function PPI = spm_peb_ppi(varargin)
% Bold deconvolution to create physio- or psycho-physiologic interactions
% FORMAT PPI = spm_peb_ppi(SPMname,ppiflag,VOI,Uu,ppiname,showGraphics)
%
% SPM          - Structure containing generic details about the analysis or
%                the fully qualified filename of such a structure.
% ppiflag      - Type of analysis. Must be one of:
%                  'simple deconvolution'          or 'sd'
%                  'psychophysiologic interaction' or 'ppi'
%                  'physiophysiologic interaction' or 'phipi'
% VOI          - Structure containing details about a VOI (as produced by
%                spm_regions) or the fully qualified filename of such a
%                structure. If a structure, then VOI should be of size 1x1
%                in the case of simple deconvolution, and psychophysiologic 
%                interactions) or 1x2, in the case of physiophysiologic
%                interactions. If a file name it should be 1xN or 2xN.
% Uu           - Matrix of input variables and contrast weights. This is an
%                [n x 3] matrix. The first column indexes SPM.Sess.U(i). The
%                second column indexes the name of the input or cause, see
%                SPM.Sess.U(i).name{j}. The third column is the contrast
%                weight. Unless there are parametric effects the second
%                column will generally be a 1.
% ppiname      - Basename of the PPI file to save. The saved file will be:
%                <PATH_TO_SPM.MAT>/PPI_<ppiname>.mat
% showGraphics - empty or 1 = yes, 0 = no.
%
%
% PPI.ppi      - (PSY*xn  or xn1*xn2) convolved with the HRF
% PPI.Y        - Original BOLD eigenvariate. Use as covariate of no interest
% PPI.P        - PSY convolved with HRF for psychophysiologic interactions,
%                or in the case of physiophysologic interactions contains
%                the eigenvariate of the second region. 
% PPI.name     - Name of PPI
% PPI.xY       - Original VOI information
% PPI.xn       - Deconvolved neural signal(s)
% PPI.psy.u    - Psychological variable or input function (PPIs only)
% PPI.psy.w    - Contrast weights for psychological variable (PPIs only)
% PPI.psy.name - Names of psychological conditions (PPIs only)
%__________________________________________________________________________
%
% This routine is effectively a hemodynamic deconvolution using full priors
% and EM to deconvolve the HRF from a hemodynamic time series to give a 
% neuronal time series [that can be found in PPI.xn].  This deconvolution 
% conforms to Wiener filtering. The neuronal process is then used to form 
% PPIs. See help text within function for more details.
%__________________________________________________________________________
% Copyright (C) 2002-2014 Wellcome Trust Centre for Neuroimaging

% Darren Gitelman
% $Id: spm_peb_ppi.m 5969 2014-05-01 14:37:22Z guillaume $


% SETTING UP A PPI THAT ACCOUNTS FOR THE HRF
% =========================================================================
% PPI's were initially conceived as a means of identifying regions whose
% reponses can be explained in terms of an interaction between activity in
% a specified source (the physiological factor) and some experimental
% effect (the psychological factor). However, a problem in setting up PPI's
% is that in order to derive a proper estimate of the interaction between
% a psychological variable (P) and measured hemodynamic signal (x), one 
% cannot simply convolve the psychological variable with the hrf (HRF) and 
% multiply by the signal. Thus:
% 
%                  conv(P,HRF).* x ~= conv((P.*xn),HRF)
%
% P   = psychological variable
% HRF = hemodynamic response function
% xn  = underlying neural signal which in fMRI is convolved with the hrf to
%       give the signal one measures -- x.
% x   = measured fmri signal
%
% It is actually the right hand side of the equation one wants.
% Thus one has to work backwards, in a sense, and deconvolve the hrf
% from x to get xn. This can then be multiplied by P and the resulting
% vector (or matrix) reconvolved with the hrf.
%
% This algorithm uses a least squares strategy to solve for xn.
%
% The source's hemodynamics are x = HRF*xn;
%
% Using the constraint that xn should have a uniform spectral density 
% we can expand x in terms of a discrete cosine set (xb)
%
%      xn  = xb*B
%       B  = parameter estimate
%
% The estimator of x is then
%
%       x  = HRF(k,:)*xn
%       x  = HRF(k,:) * xb * B
%
% This accounts for different time resolutions between our hemodynamic 
% signal and the discrete representation of the psychological variable. In 
% this case k is a vector representing the time resolution of the scans.
%
% Conditional estimates of B allow for priors that ensure uniform variance 
% over frequencies.
%
% PPI STATISTICAL MODEL
% =========================================================================
% Once the PPI.ppi interaction term has been calculated a new GLM must be
% setup to search for the interaction effects across the brain. This is
% done using a standard, first level, fMRI model, which must include 3
% covariates, PPI.ppi (interaction), PPI.Y (main effect: source region bold
% signal) and PPI.P (main effect: "psychological" condition), plus any
% nuisance regressors according to the particular design.
%
% NB: Designs that include only the interaction term without the main
% effects are not proper as inferences on the interaction will include a
% mixture of both main and interaction effects. 
%
% Once the model has been setup and run, a contrast of [1 0 0] over the
% PPI.ppi, PPI.Y and PPI.P columns respectively, will show regions with a
% positive relationship to the interaction term, discounting any main
% effects. Negative regressions can be examined with [-1 0 0]. A PPI random
% effects analysis would involve taking the con* files from the [1 0 0]
% t-contrast for each subject and forwarding them to a second level
% analysis.


