2011-02-24T10:22:08-08:00
Resource:Statistical Parametric Mapping Software
<?xml version="1.0" encoding="UTF-8"?><br />
<disco xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" format="discoToNifRegistry.xsd" version="1.0" version-date="2009-07-30"><br />
:<resource blueprint="true" disco_id="170" full_name="Images are spatially normalised into a standard space, and smoothed. Parametric statistical models are assummed at each voxel, using the General Linear Model to describe the variability in the data in terms of experimental and confounding effects, and residual variability. Hypotheses expressed in terms of the model parameters are assessed at each voxel with univariate statistics. This gives an image whose voxel values are statistics, a Statistic Image, or Statistical Parametric Map (SPM{t}, SPM{F}) Temporal convolution of the General Linear Model for fMRI enables the application of results from serially correlated regression, permitting the construction of statistic images from fMRI time series. The multiple comparisons problem of simultaneously assessing all the voxel statistics is addressed using the theory of continuous random fields, assumming the statistic image to be a good lattice representation of an underlying continuous stationary random field. Results for the Euler characteristic lead to corrected p-values for each voxel hypothesis. In addition, the theory permits the computation of corrected p-values for clusters of voxels exceeding a given threshold, and for entire sets of supra-threshold clusters, leading to more powerful statistical tests at the expense of some localising power." last-discoed="2009-08-29" nif_id="new" short_name="SPM5" url="http://www.fil.ion.ucl.ac.uk/spm"><br />
:<admin_contact email="" name="Methods Group of the Wellcome Department of Imaging Neuroscience + others" phone="" /><br />
:<comment type="desc:Version_Information" /><br />
:<comment type="desc:version_information" /><br />
:<comment type="desc:keywords">functional, NIfTI-1 support, registration, segmentation, statistical, visualization, volume, warping</comment><br />
:<keyword type="license" value="copyleft" /><br />
:<keyword type="release_date" value="SPM5 (1st Dec/ 2005)" /><br />
:<keyword type="version" value="SPM2 and SPM5b" /><br />
:</resource><br />
</disco><br />
http://www.fil.ion.ucl.ac.uk/spm
<br />
Images are spatially normalized into a standard space, and smoothed. Parametric statistical models are assummed at each voxel, using the General Linear Model to describe the variability in the data in terms of experimental and confounding effects, and residual variability. Hypotheses expressed in terms of the model parameters are assessed at each voxel with univariate statistics. This gives an image whose voxel values are statistics, a Statistic Image, or Statistical Parametric Map (SPM{t}, SPM{F}) Temporal convolution of the General Linear Model for fMRI enables the application of results from serially correlated regression, permitting the construction of statistic images from fMRI time series. The multiple comparisons problem of simultaneously assessing all the voxel statistics is addressed using the theory of continuous random fields, assumming the statistic image to be a good lattice representation of an underlying continuous stationary random field. Results for the Euler characteristic lead to corrected p-values for each voxel hypothesis. In addition, the theory permits the computation of corrected p-values for clusters of voxels exceeding a given threshold, and for entire sets of supra-threshold clusters, leading to more powerful statistical tests at the expense of some localising power. Used by the vast majority of the neuroimaging community for the analysis of fMRI and/or PET image data.<br />
nif-0000-00343
Resource:Statistical Parametric Mapping Software
2010-10-13T00:00:00
SPM
Resource
Synonym
ModifiedDate
Label
Department of Imaging Neuroscience
Is part of
Id
Image processing software
Has role
Definition
DefiningCitation
Comment