A 12-step user guide for analyzing voxel
Contents
1. 1996 Hayasaka S Phan K L Liberzon I Worsley K J amp Nichols T E Nonstationary cluster size inference with random field and permutation methods Neuroimage 22 676 687 2004 Smith S M amp Nichols T E Threshold free cluster enhancement addressing problems of smoothing threshold dependence and localisation in cluster inference Neuroimage 44 83 98 20092. A Schima W Ba Ssalamah A Kettenbach J amp Eisenhuber E Artifacts in body MR imaging their appearance and how to eliminate them Eur Radiol 17 1242 1255 2007 Graves M J amp Mitchell D G Body MRI artifacts in clinical practice a physicist s and radiologist s perspective J Magn Reson Imaging 38 269 287 2013 Rex D E et al A meta algorithm for brain extraction in MRI Neuroimage 23 625 637 2004 Dice L R Measures of the amount of ecologic association between species Ecology 26 297 302 1945 Van Leemput K Maes F Vandermeulen D amp Suetens P Automated model based tissue classification of MR images of the brain IEEE Trans Med Imaging 18 897 908 1999 Radua J Canales Rodriguez E J Pomarol Clotet E amp Salvador R Validity of modulation and optimal settings for advanced voxel based morphometry Neuroimage 86 81 90 2014 Ashburner J amp Friston K J Unified segmentation Neuroimage 26 839 851 2005 Klein A et al Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration Neuroimage 46 786 802 2009 Rohlfing T Image similarity and tissue overlaps as surrogates for image registration accuracy widely used but unreliable JEEE Trans Med Imaging 31 153 163 2012 Friston K J Holmes A Poline J B Price C J amp Frith C D Detecting activations in PET and fMRI levels of inference and power Neuroimage 4 223 235
3. for the left hemisphere third column Note that all values will be in the same order as the order of the original images in the statistical model The respective numbers can then be used for further analysis stages II and III of the statistical analysis and or visualization e g using MATLAB Excel or any statistics program NATURE PROTOCOLS VOL 10 NO 2 2015 301 npg 2015 Nature America Inc All rights reserved PROTOCOL TROUBLESHOOTING Troubleshooting advice can be found in Table 1 TABLE 1 Troubleshooting table Step Problem Possible reason Solution 1 The segmentation and or The origin in the original images Reset the origin in the native images to the normalization did not work at all is wrong anterior commissure using SPM8 s Display see Supplementary Fig 1 Failed function Fig 3 item 1 and rerun Step 1 Tissue Segmentation 1 The segmentation results are poor The original images are corrupted Remove the affected images from the analysis see Supplementary Fig 1 Failed by artifacts noise incidental Tissue Segmentation 2 and 3 pathologies and so on The bias correction is too Adapt the settings for the bias correction aggressive or too lenient First try to slightly decrease the bias regularization and then rerun Step 1 4 Some or all of the warped segments The wrong images were selected Check the selected files in the preceding are wrong i e they do not match in preceding s
4. testing the hypothesis group 1 gt group 2 will reveal regions in which group 1 has a stronger rightward asymmetry than group 2 option 1 or in which group 1 has a weaker leftward asymmetry than group 2 option 2 Both options are possible given the following considerations positive AI numbers reflect rightward asymmetry with higher numerical values in the positive range reflecting Asymmetry index Al O Rightward asymmetry Leftward asymmetry E Leftward asymmetry noise npg 2015 Nature America Inc All rights reserved PROTOCOL stronger rightward asymmetry e g an AI of 0 5 indicates a values stage II as well as the hemispheric gray matter stronger rightward asymmetry than 0 4 negative AI numbers volumes stage III In other words stage I is the application of reflect leftward asymmetry with smaller numerical values inthe the voxel wise test for group differences in asymmetry Stage II is negative range reflecting weaker leftward asymmetry e g an the cluster specific extraction of the AI values which helps inter AI of 0 4 indicates a weaker leftward asymmetry than 0 5 In preting the findings in terms of the group specific asymmetry other words the ambiguity of the effect is due to the fact that the direction and magnitude Stage III is the cluster specific extrac aforementioned hypothesis group 1 gt group 2 is true for both tion of hemispheric gray matter volumes which helps inte
5. asymmetry VBM is most sensitive to effects in the size and shape of the selected smoothing kernel Thus effects ranging above and below that particular spatial NATURE PROTOCOLS VOL 10 NO 2 2015 295 npg 2015 Nature America Inc All rights reserved PROTOCOL Box 4 The asymmetry index In symmetric space asymmetry can be quantified by comparing orginal and flipped gray matter segments One option is to compare left hemispheric and right hemispheric voxel values directly within the statistical model However this approach requires the use of more complicated statistical models than working with the metrics outlined below and the quality of the resulting analysis will be negatively affected by side effects of spatial smoothing A better option is to quantify the voxel wise asymmetry before conducting the statistical analyses which may be achieved either by calculating the simple right left difference or by calculating an AI this metric can also be found as laterality index in the literature Note that a symmetric change in brain size be it global or local will be reflected by the right left difference but not by the AI In other words if the brains of two hypothetical groups of individuals are identical but members of one group have 1 4 times bigger brains all volumes are scaled by 1 4 the right left difference will also be 1 4 times bigger although the brains are identical apart from the symmetric scaling Although the r
6. c Significant group differences group 1 lt group 2 in the cluster specific gray matter of the left hemisphere but not in the right hemisphere as revealed in stage III These results suggest that the observed stronger rightward asymmetry in group 1 b is driven by less left hemispheric gray matter in group 1 asymmetry in group 2 The data were processed according to the instructions in Steps 1 9 of this protocol and the statistical testing was applied according to Steps 10 and 11 More specifically we conducted a two sample t test using the smoothed AI images stage I see Experimental design The hypothesis for the two sample t test was group 1 gt group 2 and age and sex were included as covariates As demonstrated in Figure 5a there was one cluster indicating a significant group difference P 0 015 FWE corrected for multiple com parisons on cluster level see Step 11 of the PROCEDURE with respect to voxel wise gray matter asymmetry However as explained above see Experimental design the observed significant cluster is not unequivocally inter PROTOCOL Stage Il Asymmetry index Stage Ill Left hemispheric gray matter Right hemispheric gray matter Significant Not significant pretable 1 e the data imply that there is either a stronger rightward asymmetry or a weaker leftward asymmetry in group 1 than in group 2 thus requiring the implementation of Step 12 For this purpose we extracted the cluster
