Statistical parametric mapping: Difference between revisions

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{{Short description|Statistical technique}}

'''Statistical parametric mapping''' or '''SPM''' is a [[statistical]] technique created by Karl Friston for examining differences in [[brain]] activity recorded during [[functional neuroimaging]] experiments using [[neuroimaging]] technologies such as [[functional Magnetic Resonance Imaging|fMRI]] or [[Positron Emission Tomography|PET]]. It may also refer to a specific piece of software created by the ''Wellcome Department of Imaging Neuroscience'' (part of [[University College London]]) to carry out such analyses.

{{Other uses|SPM (disambiguation){{!}}SPM}}

{{more citations needed|date=January 2021}}

'''Statistical parametric mapping''' or ('''SPM''') is a [[statistical]] technique created by Karl Friston for examining differences in [[brain]] activity recorded during [[functional neuroimaging]] experiments. usingIt [[neuroimaging]]was technologiescreated such asby [[functionalKarl Magnetic Resonance Imaging|fMRI]] or [[Positron Emission Tomography|PETFriston]]. It may alsoalternatively refer to a specific piece of software created by the ''Wellcome Department of Imaging Neuroscience'' (part ofat [[University College London]]) to carry out such analyses.

==The statistical parametric mapping approach==

==Approach==

===Unit of measurement===

Functional neuroimaging, is one type of 'brain scanning',. It involves the measurement of brain activity. The specificmeasurement technique used to measure brain activity depends on the imaging technology being used (seee.g., [[fMRI]] and [[Positron Emission Tomography|PET]] for examples). Regardless of which technology is used, theThe scanner produces a 'map' of the area being scanned that is represented as [[voxel]]s. Each voxel typically represents the activity of a particularspecific coordinatevolume in three -dimensional space. The exact size of a voxel will varyvaries depending on the technology used, although. fMRI voxels typically represent a volume of 27  mm³ (ain cubean withequilateral 3mm length sides)cuboid.

===Experimental design===

Researchers are often interested in examiningexamine brain activity linked to a specific psychologicalmental process or processes. An experimentalOne approach to this problem might involveinvolves asking the question 'which areas of the brain are significantly more active when a person is doing task A compared to task B?'. Although eachthe tasktasks might be designed to be identical, except for the aspect of behaviour under investigation, the brain is still likely to show changes in activity between tasks due to factors other than task differences (as the brain iscoordinates involved with co-ordinating a whole range ofmany parallel functions unrelated to the experimental task). FurthermoreFurther, the signal may contain noise from the imaging process itself.

To accommodatefilter out these random effects, and to highlight the areas of activity linked specifically to the process under investigation, statistics are used to look for the most significant difference above and beyond background brain activitydifferences. This involves a multi-stage process to prepare the data, and to subsequently analyse it using a statistical method known as the [[general linear model]].

===Image pre-processing===

Images from the brain scanner may be pre-processed before any statistical comparison takes place to remove noise or correct for sampling errors.

A study will usually scanscans a subject several times. To account for the motion of the head between scans, the images willare usually betypically adjusted so each of the voxels in theeach imagesimage correspondscorrespond (approximately) to the same site in the brain. This is referred to as ''realignment'' or ''motion correction'', see [[image realignment]].

Functional neuroimaging studies usually involve severalmultiple participants, whoeach willof havewhom slightlyhave differently shaped brains. All are likely to have the same gross anatomy, but there will besaving minor differences in overall brain size, individual variation in topography of the [[gyri]] and [[Sulcus (neuroanatomy)|sulci]] of the [[cerebral cortex]], and morphological differences in deep structures such as the [[corpus callosum]]. To aid comparisons, the 3D image of each brain is transformed so that superficial structures line up, a process known asvia ''[[spatial normalization]]''. Such normalization typically involves not only translation and, rotation, but alsoand scaling and nonlinear warping of the brain surface to match a standard template. Standard brain maps such as the [[Talairach coordinates|Talairach-Tournoux]]-Tournoux or templates from the [[Montréal Neurological Institute]] (MNI) are often used to allow researchers from across the world to compare their results.

Images arecan oftenbe smoothed to make the data less noisy (similar to the 'blur' effect used in some image-editing software) by which voxels are averaged with their neighbours, typically using a [[Gaussian]] filter]] or by [[wavelet]] transformation, to make the data less noisy.

===Statistical comparison===

[[Parametric statistics|Parametric statistical]] models are assumed at each voxel, using the [[general linear model]] to describe the data variability in the data in terms of experimental and confounding effects, andwith residual variability. Hypotheses expressed in terms of the model parameters are assessed at each voxel with [[Univariate (statistics)|univariate statistics]].

Analyses may also be conducted to examine differences over a [[time series|time]] (i.e. correlations between a task variable and brain activity in a certain area) using linear [[convolution]] models of how the measured signal is caused by underlying changes in neural activity.

Because many statistical tests are being conducted, adjustments have to be made to control for [[Typetype I error]]s (false positives) potentially caused by the comparison of levels of activity atover a large number ofmany voxels. InA this case, a Typetype I error would result in falsely detectingassessing background brain activity as activity related to the task. Adjustments are made, based on the number of [[resel]]s in the image and the theory of continuous [[random field]]s in order to set a new criterion for statistical significance that adjusts for the problem of [[multiple comparisons]].

