7.1.94. 3dLocalBistat

Link to classic view

Usage: 3dLocalBistat [options] dataset1 dataset2

This program computes statistics between 2 datasets, at each voxel, based on a local neighborhood of that voxel.

  • The neighborhood is defined by the ‘-nbhd’ option.

  • Statistics to be calculated are defined by the ‘-stat’ option(s).

  • The 2 input datasets should have the same number of sub-bricks.

  • OR dataset1 should have 1 sub-brick and dataset2 can have more than 1: - In which case, the statistics of dataset2 against dataset1 are

    calculated for the #0 sub-brick of dataset1 against each sub-brick of dataset2.

OPTIONS

-nbhd ‘nnn’ = The string ‘nnn’ defines the region around each

voxel that will be extracted for the statistics calculation. The format of the ‘nnn’ string are: * ‘SPHERE(r)’ where ‘r’ is the radius in mm;

the neighborhood is all voxels whose center-to- center distance is less than or equal to ‘r’. ** A negative value for ‘r’ means that the region

is calculated using voxel indexes rather than voxel dimensions; that is, the neighborhood region is a “sphere” in voxel indexes of “radius” abs(r).
  • ‘RECT(a,b,c)’ is a rectangular block which proceeds plus-or-minus ‘a’ mm in the x-direction, ‘b’ mm in the y-direction, and ‘c’ mm in the z-direction. The correspondence between the dataset xyz axes and the actual spatial orientation can be determined by using program 3dinfo. ** A negative value for ‘a’ means that the region

    extends plus-and-minus abs(a) voxels in the x-direction, rather than plus-and-minus a mm. Mutatis mutandum for negative ‘b’ and/or ‘c’.

  • ‘RHDD(r)’ is a rhombic dodecahedron of ‘radius’ r.

  • ‘TOHD(r)’ is a truncated octahedron of ‘radius’ r.

-stat sss = Compute the statistic named ‘sss’ on the values

extracted from the region around each voxel: * pearson = Pearson correlation coefficient * spearman = Spearman correlation coefficient * quadrant = Quadrant correlation coefficient * mutinfo = Mutual Information * normuti = Normalized Mutual Information * jointent = Joint entropy * hellinger= Hellinger metric * crU = Correlation ratio (Unsymmetric) * crM = Correlation ratio (symmetrized by Multiplication) * crA = Correlation ratio (symmetrized by Addition) * L2slope = slope of least-squares (L2) linear regression of

the data from dataset1 vs. the dataset2 (i.e., d2 = a + b*d1 ==> this is ‘b’)
  • L1slope = slope of least-absolute-sum (L1) linear regression

    of the data from dataset1 vs. the dataset2

  • num = number of the values in the region:

with the use of -mask or -automask, the size of the region around any given voxel will vary; this option lets you map that size.
  • ALL = all of the above, in that order

More than one ‘-stat’ option can be used.

-mask mset = Read in dataset ‘mset’ and use the nonzero voxels
therein as a mask. Voxels NOT in the mask will not be used in the neighborhood of any voxel. Also, a voxel NOT in the mask will have its statistic(s) computed as zero (0).
-automask = Compute the mask as in program 3dAutomask.
-mask and -automask are mutually exclusive: that is, you can only specify one mask.
-weight ws = Use dataset ‘ws’ as a weight. Only applies to ‘pearson’.
-prefix ppp = Use string ‘ppp’ as the prefix for the output dataset.
The output dataset is always stored as floats.

ADVANCED OPTIONS

-histpow pp = By default, the number of bins in the histogram used
for calculating the Hellinger, Mutual Information, and Correlation Ratio statistics is n^(1/3), where n is the number of data points in the -nbhd mask. You can change that exponent to ‘pp’ with this option.
-histbin nn = Or you can just set the number of bins directly to ‘nn’.
-hclip1 a b = Clip dataset1 to lie between values ‘a’ and ‘b’. If ‘a’
and ‘b’ end in ‘%’, then these values are percentage points on the cumulative histogram.
-hclip2 a b = Similar to ‘-hclip1’ for dataset2.

++ Compile date = Dec 16 2015

Table Of Contents

This Page