spm.man

% Statistical Parametric Mapping - SPM99b
% SPM99 is the final public release of SPM'99   -   released 25/01/2000
% ______________________________________________________________________
% 
%  ___  ____  __  __
% / __)(  _ \(  \/  )  
% \__ \ )___/ )    (   Statistical Parametric Mapping
% (___/(__)  (_/\/\_)  SPM - http://www.fil.ion.ucl.ac.uk/spm
%
%    John Ashburner, Karl Friston, Andrew Holmes, Jean-Baptiste Poline
% ______________________________________________________________________
%
%    Statistical Parametric Mapping refers to the construction and
%    assessment of spatially extended statistical process used to test
%    hypotheses about [neuro]imaging data from SPECT/PET & fMRI. These
%    ideas have been instantiated in software that is called SPM.
%    This software also deals with other issues in image analysis
%    such as spatial registration and normalisation problems.
%
%                           ----------------
%
% Please refer to this version as "SPM99" in papers and communications.
%
%                           ----------------
% Contents:
%      1) SPM - The software
%      2) New features in SPM99
%      3) SPM99b: Updates since the beta release
%      4) SPM Toolboxes
% 
%
% ======================================================================
% 1)               S P M   -   T h e   s o f t w a r e
% ======================================================================
%
% SPM was written to organise and interpret our data (at the Wellcome
% Department of Cognitive Neurology, and previously at the MRC
% Cyclotron Unit, London UK). The distributed version is the same as
% that we use ourselves.
%
% SPM is made freely available to the [neuro]imaging community, to
% promote collaboration and a common analysis scheme across
% laboratories.
% 
% ______________________________________________________________________
%                                                                Authors
%
% SPM is developed under the auspices of The Wellcome Department of
% Cognitive Neurology, a department of the Institute of Neurology at
% University College London.
%
% SPM94 was written primarily by Karl Friston in the first half of
% 1994, with assistance from John Ashburner (MRC-CU), Jon Heather
% (WDoCN), and Andrew Holmes (Department of Statistics, University of
% Glasgow). Subsequent development, under the direction of Prof. Karl
% Friston at the Wellcome Department of Cognitive Neurology, has
% benefited from substantial input (technical and theoretical) from:
% John Ashburner (WDoCN), Andrew Holmes (WDoCN & Robertson Centre for
% Biostatistics, University of Glasgow, Scotland), Jean-Baptiste Poline
% (WDoCN & CEA/DRM/SHFJ, Orsay, France), Christian Buechel (WDoCN),
% Matthew Brett (MRC-CBU, Cambridge, England), Chloe Hutton (WDoCN) and
% Keith Worsley (Department of Statistics, McGill University, Montreal,
% Canada).
%
% SPM99 was developed by: John Ashburner (spatial, image handling),
% Karl Friston (project director, fMRI design), Andrew Holmes (project
% management, statistics, user interfaces), Jean-Baptiste Poline
% (statistics, batch system (with Stephanie Rouquette)), with
% contributions from Matthew Brett (WinNT implementation, misc. stats),
% and theoretical input from Keith Worsley.
%
% We would like to thank everyone who has provided feedback on SPM.
%
% We envisage that this software will be used in a diverse number of
% ways.  Although SPM has grown out of a PET background, it is now an
% established package for the analysis of fMRI data, and is also used
% for structural data.
%
% ______________________________________________________________________
%                                                              Resources
% 
% The SPMweb site is the central repository for SPM resources:
%                  http://www.fil.ion.ucl.ac.uk/spm
% Introductory material, installation details, documentation, course
% details and patches are published on the site.
%
% There is an SPM eMail discussion list, hosted at
% <spm@mailbase.ac.uk>.  The list is monitored by the authors, and
% discusses theoretical, methodological and practical issues of
% Statistical Parametric Mapping and SPM. Subscribe by sending the one
% line message: "join spm firstname lastname" to
% <mailbase@mailbase.ac.uk>. (Users at NIH or UC-Davis should join
% their local SPM feeds.) The SPMweb site has further details:
%                  http://www.fil.ion.ucl.ac.uk/spm/help
% 
%                           ----------------
%
% Unfortunately we have insufficient resources to support SPM as a
% commercial package. Thus, there is no user guide for SPM. However,
% the software itself offers help and documentation at three levels,
% described in spm_help.m
%
% In order to use the advanced spatial, statistical modelling and
% inference tools of SPM, it is vital to have at least a conceptual
% understanding of the theoretical underpinnings. Therefore, we
% recommend the theoretical articles published in the peer reviewed
% literature, and the SPMcourse notes (avaiable from the SPMweb site).
