:orphan: .. _ahelp_afni: **** afni **** .. contents:: :local: | .. code-block:: none **** Help for all AFNI programs can be found at the Web page https://afni.nimh.nih.gov/pub/dist/doc/program_help/index.html ---------------------------------------------------------------- USAGE 1: read in sessions of 3D datasets (created by to3d, etc.) ---------------------------------------------------------------- afni [options] [session_directory ...] -bysub This new [01 Feb 2018] option allows you to have 'sessions' *OR* made up from files scattered across multiple directories. -BIDS The purpose of this option is to gather all the datasets corresponding to a single subject identifier, as is done in the BIDS file hierarchy -- http://bids.neuroimaging.io/ **** There are two methods for using this option. method (1) ** In the first method, you put one or more subject identifiers, [OLDER] which are of the form 'sub-XXX' where 'XXX' is some subject code (it does not have to be exactly 3 characters). ++ If an identifier does NOT start with 'sub-', then that 4 letter string will be added to the front. This allows you to specify your subjects by their numbers 'XXX' alone. method (2) ** In the second method, you put one or more directory names, [NEWER] and all immediate sub-directories whose name starts with 'sub-' will be included. With this method, you can end up reading in an entire BIDS hierarchy of datasets, which might take a significant amount of time if there are many subjects. **** Note that if an identifier following '-bysub' on the command line is a directory name that starts with 'sub-', it will be treated using method (1), not using method (2). both methods ** In either method, the list of names following '-bysub' ends with any argument that starts with '-' (or with the end of all command line arguments). method (2) ** Each directory on the command line (after all options, and including any directories directly after the '-bysub' option) will be scanned recursively (down the file tree) for subdirectories whose name matches each 'sub-XXX' identifier exactly. All such subdirectories will have all their datasets read in (recursively down the file tree) and put into a single session for viewing in AFNI. ++ In addition, all datasets from all subjects will be available in the 'All_Datasets' session in the GUI. (Unless environment variable AFNI_ALL_DATASETS is set to NO) ++ If you do NOT put any directories or subject identifiers directly after the '-bysub' (or '-BIDS') option, the program will act as if you put '.' there, and it will search below the current working directory - the directory you were 'in' when you started the AFNI GUI. method (1) ** If a directory on the command line after this option does NOT have any subdirectories that match any of the '-bysub' identifiers, then that directory will be read in the normal way, with all the datasets in that particular directory (but not subdirectories) read into the session. both methods ** Please note that '-bysub' sessions will NOT be rescanned for new datasets that might get placed there after the AFNI GUI starts, unlike normal (single directory) sessions. method (1) ** Example (method 1): afni -bysub 10506 50073 - ~/data/OpenFMRI/ds000030 This will open the data for subjects 10506 and 50073 from the data at the specified directory -- presumably the data downloaded from https://openfmri.org/dataset/ds000030/ ++ If directory sub-10506 is found and has (say) sub-directories anat beh dwi func all AFNI-readable datasets from these sub-directories will be input and collected into one session, to be easily viewed together. ++ Because of the recursive search, if a directory named (e.g.) derivatives/sub-10506 is found underneath ~/data/OpenFMRI/ds000030, all the datasets found underneath that will also be put into the same session, so they can be viewed with the 'raw' data. ++ In this context, 'dataset' also means .png and .jpg files found in the sub-XXX directories. These images can be opened in the AFNI GUI using the Axial image viewer. (You might want to turn the AFNI crosshairs off!) ++++ If you do NOT want .png and .jpg files read into AFNI, set Unix environment variable AFNI_IMAGE_DATASETS to 'NO'. ++ You can put multiple subject IDs after '-bysub', as in the example above. You can also use the '-bysub' option more than once, if you like. Each distinct subect ID will get a distinct AFNI session in the GUI. method (2) ** Example (method 2): afni -bysub ~/data/OpenFMRI/ds000030 This will read in all datasets from all subjects. In this particular example, there are hundreds of subjects, so this command may not actually be a good idea - unless you want to go get a cup of chai or coffee, and then sip it very slowly. ** Example (method 2): afni -BIDS This will read all 'sub-*' directories from the current working directory, and is the same as 'afni -BIDS .' As noted earlier, this recursive operation may take a long time (especially if the datasets are compressed), as AFNI reads the headers from ALL datasets as it finds them, to build a table for you to use in the 'OverLay' and 'UnderLay' dataset choosers. -all_dsets Read in all datasets from all listed folders together. Has the same effect as choosing 'All_Datasets' in the GUI. Example: afni -all_dsets dir1 dir2 dir3 Can be set to default in .afnirc with ALL_DSETS_STARTUP = YES. Overridden silently by AFNI_ALL_DATASETS = NO. -purge Conserve memory by purging unused datasets from memory. [Use this if you run out of memory when running AFNI.] [This will slow the code down, so use only if needed.] [When a dataset is needed, it will be re-read from disk.] -posfunc Start up the color 'pbar' to use only positive function values. -R Recursively search each session_directory for more session subdirectories. WARNING: This will descend the entire filesystem hierarchy from each session_directory given on the command line. On a large disk, this may take a long time. To limit the recursion to 5 levels (for example), use -R5. ** Use of '-bysub' disables