7. 2 h 9 Create an explicit mask using ImCalc Fig 3 item 3 Select the DARTEL template Template_6 nii and the right hemispheric mask created in Step 5 as input images any output name will work e g GM_mask_01 n17 The required expression is i11 gt 0 1 i2 to be manually typed If necessary the resulting binary mask may be edited manually using MRIcron see Equipment The mask is used to restrict the statistical analysis to regions of the brain that are expected to contain true signal rather than noise 10 Set up the statistical model Fig 3 item 6 Selecting Specify 2nd Level will open up the Batch Editor and run the module Factorial design specification Under Design select the desired model e g two sample t test Detailed descriptions on how to set up different statistical models are provided in the VBM8 manual http dbm neuro uni jena de vbm8 VBM8 Manual pdt Under Scans select the smoothed AI images created in Step 8 Under Masking select Threshold masking and None as selecting a threshold would be detrimental to the majority of the meaningful data because asymmetry values can be positive and negative Instead we recommend applying an explicit mask created in Step 9 under Explicit mask All other settings can be left at default Finally estimate the model and set the contrasts of interests Fig 3 items 7 and 8 We recommend saving the module
8. America Inc All rights reserved PROTOCOL Box 2 Spatial normalization Asymmetry VBM requires an accurate voxel wise correspondence not only across brains as in standard VBM Box 1 but also across hemispheres Such correspondence is achieved by spatially normalizing all individual brains to a symmetric standard space atlas SPM offers two main spatial normalizations low dimensional SPM default normalization2 gt in which pre existing symmetric tissue probability maps serve as a reference atlas and high dimensional DARTEL normalization 2 in which a symmetric study specific reference atlas is created DARTEL which stands for Diffeomorphic Anatomical Registration using Exponentiated Lie algebra is a high dimensional normalization algorithm available as part of SPM8 Briefly the registration procedure starts by creating a mean of all the images which is used as an initial template Subsequently the images are registered to this mean template and averaged again thus creating a more detailed mean template than the previous one This step of registration and template generation is repeated several times resulting in deformations between the individual images and a final highly detailed mean template Finally the resulting deformations are used to generate warped versions of the initial images in the mean template space In general DARTEL has been shown to yield a better registration across brains than the SPM default norm
9. BM publications by the SPM authors10 11 13 provide an excellent theoretical framework whereas the VBM8 manual http dbm neuro uni jena de vbm8 VBM8 Manual pdf offers practical step by step instructions for standard VBM using exactly the same tools as used for asymmetry VBM Comparison with other methods The assessment of structural asymmetries in neuroimaging studies is frequently achieved by selecting a so called region of interest ROI Although ROI analyses are useful for assessing the degree of asymmetry in a specific anatomic region for which an a priori hypothesis exists they come with several limitations For example measuring asymmetry only in one region of the brain will leave possible effects elsewhere in the brain undetected creating a selection bias In addition the selection of ROIs requires a clearly definable and unambiguous structure as well as detailed protocols because the brain structure of interest needs to be delineated in exactly the same way in every individual to guarantee an acceptable level of sensitivity and specificity of the analysis For large parts of the brain however it may be difficult to precisely define or identify unambiguous boundaries possibly creating a user bias Last but not least ROI analyses are limited in that they cannot capture effects below a certain spatial scale e g in subregions of an outlined structure which limits further the sensitivity of the approach By contrast vo
10. TEL should be used in the framework of asymmetry VBM spatial normalization into a symmetric space using DARTEL Box 2 and Fig 1 Moreover special care is taken to avoid blurring of information across hemispheres and to control the possible impact of noise in the data through the application of an explicit brain mask as well as spatial smoothing Box 3 Last but not least the statistical analysis requires additional steps i e beyond calculating the initial significance maps to properly interpret the analysis outcomes Importantly all adaptations included in this protocol have been successfully applied in a recently published analysis and re applied for this protocol using independent sample data Supplementary Data 1 The protocol has been designed primarily to enable relatively inexperienced users to conduct a voxel based asymmetry analysis 9 T bb DARTEL approach EK ns Wt Figure 1 Spatial normalization a b The SPM default approach a left does not model anatomical features with the same degree of detail as the DARTEL approach b left Moreover most differences between original and flipped images are because of nonoverlapping sulci using the SPM default approach a right but not so much using DARTEL approach b right As a more objective measure the overlap between original and flipped was quantified via the dice coefficient2 23 where higher values indicate a better interhemispheric correspond
11. Then go to the next plane 1 e two planes three planes four planes etc away from midline and repeat the masking until the entire right hemisphere is marked as VOI As the AI at midline equals zero i e original flipped means subtracting the voxel value from itself the midline plane does not need to be included Select Draw Save VOT and save the file in NIFTI format e g mask nii into the study directory This step will result in one binary mask that covers all right hemispheric voxels 6 Use ImCalc Fig 3 item 3 to calculate the AI images and also to discard the left hemisphere from the MRI scans of each subject First select the warped original gray matter segment then select its corresponding warped flipped gray matter segment both output from Step 4 and finally select the right hemispheric mask see Step 5 Note that the mask is identical for each subject Selecting these three images will result in the listing of three files in exactly this order under Input Images original warped mwrp1 _affine nii flipped warped mwrp1 _affine_flipped nii and the mask mask ni7 As output file name choose the file name of the warped original gray matter segment the first input image with the prefix AI NATURE PROTOCOLS VOL 10 NO 2 2015 299 npg 2015 Nature America Inc All rights reserved PROTOCOL which will read Al_mwrp1 _affine nit The outp
12. Use SPM s image calculator ImCalc Fig 3 item 3 to flip the images at midline Select the images to be flipped and use the same name with the suffix _flipped as output file names 1 e rplimage_affine nii becomes rp1image_affine_ flipped nii As output directory select the same one where the original images are The required expression is flipud i1 which needs to be typed manually All gray and white matter segments i e all rp1 and rp2 images have to be flipped to proceed with DARTEL If one wishes to create a mean template see Step 8 to illustrate findings on the averaged sample brain rather than an existing template brain or a single brain from the sample analyzed the PVE label images need to be flipped as well An optional script is provided as Supplementary Software 2 see Equipment which will automatically perform this step To use the script type calculate in MATLAB s command window Select Step 2 and then the images that need to be flipped Running the script will generate the flipped images A CRITICAL STEP Make sure that for every tissue segment there are both flipped and unflipped versions and that the output images are named correctly i e if you do not use the automated script remember to change the output name when selecting the next segment Note that reorienting the image by changing the header using the built in Reorient Images utility in SPM i e the m