%
%                           ----------------
%
% Please report bugs to the authors at <spm-authors@fil.ion.ucl.ac.uk>
% Peculiarities may actually be features(!), and should be raised on the
% SPM eMail discussion list, <spm@mailbase.ac.uk>.
% ______________________________________________________________________
%                                                   The SPM distribution
%
% The SPM software is a suite of MATLAB functions, scripts and data
% files, with some externally compiled C routines, implementing
% Statistical Parametric Mapping. MATLAB, a commercial engineering
% mathematics package, is required to use SPM. MATLAB is produced by The
% MathWorks, Inc.  Natick, MA, USA. http://www.mathworks.com/
% eMail:info@mathworks.com. SPM requires only core MATLAB to run (no
% special toolboxes are required).
% 
% SPM99 is written for Matlab 5.2.1 under UNIX, LINUX and
% Windows(95/98/NT). (SPM will not work with versions of Matlab 5 prior
% to 5.2.1, including Matlab 4.)
% 
% The current version was developed and tested on 200MHz UltraSPARC 2
% workstations (128Mb RAM, 200Mb swap) under Solaris2.5.1
% (OpenWindows3.5.1) using MATLAB version 5.2.1.
%
% Binaries of the external C-mex routines are provided for Solaris,
% Linux and Windows only, users of other UNIX platforms need an ANSI C
% compiler to compile the supplied C source (Makefile provided:
% spm_MAKE). See http://www.fil.ion.ucl.ac.uk/spm/distrib.html for
% additional details.
% ______________________________________________________________________
%                                                  Copyright & licencing
% 
% SPM (being the collection of files given in the manifest in the
% Contents.m file) is free but copyright software, distributed under
% the terms of the GNU General Public Licence as published by the Free
% Software Foundation (either version 2, as given in file
% spm_LICENCE.man, or at your option, any later version). Further
% details on "copyleft" can be found at http://www.gnu.org/copyleft/.
%
% SPM is supplied as is.
% No formal support or maintenance is provided or implied.
% ______________________________________________________________________
%                                                           File formats
%
% The various file types included in SPM are:
% 
% spm_*.m files: ASCII files that form the main structure for SPM. 
%       Most of SPM is written as MatLab functions. These are compiled
%       at their first invocation, leading to a slight delay in the
%       startup of some routines. MatLab script files are occasionally
%       used. These are interpreted by MATLAB, but have the advantage
%       of working in the base MatLab workspace, such that their
%       results are available to the user after completion.
%
% Clearly MatLab is slower than writing everything in fully optimised
% C; however the fundamental advantage of having a didactic pseudo-code
% specification of this sort is preferred over implementational
% efficacy. Further, MatLab *is* optimised for matrix and vector
% operations, which are utilised whenever possible.
% 
% spm_*.c:       ASCII files that are complied in a MATLAB-specific
%       fashion to produce programs that can be called directly from
%       MATLAB.  Once compiled these routines are suffixed in a
%       platform dependent fashion (e.g. spm_*.mexsol or mexlx).  These
%       routines implement memory mapping and some numerical and image
%       operations that are called frequently.  Precompiled Mex files
%       are provided for Solaris2, Linux and Windows platforms, a
%       Makefile is included for other platforms.
% 
% spm_*.man:     ASCII files containing manual pages.
% 
% *.mat          MATLAB specific data files that can be loaded directly
%       into MATLAB. These files contain images and other data in matrix
%       format, usually in double precision (see MATLAB user's guide)
% 
% Where possible the user interface and computational or analytical
% aspects of the software have been segregated such that spm_*_ui.m
% sets up the user interface and assembles the appropriate input
% arguments for spm_*.m.  spm_*.m contains the statistical and
% mathematical implementation of a generic nature.  spm_*.m would be of
% greater interest to those whose wish to incorporate SPM into an
% existing package with its own 'front end'.
