, fac)

## S4 method for signature 'matrix,factor,'function''
splitReduce(x, fac, FUN)

## S4 method for signature 'BrainVector,factor,'function''
splitReduce(x, fac, FUN)

## S4 method for signature 'BrainVector,factor,missing'
splitReduce(x, fac, FUN)

Arguments

x

a numeric matrix(like) object
fac

the factor to define subsets of the object
FUN

the function to apply to each subset. if FUN is missing, than the mean of each sub-matrix column is computed.

Details

if FUN is supplied it must take a vector and return a single scalar value. If it returns more than one value, an error will occur.

if x is a BrainVector instance then voxels (dims 1:3) are treated as columns and time-series (dim 4) as rows. The summary function then is applied to groups of voxels. However, if the goal is to apply a function to groups of time-points, then this can be achieved as follows:

splitReduce(t(as.matrix(bvec)), fac)

Value

a new matrix where the original values have been "reduced" by the supplied function.

Examples

mat = matrix(rnorm(100*100), 100, 100)
fac = sample(1:3, nrow(mat), replace=TRUE)
## compute column means of each sub-matrix
ms <- splitReduce(mat, fac)
all.equal(row.names(ms), levels(fac))

## compute column medians of each sub-matrix
ms <- splitReduce(mat, fac, median)

## compute time-series means grouped over voxels. 
## Here, \code{length(fac)} must equal the number of voxels: \code{prod(dim(bvec)[1:3]}
bvec <- BrainVector(array(rnorm(24*24*24*24), c(24,24,24,24)), BrainSpace(c(24,24,24,24), c(1,1,1)))
fac <- factor(sample(1:3, prod(dim(bvec)[1:3]), replace=TRUE))
ms <- splitReduce(bvec, fac)
ms2 <- splitReduce(bvec, fac, mean)
all.equal(row.names(ms), levels(fac))
all.equal(ms,ms2)

Generic function to center/scale row-subsets of a matrix or matrix-like object

Description

Generic function to center/scale row-subsets of a matrix or matrix-like object

Usage

splitScale(x, f, center, scale)

## S4 method for signature 'matrix,factor,logical,logical'
splitScale(x, f, center = TRUE,
  scale = TRUE)

## S4 method for signature 'matrix,factor,missing,missing'
splitScale(x, f)

## S4 method for signature 'DenseBrainVector,factor,missing,missing'
splitScale(x, f)

## S4 method for signature 'DenseBrainVector,factor,logical,missing'
splitScale(x, f, center)

## S4 method for signature 'DenseBrainVector,factor,logical,logical'
splitScale(x, f, center,
  scale)

Arguments

x

a numeric matrix or matrix-like object
f

the splitting object, typically a factor or set of integer indices. must be equal to number of rows of matrix.
center

should values within each submatrix be centered? (mean removed from each column of submatrix)
scale

should values be scaled? (divide vector by standard deviation from each column of submatrix)

Value

a new matrix or matrix-like object where the original rows have been grouped by f and then centered and/or scaled for each grouping

Examples

M <- matrix(rnorm(1000), 10, 100)
fac <- factor(rep(1:2, each=5))
Ms <- splitScale(M, fac)

## correctly centered
all(abs(apply(Ms[fac == 1,], 2, mean)) < .000001)
all(abs(apply(Ms[fac == 2,], 2, mean)) < .000001)

# correctly scaled
all.equal(apply(Ms[fac == 1,], 2, sd), rep(1, ncol(Ms)))
all.equal(apply(Ms[fac == 2,], 2, sd), rep(1, ncol(Ms)))

Generic function to strip extension from file name, given a BrainFileDescriptor instance.

Description

Generic function to strip extension from file name, given a BrainFileDescriptor instance.

Usage

stripExtension(x, fileName)

## S4 method for signature 'BrainFileDescriptor,character'
stripExtension(x, fileName)

Arguments

x

descriptor instance
fileName

file name to be stripped of its extension

Value

fileName without extension

Generic function to extract a sub-vector from a BrainVector object.

Description

Generic function to extract a sub-vector from a BrainVector object.

Usage

subVector(x, i, ...)

## S4 method for signature 'SparseBrainVector,numeric'
subVector(x, i)

## S4 method for signature 'DenseBrainVector,numeric'
subVector(x, i)

Arguments

x

four-dimensional image
i

the indices of the volume(s) to extract
...

additional arguments

Value

a BrainVector object that is a sub-sequence of the supplied object.

Examples

bvec <- BrainVector(array(rnorm(24*24*24*24), c(24,24,24,24)), BrainSpace(c(24,24,24,24), c(1,1,1)))
vec <- subVector(bvec,1:2)
all.equal(2, dim(vec)[4])

vec <- subVector(bvec, c(1,3,5,7))
all.equal(4, dim(vec)[4])

mask <- LogicalBrainVolume(rep(TRUE, 24*24*24), BrainSpace(c(24,24,24), c(1,1,1)))
svec <- SparseBrainVector(array(rnorm(24*24*24*24), c(24,24,24,24)), 
BrainSpace(c(24,24,24,24), c(1,1,1)), mask)
vec <- subVector(svec, c(1,3,5))
all.equal(3, dim(vec)[4])

Generic function to extract a one or more individual volumes from a four-dimensional image

Description

Generic function to extract a one or more individual volumes from a four-dimensional image

Usage

takeVolume(x, i, ...)