13. alization2 and similar effects are expected with respect to registrations across hemispheres However such improvements may only be marginal and overall negligible in the context of asymmetry VBM perhaps not justifying the more complex procedure To establish a guideline as to which approach to use in asymmetry VBM both normalizations were compared using the sample data Supplementary Data 1 For the SPM default normalization data were processed as in previous publications For the DARTEL normalization Steps 1 4 of the PROCEDURE were applied For each approach a symmetric mean template was created from 60 brains In addition normalized original and flipped gray matter segments of the NMI single subject template http www bic mni mcgill ca ServicesAtlases Colin27 were overlaid onto each other In theory this compar ison could be extended further by applying a number of different anatomic labeling approaches to ensure that the improved registration is biologically valid However as DARTEL has previously been demonstrated to yield superior and biologically valid registration results using four different labeling approaches and as the current validation is merely a special case of this previous validation it seems reasonable to assume that the present results are valid as well The normalization outcomes Fig 1 indicate that the difference between using the SPM default normalization and DARTEL is not negligible thus suggesting that DAR
14. atomical brain asymmetries and their relevance for functional asymmetries in The Asymmetrical Brain eds Hugdahl K amp Davidson R J 187 230 The MIT Press 2003 Luders E Gaser C Jancke L amp Schlaug G A voxel based approach to gray matter asymmetries Neuroimage 22 656 664 2004 Takao H et al Gray and white matter asymmetries in healthy individuals aged 21 29 years a voxel based morphometry and diffusion tensor imaging study Hum Brain Mapp 32 1762 1773 2011 Good C D et al Cerebral asymmetry and the effects of sex and handedness on brain structure a voxel based morphometric analysis of 465 normal adult human brains Neuroimage 14 685 700 2001 NATURE PROTOCOLS VOL 10 NO 2 2015 303 npg 2015 Nature America Inc All rights reserved PROTOCOL 10 Ti 12 13 14 15 16 L 18 Dorsaint Pierre R et al Asymmetries of the planum temporale and Heschl s gyrus relationship to language lateralization Brain 129 1164 1176 2006 Watkins K E et al Structural asymmetries in the human brain a voxel based statistical analysis of 142 MRI scans Cereb Cortex 11 868 877 2001 Kurth F MacKenzie Graham A Toga A W amp Luders E Shifting brain asymmetry the link between meditation and structural lateralization Soc Cogn Affect Neurosci http dx doi org 10 1093 scan nsu029 17 March 2014 Ashburner J amp Friston K J Voxel based morphometry
15. before running it Fig 3 item 300 VOL 10 NO 2 2015 NATURE PROTOCOLS npg 2015 Nature America Inc All rights reserved PROTOCOL Box 5 Corrections for multiple comparisons Conducting voxel wise statistics necessitates a correction for multiple comparisons and the SPM8 software offers several options to apply such corrections Although the ultimate choice lies with the user the following considerations might provide some guidance As described in Box 3 and also shown in Figure 2 AI images can be affected by noise even after spatial smoothing small local variations in the AI values remain The presence of noise can have a direct effect on the statistical analysis as it may result in relatively high thresholds on voxel level which makes the correction for multiple comparisons too conservative Moreover owing to the nature of the AI Box 4 and Fig 2 noise might manifest as significant voxels that are scattered not interconnected throughout the brain and thus lack any biological meaning By contrast real gray matter effects i e the effects of scientific interest will manifest as significant voxels that are interconnected thus forming a so called significance cluster that is spatially continuous Thus a correction based on the spatial extent of the findings i e a correction on cluster level 8 may yield more appropriate results than a correction on voxel level Note that SPM s cluster level correction will onl
16. bserved positive correlation with a more rightward asymmetry for example is driven by a negative correlation with left hemispheric gray matter or by a positive correlation with right hemispheric gray matter stage III Another example for this analysis can be found in a study on meditation which describes both group differences and correlations However note that in that study the right hemisphere was discarded rather than the left hemisphere as suggested in this protocol Note Any Supplementary Information and Source Data files are available in the online version of the paper ACKNOWLEDGMENTS This work was supported by the German Ministry of Education and Research BMBF grant no 01EV0709 to C G AUTHOR CONTRIBUTIONS F K and E L developed and designed the protocol and experiments and drafted the manuscript C G developed and wrote the VBM8 tool and provided methodological guidance and feedback F K E L and C G finalized the manuscript COMPETING FINANCIAL INTERESTS The authors declare no competing financial interests Reprints and permissions information is available online at http www nature com reprints index html Toga A W Narr K L Thompson P M amp Luders E Brain Asymmetry Evolution in Encyclopedia of Neuroscience Vol 2 ed Squire L R 303 311 Academic Press 2009 Toga A W amp Thompson P M Mapping brain asymmetry Nat Rev Neurosci 4 37 48 2003 Jancke L amp Steinmetz H An
17. emplate created in Step 3 using the flow fields created in Step 3 This module can be found in the Batch Editor menu Fig 3 item under SPM Tools DARTEL Tools The normalized segments may be modulated to preserve the local gray matter amount To run this step select all flow field files starting with u_rp1 For Images select New Images and enter all original and flipped gray matter segments Note that there should be the same number of gray matter segments as there are flow fields For Modulation select Pres Amount Modulation You may create a new directory and choose it as output directory All other settings can be left at default Save the module and hit run The normalized modulated gray matter segments mwrp1 nii will be written into the new output directory A CRITICAL STEP We recommend assessing the quality of the output data see TROUBLESHOOTING TROUBLESHOOTING 5 Create a right hemispheric mask in symmetric template space to limit the analysis to the right hemisphere Creating such a mask can be achieved using MRIcron see Equipment In MRIcron load the DARTEL template Template_6 nii and select image 1 gray matter In the sagittal window go one plane to the right from the midline Select the drawing tool and click with the right mouse button into the sagittal view window The complete sagittal plane should now be marked as volume of interest VOI
18. ence c d DARTEL significantly outperforms the SPM default P 1 025 x 10757 as shown in c median quartiles 1 5 interquartile range and d histograms Note that the worst overlap obtained using DARTEL is still 1 s d better than the best overlap obtained with the SPM default normalization oO Dice coefficient SPM default 294 VOL 10 NO 2 2015 NATURE PROTOCOLS Aq SPM default approach ans Approach on their own However it may also be useful to more experienced users who wish to efficiently adapt their existing scripts or pipe lines The present protocol requires neither previous experience with the statistical parametric mapping SPM software nor a background or even interest in MATLAB scripting Nevertheless some familiarity with the concept of VBM analyses Box 1 or perhaps a previously completed standard VBM study even if just for practice purposes might be helpful Several Original Flipped Overlap additive Original Flipped Overlap additive SPM default approach DARTEL approach Frequency DARTEL 0 9 Dice coefficient npg 2015 Nature America Inc All rights reserved Box 3 Spatial smoothing PROTOCOL This procedure is generally recommended for VBM analyses for the reasons outlined in Box 1 Spatial smoothing creates a weighted average of each voxel value and its surrounding voxels basically resulting in a blurring of the brain image or respective tissue seg