% 
% ______________________________________________________________________
%                                                          Image formats
%
% SPM uses the simple header and flat binary image file format of
% ANALYZE-7 (Mayo Clinic, Rochester, USA.) http://www.mayo.edu/bir/),
% with slight customisations to the header. See "Data Format" in the
% online help [spm_format.man]. It can also read MINC & ECAT-7 images.
%
% You will either need to convert your image files to one of these
% formats (preferably Analyze), or construct an additional module for
% the SPM memory mapping subsystem to read your file format. Image
% conversion utilities for your image file format may be available in
% other packages, or may have been specially written by other SPM
% users. (Consult the SPM email discussion list, described below, by
% first searching the archives, and posting a query if necessary.)
% Unfortunately we have no resources to provide image conversion
% software, although we will collaborate in developing SPM memory
% mapping read-modules for popular image formats for inclusion in SPM.
%
%
%
%
% ======================================================================
% 2)           N e w   f e a t u r e s   i n   S P M 9 9
% ======================================================================
%
% SPM99 is a major update to the SPM software, containing substantial
% theoretical, algorithmic, organisational and interface enhancements
% over previous versions.
% 
% ______________________________________________________________________
%                                                               OVERVIEW
%
% Spatial
%  - support for re-orienting images via their ".mat" files
%  - Realign
%        - handles multiple fMRI sessions
%        - two pass procedure for PET realignment (images registered to mean)
%        - checks for stopping criterion
%        - Fourier interpolation (for data with isotropic voxels)
%        - algorithm generally improved
%        - saves realignment parameters for use as confounds to stats
%  - Segment
%        - simultaneous nonuniformity correction
%        - fixed problem with prior probability images
%  - Coregister
%        - "tweaked" for slightly better results
%        - extended for other modalities
%  - Normalize
%        - affine part made more stable by using a MAP scheme
%        - improved regularization for nonlinear part
%        - optional weighting so that out of brain structures don't contribute
%        - new templates
%  - Slice timing
%        - temporal resampling of fMRI time series
%        - to correct differences in slice timing of 2D acquisitions
%  - Enhanced cortical surface extraction for rendering
%
% Volume I/O
%  - copes with byte-swapped Analyze format data
%  - re-working of memory mapping to overcome 2 or 4 Gbyte datasize limit
%  - modular image mapping code - support for reading CTI & MINC data
%
% Completely re-structured & re-written statistics section
%  - split into three stages: setup, estimation, and assessment (results)
%    - setup saves an SPMcfg.mat file which can be run in batch mode
%    - fMRI models can be re-used: Design information saved in SPM-fMRIDesMtx.mat
%  - estimation stage can be run in batch mode
%  - enhanced event related fMRI modelling
%  - band-pass filtering for fMRI time series
%  - intrinsic temporal autocorrelation estimation
%  - enhanced PET models
%    - subject separable ready for random effects
%    - time order entry of images in PET/SPECT & basic stats
%    - enter all scans for a subject, indicate conditions by "abababa"...
%    - design matrix shows actual experimental design
%    - easier enterng of covariates (don't have to reorder)
%    - customisable via documented "design definition structure"
%  - new "basic stats designs" - t-tests, Anova, regression &c
%  - implicit masking: Dummy value for out-of-brain voxels (0 or NaN)
%  - explicit masking: mask images
%  - absolute & proportional threshold masking
%  - user-defined globals (for clinical applications)
%  - supports masked data, explicit masks, and negative data
%  - efficient handling of design spaces via space structures (JBP)
%  - statistics results files are plain image files
%    - parameter estimates, variance estimates, contrasts and SPM's
%    - mask images, showing exactly the volume analyzed
%  - integrated random effects for balanced designs, via multi-level analyses
%  - contrasts entered after estimation (in the results section)
%  - new "contrast manager": contrast naming and validity checking
%  - "F-contrasts" (JBP) to specify F-tests (& hence two-sided T-tests)
%    - flexible specification of general linear hypotheses
%    - model comparison
%    - efficiently computed from projections of parameter estimates
%  - inference directly on t & F fields
%    - no "Gaussianisation" of statistic images
%    - tabular summaries give equivalent Z-values
%  - small volume corrections (Keith Worsleys' unified theory)
%  - new conjunctions formulation (as minimum field) - with K Worsley
%  - contrast orthogonalisation for conjunctions
%  - inclusive & exclusive masking (allowing old-style conjunctions)
%  - no "F-thresholding" of results saved for analysis
%    - results available at all voxels within 
%    - only of voxels whose data is saved for plotting
%  - raw data saved at F-significant voxels in compressed format for plotting
%    - much smaller stats files than old "XA.mat" files
%  - integrated interactive results, contrast and design interrogation section
%  - "surfable" design matrices
%  - clearer tabular output of p-values and statistics
%    - active table - click to extract data or jump to location
%    - ASCII text output of p-value & statistics table
%  - enhanced plotting routines, particularly for event-related fMRI
% ...bags of other stuff... (I can't remember what SPM96 did!)
