MSTAT. The MIDAS Statistical Analysis Package. Karl Young, Patrice Weber and Norbert Schuff, 2006 University of California San Francisco

Transcription

1 MSTAT The MIDAS Statistical Analysis Package Karl Young, Patrice Weber and Norbert Schuff, 2006 University of California San Francisco Contents Introduction... 2 The User Interface... 2 Running the MSTAT Application... 2 Setting module preferences... 3 Changing the active project... 3 Exploring a project... 3 Creating a data frame... 5 Exploring a project data frames... 7 Running statistics on a project Mean and Standard deviation T-test Single Subject Data Analysis (studentized residuals)... 9 The R Statistics Routines Usage MSTAT Commands midas_check_array midas_check_files midas_csf_correct midas_exclude_mask midas_mean_stdev midas_multi_mask midas_remove_outliers midas_student_res_frame midas_student_res_one midas_ttest midas_write_file Acknowledgements

2 Introduction The Statistical Analysis Package for the MIDAS project (MSTAT) provides an interface for standard statistical analysis as well as exploratory data analysis of spatially normalized MRSI data. MSTAT offers a library of statistical functions written in the R statistical language that can be accessed via a JAVA based graphical user interface (GUI) or directly from the R command line. Analyses are performed on each and every image voxel using standard (univariate) statistical tests, generally also known as Statistical Parametric Mapping. The input to MSTAT is a set of metabolite and tissue segmentation images in a standard space defined in terms of some brain template (e.g. the Montreal Neurological Institute (MNI) template) and generated at an earlier stage of the MIDAS processing pipeline. The standard outputs from MSTAT are statistical maps, which present the test results voxel-by-voxel for the graphical representations of the results in form of an image. The standard format of the statistical maps is ANALYZE. A text format that lists results ranked by statistical significance is also provided. The lingua-franca of MSTAT is the data frame as defined and used in various implementations of the S statistical language (e.g. the open source package R of the commercial package Splus). The standard MSTAT data frame is generated from the MIDAS XML files via the JAVA GUI and contains a set of columns specifying complete local or remote paths to image and quality map file names as well as any requested metadata for a set of rows representing subjects, possibly from different projects, as specified via the GUI. MSTAT provides a set of statistical routines which operate on MSTAT data frames to generate various images such as mean and standard deviation images, as well as statistical parametric maps such as t-test maps. The User Interface Running the MSTAT Application The program can be started from the MIDAS Toolbar The Midas Statistics program (MSTAT) is composed of several independent modules that are loaded at run-time: Four modules are currently available: Project XML Browser Data frame Wizard Data frame Explorer Statistics Models To check which modules are used by the application, on the Views menu, click Preferences and then click the Midas [mainframe] tab (left of the window). Under module manager is displayed the list of modules and the order in which there are loaded. Modifying the modules order doesn t affect the program functionalities, but will change the application toolbars ordering. 2

3 Setting Module Preferences Some application modules have extra settings that can be access in the preference tab panels (see above picture). On the Views menu, click Preferences and then click the module tab (left of the window) to display the settings window. Changing the Active Project Begin the statistical session by selecting the study project that contains the data to be analyzed. Use the drop-down menu on the main toolbar to obtain a listing of the various projects. By clicking on the button labeled Active Project you can set the current active project to be the application default project for the statistical analysis. Exploring a Project Use the button to open the MIDAS browser (see picture below). The top view is a tree rendition of the project XML node structure that allows you to see the subject identification (ID) and data contained within a project. The browser top view also displays the project atlas and the statistical analyses when present. The lower view displays a list of parameters (or node attributes) and their corresponding values for a selected node in the top view. 3

4 Right click menu options: Add node comment: a simple tool used to add a comment field to any selected node in the project Duplicate subject xml: duplicate a subject.xml (for debugging purpose). Remove Subject from project: delete a subject and its associated data in the current project Remove current node: delete from a subject the selected nodes (series, process or data nodes) from a subject. You can only delete multiple nodes if they are of the same type. Export transformation: export a rigid, affine or non-linear transformation parameters to an ASCII file Export to Analyze format: a data volume to analyze format. Export to Midas format: a data volume to Midas format (.mdh,.vol). Display file: display an image file using the default viewer (see setting module preferences for more details). 4