recursive descent, since '-bysub' will do that for you. -no1D Tells AFNI not to read *.1D timeseries files from the dataset directories. The *.1D files in the directories listed in the AFNI_TSPATH environment variable will still be read (if this variable is not set, then './' will be scanned for *.1D files). -nocsv Each of these option flags does the same thing (i.e., -notsv they are synonyms): each tells AFNI not to read -notcsv *.csv or *.tsv files from the dataset directories. You can also set env AFNI_SKIP_TCSV_SCAN = YES to the same effect. -unique Tells the program to create a unique set of colors for each AFNI controller window. This allows different datasets to be viewed with different grayscales or colorscales. Note that -unique will only work on displays that support 12 bit PseudoColor (e.g., SGI workstations) or TrueColor. -orient code Tells afni the orientation in which to display x-y-z coordinates (upper left of control window). The code must be 3 letters, one each from the pairs {R,L} {A,P} {I,S}. The first letter gives the orientation of the x-axis, the second the orientation of the y-axis, the third the z-axis: R = right-to-left L = left-to-right A = anterior-to-posterior P = posterior-to-anterior I = inferior-to-superior S = superior-to-inferior The default code is RAI ==> DICOM order. This can be set with the environment variable AFNI_ORIENT. As a special case, using the code 'flipped' is equivalent to 'LPI' (this is for Steve Rao). -noplugins Tells the program not to load plugins. (Plugins can also be disabled by setting the environment variable AFNI_NOPLUGINS.) -seehidden Tells the program to show you which plugins are hidden. -DAFNI_ALLOW_ALL_PLUGINS=YES Tells the program NOT to hide plugins from you. Note that there are a lot of hidden plugins, most of which are not very useful! -yesplugouts Tells the program to listen for plugouts. (Plugouts can also be enabled by setting the environment variable AFNI_YESPLUGOUTS.) -YESplugouts Makes the plugout code print out lots of messages (useful for debugging a new plugout). -noplugouts Tells the program NOT to listen for plugouts. (This option is available to override the AFNI_YESPLUGOUTS environment variable.) -skip_afnirc Tells the program NOT to read the file .afnirc in the home directory. See README.setup for details on the use of .afnirc for initialization. -layout fn Tells AFNI to read the initial windows layout from file 'fn'. If this option is not given, then environment variable AFNI_LAYOUT_FILE is used. If neither is present, then AFNI will do whatever it feels like. -niml If present, turns on listening for NIML-formatted data from SUMA. Can also be turned on by setting environment variable AFNI_NIML_START to YES. -np PORT_OFFSET: Provide a port offset to allow multiple instances of AFNI <--> SUMA, AFNI <--> 3dGroupIncorr, or any other programs that communicate together to operate on the same machine. All ports are assigned numbers relative to PORT_OFFSET. The same PORT_OFFSET value must be used on all programs that are to talk together. PORT_OFFSET is an integer in the inclusive range [1025 to 65500]. When you want to use multiple instances of communicating programs, be sure the PORT_OFFSETS you use differ by about 50 or you may still have port conflicts. A BETTER approach is to use -npb below. -npq PORT_OFFSET: Like -np, but more quiet in the face of adversity. -npb PORT_OFFSET_BLOC: Similar to -np, except it is easier to use. PORT_OFFSET_BLOC is an integer between 0 and MAX_BLOC. MAX_BLOC is around 4000 for now, but it might decrease as we use up more ports in AFNI. You should be safe for the next 10 years if you stay under 2000. Using this function reduces your chances of causing port conflicts. See also afni and suma options: -list_ports and -port_number for information about port number assignments. You can also provide a port offset with the environment variable AFNI_PORT_OFFSET. Using -np overrides AFNI_PORT_OFFSET. -max_port_bloc: Print the current value of MAX_BLOC and exit. Remember this value can get smaller with future releases. Stay under 2000. -max_port_bloc_quiet: Spit MAX_BLOC value only and exit. -num_assigned_ports: Print the number of assigned ports used by AFNI then quit. -num_assigned_ports_quiet: Do it quietly. Port Handling Examples: ----------------------- Say you want to run three instances of AFNI <--> SUMA. For the first you just do: suma -niml -spec ... -sv ... & afni -niml & Then for the second instance pick an offset bloc, say 1 and run suma -niml -npb 1 -spec ... -sv ... & afni -niml -npb 1 & And for yet another instance: suma -niml -npb 2 -spec ... -sv ... & afni -niml -npb 2 & etc. Since you can launch many instances of communicating programs now, you need to know wich SUMA window, say, is talking to which AFNI. To sort this out, the titlebars now show the number of the bloc of ports they are using. When the bloc is set either via environment variables AFNI_PORT_OFFSET or AFNI_PORT_BLOC, or with one of the -np* options, window title bars change from [A] to [A#] with # being the resultant bloc number. In the examples above, both AFNI and SUMA windows will show [A2] when -npb is 2. -list_ports List all port assignments and quit -port_number PORT_NAME: Give port number for PORT_NAME and quit -port_number_quiet PORT_NAME: Same as -port_number but writes out number only -available_npb: Find the first available block of port numbers, print it to stdout and quit The value can be used to set the -npb option for a new set of chatty AFNI/SUMA/etc group. -available_npb_quiet: Just print the block number to stdout and quit. -com ccc This option lets you specify 'command strings' to drive AFNI after the program startup is completed. Legal command strings are described in the file README.driver. More than one '-com' option can be used, and the commands will be executed in the order they are given on the command line. N.B.: Most commands to AFNI contain spaces, so the 'ccc' command strings will need to be enclosed in quotes. -comsep 