19. from which several functions can be assessed directly B is the interactive window which indicates progress and provides options for user interaction C is the graphics window which serves to display the results The middle image shows the main menu of the program with important functions numbered in the order in which they occur in the protocol Display 1 is helpful for visual assessment and quality control To 2 allows to access the VBM8 toolbox ImCalc 3 is the image calculator Batch 4 opens the Batch Editor see right image detailed below Smooth 5 provides options for spatial smoothing Specify 2nd level 6 provides options to build the statistical model Estimate 7 provides options to estimate a specified statistical model Results 8 provides options to view the results of the statistical analysis DICOM Import 9 provides a tool to convert DICOM format to NIfTI format Util 10 provides several utilities including the option to change the working directory CD The right image illustrates the Batch Editor menu which is helpful to call certain functions e g the DARTEL tools directly under SPM The left field B lists the called function The upper right field y lists all function specific options for which settings can be selected The lower right field 6 shows the selected settings and allows adjusting them The black disc symbol allows saving
20. fter the PVE label images are written flip them as done in Step 2 to catch up with the protocol All users will continue with running Step 4 as described above but instead of selecting the original and flipped gray matter segments under Images this time select the original and flipped PVE label images In addition for Modulation select Pres Concentration No modulation Finally use ImCalc Fig 3 item 3 to create the mean of all warped PVE label images The input images will be the warped PVE label images the output file name can be anything e g Mean_Template nii The required expression is mean X to be manually typed Under Data Matrix select Yes read images into data matrix Hitting run will create the initial mean template which should be further adjusted to restrict the template to the right hemisphere only To achieve this goal use ImCalc again and select the newly created mean template Mean_Template nii and the right hemispheric mask created in Step 5 as input images any output name will work e g Template_visualize nii The required expression is 11 12 to be manually typed The resulting image of the right hemisphere reflects the mean anatomy of all subjects brains in the space in which the statistical analysis is performed and thus it is ideal for projecting the resulting significance clusters Statistical analysis TIMING 3 1
21. he left half of the images should be empty and that the output images are named correctly especially when applying the manual procedure 7 Use the module Smooth Fig 3 item 5 to smooth all AI images created in Step 6 Under Images to Smooth select the files Al_mwrp1 affine nii For the size of the smoothing kernel the default setting 8 8 8 is suitable One smoothed right hemispheric AI s is written per subject We recommend saving the module before running it Fig 3 item Mean template TIMING 1 10 h for 60 images 8 Optional Later in the process see Step 11 outcomes of the statistical analysis will need to be visualized by projecting the significance cluster s obtained in Step 11 either onto a single brain or an average of many brains the choice is entirely up to the researcher This optional step explains how to generate a study specific mean template i e an average of all brains in the study analyzed in symmetric space If users initially abstained from creating see Step 1 and flipping see Step 2 their PVE label images as perhaps they changed their minds about creating a mean template only later in the process they may retroactively perform these actions now Write PVE label images by running the VBM8 toolbox module Write already estimated segmentations and by selecting DARTEL export and affine for PVE label images unselecting the other writing options A
22. ight left difference thus reflects this symmetric scaling the AI will be the same for both groups which therefore safeguards against symmetric scaling effects If the research question includes the influence of scaling on asymmetry the right left difference will yield the desired measure However if scaling effects are not of interest which seems to be more likely for most studies the AI should be chosen The AI is therefore the method of choice for the current protocol but researchers may use the right left difference instead in Step 6 without any need for further adaptations of the protocol As illustrated in Figure 2 regions with low gray matter content render the AI susceptible to noise which may artificially enhance the AI Although the impact of noise can be controlled to some degree by spatial smoothing Box 3 we advise protocol users to exclude regions with no or low gray matter content from the analysis by applying an explicit mask see Steps 9 and 10 of the PROCEDURE Moreover as also shown in Figure 2 calculating a voxel wise AI yields redundant information in both hemispheres which can be exploited in the framework of spatial smoothing Box 3 Although spatial smoothing is necessary for asymmetry VBM it also results in a false transition of information blurring across the midline see Steps 5 and 6 of the PROCEDURE Such blurring can be avoided by simply discarding one hemisphere before performing the spatial smoothing We
23. lculated from the gray matter values on the left using the AI formula see figure More gray matter in the right hemisphere rightward asymmetry will yield positive AI values on the right and negative values on the left yellow AI values More gray matter in the left hemisphere leftward asymmetry will yield negative AI values on the right and positive values on the left pink AI values Small hemispheric differences in regions with low gray matter content e g due to noise can yield the same results orange AI values as extreme hemispheric differences pink AI values 296 VOL 10 NO 2 2015 NATURE PROTOCOLS Gray matter content t tests or analyses of variance or co variance can be applied to assess differences in asymmetry between two or more groups with or without removing the variance of a nuisance variable Finally the multiple regression model enables users to implement correlation analyses with asymmetry However in contrast to standard VBM statistical testing in asymmetry VBM does not always yield unequivocally interpret able results given the nature of the asymmetry index AI Box 4 and Fig 2 For example in standard VBM testing the hypothesis group 1 gt group 2 will reveal regions with substantially more gray matter in group 1 than in group 2 In asymmetry VBM the interpretation of a significant effect is not as clear cut because the Alcan take positive and negative values In other words