%
% New utilities
%  - enhanced image algebra (ImCalc)
%    - powerful command -line options
%  - improved display facility - *.mat file sensitive
%  - CheckReg - clickable ortho projections of two images side by side
%  - improved rendering facility with cortical surface extraction utility
%  - brain extraction routine
%  - file and directory management from the GUI
%
% Enhanced GUI & command line use
%  - range checking of input
%  - context menus (right mouse button)
%  - command line input has access to base workspace
%  - assisted input of parameters from ASCII files
%  - neater "crashing out" of GUI i/o
%    - just press "Clear", or use "crash out" ContextMenus
%  - timestamping in command window
%  - dynamic progress reporting in command window
%  - more robust GUI / no-GUI handling
%  - additional input dialogs
%  - full command line support for question answering
%    - except results section, which is GUI orientated
%  - context help and ToolTips
%  - alerts & dialogs
%  - ToolTips
%  - context help for most functions
%  - enhanced graphics editing in SPM graphics window
%  - neater window handling
%
% Re-written for Matlab version 5 (5.2.1 or greater required)
%  - no additional toolboxes required
%  - simplified structure
%  - better documented interfacces and file formats
%  - numerous i/o, file & GUI handling primitives
%  - comprehensive statistical distribution functions
%
% Cross platform support
%  - Solaris, Linux & WindowsNT supported "out of the box"
%    ( *.mexsol, *.mexlx & *.dll's provided)
%  - makefile supports many other UNIX platforms
%
% ______________________________________________________________________
%                                          FILE FORMATS & MEMORY MAPPING
%
% The functions for reading and writing images have changed.  All writing
% of images is via the functions spm_create_vol and spm_write_plane (even
% mex routines now call these functions).
% 
% A handle for an image volume is now via the "spm_vol" function:
% 	V   = spm_vol('filename.img');
% The structure "V" is a structure or a matrix/cell-array of structures
% taking a similar form to the matrix/cell-array of strings that define
% the image file names.  The form of each element of V is described in
% spm_vol.m.  Sampling the image still involves passing the handle to
% spm_slice_vol, or spm_sample_vol.  Because of the limitation on the
% amount of virtual memory allowed for a process (either 2 or 4 Gbytes,
% depending on the OS), the actual mapping is done as the data is
% required, and not at the spm_vol stage. Because mapping is done on
% the fly  (from the image filename stored in the handle structure),
% handles from spm_vol.m can be saved and used across Matlab sessions
% (provided the files haven't moved).
% 
% A number of new data types are supported, including signed and unsigned
% versions of existing types, and also byte-swapped versions. Also, the
% volume no longer needs to be contiguous, and each slice can have
% different scalefactor and offset, so formats such as ECAT 6 could in
% theory be supported.
% 
% Byte-swapped Analyze files can now be read, so mixing different computer
% architectures on the same network should be less of a problem.
% 
% ECAT 7 files can now be read, providing that they only contain a single
% frame (matrix number 1010001).  Some MINC files can also be read,
% although I'm not sure about all the different possible ways of
% representing images in the MINC format.  I'm not 100% sure about the
% MINC stuff, so it may be worth checking this out properly.  Full support
% for many other formats could be added by modifying:
% spm_vol.m, spm_get_space.m, spm_create_vol.m and spm_write_plane.m.
% 
% ______________________________________________________________________
%                                                                SPATIAL
%
% * Realign
%
% A number of modifications have been made to the realignment module.
% Many of these reflect the questions asked on the SPM discussion list.
% The realignment can be thought of as three components: parameter
% estimation, image resampling and correction of motion artifacts
% (adjustment).