% Shortcut for PPI display
%--------------------------------------------------------------------------
if nargin && ischar(varargin{1}) && strcmpi(varargin{1},'display')
    if nargin > 1
        PPI = varargin{2};
    else
        [PPI, sts] = spm_select(1,'^PPI.*\.mat$');
        if ~sts, return; end
    end
    if ischar(PPI), load(PPI); end
    display_ppi(PPI);
    return;
end

% Set up the graphical interface
%--------------------------------------------------------------------------
Finter = spm_figure('FindWin','Interactive');
if ~nargin, Finter = spm_figure('GetWin','Interactive'); end
header = get(Finter,'Name');
spm_clf(Finter); set(Finter,'name','PPI Setup');

% Check inputs
%--------------------------------------------------------------------------
if nargin && isstruct(varargin{1})
    SPM = varargin{1};
    try, swd = SPM.pwd; catch, swd = pwd; end
else
    try
        P            = varargin{1};
    catch
        [P, sts]     = spm_select(1,'^SPM\.mat$','Select SPM.mat');
        if ~sts, PPI = []; return; end
    end
    swd = spm_file(P,'fpath');
    load(fullfile(swd,'SPM.mat'));
end
SPM.swd = spm_file(swd,'cpath');
cwd     = pwd;
cd(SPM.swd)


% Ask whether to perform physiophysiologic or psychophysiologic interactions
%--------------------------------------------------------------------------
try
    ppiflag = varargin{2};
catch
    ppiflag = {'simple deconvolution',...
               'psychophysiologic interaction',...
               'physiophysiologic interaction'};
    i       = spm_input('Analysis type?',1,'m',ppiflag);
    ppiflag = ppiflag{i};
end


switch lower(ppiflag)
    
    case  {'simple deconvolution','sd'}
    %======================================================================
    if nargin>2 && isstruct(varargin{3})
        p.xY = varargin{3};
    else
        try
            VOI = varargin{3};
            p   = load(deblank(VOI(1,:)),'xY');
        catch
            spm_input('physiological variable:...  ',2,'d');
            [VOI,sts] = spm_select(1,'^VOI.*\.mat$',{'select VOI'});
            if ~sts, PPI = []; return; end
            p   = load(VOI,'xY');
        end
    end
    xY(1) = p.xY;
    Sess  = SPM.Sess(xY(1).Sess);

    case  {'physiophysiologic interaction','phipi'}
    %======================================================================
    if nargin>2 && isstruct(varargin{3})
        xY = varargin{3};
        xY = xY(:)';
        if numel(xY) ~= 2
            error('Must include 2 VOI structures for physiophysiologic interactions')
        end
    else
        try
            VOI = varargin{3};
            if size(VOI,1) ~= 2
                error('Must include 2 VOI filenames for physiophysiologic interactions')
            end
            for i = 1:2
                p     = load(deblank(VOI(i,:)),'xY');
                xY(i) = p.xY;
            end
        catch
            spm_input('physiological variables:...  ',2,'d');
            [VOI,sts] = spm_select(2,'^VOI.*\.mat$',{'select VOIs'});
            if ~sts, PPI = []; return; end
            for i = 1:2
                p     = load(deblank(VOI(i,:)),'xY');
                xY(i) = p.xY;
            end
        end
    end
    Sess = SPM.Sess(xY(1).Sess);

