FNC Toolbox Walk Through

Transcription

1 FNC Toolbox Walk Through Date: August 10 th, 2009 By: Nathan Swanson, Vince Calhoun at The Mind Research Network

2 Introduction The FNC Toolbox is an extension of the GIFT toolbox that can be run only after a GIFT analysis. After this is done the FNC Toolbox can load the GIFT parameter file and calculate lags between components that are correlated to each other. This approach has been published and applied to identify which pairs of independent components are showing changes in temporal correlation between healthy controls and schizophrenic patients [1]. Note! In addition to MATLAB,the FNC Toolbox needs the Signal Processing Toolbox to run. Installing and Running FNC Toolbox FNC Toolbox supports both UNIX and Windows and is installed as follows. 1) Extract the FncVer1e.zip to a folder where you want to store the FNC Toolbox. On a PC this is usually done by copying the file to wanted folder, then right clicking the file ZIP-file and choosing extract all from the list of choices. 2) Similarly extract the FncWalkData. 3) Start MATLAB 4) Go to the folder where the FNC source code was unzipped to. 5) Type fnctb, which will result in the form shown in figure popping up on the screen. Note that in some cases the form pops up behind the Matlab window and then you can minimize the Matlab window to see figure. This step will also add the fnctb directory to your Matlab path so you don t need to use step 4 again. 1

3 Figure 1: FNC initial parameter window. 2

4 6) Click the [Parameter File] button and a window similar to figure will pop up. Figure 2: Parameter file selector. 7) In the left list click on the giftoutput entry in the location where FncWalkData was extracted to. 3

5 8) After a while files in the right list appears and you click on the walk_ica_parameter_info.mat entry and click [OK] showing figure. Note that the number of subjects (40) are displayed in the form now in the upper right corner). Figure 3: FNC initial parameter window after loaded parameter file. 9) Enter the group numbers for each group. 10) Select Add Contrast to enter the desired contrast. Enter 1-1 in the pop-up box for a 2-way contrast. Note that, unless doing a single group contrast, all contrasts must sum to 0. For a 3-way contrast, you may enter multiple 3-way contrasts to compose a 3-way contrast matrix. You may edit an existing contrast by clicking on the resulting drop-down list with the previously entered contrast. You may also delete the contrast by clicking on the contrast, and leaving the pop-up blank, click Ok. 11) Enter 0.1 for the lower limit textbox and 0.4 for the higher limit textbox. You may also enter the filter in Hertz units if you wish by clicking on the Hertz radio 4

6 button. These are properties of how the time course should be filtered. See [1] for more details on the filtering. 12) Choose 3 sec in the Max-lag textbox. This is the maximum amount of time the time course (TC) are allowed to shift when comparing components. 13) Choose 1.5 sec in the TR textbox. This indicates the repetition time (TR) of the fmri experiment. 14) Select "Mean S All" in the GIFT Type combo box. This is currently the only option available. 15) There are two algorithms for running FNC: Lag Shift, and Granger Causality. If you wish to try Granger Causality, change the lower limit time course filter to.2 (Granger Causality requires a proper lower limit to work). For more information on Granger Causality, see (Aikins and Ray). For this walkthrough, we will choose Lag Shift. Note that running Granger Causality will not have as many features implemented as Lag Shift. 16) Click on Choose Components. Note that since the sample data was processed with GIFT on a file structure that is different than yours, the structural file path stored in the data will not correctly point to the structural file. In this case, you will get a prompt for the structural file. This file can be found in: FncWalkData\giftOutput\rest210\h _114516_ \srw img 5

7 Figure 4: FNC Explorer window (Lag-Shift Algorithm). 17) Select the components you want to include in your study (check component 2,5, 11, 12 for this walk through). Use the scrollbar to show additional components if available. 18) The "Pval - Corr. types" is a combobox that lets you choose how the p-values are computed as well as what type of correlation you want to maximize between your components (positive or absolute correlation). Choose "Pval1 - Pos". 19) Click the "Select Folder" button and choose the output folder into which an HTML page will be saved containing graphs and final results. If desired, you can manually edit the output path once the initial folder is selected. 20) The "D Corr., D Lag" text boxes let you choose the p-value threshold for the Δ-correlation and the Δ-lag across groups. Choose 0.05 (95% confidence) in the Corr. text box and 0.05 in the Lag text box. 21) The "In-Group Corr., Lag" textboxes let you choose the p-value threshold for the controls correlation and the lag where its maximum correlation occurred within group. Choose 0.05 (95% confidence) in the Corr. text box and 1 in the Lag text box. 22) The FDR checkbox is for if you wish to use False Discovery Rate correction for multiple comparisons. The Disable Lag checkbox is for if you wish to not have 6