5 Creating a Data Frame Before running a statistical analysis on a project, you will need to organize and store the data to be analyzed in a two-dimensional data object, also called a data frame. A data frame can contain columns of different type. For example, the first column can contain character data, e.g. the code name of a subject, while the second column can contain numeric data, e.g. intensity of NAA at position x,y,z. All columns in a data frame must have the same length, thus yielding a rectangular data sheet. Click the button to launch the data frame wizard and to organize the data in a data frame. Step 1: Select the data project, the modality (T1 or SI) and the resolution of the data in common space (atlas). The drop down menu will give the user the choice of the resolutions found in the project. An empty combo box means that the subject data have not been transformed to the atlas space (see registration documentation on how to do this). Step 2: Select the variables you want to include in you data frame. You can refine your criteria and filter the variable values by checking the assigned value column. Doing so will display a drop down menu for categorical variables or a range selection for continuous variables. 5

6 Step 3: Select the observation frames (a variable 3D map, like a metabolite for example) you want to include in your data frame. The wizard will display four pre-selected observations by default (the segmentation maps grey matter, white matter, CSF and Line width). Important: It is not recommended to uncheck any of the pre-selected observation frames. These observations are required to compute the quality map mask (optional) in the statistical analyses. Step 4: Select the subjects you wish to include in your analyses. Step 5: give a name to your data frame (no whitespaces allowed) and add optional comments. Click the save button to complete the wizard. 6

7 Exploring a Project Data Frames Use the button to open a new data frame explorer, which permits manipulating a data frame. Data frame list right click menu options: Delete a data frame. Export a data frame to a tab or comma delimited text file. Save the data frame as a HTML document. The data frame elements representing a 3D map (metabolites or segmentation for example) are exported as Analyze files with the data frame table. Running Statistics on a Project 1 - Mean and Standard deviation A voxel-by-voxel analysis of mean and standard deviations in metabolite images can be performed by executing the midas_mean_stdev function. Click the button to launch a new analysis window. 1) In the Data section select the data frame you want to run your analysis on. 2) In the Analysis parameters section, select the observation (NAA concentration for example) and the analysis variable (age or sex for example depending on the data frame chosen). With categorical variables you can further filter the data frame by specifying a class value. 7

8 3) In the Quality map section select if you want to apply a quality mask to limit the computations to a certain quality of spectra. The quality map is currently based on the line width observation. The required parameters are the line width cutoffs (default values 2 and 7 Hz) and the minimum number of subjects included in an average voxel (default value 5) Click on the Run button to launch the R script. At the end of the computation, a dialog box should pop up providing information if the process has been successful. At the end of the computation, the results will be saved as new analysis process under the Statistics node in the project browser. 2 - T-test A voxel-by-voxel t-test (to test whether two samples come from distributions with the same means) can be performed by executing the midas_ttest function. Click the button to launch a new analysis window. 1) In the Data section select the data frame you want to run your analysis on. 2) In the Analysis parameters section, select an observation (NAA concentration for example) and the grouping variable used to create the two populations for the t-test. For continuous 8

9 variables you have the choice of a cut-off point, the median or the mean value of the given variable within the data frame. 3) In the Hypothesis section select what kind of t-test to perform. The choices are "two.sided", "less", or "greater". The default setting is "two.sided". 4) Select a quality map option (refer to the mean and standard deviation analysis for details) 5) Click the Run button to launch the R script. At the end of the computation, a dialog box will pop up, telling the user if the process has been successful. At the end of the computation, the results will be saved as new analysis process under the Statistics node in the project browser. 3 - Single Subject Data Analysis (studentized residuals) A voxel-by-voxel test to determine whether metabolite values of a single subject are different from those of a specific group of subjects (e.g. healthy control subjects), can be performed using the midas_student_res_frame function. Click the button to launch a new analysis window. 1) In the Data section select the data frame you want to run your analysis on. By default the studentized test will be run for every subject in the data frame. To run the test on a subset of subjects, click on the Subjects check box and open a project browser to make your selection. 2) In the Analysis parameters section, select the observation and the analysis variable. 3) Select a quality map option (refer to the mean and standard deviation analysis for details) 9