    case  {'psychophysiologic interaction','ppi'}
    %======================================================================
    if nargin>2 && isstruct(varargin{3})
        p.xY = varargin{3};
    else
        try
            VOI = varargin{3};
            p   = load(deblank(VOI(1,:)),'xY');
        catch
            spm_input('physiological variable:...  ',2,'d');
            [VOI,sts] = spm_select(1,'^VOI.*\.mat$',{'select VOI'});
            if ~sts, PPI = []; return; end
            p   = load(VOI,'xY');
        end
    end
    xY(1) = p.xY;
    Sess  = SPM.Sess(xY(1).Sess);

    % get 'causes' or inputs U
    %----------------------------------------------------------------------
    U.name = {};
    U.u    = [];
    U.w    = [];
    try
        Uu = varargin{4};
        for i = 1:size(Uu,1)
            U.u           = [U.u Sess.U(Uu(i,1)).u(33:end,Uu(i,2))];
            U.name{end+1} = Sess.U(Uu(i,1)).name{Uu(i,2)};
            U.w           = [U.w Uu(i,3)];
        end
    catch
        spm_input('Psychological variable:...  ',2,'d');
        u      = length(Sess.U);
        for  i = 1:u
            for  j = 1:length(Sess.U(i).name)
                str   = ['include ' Sess.U(i).name{j} '?'];
                if spm_input(str,3,'y/n',[1 0])
                    str             = 'Contrast weight';
                    tmpw            = spm_input(str,4,'e',[],1);
                    % if tmpw==0 then don't include the column in the
                    % design. This takes care of the possibility that the
                    % user would select to include the column but then give
                    % it a 0 weight.
                    %------------------------------------------------------
                    if tmpw ~= 0
                        U.w             = [U.w tmpw];
                        U.u             = [U.u Sess.U(i).u(33:end,j)];
                        U.name{end + 1} = Sess.U(i).name{j};
                    end
                end
            end
        end
    end

    otherwise
    %======================================================================
    error('Unknown type of analysis');
    
end % (switch setup)


% Name of PPI file to be saved
%--------------------------------------------------------------------------
try
    PPI.name = varargin{5};
catch
    PPI.name = spm_input('Name of PPI',3,'s','PPI');
end


% Check if Graphical output should be shown
%--------------------------------------------------------------------------
try
    showGraphics = varargin{6};
catch
    showGraphics = 1;
end


% Setup variables
%--------------------------------------------------------------------------
RT      = SPM.xY.RT;
dt      = SPM.xBF.dt;
NT      = round(RT/dt);
fMRI_T0 = SPM.xBF.T0;
N       = length(xY(1).u);
k       = 1:NT:N*NT;                       % microtime to scan time indices


% Setup other output variables
%--------------------------------------------------------------------------
PPI.xY = xY;
PPI.RT = RT;
PPI.dt = dt;


% Create basis functions and hrf in scan time and microtime
%--------------------------------------------------------------------------
hrf = spm_hrf(dt);


% Create convolved explanatory {Hxb} variables in scan time
%--------------------------------------------------------------------------
xb  = spm_dctmtx(N*NT + 128,N);
Hxb = zeros(N,N);
for i = 1:N
    Hx       = conv(xb(:,i),hrf);
    Hxb(:,i) = Hx(k + 128);
end
xb = xb(129:end,:);


% Get confounds (in scan time) and constant term
%--------------------------------------------------------------------------
X0 = xY(1).X0;
M  = size(X0,2);


% Get response variable
%--------------------------------------------------------------------------
for i = 1:size(xY,2)
    Y(:,i) = xY(i).u;
end


% Remove confounds and save Y in ouput structure
%--------------------------------------------------------------------------
Yc    = Y - X0*inv(X0'*X0)*X0'*Y;
PPI.Y = Yc(:,1);
if size(Y,2) == 2
    PPI.P = Yc(:,2);
end


% Specify covariance components; assume neuronal response is white
% treating confounds as fixed effects
%--------------------------------------------------------------------------
Q = speye(N,N)*N/trace(Hxb'*Hxb);
Q = blkdiag(Q, speye(M,M)*1e6  );


% Get whitening matrix (NB: confounds have already been whitened)
%--------------------------------------------------------------------------
W = SPM.xX.W(Sess.row,Sess.row);