% 
% fMRI sessions are now handled differently, because of the assumption
% that there may be systematic differences between the images in
% different sessions.  The first volume of each session is now aligned
% to the first volume of the first session.  Subsequent volumes in each
% session are then aligned to the first volume of the session. This should
% acheive increased accuracy within session, since all the images are
% aligned to an image from the same session.  This also saves time,
% because subject movement between sessions tends to be larger than
% subject movement within session.  The large systematic differences
% between sessions are therefore removed in the first realignment step.
% Adjustment is performed separately for each session, but the same mask
% for writing the realigned images is used for all images of all sessions.
%  
% PET realignment is now a two pass procedure.  The first pass aligns all
% the images to the first image in the series.  A mean of the realigned
% images is created, and the second pass aligns all the images to the
% mean.  The second pass effectively matches the images to a less noisy
% template, and so should result in more accurate movement estimates.
% 
% Previously, the estimates of motion continued for a fixed number of
% iterations.  This may be OK for small movements, but was inadequate for
% large ones.  The realignment now continues until a stopping criterion
% has been acheived.
% 
% Movement estimation begins by using (faster but less accurate)
% tri-linear interpolation to resample the data.  The final iterations are
% done using sinc interpolation in order to obtain the final high
% accuracy.
% 
% The algorithm for the SPM96 motion correction was very similar to that
% of the previous version (SPM94).  However, both these implementations
% included an assumption about the rate of change of sum of squared
% difference with respect to parameter changes that only held when the
% rotations were small.  This has been fixed.
% 
% Occasionally it may be necessary to attempt to sample voxels that lie
% outside the field of view of the original image.  The parameter
% estimates in the SPM96 implementation assumed that the values of such
% voxels should be zero.  In SPM99, these voxels are excluded from the
% computation of motion estimates.
% 
% The sinc interpolation has changed.  In the previous version, the width
% of the hanning window was 1 pixel narrower than the optimal width.  This
% has been fixed.  Also, the integral under the sinc kernal has been set
% to one (by a renormalizing step).  Both odd and even numbers of
% neighbours can now be used by the interpolation.  Windowed sinc
% interpolation schemes are acheived by passing a negative hold to
% functions such as spm_sample_vol.  Positive holds will result in
% polynomial (Lagrange) interpolation being used, whereas negative holds
% use sinc interpolation.  The gradient of the images can also be directly
% obtained by spm_sample_vol.
% 
% A three dimensional Fourier interpolation has been implemented that is
% based on the paper by Eddy et al.  This method is used for resampling
% fMRI data, but it requires the voxel sizes of the images to all be
% isotropic (since zooming can not be performed using this method).
% Preliminary tests showed that this method did not greatly reduce the
% resampling errors.
% 
% The fMRI adjustment has been modified.
% Rather than removing signal that is correlated to functions of the six
% parameters describing the rigid body movement of the subject, the
% adjustment now uses functions that are dependant upon the displacement
% of each voxel.  This requires three parameters to describe it rather
% than six, but it is now a different function for each voxel.  The
% functions that are covaried from the data are periodic in terms of the
% number of voxels displaced, and are based upon sines and cosines of the
% number of voxels displaced.  e.g., the functions for a displacement of
% two voxels are identical to the functions for a displacement of one
% voxel.  The functions were appropriate for simulated data, and with
% phantom data that was "moved" by moving the field of view of the
% scanner.  However, the method is not quite so good for real subject
% movement within the scanner.
% 
% The adjustment step is now regularized.
% This regularization is based upon Bayesian statistics which states that:
% 	p(a|b) \propto p(b|a) \times p(a)
% If (a) is the coefficients of the regressors, and (b) is the data, we
% wish to find the suitable values for (a) which maximize the posteriori
% probability of (a) given (b).  Taking the Gibb's transform of these
% functions gives us:
% 	H(a|b) = H(b|a) + H(a) + c
% which expresses the Bayesian furmulation in terms of energy cost
% functions.  The objective is now to find the coefficients that minimize
% the cost function.  The model assumes that the errors on (b) are
% normally distributed, and that H(b|a) is proportional to the residual
% sum of squares between the data and the fitted function.  Probability
% distributions for H(a) are estimated by translating the first image of
% the series by different amounts, using the windowed sinc function, and
% Fourier interpolation.  This allows an estimate to be made for the a
% priori distribution of the errors.
