NIFTI1_INTENT_CODES(3)

Library Functions Manual

NAME

NIFTI1_INTENT_CODES -

nifti1 intent codes, to describe intended meaning of dataset contents

Defines

#define NIFTI_INTENT_CORREL 2
#define NIFTI_INTENT_TTEST 3
#define NIFTI_INTENT_FTEST 4
#define NIFTI_INTENT_ZSCORE 5
#define NIFTI_INTENT_CHISQ 6
#define NIFTI_INTENT_BETA 7
#define NIFTI_INTENT_BINOM 8
#define NIFTI_INTENT_GAMMA 9
#define NIFTI_INTENT_POISSON 10
#define NIFTI_INTENT_NORMAL 11
#define NIFTI_INTENT_FTEST_NONC 12
#define NIFTI_INTENT_CHISQ_NONC 13
#define NIFTI_INTENT_LOGISTIC 14
#define NIFTI_INTENT_LAPLACE 15
#define NIFTI_INTENT_UNIFORM 16
#define NIFTI_INTENT_TTEST_NONC 17
#define NIFTI_INTENT_WEIBULL 18
#define NIFTI_INTENT_CHI 19
#define NIFTI_INTENT_INVGAUSS 20
#define NIFTI_INTENT_EXTVAL 21
#define NIFTI_INTENT_PVAL 22
#define NIFTI_INTENT_LOGPVAL 23
#define NIFTI_INTENT_LOG10PVAL 24
#define NIFTI_FIRST_STATCODE 2
#define NIFTI_LAST_STATCODE 24
#define NIFTI_INTENT_ESTIMATE 1001
#define NIFTI_INTENT_LABEL 1002
#define NIFTI_INTENT_NEURONAME 1003
#define NIFTI_INTENT_GENMATRIX 1004
#define NIFTI_INTENT_SYMMATRIX 1005
#define NIFTI_INTENT_DISPVECT 1006
#define NIFTI_INTENT_VECTOR 1007
#define NIFTI_INTENT_POINTSET 1008
#define NIFTI_INTENT_TRIANGLE 1009
#define NIFTI_INTENT_QUATERNION 1010
#define NIFTI_INTENT_DIMLESS 1011
#define NIFTI_INTENT_TIME_SERIES 2001
#define NIFTI_INTENT_NODE_INDEX 2002
#define NIFTI_INTENT_RGB_VECTOR 2003
#define NIFTI_INTENT_RGBA_VECTOR 2004
#define NIFTI_INTENT_SHAPE 2005

Detailed Description

nifti1 intent codes, to describe intended meaning of dataset contents

[C2, chap 32] Correlation coefficient R (1 param): p1 = degrees of freedom R/sqrt(1-R*R) is t-distributed with p1 DOF.

Define Documentation

#define NIFTI_FIRST_STATCODE 2 Smallest intent_code that indicates a statistic.

#define NIFTI_INTENT_BETA 7 [C2, chap 25] Beta distribution (2 params): p1=a, p2=b. Density(x) proportional to x^(a-1) * (1-x)^(b-1).

#define NIFTI_INTENT_BINOM 8 [U, chap 3] Binomial distribution (2 params): p1 = number of trials, p2 = probability per trial. Prob(x) = (p1 choose x) * p2^x * (1-p2)^(p1-x), for x=0,1,...,p1.

#define NIFTI_INTENT_CHI 19 [C1, chap 18] Chi distribution (1 param): p1 = DOF. Density(x) proportional to x^(p1-1) * exp(-x^2/2) for x > 0. p1 = 1 = 'half normal' distribution p1 = 2 = Rayleigh distribution p1 = 3 = Maxwell-Boltzmann distribution.

#define NIFTI_INTENT_CHISQ 6 [C1, chap 18] Chi-squared (1 param): p1 = DOF. Density(x) proportional to exp(-x/2) * x^(p1/2-1).

#define NIFTI_INTENT_CHISQ_NONC 13 [C2, chap 29] Noncentral chi-squared statistic (2 params): p1 = DOF, p2 = noncentrality parameter.

#define NIFTI_INTENT_DIMLESS 1011 Dimensionless value - no params - although, as in _ESTIMATE the name of the parameter may be stored in intent_name.

#define NIFTI_INTENT_DISPVECT 1006 To signify that the vector value at each voxel is to be taken as a displacement field or vector:

dataset must have a 5th dimension
intent_code must be NIFTI_INTENT_DISPVECT
dim[5] must be the dimensionality of the displacment vector (e.g., 3 for spatial displacement, 2 for in-plane)

#define NIFTI_INTENT_ESTIMATE 1001 To signify that the value at each voxel is an estimate of some parameter, set intent_code = NIFTI_INTENT_ESTIMATE. The name of the parameter may be stored in intent_name.