10 The R Statistics Routines The statistical routines are written using R an open-source software environment for statistical computing and graphics. Information is available at The statistical routines called by the MSTAT interface are contained in a single R source file Midas.R. The intention is to provide a single source file that can be loaded either at the R command line or used transparently by the GUI when invoking the R interpreter. The routines utilize the fast Analyze I/O routines provided by the AnalyzeFMRI package (J.L. Marchini, R Package: AnalyzeFMRI: Functions for analysis of fmri datasets stored in the ANALYZE format, (2004). Available at Combined with utilization of R s ability to perform vectorized calculations, and avoidance of loops where possible, which allows MSTAT to perform large 3D image calculations efficiently. Usage To use the MSTAT routines from the command line (which allows for greater flexibility regarding performing exploratory data analysis) one starts the R interpreter (Start -> Programs -> R -> R 2.x.x), and at the R prompt enters: > Midas.R= Midas_Intallation_Path/Bin/MSTAT/Midas.R > source( Midas.R ) At that point any of the MSTAT commands are available. 10

11 MSTAT Commands midas_check_array Checks arrays for missing values, NaN's, and Inf's Description Usage Checks arrays for missing values, NaN's, and Inf's midas_check_array(checkarray, domissing = TRUE, missing = 0, donan = TRUE, nan = 0, doinf = TRUE, inf = 0) Arguments checkarray array to check domissing TRUE, FALSE re. checking for missing values. default = TRUE missing what to replace missing values with, default = 0.0 donan TRUE, FALSE re. checking for NaN's values. default = TRUE nan what to replace NaN's with, default = 0.0 doinf TRUE, FALSE re. checking for Inf's values. default = TRUE inf what to replace Inf's with, default = 0.0 Value Array with missing values and/or NaN's and/or Inf's replaced Warning Don't use with large image files (resolution ~ 1 mm) this function gets really really slow for big arrays - in that case try to make sure the incoming arrays are ok before using mstat routines Author(s) Karl Young, CIND, UCSF midas_check_files Checks dataframe files Description Checks that image files specified in a row and read in from a dataframe actually exist Usage 11

12 midas_check_files(datatable, subject, imcol, imfile = TRUE, maskfile = TRUE, csffile = TRUE, gmfile = TRUE, wmfile = TRUE) Arguments datatable subject dataframe column names subject name for datafrmae row imcol a variable containing the name of the column specifying the image quantity to be used. default = "NAcetylAspartate_Norm". imfile maskfile csffile gmfile wmfile whether to check image file, default=true whether to check mask file, default=true whether to check csf file, default=true whether to check gm file, default=true whether to check wm file, default=true Value 1 if all requested file checks passed 0 if one or more file checks failed Author(s) Karl Young, CIND, UCSF midas_csf_correct Performs CSF correction on an image file Description Performs CSF correction on an image file using a modified sigmoid function Usage midas_csf_correct(mytable, subject, csfthresh = 0.75, sigparam = 15) Arguments mytable subject dataframe containing subject subject id (row) in data frame csfthresh threshold parameter for sigmoid function. default = 0.75 sigparam curvature parameter for sigmoid function. default = 15 Details 12