% Create structure for spm_PEB
%--------------------------------------------------------------------------
clear P
P{1}.X = [W*Hxb X0];        % Design matrix for lowest level
P{1}.C = speye(N,N)/4;      % i.i.d assumptions
P{2}.X = sparse(N + M,1);   % Design matrix for parameters (0's)
P{2}.C = Q;


switch ppiflag

    case  {'simple deconvolution','sd'}
    %======================================================================
    C  = spm_PEB(Y,P);
    xn = xb*C{2}.E(1:N);
    xn = spm_detrend(xn);

    % Save variables (NOTE: xn is in microtime and does not account for
    % slice timing shifts). To convert to BOLD signal convolve with a hrf.
    % Use a microtime to scan time index to convert to scan time: e.g.,
    % k = 1:NT:N*NT; where NT = number of bins per TR = TR/dt or SPM.xBF.T
    % and N = number of scans in the session. Finally account for slice
    % timing effects by shifting the index accordingly.
    %----------------------------------------------------------------------
    PPI.xn = xn;

    case  {'physiophysiologic interaction','phipi'}
    %======================================================================
    C    = spm_PEB(Y(:,1),P);
    xn1  = xb*C{2}.E(1:N);
    C    = spm_PEB(Y(:,2),P);
    xn2  = xb*C{2}.E(1:N);
    xn1  = spm_detrend(xn1);
    xn2  = spm_detrend(xn2);
    xnxn = xn1.*xn2;

    % Convolve, convert to scan time, and account for slice timing shift
    %----------------------------------------------------------------------
    ppi = conv(xnxn,hrf);
    ppi = ppi((k-1) + fMRI_T0);

    % Save variables
    %----------------------------------------------------------------------
    PPI.xn  = [xn1 xn2];
    PPI.ppi = spm_detrend(ppi);

    case  {'psychophysiologic interaction','ppi'}
    %======================================================================

    % COMPUTE PSYCHOPHYSIOLOGIC INTERACTIONS
    % use basis set in microtime
    %----------------------------------------------------------------------
    % get parameter estimates and neural signal; beta (C) is in scan time
    % This clever trick allows us to compute the betas in scan time which
    % is much quicker than with the large microtime vectors. Then the betas
    % are applied to a microtime basis set generating the correct neural
    % activity to convolve with the psychological variable in microtime
    %----------------------------------------------------------------------
    C  = spm_PEB(Y,P);
    xn = xb*C{2}.E(1:N);
    xn = spm_detrend(xn);

    % Setup psychological variable from inputs and contast weights
    %----------------------------------------------------------------------
    PSY = zeros(N*NT,1);
    for i = 1:size(U.u,2)
        PSY = PSY + full(U.u(:,i) * U.w(i));
    end
    % PSY = spm_detrend(PSY);  % removed centering of psych variable prior
                               % to multiplication with xn in r3271.

    % Multiply psychological variable by neural signal
    %----------------------------------------------------------------------
    PSYxn = PSY.*xn;

    % Convolve, convert to scan time, and account for slice timing shift
    %----------------------------------------------------------------------
    ppi = conv(PSYxn,hrf);
    ppi = ppi((k-1) + fMRI_T0);

    % Convolve psych effect, convert to scan time, and account for slice
    % timing shift
    %----------------------------------------------------------------------
    PSYHRF = conv(PSY,hrf);
    PSYHRF = PSYHRF((k-1) + fMRI_T0);

    % Save psychological variables
    %----------------------------------------------------------------------
    PPI.psy = U;
    PPI.P   = PSYHRF;
    PPI.xn  = xn;
    PPI.ppi = spm_detrend(ppi);
    
end % (switch)

% Display
%--------------------------------------------------------------------------
if showGraphics
    display_ppi(PPI);
end

% Save
%--------------------------------------------------------------------------
str    = ['PPI_' PPI.name '.mat'];

save(fullfile(SPM.swd,str),'PPI', spm_get_defaults('mat.format'))

cmd = 'spm_peb_ppi(''display'',''%s'')';
fprintf('   PPI saved as %s\n',spm_file(fullfile(SPM.swd,str),'link',cmd));

% Clean up
%--------------------------------------------------------------------------
set(Finter,'name',header);
cd(cwd);


%==========================================================================
% function display_ppi(PPI)
%==========================================================================
function display_ppi(PPI)

