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Major updates to HRF_Est_Toolbox2 to allow passing in of SPM.mat file…
…s to estimate the HRF
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CanlabCore/HRF_Est_Toolbox2/EstHRF_inAtlas/EstimateHRF_inAtlas.m
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CanlabCore/HRF_Est_Toolbox2/EstHRF_inAtlas/extractHRF.m
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,123 @@ | ||
| function [HRF, tc] = extractHRF(HRF_OBJ, CondNames, at, rois) | ||
| % Passed in HRF_OBJ is a cell array of fmri_data for each condition. | ||
| % CondNames should be a cell array of charstr for each condition | ||
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| % Initialize the parallel pool if it's not already running | ||
| if isempty(gcp('nocreate')) | ||
| parpool; | ||
| end | ||
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| % Preallocation of tc_local before the parfor loop | ||
| HRF= cell(1, numel(rois)); | ||
| tc= cell(1, numel(rois)); | ||
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| CondNames=matlab.lang.makeValidName(CondNames); | ||
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| % Now, handle the fourth dimension which varies with 'd' | ||
| numCondNames = numel(CondNames); | ||
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| % HRF_local{d} = cell(1, numel(rois)); | ||
| % tc_local{d} = cell(1, numel(rois)); | ||
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| % Consider doing apply_parcellation instead of mean(apply_mask(HRF_OBJ{c}, at.select_atlas_subset(rois(r), 'exact')).dat); | ||
| % [parcel_means, parcel_pattern_expression, parcel_valence, rmsv_pos, rmsv_neg] = apply_parcellation(dat,at); | ||
| % nps=load_image_set('npsplus'); | ||
| % nps = get_wh_image(nps,1); | ||
| % [parcel_means, parcel_pattern_expression, parcel_valence, rmsv_pos, rmsv_neg] = apply_parcellation(dat,at, 'pattern_expression', nps); | ||
| % r=region(at,'unique_mask_values'); | ||
| % wh_parcels=~all(isnan(parcel_means)) | ||
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| for r=1:numel(rois) | ||
| HRF{r} = struct; | ||
| tc{r} = cell(1, numCondNames); | ||
| tic | ||
| for c=1:numel(CondNames) | ||
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| try | ||
| tc{r}{c}=mean(apply_mask(HRF_OBJ{c}, at.select_atlas_subset(rois(r), 'exact')).dat); | ||
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| HRF{r}.(CondNames{c}).model=tc{r}{c}; | ||
| [HRF{r}.(CondNames{c}).peaks, HRF{r}.(CondNames{c}).troughs]=detectPeaksTroughs(tc{r}{c}', false); | ||
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| [~, regionVoxNum, ~, ~]=at.select_atlas_subset(rois(r), 'exact').get_region_volumes; | ||
| HRF{r}.(CondNames{c}).model_voxnormed=tc{r}{c}/regionVoxNum; | ||
| [HRF{r}.(CondNames{c}).peaks_voxnormed, HRF{r}.(CondNames{c}).troughs_voxnormed]=detectPeaksTroughs(tc{r}{c}'/regionVoxNum, false); | ||
| catch | ||
| % disp(t); | ||
| disp(c); | ||
| disp(rois{r}); | ||
| tc{r}{c} | ||
| mean(apply_mask(HRF_OBJ{c}, at.select_atlas_subset(rois(r), 'exact')).dat) | ||
| {apply_mask(HRF_OBJ{c}, at.select_atlas_subset(rois(r), 'exact')).dat} | ||
| HRF_OBJ | ||
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| end | ||
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| % Number of phases | ||
| start_times = [HRF{r}.(CondNames{c}).peaks.start_time, HRF{r}.(CondNames{c}).troughs.start_time]; | ||