13 Typical CSF corrections use a multiplier consisting of linear increase up to a threshold and zero beyond that (i.e. "mostly" CSF beyond that). The problem with this is that the large difference at the threshold leads to distracting image artifacts so it was felt a smoother correction would be desirable. Hence this function uses an "inverted" sigmoid (i.e. going from 1 to 0) through a threshold, multiplied by the previously mentioned function, i.e. a linear increase up to the threshold. Value Array containing voxel CSF correction multiplier values Author(s) Karl Young, CIND, UCSF midas_exclude_mask Generate an image mask from an image and truncation values Description Generate an image mask from an image and truncation values; e.g. current typical usage is to generate a mask from a linewidth image image specifying upper and lower linewidth criteria. Usage midas_exclude_mask(maskfile, highthresh = upmasklim, lowthresh = lowmasklim) Arguments maskfile image file to generate a mask from highthresh upper threshold for image file values used to generate mask. currently uses default upper linewidth limit of 11 Hz as default lowthresh lower threshold for image file values used to generate mask. currently uses default lower linewidth limit of 2 Hz as default Value Array containing mask Author(s) Karl Young, CIND, UCSF 13

14 midas_mean_stdev Calculate Mean and Standard Deviation Images Description Calculate mean and standard deviation images from a set of images whose filenames reside in a dataframe passed as argument Usage midas_mean_stdev(dataframe, imcol = mycol, leavemout = c(0), classcol = "None", class = "class", maskhigh = upmasklim, masklow = lowmasklim, minvox = 5, outfile = "nosave") Arguments dataframe Value imcol leavemout classcol class maskhigh masklow minvox outfile a dataframe containing rows which either contain subject metadata or full paths to subject image files to be processed a variable containing the name of the column specifying the image quantity to be used. default = "NAcetylAspartate_Norm" a vector of integers specifying which rows of the dataframe to ignore when calculating the mean and standard deviation - handy when calling midas_mean_stdev from other functions.default = c(0) if mean and standard deviation images are to be restricted to some class (e.g. "female") this variable contains the name of the column containing the class specifier (e.g. "Subject_sex"). default = "None" if mean and standard deviation images are to be restricted to some class this variable contains the name of the class (e.g. "female") and must be accompanied by specification of the column variable, classcol, containing the class specifier (e.g. "Subject_sex"). default = "class" a value specifying the upper limit in a mask image for any voxel to be included in the calculations; if violated the voxel will be masked out. Currently uses linewidth in Hz with default = 11 a value specifying the lower limit in a mask image for any voxel to be included in the calculations; if violated the voxel will be masked out. Currently uses linewidth in Hz with default = 2 the minimum number of voxels (i.e. subjects with good data in that voxel) required for a voxel to be included in the final mean and standard deviation images, if violated the voxel will be masked out.default = 5 portion of a file name for output images (if specified mean, standard deviation and mask analyze image, header pairs will be output with appropriate strings appended to the filename). The default is to not save any files.default = "nosave" 14

15 An array of 3 images: 1) mean image, 2) standard deviation image, 3) mask image (0 where mean and std were calculated, 1 where no image value was returned) Warning Large sets (> 10) of large image files (resolution ~ 1 mm) have caused crashes due to exceeding R's memory limits Note Currently a CSF correction based on a modified sigmoid function is applied to the image values at each voxel and requires that the fields CSF_SIMap_Norm, GM_SIMap_Norm, and WM_SIMap_Norm exist in the datframe passed in and point to the appropriate image files for each subject. See the documentation for midas_csf_correct for details. Author(s) Karl Young, CIND, UCSF See Also midas_csf_correct, midas_exclude_mask, midas_csf_correct,midas_exclude_mask Examples ##---- The following example depends on the files Midas.R and the dataframe, ## myframe.txt, existing in the current directory or path and that ## myframe.txt has column entries pointing to analyze image files either ## in the directory, path or having specified path names source("midas.r") mean_stdev <- midas_mean_stdev(dataframe="myframe.txt",imcol="tcholine_norm",maskhigh= 10.0,masklow=3.0,outfile="my_tCholine",minvox=4.0) midas_multi_mask Generate a mask from multiple images Description Generate a threshold based mask from a dataframe like the one used for t-tests and other statistical tests Usage midas_multi_mask(dataframe, classcol = "None", class = "class", cutpoint = 0, cutstyle = "greater", maskmin = 0.01) Arguments dataframe a dataframe containing rows which either contain subject metadata or full paths to subject image files to be processed classcol if mean and standard deviation images are to be restricted to some class (e.g. "female") this variable contains the name of the column containing the class specifier (e.g. "Subject_sex"). default = "None" 15