24. mages The original anatomical differences however are coded in the deformation fields resulting from the normalization By using this information and by applying a modulation i e multiplying the normalized gray matter segments with the Jacobian determinant from the deformation matrix the induced volume changes will be corrected and the original local volumes will be preserved even in the new space Although some controversy exists with respect to modulation we recommend implementing it as part of the present protocol however modulation may be omitted on the basis of the user s preference Regardless of whether modulation is implemented or not the normalized tissue segments are then convoluted with a Gaussian function which is commonly referred to as spatial smoothing Spatial smoothing ensures that the random errors have a Gaussian distribution this is a prerequisite for parametric tests compensates for small inaccuracies in spatial normalization even applying high dimensional DARTEL does not yield a perfect voxel wise correspondence and determines the spatial scale at which effects are most sensitively detected in order to discriminate true effects from random noise the smoothing kernel size should match the expected size of the effect The spatially normalized and smoothed gray matter segments then constitute the input for the voxel wise statistical analyses 11 NATURE PROTOCOLS VOL 10 NO 2 2015 293 npg 2015 Nature
25. me estimations Although the protocol is primarily designed to enable relatively inexperienced users to conduct a voxel based asymmetry analysis on their own it may also be useful to experienced users who wish to efficiently adapt their existing scripts or pipelines INTRODUCTION Structural asymmetries of the brain are of major interest to the scientific community However the detection and accurate quantification of anatomical hemispheric differences requires methods that are sufficiently sensitive with respect to asymmetry location direction and magnitude In this protocol we describe a fully automated VBM based approach to assess structural asym metries in T1 weighted brain data obtained via MRI VBM has been proven capable of capturing gray matter asymmetries with an extremely high voxel based regional specificity as evidenced by existing research Nevertheless the number of published VBM based asymmetry studies seems rather low possibly because of the lack of a standard guideline and missing step by step instructions Therefore we designed a detailed proto col that will enable interested users including newcomers to successfully conduct their own voxel based asymmetry analysis Furthermore we provide background information as well as simulations to demonstrate how and why VBM standard routines should be adapted in the framework of asymmetry analyses to further improve accuracy Development of the protocol Our pro
26. ment Smoothing also increases the signal to noise ratio and as noise may constitute a problem in asymmetry VBM Box 4 and Fig 2 diminishing its influence is desirablet8 The figure shown above demonstrates the desired smoothing effect a The first column shows a synthetic left right image simulat ing the left and right hemispheres of the brain The left half of the column consists of three fields with different values 0 1 0 3 and 0 6 and the right half of the brain has a consistent value of 1 these arbitrary values simulate different local gray matter volumes Calculating the AI images by applying the AI formula as detailed in Fig 2 to the aforementioned values results in AI 1 6 AI 1 1 and AI 0 5 as displayed in the color coded right image second column These values are preserved after smoothing third column b Replicated are the left right images of a only with noise added As demonstrated noise may severely affect the AI values when no smoothing is applied second column Note that AI values for the three different fields are not as distinct in the noisy image as would be expected on the basis of the data above and that artificial AI values are assigned to areas surrounding the actual image bluish color However smoothing restores the field specific differences and it also eliminates the false AI values surrounding the image third column Thus spatial smoothing is particularly necessary when conducting asymmetry V
27. nalysis is directly dependent Step 5 on the quality of the segmented images resulting from Step 1 Check gray and Right ten 7 ane white matter segments separately eae aa ee asymmetry A CRITICAL STEP As a general nee recommendation we suggest saving the module before running it Fig 3 Right Warped Step 1 Tissue Step 4 p ti segments eaves segments Structural image index image DARTEL Step 6 template Warped flipped tissue segments Flipped tissue segments Figure 4 Workflow of the protocol a Pre processing Steps 1 7 are needed to create the smoothed asymmetry index images which are used for the statistical analysis b Statistical analysis Steps 9 12 yield index images ane si EI uster specific ae Statistical Step 11 significance Step 12 PEE a ioe an significant clusters as well as asymmetry mous map hemispheric gray matter indices and hemispheric gray matter volumes Explicit for each cluster The optional step Step 8 mask which creates a mean template for visualization Step 9 is not depicted b Smoothed asymmetry Step 10 298 VOL 10 NO 2 2015 NATURE PROTOCOLS npg 2015 Nature America Inc All rights reserved PROTOCOL item so that if problems arise later on in the PROCEDURE it will be easier to modify and rerun the workflow The same cau tionary approach is recommended in Steps 3 4 7 and 10 of the PROCEDURE TROUBLESHOOTING 2
28. npg 2015 Nature America Inc All rights reserved PROTOCOL A 12 step user guide for analyzing voxel wise gray matter asymmetries in statistical parametric mapping SPM Florian Kurth Christian Gaser 3 amp Eileen Luders 1Department of Neurology University of California Los Angeles UCLA School of Medicine Los Angeles California USA 7Department of Psychiatry Jena University Hospital Jena Germany Department of Neurology Jena University Hospital Jena Germany Correspondence should be addressed to EK fkurth mednet ucla edu Published online 15 January 2015 doi 10 1038 nprot 2015 014 Voxel based morphometry VBM has been proven capable of capturing cerebral gray matter asymmetries with a high voxel wise regional specificity However a standardized reference on how to conduct voxel wise asymmetry analyses is missing This protocol provides the scientific community with a carefully developed guide describing in 12 distinct steps how to take structural images from data pre processing via statistical analysis to the final interpretation of the significance maps Key adaptations compared with the standard VBM workflow involve establishing a voxel wise hemispheric correspondence capturing the direction and degree of asymmetry and preventing a blurring of information across hemispheres The workflow incorporates the most recent methodological developments including high dimensional spatial normalization and partial volu
29. or during the analysis when following the stepwise PROCEDURE as subsequently detailed 1 Run the VBM8 toolbox Fig 3 item 2 and start the module estimate and write All of the T1 weighted MRI scans in NIfTI format see Reagents can be processed at once and they should be selected under Volumes Next under Tissue Probability Map select the symmetric tissue probability maps see Reagents downloaded from the internet see Reagent Setup Under writing options select the option DARTEL export and affine for Gray matter White matter and PVE label image Selecting the PVE label image may be omitted if you do not wish to create a mean template for visualization see Step 8 No additional output images need to be written All other settings can be left at default Before hitting run save the module Fig 3 item The following images will be written rp1 _affine nii gray matter segments rp2 _affine niv white matter segments and rp0 _affine nii PVE label images if selected A CRITICAL STEP We recommend assessing the quality of the resulting output data Examples of one successful and three failed tissue segmentations are provided in Supplementary Figure 1 The VBM8 toolbox provides convenient tools for quality control as described in the VBM8 manual http dbm neuro uni jena de vbm8 VBM8 Manual pdf en Keep in mind that the quality of the mask overall a