#define NIFTI_INTENT_EXTVAL 21 [C2, chap 22] Extreme value type I (2 params): p1 = location, p2 = scale cdf(x) = exp(-exp(-(x-p1)/p2)).

#define NIFTI_INTENT_FTEST 4 [C2, chap 27] Fisher F statistic (2 params): p1 = numerator DOF, p2 = denominator DOF.

#define NIFTI_INTENT_FTEST_NONC 12 [C2, chap 30] Noncentral F statistic (3 params): p1 = numerator DOF, p2 = denominator DOF, p3 = numerator noncentrality parameter.

#define NIFTI_INTENT_GAMMA 9 [C1, chap 17] Gamma distribution (2 params): p1 = shape, p2 = scale. Density(x) proportional to x^(p1-1) * exp(-p2*x).

#define NIFTI_INTENT_GENMATRIX 1004 To store an M x N matrix at each voxel:

dataset must have a 5th dimension (dim[0]=5 and dim[5]>1)
intent_code must be NIFTI_INTENT_GENMATRIX
dim[5] must be M*N
intent_p1 must be M (in float format)
intent_p2 must be N (ditto)
the matrix values A[i][[j] are stored in row-order:

•: A[0][0] A[0][1] ... A[0][N-1]
•: A[1][0] A[1][1] ... A[1][N-1]
•: etc., until
•: A[M-1][0] A[M-1][1] ... A[M-1][N-1]

#define NIFTI_INTENT_INVGAUSS 20 [C1, chap 15] Inverse Gaussian (2 params): p1 = mu, p2 = lambda Density(x) proportional to exp(-p2(x-p1)^2/(2p1^2*x)) / x^3 for x > 0.

#define NIFTI_INTENT_LABEL 1002 To signify that the value at each voxel is an index into some set of labels, set intent_code = NIFTI_INTENT_LABEL. The filename with the labels may stored in aux_file.

#define NIFTI_INTENT_LAPLACE 15 [C2, chap 24] Laplace distribution (2 params): p1 = location, p2 = scale. Density(x) proportional to exp(-abs(x-p1)/p2).

#define NIFTI_INTENT_LOG10PVAL 24 Data is log10(p-value) (no params). To be safe, a program should compute p = pow(10.,-abs(this_value)). The nifti_stats.c library returns this_value as positive, so that this_value = -log10(p).

#define NIFTI_INTENT_LOGISTIC 14 [C2, chap 23] Logistic distribution (2 params): p1 = location, p2 = scale. Density(x) proportional to sech^2((x-p1)/(2*p2)).

#define NIFTI_INTENT_LOGPVAL 23 Data is ln(p-value) (no params). To be safe, a program should compute p = exp(-abs(this_value)). The nifti_stats.c library returns this_value as positive, so that this_value = -log(p).

#define NIFTI_INTENT_NEURONAME 1003 To signify that the value at each voxel is an index into the NeuroNames labels set, set intent_code = NIFTI_INTENT_NEURONAME.

#define NIFTI_INTENT_NODE_INDEX 2002 To signify that the value at each location is a node index, from a complete surface dataset.

#define NIFTI_INTENT_NORMAL 11 [C1, chap 13] Normal distribution (2 params): p1 = mean, p2 = standard deviation.

#define NIFTI_INTENT_POINTSET 1008 To signify that the vector value at each voxel is really a spatial coordinate (e.g., the vertices or nodes of a surface mesh):

dataset must have a 5th dimension
intent_code must be NIFTI_INTENT_POINTSET
dim[0] = 5
dim[1] = number of points
dim[2] = dim[3] = dim[4] = 1
dim[5] must be the dimensionality of space (e.g., 3 => 3D space).
intent_name may describe the object these points come from (e.g., 'pial', 'gray/white' , 'EEG', 'MEG').

#define NIFTI_INTENT_POISSON 10 [U, chap 4] Poisson distribution (1 param): p1 = mean. Prob(x) = exp(-p1) * p1^x / x! , for x=0,1,2,....

#define NIFTI_INTENT_PVAL 22 Data is a 'p-value' (no params).

#define NIFTI_INTENT_QUATERNION 1010 To signify that the vector value at each voxel is a quaternion:

dataset must have a 5th dimension
intent_code must be NIFTI_INTENT_QUATERNION
dim[0] = 5
dim[5] = 4
datatype should be a floating point type

#define NIFTI_INTENT_RGB_VECTOR 2003 To signify that the vector value at each location is an RGB triplet, of whatever type.

dataset must have a 5th dimension
dim[0] = 5
dim[1] = number of nodes
dim[2] = dim[3] = dim[4] = 1
dim[5] = 3

#define NIFTI_INTENT_RGBA_VECTOR 2004 To signify that the vector value at each location is a 4 valued RGBA vector, of whatever type.

dataset must have a 5th dimension
dim[0] = 5
dim[1] = number of nodes
dim[2] = dim[3] = dim[4] = 1
dim[5] = 4

#define NIFTI_INTENT_SHAPE 2005 To signify that the value at each location is a shape value, such as the curvature.