| end_times = [HRF{r}.(CondNames{c}).peaks.end_time, HRF{r}.(CondNames{c}).troughs.end_time]; | ||
| phases = [start_times(:), end_times(:)]; | ||
| unique_phases = unique(phases, 'rows'); | ||
| HRF{r}.(CondNames{c}).phases = mat2cell(unique_phases, ones(size(unique_phases, 1), 1), 2); | ||
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| % Loop over phases | ||
| for p = 1:numel(HRF{r}.(CondNames{c}).phases) | ||
| features = {'peaks', 'troughs'}; | ||
| for f = 1:2 | ||
| feat = features{f}; | ||
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| % Count the number of features (peaks or troughs) | ||
| start_times = cell2mat({HRF{r}.(CondNames{c}).(feat).start_time}); | ||
| current_phase_start = HRF{r}.(CondNames{c}).phases{p}(1); | ||
| HRF{r}.(CondNames{c}).phase(p).(feat) = sum(start_times == current_phase_start); | ||
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| if HRF{r}.(CondNames{c}).phase(p).(feat) > 0 | ||
| display(['Estimating ' , '_', rois{r}, '_', CondNames{c}, '_', ' Now...!']); | ||
| display(['Phase ' , num2str(p), 'Feature ', feat]); | ||
| idx = find(start_times == current_phase_start); | ||
| auc = cell2mat({HRF{r}.(CondNames{c}).(feat).AUC}); | ||
| height = cell2mat({HRF{r}.(CondNames{c}).(feat).height}); | ||
| time_to_peak = cell2mat({HRF{r}.(CondNames{c}).(feat).time_to_peak}); | ||
| half_height = cell2mat({HRF{r}.(CondNames{c}).(feat).half_height}); | ||
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| feat_voxnormed = strcat(feat, '_voxnormed'); | ||
| auc_voxnormed = cell2mat({HRF{r}.(CondNames{c}).(feat_voxnormed).AUC}); | ||
| height_voxnormed = cell2mat({HRF{r}.(CondNames{c}).(feat_voxnormed).height}); | ||
| time_to_peak_voxnormed = cell2mat({HRF{r}.(CondNames{c}).(feat_voxnormed).time_to_peak}); | ||
| half_height_voxnormed = cell2mat({HRF{r}.(CondNames{c}).(feat_voxnormed).half_height}); | ||
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| HRF{r}.(CondNames{c}).phase(p).auc = unique(auc(idx)); | ||
| HRF{r}.(CondNames{c}).phase(p).auc_voxnormed = unique(auc_voxnormed(idx)); | ||
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| HRF{r}.(CondNames{c}).phase(p).height = height(idx); | ||
| HRF{r}.(CondNames{c}).phase(p).height_voxnormed = height_voxnormed(idx); | ||
| HRF{r}.(CondNames{c}).phase(p).time_to_peak = time_to_peak(idx); | ||
| HRF{r}.(CondNames{c}).phase(p).time_to_peak_voxnormed = time_to_peak_voxnormed(idx); | ||
| HRF{r}.(CondNames{c}).phase(p).half_height = half_height(idx); | ||
| HRF{r}.(CondNames{c}).phase(p).half_height_voxnormed = half_height_voxnormed(idx); | ||
| end | ||
| end | ||
| end | ||
| end | ||
| display(strjoin({' Done in ', num2str(toc), ' seconds with ', rois{r}})); | ||
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| end | ||
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| % temp_HRF_fit = HRF_local; | ||
| % Save the results for this ROI | ||
| % display([num2str(t), ' Done!']) | ||
| % temp_HRF_fit{t} = HRF_local; | ||
| % tc{t} = tc; | ||
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| % Transfer the results from the temporary cell array to the HRF structure | ||
| % HRF.fit = temp_HRF_fit; | ||
| % HRF.params=HRF_PARAMS; | ||
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| delete(gcp('nocreate')); | ||
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| end |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,164 @@ | ||
| function [params_obj, hrf_obj, params_obj_dat, hrf_obj_dat, info] = hrf_fit(SPM,T,method,mode) | ||