30. ost commonly used procedure for flipping will not work with DARTEL 3 Create a symmetric DARTEL template and the respective nonlinear transformations between tissue segments and DARTEL template space from the original and flipped gray matter and white matter segments To achieve this goal use the module Run DARTEL create Templates The module can be found in the Batch Editor menu Fig 3 item a under SPM Tools gt DARTEL Tools Under Images select New Images twice For the first Images select all original and flipped gray matter segments 1 e all rp1 affine niv and rp1 affine_flipped nii images For the second Images select all original and flipped white matter segments in exactly the same order i e all rp2 affine nii and rp2 affine_flipped nii images All other settings can be left at default Next save the module and hit run to write the DARTEL template as seven separate files Template_O nii Template_6 nii Template_6 n1i should look like a relatively crisp tissue segment when opened with MRIcron image 1 is the gray matter segment and image 2 is the white matter segment Furthermore the flow fields containing the nonlinear transformations for every original and flipped gray matter segment u_rp1 nii will be written 4 Run the module Create Warped to warp the original and flipped tissue segments created in Step 2 to the symmetric DARTEL t
31. r size 18 In SPM s interactive window Fig 3 item B press save select thresholded SPM and type in a name for the saved image The saved images constitute the input for Step 12 and they can be used for visualizing the results by overlaying them onto the mean template in MRIcron see Equipment 12 Run the extract script Supplementary Software 1 to calculate the mean AI and hemispheric gray matter content for the significance cluster s for each subject First change the current working directory in MATLAB back to the original working directory see Reagent Setup which contains the file extract m Subsequently type extract in the MATLAB command window the script will then ask for the needed input First select the thresholded SPM map of interest T_ ni7 this is the image saved in Step 11 Next choose an output directory to which the results should be written Next select all AI images as well as all warped original images and all warped flipped images the script will ask for each Expect the following output All clusters within the thresholded SPM map will be written as single volumes into the output directory Furthermore text files for each cluster will be saved in the same directory These cluster specific text files contain the mean AI for every subject first column the cluster s gray matter volume in mm for the right hemisphere second column and the cluster s gray matter volume in mm
32. re acceptable and will be EQUIPMENT corrected during tissue segmentation Helpful illustrations of MRI A computer running MATLAB http www mathworks com products artifacts as well as explanations on why they occur and how their matlab version 7 1 or newer The computer should have 20 50 GB of free occurrence may be minimized are provided elsewhere 20 Data from disc space in addition to the minimum requirements for running MATLAB subjects with any pathologies or abnormalities should be used with http www mathworks com support sysreq current_release gt itch Editor B l Spatial gt Stats gt MM8 Estimate amp Write VBM Estimate amp Wij M EEG gt stimate amp Write Util gt Sendmail DARTEL Tools Edit Defaults FieldMap High Dimensional Warping Specify 1st level LST i Rendering i 7 peeremensio New Segment x Specify 2nd level i Shoot Tools Enada Template O Matic 0 15 z i any pi Cleanup Results Dis T 4 SPMMouse yee E reek Sot Estimate TFCE Current tem Volum Realign longitudinal data Dynamic Causal Modelling __VBM8 Select raw data e g T1 images for processing This assumes that there is one scan for each subject Note that multi spectral when there are two or more registered images of different contrasts processing is not yet implemented for this method Figure 3 The left image shows SPM s three windows A is the main menu of the program
33. rpret cases 1 e for 0 5 gt 0 4 as well as for 0 4 gt 0 5 ing the findings in terms of the group specific left hemispheric To solve this ambiguity and to determine which of these and right hemispheric gray matter volumes A concrete example two options is correct it is necessary to extend the standard for a statistical analysis including the three step follow up as VBM approach beyond calculating the initial significance maps well as visualization of outcomes resulting from stages I III is stage I by inspecting the individual gray matter asymmetry described in the ANTICIPATED RESULTS MATERIALS REAGENTS caution However rather than always excluding these data by default T CAUTION Ensure that the study protocol is approved by the appropriate we recommend removing the affected images only in case of poor ethical review board and that all subjects gave informed consent segmentation outcomes Performing an initial quality inspection also T1 weighted MRI scans These are the brain images you wish to analyze enables researchers to recognize the relationship between the actual data A CRITICAL Rather than working with raw data e g Digital Imaging and the view that is displayed on the screen to achieve a correct assignment and Communications in Medicine DICOM format make sure to first of MNI coordinates left should display as left in SPM Along these lines convert all images to NIfTI format i e the format required in SPM8 a
34. specific mean AI values and also the cluster specific gray matter volumes and then we compared group 1 and group 2 with respect to these measures The first follow up analysis of the cluster specific mean AI stage IT revealed a stronger rightward asymmetry in group 1 than in group 2 Fig 5b For the curious reader it was group 1 that initially also showed the larger global gray matter asymmetry so the detected larger local voxel wise asymmetry in group 1 was expected In other words the outcomes are without any real meaning as both samples were artificially compiled solely for demonstration purposes The second follow up analysis of the cluster specific gray matter volumes stage ITI revealed this additional information although individuals in group 1 had significantly less cluster specific gray matter in the left hemisphere than group 2 there were no group differences with respect to the cluster specific gray matter in the right hemisphere Fig 5c Implementation of stages I III described here in detail for group comparisons is also indicated when conducting correlation analyses Briefly after establishing the initial significance cluster indicating for example that age is positively correlated with brain asymmetry stage I it makes sense to conduct follow up analyses to determine whether age is associated with less leftward asymmetry or with more rightward asymmetry stage II Subsequently investigators may want to clarify whether the o
35. suggest keeping the right hemisphere so that positive AI values indicate a rightward asymmetry which may remind of the standard convention of positive values in the MNI coordinate space labeling the right hemisphere scale as well as effects that largely deviate from the shape of the applied filter may not be captured when applying asymmetry VBM Moreover in its current form the protocol is limited to the analysis of structural imaging data with particular focus on voxel wise gray matter Although it may be adapted to the analy sis of other structural measures point wise cortical thickness voxel wise fractional anisotropy and so on or even functional measures brain activity such adaptations will require additional considerations that are currently beyond the scope of this pro tocol Note however that there is a toolbox available which enables users to assess functional asymmetry lateralization of brain activity albeit using an entirely different approach i e one that does not easily accommodate a structural asymmetry analysis using VBM Experimental design Statistical tests in asymmetry VBM can be applied as in standard VBM That is one sample t tests can be used to detect asymmetry in general i e as a significant deviation from zero Two sample Figure 2 The asymmetry index AI Left model of voxel wise gray matter content with 1 100 gray matter and 0 no gray matter Right respective AI values ca