'c' Use character 'c' as a separator for commands. In this way, you can put multiple commands in a single '-com' option. Default separator is ';'. N.B.: The command separator CANNOT be alphabetic or numeric (a..z, A..Z, 0..9) or whitespace or a quote! N.B.: -comsep should come BEFORE any -com option that uses a non-semicolon separator! Example: -com 'OPEN_WINDOW axialimage; SAVE_JPEG axialimage zork; QUIT' N.B.: You can also put startup commands (one per line) in the file '~/.afni.startup_script'. For example, OPEN_WINDOW axialimage to always open the axial image window on startup. * If no session_directories are given, then the program will use the current working directory (i.e., './'). * The maximum number of sessions is now set to 199. * The maximum number of datasets per session is 8192. * To change these maximums, you must edit file '3ddata.h' and then recompile this program. Global Options (available to all AFNI/SUMA programs) -h: Mini help, at time, same as -help in many cases. -help: The entire help output -HELP: Extreme help, same as -help in majority of cases. -h_view: Open help in text editor. AFNI will try to find a GUI editor -hview : on your machine. You can control which it should use by setting environment variable AFNI_GUI_EDITOR. -h_web: Open help in web browser. AFNI will try to find a browser. -hweb : on your machine. You can control which it should use by setting environment variable AFNI_GUI_EDITOR. -h_find WORD: Look for lines in this programs's -help output that match (approximately) WORD. -h_raw: Help string unedited -h_spx: Help string in sphinx loveliness, but do not try to autoformat -h_aspx: Help string in sphinx with autoformatting of options, etc. -all_opts: Try to identify all options for the program from the output of its -help option. Some options might be missed and others misidentified. Use this output for hints only. -overwrite: Overwrite existing output dataset. Equivalent to setting env. AFNI_DECONFLICT=OVERWRITE -ok_1D_text: Zero out uncommented text in 1D file. Equivalent to setting env. AFNI_1D_ZERO_TEXT=YES -Dname=val: Set environment variable 'name' to value 'val' For example: -DAFNI_1D_ZERO_TEXT=YES -Vname=: Print value of environment variable 'name' to stdout and quit. This is more reliable that the shell's env query because it would include envs set in .afnirc files and .sumarc files for SUMA programs. For example: -VAFNI_1D_ZERO_TEXT= -skip_afnirc: Do not read the afni resource (like ~/.afnirc) file. -pad_to_node NODE: Output a full dset from node 0 to MAX_NODE-1 ** Instead of directly setting NODE to an integer you can set NODE to something like: ld120 (or rd17) which sets NODE to be the maximum node index on an Icosahedron with -ld 120. See CreateIcosahedron for details. d:DSET.niml.dset which sets NODE to the maximum node found in dataset DSET.niml.dset. ** This option is for surface-based datasets only. Some programs may not heed it, so check the output if you are not sure. -pif SOMETHING: Does absolutely nothing but provide for a convenient way to tag a process and find it in the output of ps -a -echo_edu: Echos the entire command line to stdout (without -echo_edu) for edification purposes SPECIAL PURPOSE ARGUMENTS TO ADD *MORE* ARGUMENTS TO THE COMMAND LINE ------------------------------------------------------------------------ Arguments of the following form can be used to create MORE command line arguments -- the principal reason for using these type of arguments is to create program command lines that are beyond the limit of practicable scripting. (For one thing, Unix command lines have an upper limit on their length.) This type of expanding argument makes it possible to input thousands of files into an AFNI program command line. The generic form of these arguments is (quotes, 'single' or "double", are required for this type of argument): '< use only sub-brick #5 fred+orig[5,9,17] ==> use #5, #9, and #17 fred+orig[5..8] or [5-8] ==> use #5, #6, #7, and #8 fred+orig[5..13(2)] or [5-13(2)] ==> use #5, #7, #9, #11, and #13 Sub-brick indexes start at 0. You can use the character '$' to indicate the last sub-brick in a dataset; for example, you can select every third sub-brick by using the selection list fred+orig[0..$(3)] N.B.: The sub-bricks are read in the order specified, which may not be the order in the original dataset. For example, using fred+orig[0..$(2),1..$(2)] will cause the sub-bricks in fred+orig to be input into memory in an interleaved fashion. Using fred+orig[$..0] will reverse the order of the sub-bricks. N.B.: You may also use the syntax after the name of an input dataset to restrict the range of values read in to the numerical values in a..b, inclusive. For example, fred+orig[5..7]<100..200> creates a 3 sub-brick dataset with values less than 100 or greater than 200 from the original set to zero. If you use the <> sub-range selection without the [] sub-brick selection, it is the same as if you had put [0..$] in front of the sub-range selection. N.B.: Datasets using sub-brick/sub-range selectors are treated as: - 3D+time if the dataset is 3D+time and more than 1 brick is chosen - otherwise, as bucket datasets (-abuc or -fbuc) (in particular, fico, fitt, etc datasets are converted to fbuc!) N.B.: The characters '$ ( ) [ ] < >' are special to the shell, so you will have to escape them. This is most easily done by putting the entire dataset plus selection list inside forward single quotes, as in 'fred+orig[5..7,9]', or double quotes "x". CATENATED AND WILDCARD DATASET NAMES ------------------------------------ Datasets may also be catenated or combined in memory, as if one first ran 3dTcat or 3dbucket. An input with space-separated elements will be read as a concatenated dataset, as with 'dset1+tlrc dset2+tlrc dset3+tlrc', or with paths, 'dir/dset1+tlrc dir/dset2+tlrc dir/dset3+tlrc'. The datasets will be combined (as if by 3dTcat) and then treated as a single input dataset. Note that the quotes are required to specify them as a single argument. Sub-brick selection using '[]' works with space separated dataset names. If the selector is at the end, it is considered global and applies to all inputs. Otherwise, it applies to the adjacent input. For example: local: 'dset1+tlrc[2,3] dset2+tlrc[7,0,1] dset3+tlrc[5,0,$]' global: 'dset1+tlrc dset2+tlrc dset3+tlrc[5,6]' N.B. If AFNI_PATH_SPACES_OK is set to Yes, will be considered as part of the dataset name, and not as a separator between them. Similar treatment applies when specifying datasets using a wildcard pattern, using '*' or '?', as in: 'dset*+tlrc.HEAD'. Any sub-brick selectors would apply to all matching datasets, as with: 'dset*+tlrc.HEAD[2,5,3]' N.B.: complete filenames are required when using wildcard matching, or no files will exist to match, e.g. 