===Graphical representations===

[[Image:FMRIFunctional magnetic resonance imaging.jpg|thumb|leftright|Brain activation from fMRI shown as patch of colour on MRI scan]]

Differences in measured brain activity can be represented in a number of ways.

Differences in measured brain activity can be represented in a number ofvarious ways.

Most simply, they can be presented as a table, displaying coordinates that show the most significant differences in activity between tasks. However, differences in brain activity are more often shown as patches of colour on an MRI brain 'slice', with the colours representing the ___location of voxels that have shown statistically significant differences between conditions. The gradient of color is mapped to statistical values, such as t-values or z-scores. This creates an intuitive and visually appealing means of delineating the relative statistical strength of a given area of activation. Recently, an [http://homepages.uni-tuebingen.de/matthias.reimold/mascoi/ alternative approach] has been suggested, in which the statistical map is combined with the map of the original difference in brain activity (or, more generally speaking, with the original ''contrast'') and colorcodes are attributed to the latter.

Most simply, theyThey can be presented as a table, displaying coordinates that show the most significant differences in activity between tasks. HoweverAlternatively, differences in brain activity arecan more oftenbe shown as patches of colour on an MRIa brain 'slice', with the colours representing the ___location of voxels that have shownwith statistically significant differences between conditions. The color gradient of color is mapped to statistical values, such as t-values or z-scores. This creates an intuitive and visually appealing meansmap of delineating the relative statistical strength of a given area of activation. Recently, an [http://homepages.uni-tuebingen.de/matthias.reimold/mascoi/ alternative approach] has been suggested, in which the statistical map is combined with the map of the original difference in brain activity (or, more generally speaking, with the original ''contrast'') and colorcodes are attributed to the latter.

Differences in activity may also be represented as a 'glass brain', a representation of three outline views of the brain as if it were transparent. Only the patches of activation are visible as areas of shading. This is useful as a quick means of summarizing the total area of significant change in a given statistical comparison.

Differences in activity may alsocan be represented as a 'glass brain', a representation of three outline views of the brain as if it were transparent. Only the patches of activation are visible as areas of shading. This is useful as a quick means of summarizing the total area of significant change in a given statistical comparison.

==Software==

{|

SPM is software written by the [http://www.fil.ion.ucl.ac.uk Wellcome Department of Imaging Neuroscience] at [[University College London]] to aid in the analysis of functional neuroimaging data. It is written using [[MATLAB]] and is distributed as [[free software]].<ref>{{Cite web|url=https://www.fil.ion.ucl.ac.uk/spm/|title=SPM - Statistical Parametric Mapping|website=www.fil.ion.ucl.ac.uk|access-date=2019-10-03}}</ref>

[[Image:FMRI.jpg|thumb|left|Brain activation from fMRI shown as patch of colour on MRI scan]]

|

<!-- Image with unknown copyright status removed: [[Image:glassbrain.png|thumb|center|Brain activation from fMRI shown in 'glass brain' format]] -->

|}

==SPM software==

SPM is software written by the [http://www.fil.ion.ucl.ac.uk Wellcome Department of Imaging Neuroscience] at [[University College London]] to aid in the analysis of functional neuroimaging data. It is written using [[MATLAB]] and is distributed as [[free software]].

==See also==

* [[cognitiveCognitive neuroscience]]

* [[Functional integration (neurobiology)]]

* [[cognitive neuroscience]]

* [[functionalFunctional magnetic resonance imaging]]

* [[functionalFunctional neuroimaging]]

* [[generalGeneral linear model]]

* [[dynamicDynamic causal modelling]]

* [[neuroimagingNeuroimaging]]

* [[Analysis of Functional NeuroImages|AFNI]]

* [[FreeSurfer]]

* [[Computational anatomy toolbox]]

* [[FMRIB Software Library|FSL]]

==References==

{{nofootnotes|date=November 2010}}

{{reflist}}

{{unreferenced|date=November 2010}}

==External links==

* [[b:SPM|Wikibooks]] SPM Wikibook.

* [http://www.sphmccauslandcenter.sc.edu/comdCRNL/rorden/fmri_guide/index.html fMRI guide by Chris Rorden]

* [http://cogprints.org/6193/ Introduction to fMRI: experimental design and data analysis]

* [http://www.fil.ion.ucl.ac.uk/spm/ SPM] software and documentation from the Wellcome Department of Imaging Neuroscience.

* [http://www.mrc-cbu.cam.ac.uk/Imaging/Common/ Cambridge Imagers] - Neuroimaging information and tutorials.

* [http://www.fil.ion.ucl.ac.uk/~mgray/Presentations/Buttons%20in%20SPM5.ppt Buttons in SPM5] PowerPoint presentation from the SPM for dummies course

* [[b:SPM-Information to include in papers|SPM-Information to include in papers]]

* [http://spect.yale.edu ISAS (Ictal-Interictal SPECT Analysis by SPM)] - Yale University

* [http://www.imagilys.com/autospm.html AutoSPM: Automated SPM for Surgical Planning]

[[Category:Biostatistics]]

[[Category:Computing in medical imaging]]

[[Category:Neuroimaging]]

[[Category:Neuroimaging software]]

[[fr:Cartographie statistique paramétrique]]

[[it:Mappatura statistica parametrica]]