36. teps modules correct them and rerun the affected the normalization template or do not step s look like a brain segment The names of the flipped tissue Check the names of the flipped tissue segments have been misspelled segments correct them and rerun Step 2 or mixed up in Step 2 Step 3 and or Step 4 TIMING Pre processing Steps 1 7 processing 60 images will take 25 35 h plus another 2 4 h for quality control Step 1 5 15 min per subject depending on computing resources and settings As all images can be processed at once consider running this step overnight Step 2 1 2 min per tissue segment Step 3 The duration of this step is determined by the number of subjects in the experiment because all images must be processed at once The required time in minutes can be approximated by 6 5N 40 in minutes with N being the number of subjects as evaluated on a Macbook Pro 2 3 GHz with 16 GB memory For large experiments i e many images consider running this step overnight as processing images from 100 subjects may take close to 12 h Step 4 lt 1 min per tissue segment Step 5 0 5 1 h Step 6 2 min per subject Step 7 2 20 min Step 8 mean template optional creating a mean template from 60 images will take 1 10 h the duration of this step largely depends on how much has been prepared in previous steps Statistical analysis Steps 9 12 the statistical analysis will take 3 12 h and it depends on the complexity of the statis
37. the adjusted settings the green arrow right next to the disc symbol allows running the function NATURE PROTOCOLS VOL 10 NO 2 2015 297 npg 2015 Nature America Inc All rights reserved PROTOCOL e SPM8 http www fil ion ucl ac uk spm software spm8 see VBM8 manual http dbm neuro uni jena de vbm8 VBM8 Manual pdf e The VBM8 toolbox http dbm neuro uni jena de vbm download Note that the directory that will be accessed and or referred to by MATLAB e MRicron http www mccauslandcenter sc edu mricro mricron and also where output is written is the so called working directory e A MATLAB script called extract m This script is needed for Step 12 Thus we recommend making your study directory 1 e the one named It is available as a zip file Supplementary Software 1 Asymmetry_study see above the working directory of MATLAB Changing e Another optional MATLAB script called calculate m This script may MATLAB s working directory can be achieved either directly in SPM via but does not need to be used in Steps 2 and 6 It is also available as a zip Util Fig 3 item 10 under the option CD or by manually typing in file Supplementary Software 2 MATLAB s command window cd c work Asymmetry_study in Windows EQUIPMENT SETUP or cd Users me work Asymmetry_study in OSX or Linux Setting up the MATLAB environment Create a new folder for your study REAGENT SETUP This folder will be your s
38. the methods Neuroimage 11 805 821 2000 Ashburner J amp Friston K J Why voxel based morphometry should be used Neuroimage 14 1238 1243 2001 Ashburner J A fast diffeomorphic image registration algorithm Neuroimage 38 95 113 2007 Ashburner J amp Friston K Voxel Based Morphometry in Statistical Parametric Mapping the Analysis of Functional Brain Images eds Friston K et al 92 100 Elsevier 2007 Tohka J Zijdenbos A amp Evans A Fast and robust parameter estimation for statistical partial volume models in brain MRI Neuroimage 23 84 97 2004 Rajapakse J C Giedd J N amp Rapoport J L Statistical approach to segmentation of single channel cerebral MR images IEEE Trans Med Imaging 16 176 186 1997 Manjon J V Coupe P Marti Bonmati L Collins D L amp Robles M Adaptive non local means denoising of MR images with spatially varying noise levels J Magn Reson Imaging 31 192 203 2010 Luders E Kurth F Toga A W Narr K L amp Gaser C Meditation effects within the hippocampal complex revealed by voxel based morphometry and cytoarchitectonic probabilistic mapping Front Psychol 4 398 2013 Wilke M amp Lidzba K LI tool a new toolbox to assess lateralization in functional MR data J Neurosci Methods 163 128 136 2007 304 VOL 10 NO 2 2015 NATURE PROTOCOLS 19 20 21 22 23 24 25 26 27 28 29 30 Stadler
39. tical design and or hypotheses to be tested Step 9 0 1 5 h depending on the amount of manual editing needed Step 10 0 5 2 h Step 11 1 10 h depending on the complexity of the statistical design and of the hypotheses to be tested Step 12 10 min ANTICIPATED RESULTS To provide example results of the implementation of this protocol we will perform a VBM asymmetry analysis on an artificially compiled data set in which data from 60 subjects were divided into two groups group 1 n 30 with a large global asym metry and group 2 n 30 with a small global asymmetry Supplementary Data 1 As groups 1 and 2 differed largely in terms of their global volumetric asymmetry we expected and predicted significant local voxel wise asymmetry differences which are needed to illustrate the usefulness of the methodology presented in this protocol In other words we set out to assess whether the voxel wise gray matter asymmetry in group 1 is significantly different from the voxel wise gray matter 302 VOL 10 NO 2 2015 NATURE PROTOCOLS npg 2015 Nature America Inc All rights reserved Figure 5 Statistical outcomes and follow up stages I III a Significant group differences group 1 gt group 2 in gray matter asymmetry as revealed in stage I b Significant group differences group 1 gt group 2 in the cluster specific mean asymmetry as revealed in stage II group 1 shows a rightward asymmetry group 2 shows no asymmetry
40. tocol describes in 12 distinct steps how to perform a voxel based gray matter asymmetry analysis taking structural images from initial data pre processing via statistical analyses to the final interpretation of significance maps As the proposed protocol constitutes an adapted workflow for VBM Box 1 it requires similar processing steps as standard VBM analyses but additional modifications are necessary A key adaptation involves establish ing an accurate voxel wise correspondence not only across indi viduals but also across both hemispheres which is ensured by Box 1 Standard voxel based morphometry VBM enables investigators to assess local differences and or changes in tissue volume with high regional specificity throughout the brain The standard workflow starts with the classification of the brain into gray matter white matter and cerebrospinal fluid This so called tissue segmentation is followed by spatial normalization whereby the individual tissue segments of interest usually the gray matter are brought into a common space via registration to a standard stereotactic atlas to ensure voxel wise correspondence across different brains Spatial normalization changes the volume of the tissue segments locally some regions expand whereas others contract In fact the implemented high dimensional DARTEL registration leaves only very small differences between template and individual images and thus also across individual i