8 lag significance testing in your results (i.e. correlation significance only). For this demo leave both unchecked. 23) Finally click [Calc & Graph] and the FNC Toolbox will calculate the results for you. Calculations may take several minutes depending upon which hardware you have, resulting in a HTML page containing the statistical information in accordance to figure (scroll down to see additional graphs). The HTML page and a GraphAndData.mat file (explained later) is saved in the param folder you selected. 7

9 Figure 5: Top of resulting HTML report. 8

10 This is the complete cycle to explore the lags/correlations between selected GIFT components. However there are additional features that can save time by bypassing recalculations if you just want to change a few parameters (see below). 9

11 Redisplay graph with alternate parameters. After the HTML page with graphs has been displayed you may change any parameter configurable in figure. An example of how this is done follows. 1) Close the HTML page displayed in previous section and figure shows. Notice that the buttons [Save Graph] and [Show Graph] now are enabled. NOTE! Do not hit any checkboxes as they now will disable the [Show Graph] button. 2) Change the "Pval - Corr. types" combobox to "Pval1 - Abs". 3) Change the "D Corr., Lag" textboxes to 0.03 (97% confidence) in the Corr. text box and 0.2 (20% confidence) in the Lag text box. 4) Change the " Corr., Lag" textboxes (for controls) to 0.04 (96% confidence) in the Corr. textbox and 0.7 (30% confidence) in the Lag text box. 5) Click [Show Graph] and a new HTML page with graphs and calculations will show in accordance to the new constraints that you recently entered. Note how much faster the HTML page displayed this second time. This is since most calculations now were saved to disc and retrieved from there. Note too that this second result has been saved in the new param folder. Load previous run. Your calculations have automatically been saved for later reuse. To reload the previous run you do the following: 1) Close down entire Matlab and restart Matlab and FNCTB (steps 2 and 4 under Installing and running FNC Toolbox) and figure will show again. 2) Click [Load Graph] and figure will show (different for PC and UNIX). 10

12 3) In UNIX you select folders in the left Directories list and then GraphAndData.mat in the Files list. Then click the [Open] button. PC has only one list, but works similarly. Figure 6: Matlab load file dialog box (UNIX on top, PC below). 4) Enter the output folder again and then click [Show Graph], and your first graph will show again. The new data will be saved in the new location. 11

13 Troubleshooting Here are several common mistakes that users have when using FNC. 1) Switching Platforms (Windows/Unix) between using GIFT and FNC (not supported) 2) Moving or altering the file structure of the GIFT output before using FNC 3) Having GIFT zip the output files, and not unzipping (all) the output before running FNC 4) Using a 6.x Matlab version or earlier when running FNC The GIFT output will store the file paths of certain output files at the completion of running GIFT. FNC will assume that these locations are valid when running FNC. Moving the files of the GIFT output will invalidate these paths. Also, note that GIFT will default to zipping up output. If this is the case, there will be multiple zip files. Be sure to either manually unzip each zipped file to the file they exist in, or select of GIFT not to zip up the output when running GIFT. Finally, FNC has not been tested on versions before 7.0. If the error: Group ICA Error Information: Error using ==> fnctb_ica_spm_slice_vol Bad image handle dimensions. It may mean the several MEX binaries supplied do not work for your particular version of Matlab. Please try the following: a) If SPM5 "Display" is working fine, mat2file.mexa64, spm_slice_vol.mexa64 and spm_sample_vol.mexa64 need to be copied to fnctb/fnctb_spm5_files and appropriately prefixed. b) If SPM5 "Display" is not working, cd to spm5/src in shell. Use "make all install" command to compile the MEX binaries. These binaries need to be moved to fnctb_spm5_files as suggested in a). Final note: Version 2.X has the 1-sample significance correlation results not taken into account when calculating the 2-sample results. One should interpret any 2-sample results cautiously (i.e. the directionality of the arrows) if the respect 1-sample correlations are not significant. REFERENCES [1] M. Jafri, G. D. Pearlson, M. Stevens, and V. D. Calhoun, "A Method for Functional Network Connectivity Among Spatially Independent Resting-State Components in Schizophrenia," NeuroImage, vol. 39, pp ,

14 [2] O. Demirci, M. C. Stevens, N. C. Andreasen, A. Michael, J. Liu, T. White, G. D. Pearlson, V. P. Clark, and V. D. Calhoun, "Investigation of relationships between fmri brain networks in the spectral domain using ICA and Granger causality reveals distinct differences between schizophrenia patients and healthy controls," NeuroImage, vol. 46, pp , [3] Aikins D, Ray WJ Frontal lobe contributions to hypnotic susceptibility: a neuropsychological screening of executive functioning. Int J Clin Exp Hypn 49(4):