% 
%                           ----------------
% * Slice timing
%
% A button has been added for correcting fMRI time series data for the
% differences in image acquisition time between slices.  It is based on
% ACQCORRECT by Mark D'Exposito, Geof Aquirre and Eric Zarahn at U. Penn
% which was modified (to NMH_ACQCORRECT) by Darren Gitelman at
% Northwestern U and in turn modified by Rick Henson and Christian Buechel
% and John Ashburner at the FIL.
% Note that there is nothing at all spatial about this procedure.  It is
% included here because it is an optional pre-processing step that should
% improve results - especially for event related studies.
% 
%                           ----------------
% * Segment
%
% An error with the a priori probability images has been fixed (see
% Problem with Templates and Probability Images).
% 
% Segmentation now includes a bias correction, but extensive evaluation
% of this has not been performed. The theory for the bias correction was
% described by a poster at HBM'98:
% J. Ashburner and K. J. Friston (1998). "MRI Sensitivity Correction and
% Tissue Classification". NeuroImage 7(4):S107.
% The basic partitioning algorithm is described in:
% Ashburner and Friston (1997), NeuroImage 6(3):209-217.
% 
%                           ----------------
% * Coregister
%
% Inter-modality registration is normally a three step procedure.  The
% first step involves simultaneous constrained affine registration of the
% images to a template.  The affine registration in this step is now more
% stable because it has been regularized as described in:
% J. Ashburner, P. Neelin, D. L. Collins, A. C. Evans and K. J. Friston
% (1997).  "Incorporating Prior Knowledge into Image Registration".
% NeuroImage 6:344-352.
% New templates have been developed for this stage, including an EPI
% template for T2* fMRI.  The new templates are described in
% templates.man.
% 
% The second step is segmentation, which has been improved by having more
% correct images of prior probability.  Other tweeks have also been done
% in order to improve the results slightly.  For example, better PET/MR
% registration is possible when the CSF partition is not used.  The method
% is described and evaluated on a small dataset in:
% J. Ashburner and K. J. Friston (1997).  "Multimodal Image Coregistration
% and Partitioning - a Unified Framework".  NeuroImage 6(3):209-217
% 
%                           ----------------
% * Normalize
%
% The estimation of spatial normalization parameters consists of two
% parts: affine registration and basis function registration.  The affine
% registration has been made more robust by placing it within a Bayesian
% framework.  Zooms and shears from a large number of different brains
% provide knowledge of the a priori variability of brain sizes.  The full
% method is described in:
% J. Ashburner, P. Neelin, D. L. Collins, A. C. Evans and K. J. Friston
% (1997).  "Incorporating Prior Knowledge into Image Registration".
% NeuroImage 6:344-352.
% 
% The basis function registration is now more stable.  Part of the
% problem was because of edge effects due to the smoothing (because
% values outside the FOV are unknown).  Voxels less than 8mm from the
% edge are now used in the intensity matching.  The regularization has
% been increased in order to improve stability.  Improved estimates of
% the likelihood potential have been made by considering the image
% smoothness when estimating the degrees of freedom.  A bug was found in
% SPM96 in the computation of the rate of change of the cost function
% with respect to changes in the parameters.  This occured because the
% gradient computation was based on the gradient computation code from
% SPM95 (which was also slightly wrong).  It has subsequently been fixed.
% 
% In principle, the spatial normalization can fit an image to a linear
% combination of templates.  This didn't work in SPM96, but has now been
% fixed.  An error with the templates has also been remedied (see Problem
% with Templates and Probability Images).
% 
% Weighting images can now be used to mask out regions of the images not
% to be included in the computations.  Use of a weighting image to mask
% out the effects of scalp is described briefly in:
% J. Ashburner, C. Hutton, R.S.J. Frackowiak, I. Johnsrude, C. Price and
% K. J. Friston (1998).  "Identifying Global Anatomical Differences:
% Deformation-Based Morphometry".  Human Brain Mapping 6(5):348-357
% A weighting image can also be used to mask out strokes, tumours etc.
% 
% Graphical display of spatially normalized images is improved - (see
% Check Reg)
%
% New templates for spatial normalization are included with this release.