'dset*+tlrc' would not work. N.B.: '[]' are processed as sub-brick or time point selectors. They are therefore not allowed as wildcard characters in this context. Space and wildcard catenation can be put together. In such a case, spaces divide the input into wildcard pieces, which are processed individually. Examples (each is processed as a single, combined dataset): 'dset1+tlrc dset2+tlrc dset3+tlrc' 'dset1+tlrc dset2+tlrc dset3+tlrc[2,5,3]' 'dset1+tlrc[3] dset2+tlrc[0,1] dset3+tlrc[3,0,1]' 'dset*+tlrc.HEAD' 'dset*+tlrc.HEAD[2,5,3]' 'dset1*+tlrc.HEAD[0,1] dset2*+tlrc.HEAD[7,8]' 'group.*/subj.*/stats*+tlrc.HEAD[7]' CALCULATED DATASETS ------------------- Datasets may also be specified as runtime-generated results from program 3dcalc. This type of dataset specifier is enclosed in quotes, and starts with the string '3dcalc(': '3dcalc( opt opt ... opt )' where each 'opt' is an option to program 3dcalc; this program is run to generate a dataset in the directory given by environment variable TMPDIR (default=/tmp). This dataset is then read into memory, locked in place, and deleted from disk. For example afni -dset '3dcalc( -a r1+orig -b r2+orig -expr 0.5*(a+b) )' will let you look at the average of datasets r1+orig and r2+orig. N.B.: using this dataset input method will use lots of memory! ------------------------------- GENERAL OPTIONS (for any usage) ------------------------------- -papers Prints out the list of AFNI papers, and exits. -q Tells afni to be 'quiet' on startup -Dname=val Sets environment variable 'name' to 'val' inside AFNI; will supersede any value set in .afnirc. -gamma gg Tells afni that the gamma correction factor for the monitor is 'gg' (default gg is 1.0; greater than 1.0 makes the image contrast larger -- this may also be adjusted interactively) -install Tells afni to install a new X11 Colormap. This only means something for PseudoColor displays. Also, it usually cause the notorious 'technicolor' effect. -ncolors nn Tells afni to use 'nn' gray levels for the image displays (default is 80) -xtwarns Tells afni to show any Xt warning messages that may occur; the default is to suppress these messages. -XTWARNS Trigger a debug trace when an Xt warning happens. -tbar name Uses 'name' instead of 'AFNI' in window titlebars. -flipim and The '-flipim' option tells afni to display images in the -noflipim 'flipped' radiology convention (left on the right). The '-noflipim' option tells afni to display left on the left, as neuroscientists generally prefer. This latter mode can also be set by the Unix environment variable 'AFNI_LEFT_IS_LEFT'. The '-flipim' mode is the default. -trace Turns routine call tracing on, for debugging purposes. -TRACE Turns even more verbose tracing on, for more debugging. -motif_ver Show the applied motif version string. -no_detach Do not detach from the terminal. -no_frivolities Turn of all frivolities/fun stuff. -get_processed_env Show applied AFNI/NIFTI environment variables. -global_opts Show options that are global to all AFNI programs. -goodbye [n] Print a 'goodbye' message and exit (just for fun). If an integer is supplied afterwards, will print that many (random) goodbye messages. -startup [n] Similar to '-goodbye', but for startup tips. [If you want REAL fun, use '-startup ALL'.] -julian Print out the current Julian date and exit. -ver Print the current AFNI version and compile date, then exit. Useful to check how up-to-date you are (or aren't). -vnum Print just the current AFNI version number (i.e., AFNI_A.B.C), then exit. -package Print just the current AFNI package (i.e., linux_ubuntu_12_64, macos_10.12_local, etc.), then exit. -tips Print the tips for the GUI, such as key presses and other useful advice. This is the same file that would be displayed with the 'AFNI Tips' button in the GUI controller. Exit after display. -env Print the environment variables for AFNI, which a user might set in their ~/.afnirc file (wait, you *do* have one on your computer, right?). Exit after display. N.B.: Many of these options, as well as the initial color set up, can be controlled by appropriate X11 resources. See the files AFNI.Xdefaults and README.environment for instructions and examples. ----------------------------------------------------------- Options that affect X11 Display properties: '-XXXsomething' ----------------------------------------------------------- My intent with these options is that you use them in aliases or shell scripts, to let you setup specific appearances for multiple copies of AFNI. For example, put the following command in your shell startup file (e.g., ~/.cshrc or ~/.bashrc) alias ablue afni -XXXfgcolor white -XXXbgcolor navyblue Then the command 'ablue' will start AFNI with a blue background and using white for the default text color. Note that these options set 'properties' on the X11 server, which might survive after AFNI exits (especially if AFNI crashes). If for some reason these settings cause trouble after AFNI exits, use the option '-XXX defaults' to reset the X11 properties for AFNI back to their default values. Also note that each option is of the form '-XXXsomething', followed by a single argument. -XXXfgcolor colorname = set the 'foreground' color (text color) to 'colorname' [default = yellow] ++ This