#define NIFTI_INTENT_SYMMATRIX 1005 To store an NxN symmetric matrix at each voxel:

dataset must have a 5th dimension
intent_code must be NIFTI_INTENT_SYMMATRIX
dim[5] must be N*(N+1)/2
intent_p1 must be N (in float format)
the matrix values A[i][[j] are stored in row-order:

•: A[0][0]
•: A[1][0] A[1][1]
•: A[2][0] A[2][1] A[2][2]
•: etc.: row-by-row

#define NIFTI_INTENT_TIME_SERIES 2001 To signify that the value at each location is from a time series.

#define NIFTI_INTENT_TRIANGLE 1009 To signify that the vector value at each voxel is really a triple of indexes (e.g., forming a triangle) from a pointset dataset:

dataset must have a 5th dimension
intent_code must be NIFTI_INTENT_TRIANGLE
dim[0] = 5
dim[1] = number of triangles
dim[2] = dim[3] = dim[4] = 1
dim[5] = 3
datatype should be an integer type (preferably DT_INT32)
the data values are indexes (0,1,...) into a pointset dataset.

#define NIFTI_INTENT_TTEST 3 [C2, chap 28] Student t statistic (1 param): p1 = DOF.

#define NIFTI_INTENT_TTEST_NONC 17 [C2, chap 31] Noncentral t statistic (2 params): p1 = DOF, p2 = noncentrality parameter.

#define NIFTI_INTENT_UNIFORM 16 [C2, chap 26] Uniform distribution: p1 = lower end, p2 = upper end.

#define NIFTI_INTENT_WEIBULL 18 [C1, chap 21] Weibull distribution (3 params): p1 = location, p2 = scale, p3 = power. Density(x) proportional to ((x-p1)/p2)^(p3-1) * exp(-((x-p1)/p2)^p3) for x > p1.

#define NIFTI_INTENT_ZSCORE 5 [C1, chap 13] Standard normal (0 params): Density = N(0,1).

#define NIFTI_LAST_STATCODE 24 Largest intent_code that indicates a statistic.

Author

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nifti1_io

NIFTI1_INTENT_CODES(3)

NAME

Defines

Detailed Description

Define Documentation

#define NIFTI_FIRST_STATCODE 2 Smallest intent_code that indicates a statistic.

#define NIFTI_INTENT_BETA 7 [C2, chap 25] Beta distribution (2 params): p1=a, p2=b. Density(x) proportional to x^(a-1) * (1-x)^(b-1).

#define NIFTI_INTENT_BINOM 8 [U, chap 3] Binomial distribution (2 params): p1 = number of trials, p2 = probability per trial. Prob(x) = (p1 choose x) * p2^x * (1-p2)^(p1-x), for x=0,1,...,p1.

#define NIFTI_INTENT_CHI 19 [C1, chap 18] Chi distribution (1 param): p1 = DOF. Density(x) proportional to x^(p1-1) * exp(-x^2/2) for x > 0. p1 = 1 = 'half normal' distribution p1 = 2 = Rayleigh distribution p1 = 3 = Maxwell-Boltzmann distribution.

#define NIFTI_INTENT_CHISQ 6 [C1, chap 18] Chi-squared (1 param): p1 = DOF. Density(x) proportional to exp(-x/2) * x^(p1/2-1).

#define NIFTI_INTENT_CHISQ_NONC 13 [C2, chap 29] Noncentral chi-squared statistic (2 params): p1 = DOF, p2 = noncentrality parameter.

#define NIFTI_INTENT_DIMLESS 1011 Dimensionless value - no params - although, as in _ESTIMATE the name of the parameter may be stored in intent_name.

#define NIFTI_INTENT_DISPVECT 1006 To signify that the vector value at each voxel is to be taken as a displacement field or vector:

#define NIFTI_INTENT_ESTIMATE 1001 To signify that the value at each voxel is an estimate of some parameter, set intent_code = NIFTI_INTENT_ESTIMATE. The name of the parameter may be stored in intent_name.

#define NIFTI_INTENT_EXTVAL 21 [C2, chap 22] Extreme value type I (2 params): p1 = location, p2 = scale cdf(x) = exp(-exp(-(x-p1)/p2)).

#define NIFTI_INTENT_FTEST 4 [C2, chap 27] Fisher F statistic (2 params): p1 = numerator DOF, p2 = denominator DOF.

#define NIFTI_INTENT_FTEST_NONC 12 [C2, chap 30] Noncentral F statistic (3 params): p1 = numerator DOF, p2 = denominator DOF, p3 = numerator noncentrality parameter.