16 class if mean and standard deviation images are to be restricted to some class this variable contains the name of the class (e.g. "female") and must be accompanied by specification of the column variable, classcol, containing the class specifier (e.g. "Subject_sex"). default = "class" cutpoint a threshold value, default = 0 cutstyle whether to keep values greater or less than the cutpoint threshold. default = "greater" maskmin a minimum value for generating the mask with Value An array containing the generated mask Author(s) Karl Young, CIND, UCSF midas_remove_outliers Remove outliers from a subject image Description Remove outliers from a subject image where the criteria for a voxel containing a value considered to be an outlier is obtained from the mean and standard deviation images of the remainder of the subjects in the specified dataframe (note similar to truncating a studentized residual statistic image) Usage midas_remove_outliers(dataframe, sigmult = 3, imcol = mycol, classcol = "None", class = "class", maskhigh = upmasklim, masklow = lowmasklim, minvox = 5) Arguments dataframe a dataframe containing rows which either contain subject metadata or full paths to subject image files to be processed sigmult imcol classcol class the number of standard deviations beyond which to consider a value an outlier, default = 3 a variable containing the name of the column specifying the image quantity to be used. default = "NAcetylAspartate_Norm" if mean and standard deviation images are to be restricted to some class (e.g. "female") this variable contains the name of the column containing the class specifier (e.g. "Subject_sex"). default = "None" if mean and standard deviation images are to be restricted to some class this variable contains the name of the class (e.g. "female") and must be accompanied by specification of the column variable, classcol, containing the class specifier (e.g. "Subject_sex"). default = "class" 16

17 maskhigh masklow a value specifying the upper limit in a mask image for any voxel to be included in the calculations; if violated the voxel will be masked out. Currently uses linewidth in Hz with default = 11 a value specifying the lower limit in a mask image for any voxel to be included in the calculations; if violated the voxel will be masked out. Currently uses linewidth in Hz with default = 2 minvox the minimum number of voxels (i.e. subjects with good data in that voxel) required for a voxel to be included in the final mean and standard deviation images, if violated the voxel will be masked out.default = 5 Value No return value, only analyze outlier images are produced Note Currently only outlier images are produced but this can easily be changed so that the function returns the outlier images; currently it was felt that this would rarely be desired and since for large images this could lead to memory problems and crashes was left out Author(s) Karl Young, CIND, UCSF See Also midas_student_res_one, midas_student_res_frame, midas_student_res_one, midas_student_res_frame Examples ##---- The following example depends on the files Midas.R and the dataframe, ## myframe.txt, existing in the current directory or path and that ## myframe.txt has column entries pointing to analyze image files either ## in the directory, path or having specified path names source("midas.r") midas_remove_outliers(dataframe="myframe.txt",sigmult = 4.0,imcol="tCholine_Norm",maskhigh=10.0,masklow=3.0,minvox=4.0) midas_student_res_frame Calculates a set of studentized residual statistic images Description Calculates a set of studentized residual statistic images for all subjects in a specified dataframe Usage midas_student_res_frame(dataframe, imcol = mycol, leavemout = c(0), classcol = "None", class = "class", maskhigh = upmasklim, masklow = lowmasklim, minvox = 5, outfile = "nosave") 17