should be a bright color, to contrast the background color. ++ You can find a list of X11 color names at https://en.wikipedia.org/wiki/X11_color_names However, if you use a name like Dark Cyan (with a space inside the name), you must put the name in quotes: 'Dark Cyan', or remove the space: DarkCyan. ++ Another way to specify X11 colors is in hexadecimal, as in '#rgb' or '#rrggbb', where the letters shown are replaced by hex values from 0 to f. For example, '#ffcc00' is an orange-yellow mixture. -XXXbgcolor colorname = set the 'background' color to 'colorname' [default = gray22] ++ This should be a somewhat dark color, or parts of the interface may be hard to read. ++ EXAMPLE: afni -XXXfgcolor #00ffaa -XXXbgcolor #330000 -plus You can create command aliases to open AFNI with different color schemes, to make your life simpler. -XXXfontsize plus = set all the X11 fonts used by AFNI to be one *OR* size larger ('plus') or to be one size smaller -XXXfontsize minus ('minus'). The 'plus' version I find useful for *OR* a screen resolution of about 100 dots per inch -XXXfontsize big (40 dots per cm) -- you can find what the system *OR* thinks your screen resolution is by the command -big xdpyinfo | grep -i resolution *OR* ++ Applying 'plus' twice is the same as 'big'. -plus ++ Using 'big' will use large Adobe Courier fonts. *OR* ++ Alternatively, you can control each of the 4 fonts -minus that AFNI uses, via the 4 following options ... *OR* ++ You can also set the fontsize for your copy -norm of AFNI in your ~/.afnirc file by setting environment variable AFNI_FONTSIZE to one of: big *OR* minus *or* plus ++ Using 'norm' gives the default AFNI font sizes. -XXXfontA fontname = set the X11 font name for the main AFNI controller [default = 9x15bold] ++ To see a list of all X11 font names, type the command xlsfonts | more *or* more elaborately (to show only fixed width fonts): xlsfonts | grep -e '-[cm]-' | grep -e '-iso8859-1$' | grep -e '-medium-' \ | grep -e '-r-normal-' | grep -v -e '-0-0-' | sort -t '-' -k 8 -n | uniq ++ It is best to use a fixed width font (e.g., not Helvetica), or the AFNI buttons won't line up nicely! ++ If you use an illegal font name here, you might make it hard to use the AFNI GUI! ++ The default fonts are chosen for 'normal' screen resolutions (about 72 dots per inch = 28 dots per cm). For higher resolutions ('Retina'), you might want to use larger fonts. Adding these '-XXXfont?' options is one way to address this problem. (Also see '-plus' above.) ++ An example of two quite large fonts on my computer (which at this time has a 108 dot per inch display): '-adobe-courier-bold-r-normal--34-240-100-100-m-200-iso8859-1 '-b&h-lucidatypewriter-medium-r-normal-sans-34-240-100-100-m-200-iso8859-1' Note that to use the latter font on the command line, you have to enclose the name in quotes, as shown above, since the 'foundry name' includes the character '&'. To use it in an alias, you need to do something like alias abig -XXXfontA '-b\&h-lucidatypewriter-medium-r-normal-sans-34-240-100-100-m-200-iso8859-1' ++ When setting the fonts, it is often helpful to set the colors as well. -XXXfontB fontname = set the X11 font name for somewhat smaller text [default = 8x13bold] -XXXfontC fontname = set the X11 font name for even smaller text [default = 7x13] -XXXfontD fontname = set the X11 font name for the smallest text [default = 6x10] -XXX defaults = set the X11 properties to the AFNI defaults (the purpose of this is to restore things ) (to normal if the X11 settings get mangled) -XXXnpane P = set the number of 'panes' in the continuous colorscale to the value 'P', where P is an even integer between 256 and 2048 (inclusive). Probably will work best if P is an integral multiple of 256 (e.g., 256, 512, 1024, 2048). [This option is for the mysterious Dr ZXu.] -------------------------------------- Educational and Informational Material -------------------------------------- * The presentations used in our AFNI teaching classes at the NIH can all be found at https://afni.nimh.nih.gov/pub/dist/edu/latest/ (PowerPoint directories) https://afni.nimh.nih.gov/pub/dist/edu/latest/afni_handouts/ (PDF directory) * And for the interactive AFNI program in particular, see https://afni.nimh.nih.gov/pub/dist/edu/latest/afni01_intro/afni01_intro.pdf https://afni.nimh.nih.gov/pub/dist/edu/latest/afni03_interactive/afni03_interactive.pdf * For the -help on all AFNI programs, plus the README files, and more, please see https://afni.nimh.nih.gov/pub/dist/doc/program_help/index.html * For indvidualized help with AFNI problems, and to keep up with AFNI news, please use the AFNI Message Board: https://discuss.afni.nimh.nih.gov * If an AFNI program crashes, please include the EXACT error messages it outputs in your message board posting, as well as any other information needed to reproduce the problem. Just saying 'program X crashed, what's the problem?' is not helpful at all! In all message board postings, detail and context are highly relevant. * Also, be sure your AFNI distribution is up-to-date. You can check the date on your copy with the command 'afni -ver'. If it is more than a few months old, you should update your AFNI binaries and try the problematic command again -- it is quite possible the problem you encountered was already fixed! **************************************************** ***** This is a list of papers about AFNI, SUMA, ***** ****** and various algorithms implemented therein ****** ---------------------------------------------------------------------------- RW Cox. AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29: 162-173, 1996. * The very first AFNI paper, and the one I prefer you cite if you want to refer to the AFNI package as a whole. * https://sscc.nimh.nih.gov/sscc/rwcox/papers/CBM_1996.pdf ---------------------------------------------------------------------------- RW Cox, A Jesmanowicz, JS Hyde. Real-time functional magnetic resonance imaging. Magnetic Resonance in Medicine, 33: 230-236, 1995. * The first paper