#define NIFTI_INTENT_GAMMA 9 [C1, chap 17] Gamma distribution (2 params): p1 = shape, p2 = scale. Density(x) proportional to x^(p1-1) * exp(-p2*x).

#define NIFTI_INTENT_GENMATRIX 1004 To store an M x N matrix at each voxel:

#define NIFTI_INTENT_INVGAUSS 20 [C1, chap 15] Inverse Gaussian (2 params): p1 = mu, p2 = lambda Density(x) proportional to exp(-p2*(x-p1)^2/(2*p1^2*x)) / x^3 for x > 0.

#define NIFTI_INTENT_LABEL 1002 To signify that the value at each voxel is an index into some set of labels, set intent_code = NIFTI_INTENT_LABEL. The filename with the labels may stored in aux_file.

#define NIFTI_INTENT_LAPLACE 15 [C2, chap 24] Laplace distribution (2 params): p1 = location, p2 = scale. Density(x) proportional to exp(-abs(x-p1)/p2).

#define NIFTI_INTENT_LOG10PVAL 24 Data is log10(p-value) (no params). To be safe, a program should compute p = pow(10.,-abs(this_value)). The nifti_stats.c library returns this_value as positive, so that this_value = -log10(p).

#define NIFTI_INTENT_LOGISTIC 14 [C2, chap 23] Logistic distribution (2 params): p1 = location, p2 = scale. Density(x) proportional to sech^2((x-p1)/(2*p2)).

#define NIFTI_INTENT_LOGPVAL 23 Data is ln(p-value) (no params). To be safe, a program should compute p = exp(-abs(this_value)). The nifti_stats.c library returns this_value as positive, so that this_value = -log(p).

#define NIFTI_INTENT_NEURONAME 1003 To signify that the value at each voxel is an index into the NeuroNames labels set, set intent_code = NIFTI_INTENT_NEURONAME.

#define NIFTI_INTENT_NODE_INDEX 2002 To signify that the value at each location is a node index, from a complete surface dataset.

#define NIFTI_INTENT_NORMAL 11 [C1, chap 13] Normal distribution (2 params): p1 = mean, p2 = standard deviation.

#define NIFTI_INTENT_POINTSET 1008 To signify that the vector value at each voxel is really a spatial coordinate (e.g., the vertices or nodes of a surface mesh):

#define NIFTI_INTENT_POISSON 10 [U, chap 4] Poisson distribution (1 param): p1 = mean. Prob(x) = exp(-p1) * p1^x / x! , for x=0,1,2,....

#define NIFTI_INTENT_PVAL 22 Data is a 'p-value' (no params).

#define NIFTI_INTENT_QUATERNION 1010 To signify that the vector value at each voxel is a quaternion:

#define NIFTI_INTENT_RGB_VECTOR 2003 To signify that the vector value at each location is an RGB triplet, of whatever type.

#define NIFTI_INTENT_RGBA_VECTOR 2004 To signify that the vector value at each location is a 4 valued RGBA vector, of whatever type.

#define NIFTI_INTENT_SHAPE 2005 To signify that the value at each location is a shape value, such as the curvature.

#define NIFTI_INTENT_SYMMATRIX 1005 To store an NxN symmetric matrix at each voxel:

#define NIFTI_INTENT_TIME_SERIES 2001 To signify that the value at each location is from a time series.

#define NIFTI_INTENT_TRIANGLE 1009 To signify that the vector value at each voxel is really a triple of indexes (e.g., forming a triangle) from a pointset dataset:

#define NIFTI_INTENT_TTEST 3 [C2, chap 28] Student t statistic (1 param): p1 = DOF.

#define NIFTI_INTENT_TTEST_NONC 17 [C2, chap 31] Noncentral t statistic (2 params): p1 = DOF, p2 = noncentrality parameter.

#define NIFTI_INTENT_UNIFORM 16 [C2, chap 26] Uniform distribution: p1 = lower end, p2 = upper end.

#define NIFTI_INTENT_WEIBULL 18 [C1, chap 21] Weibull distribution (3 params): p1 = location, p2 = scale, p3 = power. Density(x) proportional to ((x-p1)/p2)^(p3-1) * exp(-((x-p1)/p2)^p3) for x > p1.

#define NIFTI_INTENT_ZSCORE 5 [C1, chap 13] Standard normal (0 params): Density = N(0,1).

#define NIFTI_LAST_STATCODE 24 Largest intent_code that indicates a statistic.

Author

#define NIFTI_INTENT_INVGAUSS 20 [C1, chap 15] Inverse Gaussian (2 params): p1 = mu, p2 = lambda Density(x) proportional to exp(-p2(x-p1)^2/(2p1^2*x)) / x^3 for x > 0.