18 Arguments dataframe imcol leavemout classcol class maskhigh masklow minvox a dataframe containing rows which either contain subject metadata or full paths to subject image files to be processed. Studentized residual images will be calculated for all subjects in the data frame a variable containing the name of the column specifying the image quantity to be used. default = "NAcetylAspartate_Norm" a vector of integers specifying which rows of the dataframe to ignore when calculating the mean and standard deviation - handy when calling midas_mean_stdev from other functions.default = c(0) if mean and standard deviation images are to be restricted to some class (e.g. "female") this variable contains the name of the column containing the class specifier (e.g. "Subject_sex"). default = "None" if mean and standard deviation images are to be restricted to some class this variable contains the name of the class (e.g. "female") and must be accompanied by specification of the column variable, classcol, containing the class specifier (e.g. "Subject_sex"). default = "class" a value specifying the upper limit in a mask image for any voxel to be included in the calculations; if violated the voxel will be masked out. Currently uses linewidth in Hz with default = 11 a value specifying the lower limit in a mask image for any voxel to be included in the calculations; if violated the voxel will be masked out. Currently uses linewidth in Hz with default = 2 the minimum number of voxels (i.e. subjects with good data in that voxel) required for a voxel to be included in the final mean and standard deviation images, if violated the voxel will be masked out.default = 5 outfile portion of a file name for output image (if specified the studentized residual statistic images will be output with an appropriate string appended to the filename, one.img,.hdr pair for each subject in the dataframe). The default is to not save any files.default = "nosave" Details midas_student_res_all, for each subject in the dataframe, calls midas_mean_stdev to calculate the mean and standard deviation images for all subjects in the dataframe except for the specified subject and uses those to calculate the studentized residual statistic image for the specified subject). 18

19 Thanks to Jonathan Taylor of Stanford for the suggestion to use studentized residuals for the metabolite images. Value An array of studentized residual statistic images, one for each subject in the datframe. Author(s) Karl Young, CIND, UCSF Examples ##---- The following example depends on the files Midas.R and the dataframe, ## myframe.txt, existing in the current directory or path and that ## myframe.txt has column entries pointing to analyze image files either ## in the directory, path or having specified path names, and that ## "subject3" exist in myframe.txt, i.e. there is a row in ## myfrmae.txt such that the column "Subject_ID" has the entry "subject3" source("midas.r") sr <- midas_student_res_frame(dataframe="myframe.txt",imcol="tcholine_norm",mas khigh=10.0,masklow=3.0,outfile="my_student_resid",minvox=4.0) midas_student_res_one Calculates a studentized residual statistic image Description Calculates a studentized residual statistic image for a specified subject, against the other subjects in the specified dataframe Usage midas_student_res_one(dataframe, subcode, imcol = mycol, leavemout = c(0), classcol = "None", class = "class", maskhigh = upmasklim, masklow = lowmasklim, minvox = 5, outfile = "nosave") Arguments dataframe a dataframe containing rows which either contain subject metadata or full paths to subject image files to be processed subcode imcol an integer specifying the row number in the dataframe for the subject to calculate a studentized residual image for a variable containing the name of the column specifying the image quantity to be used. default = "NAcetylAspartate_Norm" leavemout a vector of integers specifying which rows of the dataframe to ignore when calculating the mean and standard deviation - handy when calling midas_mean_stdev from other functions.default = c(0) 19

20 classcol class maskhigh masklow minvox if mean and standard deviation images are to be restricted to some class (e.g. "female") this variable contains the name of the column containing the class specifier (e.g. "Subject_sex"). default = "None" if mean and standard deviation images are to be restricted to some class this variable contains the name of the class (e.g. "female") and must be accompanied by specification of the column variable, classcol, containing the class specifier (e.g. "Subject_sex"). default = "class" a value specifying the upper limit in a mask image for any voxel to be included in the calculations; if violated the voxel will be masked out. Currently uses linewidth in Hz with default = 11 a value specifying the lower limit in a mask image for any voxel to be included in the calculations; if violated the voxel will be masked out. Currently uses linewidth in Hz with default = 2 the minimum number of voxels (i.e. subjects with good data in that voxel) required for a voxel to be included in the final mean and standard deviation images, if violated the voxel will be masked out.default = 5 outfile portion of a file name for output image (if specified the studentized residual statistic image will be output with an appropriate string appended to the.img,.hdr filenames). The default is to not save any files. default = "nosave" Details midas_student_res_one reads in the dataframe, calls midas_mean_stdev to calculate the mean and standard deviation images for all subjects in the dataframe except for the specified subject and uses those to calculate the studentized residual statistic image for the specified subject). Thanks to Jonathan Taylor of Stanford for the suggestion to use studentized residuals for the metabolite images. Value The studentized residual statistic image for the specified subject Note See the documentation on midas_mean_stdev for details on how the mean and standard deviation are calculated, e.g. how voxels are masked out or chosen to be included Author(s) Karl Young, CIND, UCSF See Also midas_mean_stdev, midas_mean_stdev Examples 20