on realtime FMRI; describes the algorithm used in in the realtime plugin for time series regression analysis. * https://sscc.nimh.nih.gov/sscc/rwcox/papers/Realtime_FMRI.pdf ---------------------------------------------------------------------------- RW Cox, JS Hyde. Software tools for analysis and visualization of FMRI Data. NMR in Biomedicine, 10: 171-178, 1997. * A second paper about AFNI and design issues for FMRI software tools. ---------------------------------------------------------------------------- RW Cox, A Jesmanowicz. Real-time 3D image registration for functional MRI. Magnetic Resonance in Medicine, 42: 1014-1018, 1999. * Describes the algorithm used for image registration in 3dvolreg and in the realtime plugin. * The first paper to demonstrate realtime MRI volume image registration running on a standard workstation (not a supercomputer). * https://sscc.nimh.nih.gov/sscc/rwcox/papers/RealtimeRegistration.pdf ---------------------------------------------------------------------------- ZS Saad, KM Ropella, RW Cox, EA DeYoe. Analysis and use of FMRI response delays. Human Brain Mapping, 13: 74-93, 2001. * Describes the algorithm used in 3ddelay (cf. '3ddelay -help'). * https://sscc.nimh.nih.gov/sscc/rwcox/papers/Delays2001.pdf ---------------------------------------------------------------------------- ZS Saad, RC Reynolds, BD Argall, S Japee, RW Cox. SUMA: An interface for surface-based intra- and inter-subject analysis within AFNI. 2004 IEEE International Symposium on Biomedical Imaging: from Nano to Macro. IEEE, Arlington VA, pp. 1510-1513. * A brief description of SUMA. * https://dx.doi.org/10.1109/ISBI.2004.1398837 * https://sscc.nimh.nih.gov/sscc/rwcox/papers/SUMA2004paper.pdf ---------------------------------------------------------------------------- ZS Saad, G Chen, RC Reynolds, PP Christidis, KR Hammett, PSF Bellgowan, RW Cox. FIAC Analysis According to AFNI and SUMA. Human Brain Mapping, 27: 417-424, 2006. * Describes how we used AFNI to analyze the FIAC contest data. * https://dx.doi.org/10.1002/hbm.20247 * https://sscc.nimh.nih.gov/sscc/rwcox/papers/FIAC_AFNI_2006.pdf ---------------------------------------------------------------------------- BD Argall, ZS Saad, MS Beauchamp. Simplified intersubject averaging on the cortical surface using SUMA. Human Brain Mapping 27: 14-27, 2006. * Describes the 'standard mesh' surface approach used in SUMA. * https://dx.doi.org/10.1002/hbm.20158 * https://sscc.nimh.nih.gov/sscc/rwcox/papers/SUMA2006paper.pdf ---------------------------------------------------------------------------- ZS Saad, DR Glen, G Chen, MS Beauchamp, R Desai, RW Cox. A new method for improving functional-to-structural MRI alignment using local Pearson correlation. NeuroImage 44: 839-848, 2009. * Describes the algorithm used in 3dAllineate (and thence in align_epi_anat.py) for EPI-to-structural volume image registration. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2649831/ * https://dx.doi.org/10.1016/j.neuroimage.2008.09.037 * https://sscc.nimh.nih.gov/sscc/rwcox/papers/LocalPearson2009.pdf ---------------------------------------------------------------------------- H Sarin, AS Kanevsky, SH Fung, JA Butman, RW Cox, D Glen, R Reynolds, S Auh. Metabolically stable bradykinin B2 receptor agonists enhance transvascular drug delivery into malignant brain tumors by increasing drug half-life. Journal of Translational Medicine, 7: #33, 2009. * Describes the method used in AFNI for modeling dynamic contrast enhanced (DCE) MRI for analysis of brain tumors. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2689161/ * https://dx.doi.org/10.1186/1479-5876-7-33 ---------------------------------------------------------------------------- HJ Jo, ZS Saad, WK Simmons, LA Milbury, RW Cox. Mapping sources of correlation in resting state FMRI, with artifact detection and removal. NeuroImage, 52: 571-582, 2010. * Describes the ANATICOR method for de-noising FMRI datasets. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2897154/ * https://dx.doi.org/10.1016/j.neuroimage.2010.04.246 ---------------------------------------------------------------------------- A Vovk, RW Cox, J Stare, D Suput, ZS Saad. Segmentation Priors From Local Image Properties: Without Using Bias Field Correction, Location-based Templates, or Registration. Neuroimage, 55: 142-152, 2011. * Describes the earliest basis for 3dSeg. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3031751/ * https://dx.doi.org/10.1016/j.neuroimage.2010.11.082 ---------------------------------------------------------------------------- G Chen, ZS Saad, DR Glen, JP Hamilton, ME Thomason, IH Gotlib, RW Cox. Vector Autoregression, Structural Equation Modeling, and Their Synthesis in Neuroimaging Data Analysis. Computers in Biology and Medicine, 41: 1142-1155, 2011. * Describes the method implemented in 1dSVAR (Structured Vector AutoRegression). * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3223325/ * https://dx.doi.org/10.1016/j.compbiomed.2011.09.004 ---------------------------------------------------------------------------- RW Cox. AFNI: what a long strange trip it's been. NeuroImage, 62: 747-765, 2012. * A Brief History of AFNI, from its inception to speculation about the future. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3246532/ * https://dx.doi.org/10.1016/j.neuroimage.2011.08.056 ---------------------------------------------------------------------------- ZS Saad, RC Reynolds. SUMA. Neuroimage. 62: 768-773, 2012. * The biography of SUMA. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3260385/ * https://dx.doi.org/10.1016/j.neuroimage.2011.09.016 ---------------------------------------------------------------------------- G Chen, ZS Saad, AR Nath, MS Beauchamp, RW Cox. FMRI Group Analysis Combining Effect Estimates and Their Variances. Neuroimage, 60: 747-765, 2012. * The math behind 3dMEMA (Mixed Effects Meta-Analysis) -- AKA super-3dttest. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3404516/ * https://dx.doi.org/10.1016/j.neuroimage.2011.12.060 ---------------------------------------------------------------------------- ZS Saad, SJ Gotts, K Murphy, G Chen, HJ Jo, A Martin, RW Cox. Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression. Brain Connectivity, 2: 25-32, 2012. * Our first paper on why Global Signal Regression in resting state FMRI is a bad idea when doing any form of group analysis. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3484684/ * https://dx.doi.org/10.1089/brain.2012.0080 ---------------------------------------------------------------------------- SJ Gotts, WK Simmons, LA Milbury, GL Wallace, RW Cox, A Martin. Fractionation of Social Brain Circuits in Autism Spectrum Disorders. Brain, 135: 2711-2725, 2012. * In our humble opinion, this shows how to use resting state FMRI correctly when making inter-group comparisons (hint: no global signal regression is used). * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3437021/ * https://dx.doi.org/10.1093/brain/aws160 ---------------------------------------------------------------------------- HJ Jo, ZS Saad, SJ Gotts, A Martin, RW Cox. Quantifying Agreement between Anatomical and Functional Interhemispheric Correspondences in the Resting Brain. PLoS ONE, 7: art.no. e48847, 2012. * A numerical method for measuring symmetry in brain functional imaging data. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3493608/ * https://dx.doi.org/10.1371/journal.pone.0048847 ---------------------------------------------------------------------------- ZS Saad, SJ Gotts, K Murphy, G Chen, HJ Jo, A Martin, RW Cox. Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression. Brain Connectivity, 2012: 25-32. * Another paper in the battle against Global Signal Regression. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3484684/ * https://dx.doi.org/10.1089/brain.2012.0080 ---------------------------------------------------------------------------- G Chen, ZS Saad, JC Britton, DS Pine, RW Cox Linear mixed-effects modeling approach to FMRI group analysis. NeuroImage, 73: 176-190, 2013. * The math behind 3dLME. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3404516/ * https://dx.doi.org/10.1016/j.neuroimage.2011.12.060 ---------------------------------------------------------------------------- SJ Gotts, ZS Saad, HJ Jo, GL Wallace, RW Cox, A Martin. The perils of global signal regression for group comparisons: A case study of Autism Spectrum Disorders. Frontiers in Human Neuroscience: art.no. 356, 2013. * The long twilight struggle against Global Signal Regression continues. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3709423/ * https://dx.doi.org/10.3389/fnhum.2013.00356 ---------------------------------------------------------------------------- HJ Jo, SJ Gotts, RC Reynolds, PA Bandettini, A Martin, RW Cox, ZS Saad. Effective preprocessing procedures virtually eliminate distance-dependent motion artifacts in resting state FMRI. Journal of Applied Mathematics: art.no. 935154, 2013. * A reply to the Power 2012 paper on pre-processing resting state FMRI data, showing how they got it wrong. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3886863/ * https://dx.doi.org/10.1155/2013/935154 ---------------------------------------------------------------------------- SJ Gotts, HJ Jo, GL Wallace, ZS Saad, RW Cox, A Martin. Two distinct forms of functional lateralization in the human brain. PNAS, 110: E3435-E3444, 2013. * More about methodology and results for symmetry in brain function. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3767540/ * https://dx.doi.org/10.1073/pnas.1302581110 ---------------------------------------------------------------------------- ZS Saad, RC Reynolds, HJ Jo, SJ Gotts, G Chen, A Martin, RW Cox. Correcting Brain-Wide Correlation Differences in Resting-State FMRI. Brain Connectivity, 2013: 339-352. * Just when you thought it was safe to go back into the waters of resting state FMRI, another paper explaining why global signal regression is a bad idea and a tentative step towards a different solution. * https://www.ncbi.nlm.nih.gov/pubmed/23705677 * https://dx.doi.org/10.1089/brain.2013.0156 ---------------------------------------------------------------------------- P Kundu, ND Brenowitz, V Voon, Y Worbe, PE Vertes, SJ Inati, ZS Saad, PA Bandettini, ET Bullmore. Integrated strategy for improving functional connectivity mapping using multiecho fMRI. PNAS 110: 16187-16192, 2013. * A data acquistion and processing strategy for improving resting state FMRI. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3791700/ * https://dx.doi.org/10.1073/pnas.1301725110 ---------------------------------------------------------------------------- PA Taylor, ZS Saad. FATCAT: (An Efficient) Functional And Tractographic Connectivity Analysis Toolbox. Brain Connectivity 3:523-535, 2013. * Introducing diffusion-based tractography tools in AFNI, with particular emphases on complementing FMRI analysis and in performing interactive visualization with SUMA. * https://www.ncbi.nlm.nih.gov/pubmed/23980912 * https://dx.doi.org/10.1089/brain.2013.0154 ---------------------------------------------------------------------------- G Chen, NE Adleman, ZS Saad, E Leibenluft, RW Cox. Applications of multivariate modeling to neuroimaging group analysis: A comprehensive alternative to univariate general linear model. NeuroImage 99:571-588, 2014. * The fun stuff behind 3dMVM == more complex linear modeling for groups. * https://dx.doi.org/10.1016/j.neuroimage.2014.06.027 * https://sscc.nimh.nih.gov/pub/dist/doc/papers/3dMVM_2014.pdf ---------------------------------------------------------------------------- Taylor PA, Chen G, Cox RW, Saad ZS. Open Environment for Multimodal Interactive Connectivity Visualization and Analysis. Brain Connectivity 6(2):109-21, 2016. * Visualization and MVM stats tools using tracking (or even functional connectivity). * https://dx.doi.org/10.1089/brain.2015.0363 * https://sscc.nimh.nih.gov/pub/dist/papers/ASF_2015_draft_BCinpress.pdf ---------------------------------------------------------------------------- G Chen, Y-W Shin, PA Taylor, DR GLen, RC Reynolds, RB Israel, RW Cox. Untangling the relatedness among correlations, part I: Nonparametric approaches to inter-subject correlation analysis at the group level. NeuroImage 142:248-259, 2016. Proper statistical analysis (FPR control) when correlating FMRI time series data amongst multiple subjects, using nonparametric methods. * https://doi.org/10.1016/j.neuroimage.2016.05.023 ---------------------------------------------------------------------------- G Chen, PA Taylor, Y-W Shin, RC Reynolds, RW Cox. Untangling the relatedness among correlations, Part II: Inter-subject correlation group analysis through linear mixed-effects modeling. NeuroImage 147:825-840 2017. * Just when you thought it was safe to go back into the brain data: this time, using parametric methods. * https://doi.org/10.1016/j.neuroimage.2016.08.029 ---------------------------------------------------------------------------- G Chen, PA Taylor, X Qu, PJ Molfese, PA Bandettini, RW Cox, ES Finn. Untangling the relatedness among correlations, part III: Inter-subject correlation analysis through Bayesian multilevel modeling for naturalistic scanning. NeuroImage, 2019. * https://doi.org/10.1016/j.neuroimage.2019.116474 * https://www.ncbi.nlm.nih.gov/pubmed/31884057 * https://www.biorxiv.org/content/10.1101/655738v1.full ---------------------------------------------------------------------------- RW Cox, G Chen, DR Glen, RC Reynolds, PA Taylor. fMRI clustering and false-positive rates. PNAS 114:E3370-E3371, 2017. * Response to Eklund's (et al.) paper about clustering in PNAS 2016. * https://arxiv.org/abs/1702.04846 * https://doi.org/10.1073/pnas.1614961114 ---------------------------------------------------------------------------- RW Cox, G Chen, DR Glen, RC Reynolds, PA Taylor. FMRI Clustering in AFNI: False Positive Rates Redux. Brain Connectivity 7:152-171, 2017. * A discussion of the cluster-size thresholding updates made to AFNI in early 2017. * https://arxiv.org/abs/1702.04845 * https://doi.org/10.1089/brain.2016.0475 ---------------------------------------------------------------------------- S Song, RPH Bokkers, MA Edwardson, T Brown, S Shah, RW Cox, ZS Saad, RC Reynolds, DR Glen, LG Cohen LG, LL Latour. Temporal similarity perfusion mapping: A standardized and model-free method for detecting perfusion deficits in stroke. PLoS ONE 12, Article number e0185552, 2017. * Applying AFNI's InstaCorr module to stroke perfusion mapping. * https://doi.org/10.1371/journal.pone.0185552 * https://www.ncbi.nlm.nih.gov/pubmed/28973000 ---------------------------------------------------------------------------- G Chen, PA Taylor, SP Haller, K Kircanski, J Stoddard, DS Pine, E Leibenluft, Brotman MA, RW Cox. Intraclass correlation: Improved modeling approaches and applications for neuroimaging. Human Brain Mapping, 39:1187-1206 2018. * Discussion of ICC methods, and distinctions among them. * https://doi.org/10.1002/hbm.23909 * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5807222/ ---------------------------------------------------------------------------- PA Taylor, G Chen, DR Glen, JK Rajendra, RC Reynolds, RW Cox. FMRI processing with AFNI: Some comments and corrections on 'Exploring the Impact of Analysis Software on Task fMRI Results'. * https://www.biorxiv.org/content/10.1101/308643v1.abstract * https://doi.org/10.1101/308643 ---------------------------------------------------------------------------- RW Cox. Equitable Thresholding and Clustering: A Novel Method for Functional Magnetic Resonance Imaging Clustering in AFNI. Brain Connectivity 9:529-538, 2019. * https://doi.org/10.1089/brain.2019.0666 ---------------------------------------------------------------------------- G Chen, RW Cox, DR Glen, JK Rajendra, RC Reynolds, PA Taylor. A tail of two sides: Artificially doubled false positive rates in neuroimaging due to the sidedness choice with t-tests. Human Brain Mapping 40:1037-1043, 2019. * https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6328330/ * https://dx.doi.org/10.1002/hbm.24399 ---------------------------------------------------------------------------- G Chen, Y Xiao, PA Taylor, JK Rajendra, T Riggins, F Geng, E Redcay, RW Cox. Handling Multiplicity in Neuroimaging Through Bayesian Lenses with Multilevel Modeling. Neuroinformatics 17:515-545, 2019. * https://link.springer.com/article/10.1007/s12021-018-9409-6 * https://www.biorxiv.org/content/10.1101/238998v1.abstract ---------------------------------------------------------------------------- DR Glen, PA Taylor, BR Buchsbaum, RW Cox, and RC Reynolds. Beware (Surprisingly Common) Left-Right Flips in Your MRI Data: An Efficient and Robust Method to Check MRI Dataset Consistency Using AFNI. Frontiers in Neuroinformatics, 25 May 2020. * https://doi.org/10.3389/fninf.2020.00018 * https://www.medrxiv.org/content/10.1101/19009787v4 ---------------------------------------------------------------------------- V Roopchansingh, JJ French Jr, DM Nielson, RC Reynolds, DR Glen, P D’Souza, PA Taylor, RW Cox, AE Thurm. EPI Distortion Correction is Easy and Useful, and You Should Use It: A case study with toddler data. * https://www.biorxiv.org/content/10.1101/2020.09.28.306787v1 ---------------------------------------------------------------------------- G Chen, TA Nash, KM Cole, PD Kohn, S-M Wei, MD Gregory, DP Eisenberg, RW Cox, KF Berman, JS Kippenham. Beyond linearity in neuroimaging: Capturing nonlinear relationships with application to longitudinal studies. * https://doi.org/10.1016/j.neuroimage.2021.117891 * https://pubmed.ncbi.nlm.nih.gov/33667672/ ---------------------------------------------------------------------------- POSTERS on varied subjects from the AFNI development group can be found at * https://afni.nimh.nih.gov/sscc/posters SLIDE IMAGES to help with learning the AFNI GUI can be found at * https://afni.nimh.nih.gov/pub/dist/doc/program_help/images/afni03/