% 
% The basis function spatial normalization is described in:
% J. Ashburner and K. J. Friston (submitted).  "Nonlinear Spatial
% Normalization using Basis Functions".  Human Brain Mapping
%  
%                           ----------------
% * Brain Extraction
%
% A utility has been added that will take grey and white matter segments
% (from Segment) and clean them up to produce an extracted brain.  This
% will also produce optional rendering images that can be used by the
% Render button. 
%
%                           ----------------
% * Render
%
% Rendering can now display up to three sets of blobs.  Each pattern of
% blobs is displayed in red, green or blue in a semi-transparent brain.
% The intensity of voxels inside brain decays exponentially with distance.
% Extra pre-defined rendering images have been included.  There is a
% higher resolution image of a single subject brain, and also a smooth
% average brain surface (which I prefer because it is not as misleading).
% Other brain surfaces can also be used (see Brain Extraction) below.
% 
%                           ----------------
% * Check Reg
%
% Allows orthogonal sections through any pair of registered images to be
% displayed.  Clicking anywhere in one of the images shifts the centre of
% the orthogonal sections (see Display).  Intended for checking
% registration and spatial normalization results.
%
%                           ----------------
% * Display
% The display utility has been re-written to be an interactive orthogonal
% image viewer (see Check Reg).  It allows images to be viewed in different 
% orientations and to be re-oriented via their ".mat" files.  The images
% can be zoomed and visualized using different interpolation methods.
% Translucent activation blobs can be superimposed in different colours on
% the displayed image.
%
%                           ----------------
%
% * Problem with Templates and Probability Images.
%
% A problem was identified in the conversion of the original MINC files to
% Analyze format.  When the files were converted, we did not realize that
% all planes in the volumes were scaled to their own maximum values.
% The affected files were:
%     templates/T1.img, canonical/avg305T1.img, canonical/T1.img,
%     apriori/gray.img, apriori/white.img & apriori/csf.img.
% The error has been remedied.  Also, new gray, white and csf images have
% been obtained from Montreal that are based upon 151 subjects (see
% templates.man).  New template images are now included that are mostly
% based on 152 brain averages (see spm_templates.man).
% 
% -John
%
% ______________________________________________________________________
%                                                            STATISTICAL
%
% SPM99 features completely redesigned and re-written statistics.
%
% The new features are fully described in the help text of the
% statistical routines, which may be browsed using the spm_help
% viewer.
%
% For PET/SPECT/Basic stats design setup, see: spm_spm_ui.m
% For fMRI design setup, see:                  spm_fmri_spm_ui.m
% For random effects issues, see:              spm_RandFX.man
% For contrasts, and F-contrasts, see:         spm_conman.m
% For estimation, see:                         spm_spm.m
% For contrast estimation & images, see:       spm_getSPM.m
% For the new results section, see:            spm_results_ui.m
%
%
% ======================================================================
% 3)  SPM99b: Updates since the beta release
% ======================================================================
%
% A public beta release of SPM99, SPM99b, was released on 17/05/1999.
%
% ______________________________________________________________________
%                                                                   BUGS
%
% During beta testing, a number of bugs were discovered. Few were
% serious, but all are documented in the SPM99b "bugs & fixes" area of
% the SPMweb site:
%       http://ftp.fil.ion.ucl.ac.uk/spm/spm99.html#Bugs99b
%
% Those of note include:
% * spm_sn3d.m (2.19) - 18/05/1999
%   Write normalised failed with a non-integer target pixel size.
% * spm_fMRI_design.m (2.17) & spm_fmri_spm_ui.m (2.26) - 19/05/1999
%   "barfed" when no conditions were used in the fMRI stats.
% * spm_P.m (2.3) - 01/06/1999
%   Omission of a term b in equation for EC density, containing the
%   gamma function, possibly leading to p-values that are too liberal.
% * spm_spm_ui.m (2.26) 08/06/1999
%   Maximum number of groups for AnCova & Anova models hardcoded to 1!
% * spm_hread.m (2.5) 10/06/1999 & spm_hread.m (2.5) 06/07/1999
%   Problems with string conversion, including spaces in filenames.
% * spm_spm.m (2.28)
%   Bug in explicit masking code produced "tube" like masks!