41. ttaching an MRI visible marker during the scanning such as a vitamin before running the protocol This conversion can be achieved either E gel capsule to the left or right side of the subject s head or cheek will directly in SPM8 e g using its DICOM import function Fig 3 item 9 later help identify the left and right hemispheres or through one of the many DICOM converters available on the web Symmetric tissue probability maps These are the symmetric brain A CRITICAL Given that corrupted image data or incidental pathologies maps required in Step 1 of the PROCEDURE they are based on the may substantially influence the results we advise protocol users to visually asymmetric tissue probability maps that are provided with SPMB inspect the structural images Use SPM8 s display function Fig 3 item 1 However as asymmetry VBM requires symmetric tissue probability maps or MRIcron see Equipment to make sure that no parts of the brain are we calculated the symmetric maps by averaging the asymmetric maps cut off distorted or wrapped and that images are also not corrupted by with their mirrored versions by flipping the images at midline any other factors such as motion related blurring zipper artifacts or The symmetric tissue probability maps are freely available for download extreme inhomogeneity However note that slight and smooth intensity at http dbm neuro uni jena de vbm8 TPM_symmetric nil inhomogeneities that span the whole image a
42. tudy directory e g Asymmetry_study All data T1 weighted MRI scans We recommend first creating a new subfolder in the files or folders that are needed or created in the study will be located here study directory e g T1_scans and then copying the T1 weighted MRI scans Download the first MATLAB script extract m zip Supplementary in NIfTI format into the T1_scans folder Software 1 as well as the second MATLAB script calculate m zip Symmetric tissue probability maps After downloading the symmetric tissue Supplementary Software 2 Unpack the two zip files and copy the probability maps http dbm neuro uni jena de vbm8 TPM_symmetric nii resulting files into your study directory Finally start MATLAB followed we recommend copying them into the assigned study directory named by starting SPM8 as well as the VBM8 toolbox in MATLAB for instructions Asymmetry_study see above PROCEDURE Pre processing TIMING 25 35 h for 60 images plus another 2 4 h for quality control A CRITICAL Figure 4 shows the overall workflow of the PROCEDURE which can be roughly divided into two phases the first phase is aimed at data pre processing and the second phase is aimed at the statistical analysis of the processed data A CRITICAL Before starting the PROCEDURE it might be helpful to get acquainted with SPM8 s user interface Fig 3 Depending on the user s preferences this may be done either before the analysis
43. ut directory can be the same as the one in which the warped segments are We suggest using the AI for which one needs to type under Expression 11 12 11 12 0 5 13 Note that by applying this formula users calculate the AI and discard the left hemisphere masking in one combined step 11 original warped image 12 flipped warped image 13 right hemispheric mask image Run this module for scans from every subject For an automated procedure the user is referred to the optional calculate MATLAB script Supplementary Software 2 To use the script type calculate in MATLAB s command window Select Step 6 and then select the original warped gray matter images and also the hemispheric mask the script will ask for each of those Running the script will generate the masked AI images As a side note rather than calculating the more complex AI investigators may also choose to calculate the simple right left difference instead see Box 4 for more information on these two measures To calculate the right left difference one needs to replace the AI formula see above with the formula i1 12 13 using either the manual approach or the automated procedure A CRITICAL STEP Implementing this step will result in the generation of one AI image per subject encoded within the right hemisphere the left hemisphere has been discarded during the masking procedure Check that all AI images were generated properly i e t
44. xel based analyses enable researchers to objectively examine hemispheric differences with an extremely high voxel wise regional specificity and without confining the analysis to a specific area Voxel wise hemi spheric differences e g gray matter asymmetries can be assessed using an adaptation of the standard VBM workflow Box 1 but the accuracy of the analysis and thus validity of findings strongly depends on the proper adaptation of the standard VBM processing stream which is described in this protocol Applications A wealth of structural brain images has been acquired for research purposes either alone or in combination with the acquisition of functional data In addition structural brain images are obtained routinely in clinical settings Therefore a vast pool of data exists for which asymmetry analyses may seem appropriate and indicated With this protocol we aim to provide a user friendly guide on how to use VBM to assess hemispheric asymmetries with respect to voxel wise gray matter Strengths of the proposed VBM based workflow include the integration of sophisticated tissue classifi cation tools 4 6 which do not depend on prior shape and thus asymmetry assumptions as well as the use of high dimensional warping 2 which enables an accurate spatial registration not only across subjects but also across hemispheres Limitations As previously discussed according to the matched filter theorem VBM and thus also
45. xel level values are not affected by enabling SPM s nonstationarity correction i1 To save the significant clusters only run the VBM8 toolbox Fig 3 item 2 and its function Threshold and transform spmT maps under Data presentation Locate the Tmap spmT_ ni1 in the folder that contains the SPM mat file the number of the Tmap matches the contrast one is looking at and select apply thresholds without conversion under Convert t value to For Threshold type peak level choose uncorrected as well as the desired cluster forming threshold e g the default of 0 001 Under Cluster extent threshold select FWE and also make sure that Correct for non isotropic smoothness is set to yes The latter setting will correct for the expected nonstationarity as discussed in Box 5 Running the tool will save the thresholded SPM map T_ ni1 into the folder that contains the SPM mat file The saved images constitute the input for Step 12 and they can be used for visualizing the results by overlaying them onto the mean template in MRIcron see Equipment B Correction for multiple comparisons at the voxel level 1 Choose the desired correction directly by selecting one of the options 1 e familywise error rate FWE or false discovery rate FDR provided by SPM8 To avoid spurious findings that are driven by noise we recommend applying an extent threshold e g a minimum cluste
46. y yield valid results if the correction for nonstationarity is enabled see Step 11 of the PROCEDURE owing to the expected nonstationarity of the asymmetry data Alternative correction methods such as threshold free cluster enhancement may become available in future versions of SPM 11 View the results of the asymmetry analysis via the Results button Fig 3 item 8 and selecting the respective SPM mat file followed by defining the contrast s of interest As discussed in Experimental design we advise performing follow up analyses for better interpretation of the resulting significance maps For this purpose a thresholded cluster map ideally corrected for multiple comparisons needs to be saved For theoretical reasons as discussed in Box 5 a cluster level correction is recommended However the ultimate decision lies with the investigator who may choose any valid correction method either at the cluster level option A or at the voxel level option B A Correction for multiple comparisons at the cluster level 1 To see whether any clusters remain significant when correcting for multiple comparisons enable SPM s nonstationarity correction by typing spm_get_defaults stats rft nonstat 1 in the MATLAB command window As a consequence the results table to be opened with the whole brain button in SPM s interactive window Fig 3 item B will provide the corrected P values for each cluster the vo
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