## S4 method for signature 'SparseBrainVector,numeric'
takeVolume(x, i, merge = FALSE)

## S4 method for signature 'BrainVector,numeric'
takeVolume(x, i, merge = FALSE)

Arguments

x

four-dimensional image
i

the indices of the volume(s) to extract
...

additional arguments
merge

concatenate extracted volumes

Value

a list of BrainVolume elements

Examples

bvec <- BrainVector(array(rnorm(24*24*24*24), c(24,24,24,24)), BrainSpace(c(24,24,24,24), c(1,1,1)))
vol <- takeVolume(bvec,1)
all.equal(dim(vol), c(24,24,24))

vol <- takeVolume(bvec,1:3)
length(vol) == 3
class(vol) == "list"

Generic getter to extract image coordinate transformation

Description

Generic getter to extract image coordinate transformation

Usage

trans(x)

## S4 method for signature 'BrainMetaInfo'
trans(x)

## S4 method for signature 'NIfTIMetaInfo'
trans(x)

## S4 method for signature 'BrainSpace'
trans(x)

## S4 method for signature 'BrainData'
trans(x)

Arguments

x

an object with a transformation

Details

This function returns a transformation that can be used to go from "grid coordinates" to "real world coordinates" in millimeters.

This function returns a transformation that can be used to go from "grid coordinates" to "real world coordinates" in millimeters. see BrainSpace

Examples

bspace <- BrainSpace(c(10,10,10), c(2,2,2))
trans(bspace)
all.equal(dim(trans(bspace)), c(4,4))

Generic function to extract data values of object

Description

Generic function to extract data values of object

Usage

values(x, ...)

## S4 method for signature 'ROIVolume'
values(x, ...)

## S4 method for signature 'ROIVector'
values(x, ...)

Arguments

x

the object to get values from
...

additional arguments

extract voxel coordinates

Description

extract voxel coordinates

Usage

voxels(x, ...)

## S4 method for signature 'Kernel'
voxels(x, centerVoxel = NULL)

Arguments

x

the object to extract voxels from
...

additional arguments to function
centerVoxel

the absolute location of the center of the voxel, default is (0,0,0)

Generic function to write a sequence of elements from an input source

Description

Generic function to write a sequence of elements from an input source

writeElements

Usage

writeElements(x, els)

## S4 method for signature 'BinaryWriter,numeric'
writeElements(x, els)

Arguments

x

the output channel
els

the elements to write

Generic function to write a 4D image vector to disk

Description

Generic function to write a 4D image vector to disk

Usage

writeVector(x, fileName, format, dataType)

## S4 method for signature 'BrainVector,character,missing,missing'
writeVector(x, fileName)

## S4 method for signature 'BrainVector,character,character,missing'
writeVector(x, fileName,
  format)

## S4 method for signature 'BrainVector,character,missing,character'
writeVector(x, fileName,
  dataType)

Arguments

x

an image object, typically a BrainVector instance.
fileName

output file name.
format

file format string. Since "NIFTI" is the only currently supported format, this parameter can be safely ignored and omitted.
dataType

the numeric data type. If specified should be a character vector of: "BINARY", "UBYTE", "SHORT", "INT", "FLOAT", "DOUBLE". Otherwise output format will be inferred from R the datatype of the image.

Examples

bvec <- BrainVector(array(0, c(10,10,10,10)), BrainSpace(c(10,10,10,10), c(1,1,1)))
## Not run: 
writeVector(bvol, "out.nii")
writeVector(bvol, "out.nii.gz")
writeVector(bvec, "out.nii")
writeVector(bvec, "out.nii.gz")

## End(Not run)

Generic function to write a 3D image volume to disk

Description

Generic function to write a 3D image volume to disk

Usage

writeVolume(x, fileName, format, dataType)

## S4 method for signature 'BrainVolume,character,missing,missing'
writeVolume(x, fileName)

## S4 method for signature 'ClusteredBrainVolume,character,missing,missing'
writeVolume(x,
  fileName)

## S4 method for signature 'BrainVolume,character,character,missing'
writeVolume(x, fileName,
  format)

## S4 method for signature 'BrainVolume,character,missing,character'
writeVolume(x, fileName,
  dataType)

Arguments

x

an image object, typically a BrainVolume instance.
fileName

output file name
format

file format string. Since "NIFTI" is the only currently supported format, this parameter can be safely ignored and omitted.
dataType

output data type, If specified should be a character vector of: "BINARY", "UBYTE", "SHORT", "INT", "FLOAT", "DOUBLE". Otherwise output format will be inferred from R the datatype of the image.

Details

The output format will be inferred from file extension.

The output format will be inferred from file extension. writeVolume(x, "out.nii") outputs a NIFTI file. writeVolume(x, "out.nii.gz") outputs a gzipped NIFTI file.

No other file output formats are currently supported.

Examples

bvol <- BrainVolume(array(0, c(10,10,10)), BrainSpace(c(10,10,10), c(1,1,1)))
## Not run: 
writeVolume(bvol, "out.nii")
writeVolume(bvol, "out.nii.gz")

## End(Not run)