% * spm_sp.m (2.8) 01/05/1999
%   Nasty bug affecting designs where the number of parameter is greater
%   than half the number of scans, AND when the projection on the
%   residual space is done on a bunch of voxels which number is greater
%   than 5 times the number of scans.
%   (Discussed in http://www.mailbase.ac.uk/lists/spm/1999-07/0085.html)
% * spm_graph.m (2.20) 15/07.1999
%   Problem plotting fitted and adjusted responses against an
%   explanatory variable.
% * spm_sp.m (2.10) & spm_SpUtil.m (2.13) 11/08/1999
%   Enhancements for large design matrices.
% * spm_orthviews.m (2.17) 18/08/1999
%   Crashed with single slice data.
% * spm_write_plane.m (2.9) 16/09/1999
%   Problems handling image filenames with "=" in the name.
% * spm_transverse.m (2.15) 14/10/1999
%   Slight mismatch in "overlay-slices".
%
% Other bugs were on the whole either cosmetic or platform specific fixes.
%
% ______________________________________________________________________
%                                                           ENHANCEMENTS
%
% The following enhancements have been added to SPM99 since the beta release:
%
% Spatial:
% * Image registration based on Mutual Information
% * Speeded up parameter estimation by sampling only the most useful voxels
% * Extra options from <Display>
% * <Check Reg> now accepts variable number of images
% * Support for ToolBox launching
% * <Xtract Brain> option for generating brain surfaces (Matlab 5.3+ omly)
% * Support for datatypes other than double when sampling Matlab 3D arrays
% * Cosmetic improvements to spm_coregister & spm_realign
%
% Statistical:
% * fMRI design allows trains of onsets to emulate variable length epochs
% * Increased efficiency in fMRI model estimation
% * Various robustness and speed improvements in stats estimation
% * Smoothness estimator computed on the basis of the averge determinant
%   - allowing for nonstationary smoothness
% * More efficient contrast handling (particulaly relevant for F-contrasts)
% * More efficient handling of larger design matrices
% * Cross-platform handling of results files
% * Uncorrected threshold for a conjunction now the p-value for the
%   conjunction SPM, rather than for the component SPM's.
% * Specification by mask image of search volume for corrected
%   significance (SVC)
% * Design orthogonality explorer
% * "surfable" contrast plots
% * Double-click extraction of data from design matrices & contrast weights
% * dynamic co-ordinate highlighting in results tables
% * spm_regions.m timecourse extraction integrated into results section
%
% Misc:
% * Batch system: See spm_bch.man
%
% ______________________________________________________________________
%                                                       PLANNED FEATURES
%
% Planned features due to be implemented for SPM99, which weren't:
%    * Contrast utilities & exploration: Contrast summary, contrast
%      orthogonality, design space partitioning display
%    * Dynamic thresholding in Results interface
%    * New robust measure of "global" background scan intensity
%      - to appear as an SPM ToolBox
%    * Model diagnostic plots: fitted vs. residuals, Q-Q plots
%    * Voxel statistics display: Statistics, significances, R^2,
%      partial correlations
%    * Additional user interface enhancements
%    * Conversion program for SPM96/7 Stats results files
%
% ======================================================================
% 4)                    S P M   T o o l B o x e s
% ======================================================================
%
% SPM toolboxes are MatLab applications that enhance or build upon the
% SPM software platform. Authors are encouraged to collaborate with the
% SPM authors in the production of SPM ToolBoxes for open
% distribution.
%
% Toolboxes should conform to SPM user interface and coding standards.
%
% The "ToolBox" PullDown on the SPM99 Menu window allows easy launching
% of a ToolBox GUI. See spm.m for details of thie feature.
%
% ______________________________________________________________________
%                                                      PLANNED TOOLBOXES
%
% Planned toolboxes:
%    * SPMvis : Visualisation toolbox                (CHutton)
%    * SnPM   : Non-parametric inference             (AHolmes & TNichols)
%    * SPMmv  : Multivariate statistics toolbox      (JB Poline)
%    * SPMsc  : Effective connectivity toolbox       (CBuechel)
%    * SPM??  : Morphometry toolbox                  (JAshburner)
%    * SPM??  : Robust global effect estimation      (Jandersson)
%
% ______________________________________________________________________
% @(#)spm.man	2.8 The FIL methods group 00/01/25