21 ##---- The following example depends on the files Midas.R and the dataframe, ## myframe.txt, existing in the current directory or path and that ## myframe.txt has column entries pointing to analyze image files either ## in the directory, path or having specified path names, and that ## "subject3" exist in myframe.txt, i.e. there is a row in ## myfrmae.txt such that the column "Subject_ID" has the entry "subject3" source("midas.r") sr <- midas_student_res_one(dataframe="myframe.txt",subcode="subject3",imcol="tc holine_norm",maskhigh=10.0,masklow=3.0,outfile="my_student_resid",minvox=4.0) midas_ttest Perform voxel t-tests for a set of images Description Given a dataframe and appropriate classes in the dataframe, performs voxel by voxel t-tests and allows for Bonferoni or Random Field Theory significance corrections. Usage midas_ttest(dataframe, classcol, imcol = mycol, cuttype = "Mean", cutpoint = 0, class1 = "class1", class2 = "class2", thresh = "RFT", pval = 0.05, maskhigh = upmasklim, masklow = lowmasklim, outfile = "nosave", alternative = "two.sided") Arguments dataframe a dataframe containing rows which either contain subject metadata or full paths to subject image files to be processed classcol cuttype cutpoint class1 column containing the class specifier (e.g. "Subject_sex") in the dataframe to use for separating the groups to perform t-tests for imcol a variable containing the name of the column specifying the image quantity to be used. default = "NAcetylAspartate_Norm" if classcol is numerical, cuttype specifies how to divide the group into classes based on values in that column. Choices are "Mean", "Median", and "User" where "User" allows the user to enter a cutpoint. default = "Mean" user defined cutpoint to be specified if the "User" option for cuttype is specified. default = 0 If classcol is categorical class1 specifies the first class to use for the t-tests, e.g. "Female". default = "class1" 21

22 class2 thresh If classcol is categorical class2 specifies the second class to use for the t-tests, e.g. "Male". default = "class2" specifies whether to use Bonferoni ("Bonferoni") or Random Field Theory ("RFT") for generating a significance threshold. default = "RFT" pval pval to use for the significance threshold calculation. default = 0.05 maskhigh masklow outfile a value specifying the upper limit in a mask image for any voxel to be included in the calculations; if violated the voxel will be masked out. Currently uses linewidth in Hz with default = 11 a value specifying the lower limit in a mask image for any voxel to be included in the calculations; if violated the voxel will be masked out. Currently uses linewidth in Hz with default = 2 portion of a file name for output images (if specified mask, t statistic, p value, and over threshold analyze image, header pairs will be output with appropriate strings appended to the filename). The default is to not save any files.default = "nosave" alternative what kind of t-test to perform; choices = "two.sided", "less", or "greater". default = "two.sided" Value An array containing mask, t statistic, p value, and over threshold images Author(s) Karl Young, UCSF, CIND Examples ##---- The following example depends on the files Midas.R and the dataframe, ## myframe.txt, existing in the current directory or path and that ## myframe.txt has column entries pointing to analyze image files either ## in the directory, path or having specified path names source("midas.r") myims <- midas_ttest(dataframe="myframe.txt",classcol="subject_condition",imcol="tcholi ne_norm",class1="norm",class2="sub",maskhigh=10.0,masklow=3.0,outfile="my _ttest",minvox=4.0) midas_write_file Given an array, writes an analyze.img,.hdr pair Description Given an array, writes an analyze.img,.hdr pair 22

23 Usage midas_write_file(outimage, outfile, inheader, size) Arguments outimage image to output outfile inheader name of analyze output file an analyze header with appropriate values (usually taken from an analyze image that was read in previously) size type of image value (i.e. specifies size), e.g. "Float" Value No explicit return value, outputs analyze images Author(s) Karl Young, UCSF, CIND Acknowledgements This application was developed under NIH grant R01EB