Fgraph = spm_figure('GetWin','PPI');
FS     = spm('FontSizes');
spm_clf(Fgraph);

try
    RT = PPI.RT;
catch
    RT = spm_get_defaults('stats.fmri.t')*PPI.dt; % backward compatibility
end
N  = length(PPI.xY(1).u);
NT = round(RT/PPI.dt);
t  = PPI.RT*[1:N];
T  = PPI.dt*[1:(N*NT)];

%-Simple deconvolution
%--------------------------------------------------------------------------
if numel(PPI.xY) == 1 && ~isfield(PPI,'psy')
    
    ax = subplot(1,1,1);
    plot(t,PPI.Y,T,PPI.xn)
    title('hemodynamic and neuronal responses')
    xlabel(['time (secs)'])
    axis tight square
    grid on
    legend('BOLD','neuronal')
    
    str = sprintf('Simple Deconvolution: %s\n',PPI.name);
    str = [str sprintf('VOI file: %s',PPI.xY.name)];
    
%-Physiophysiologic interaction
%--------------------------------------------------------------------------
elseif numel(PPI.xY) == 2
    
    ax = subplot(2,1,1);
    plot(t,PPI.ppi)
    title('PPI')
    xlabel(['time (secs)'])
    axis tight square
    grid on
    
    subplot(2,2,3)
    plot(t,PPI.Y(:,1),T,PPI.xn(:,1))
    title('hemodynamic and neuronal responses (1st)')
    xlabel(['time (secs)'])
    axis tight square
    grid on
    legend('BOLD','neuronal')
    
    subplot(2,2,4)
    plot(t,PPI.P,T,PPI.xn(:,2))
    title('hemodynamic and neuronal responses (2nd)')
    xlabel(['time (secs)'])
    axis tight square
    grid on
    legend('BOLD','neuronal')
    
    str = sprintf('Physiophysiologic Interaction: %s\n',PPI.name);
    str = [str, sprintf('VOI File 1: %s\n',PPI.xY(1).name)];
    str = [str, sprintf('VOI File 2: %s',PPI.xY(2).name)];
    
%-Psychophysiologic interaction
%--------------------------------------------------------------------------
elseif isfield(PPI,'psy')
    
    PSY = zeros(N*NT,1);
    for i = 1:size(PPI.psy.u,2)
        PSY = PSY + full(PPI.psy.u(:,i)*PPI.psy.w(:,i));
    end
    
    ax = subplot(2,1,1);
    plot(t,PPI.Y,T,PPI.xn(:,1))
    title('hemodynamic and neuronal responses')
    xlabel(['time (secs)'])
    axis tight square
    grid on
    legend('BOLD','neuronal')
    
    subplot(2,2,3)
    plot(T,PSY,'LineStyle','--','Color',[0 .65 0]);
    hold on
    plot(t,PPI.P,'LineStyle','-','LineWidth',1,'Color','b');
    hold off
    title('[convolved] psych. variable')
    xlabel(['time (secs)'])
    axis tight square
    grid on
    
    subplot(2,2,4)
    plot(t,PPI.ppi)
    title('PPI')
    xlabel(['time (secs)'])
    axis tight square
    grid on
    
    str = sprintf('Psychophysiologic Interaction: %s\n',PPI.name);
    str = [str, sprintf('VOI File: %s\n',PPI.xY(1).name)];
    str = [str, sprintf('Factors: ')];
    for i = 1:numel(PPI.psy.name)
        str = [str, sprintf('%s [%0.0f]',PPI.psy.name{i},PPI.psy.w(i))];
        if i < numel(PPI.psy.name)
            str = [str, sprintf('; ')];
        end
    end
    
else
    error('Unknown PPI type.');
end

hAx = axes('Position',[0 0.90 1 0.1],...
    'DefaultTextFontSize',FS(8),...
    'DefaultTextInterpreter','None',...
    'DefaultTextVerticalAlignment','Baseline',...
    'Parent',Fgraph,...
    'Units','points',...
    'Visible','off');
AxPos = get(hAx,'Position');
set(hAx,'XLim',[0,AxPos(3)]); set(hAx,'YLim',AxPos(2)+[0,AxPos(4)])
dy    = FS(9);
h     = text(dy,floor(AxPos(2)+AxPos(4))-2*dy,str);
set(h,'Parent',hAx);