| % HRF estimation on fmri_data class object | ||
| % | ||
| % HRF estimation function for a single voxel; | ||
| % | ||
| % Implemented methods include: IL-model (Deterministic/Stochastic), FIR | ||
| % (Regular/Smooth), and HRF (Canonical/+ temporal/+ temporal & dispersion). | ||
| % With SPM.mat, TR, and experimental design are imported. | ||
| % | ||
| % :Inputs: | ||
| % | ||
| % **SPM** | ||
| % SPM.mat file | ||
| % | ||
| % **T** | ||
| % length of estimated HRF ij seconds | ||
| % | ||
| % **type** | ||
| % Model type: 'FIR', 'IL', or 'CHRF' | ||
| % | ||
| % **mode** | ||
| % Mode | ||
| % | ||
| % :Model Types: | ||
| % | ||
| % A. **Fit HRF using IL-function** | ||
| % Choose mode (deterministic/stochastic) | ||
| % - 0 - deterministic aproach | ||
| % - 1 - simulated annealing approach | ||
| % | ||
| % Please note that when using simulated annealing approach you | ||
| % may need to perform some tuning before use. | ||
| % | ||
| % B. **Fit HRF using FIR-model** | ||
| % Choose mode (FIR/sFIR) | ||
| % - 0 - FIR | ||
| % - 1 - smooth FIR | ||
| % | ||
| % C. **Fit Canonical HRF** | ||
| % Choose mode (FIR/sFIR) | ||
| % - 0 - FIR | ||
| % - 1 - smooth FIR | ||
| % | ||
| % | ||
| % .. | ||
| % Created by Michael Sun on 02/20/24 | ||
| % .. | ||
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| if isstring(SPM) || ischar(SPM) | ||
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| load(SPM); | ||
| if ~exist('SPM', 'var') | ||
| error('Passed in filepath is not an SPM.mat file.') | ||
| end | ||
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| end | ||
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| if isstruct(SPM) | ||
| fnames=unique({SPM.xY.VY.fname})'; | ||
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| parfor i=1:numel(fnames) | ||
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| if contains(fnames{i}, '/') && ispc | ||
| % PC Path conversion from Unix | ||
| if strcmp(fnames{i}(1,1:2), '\\') | ||
| d{i}=fmri_data(strrep(fnames{i}, '/', '\')); | ||
| else | ||
| d{i}=fmri_data(['\',strrep(fnames{i}, '/', '\')]); | ||
| end | ||
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| else | ||
| d{i}=fmri_data(fnames{i}); | ||
| end | ||
| end | ||
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| % Transform timeseries data into the way SPM desires. | ||
| gkwy_d=spmify(d,SPM); | ||
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| % Extract TR | ||
| TR = SPM.xY.RT; | ||
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| if isempty(T) | ||
| disp('T is empty, generating T as 2 times the maximum duration for each task regressor.'); | ||
| for i = 1:numel(SPM.Sess) | ||
| T{i}=cellfun(@(cellArray) 2*ceil(max(cellArray)), {SPM.Sess(i).U.dur}); | ||
| end | ||
| elseif numel(SPM.Sess)>1 && ~iscell(T) | ||
| % error('SPM structures concatenating multiple runs must have time windows passed in with a matching number of cell arrays.'); | ||
| T=repmat({T}, 1, numel(SPM.Sess)); | ||
| elseif iscell(T) && numel(SPM.Sess)~=numel(T) | ||
| error('Incompatible number of runs in SPM and in cell-array T.') | ||
| end | ||
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| % %% ONE WAY | ||
| % % run hrf_fit separately for every d | ||
| for i=1:numel(d) | ||
| % Reassign data. | ||
| d{i}.dat=gkwy_d{i}; | ||
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| % Extract TR | ||
| TR = SPM.xY.RT; | ||
| Runc=generateConditionTS(numel(SPM.Sess(i).row), [SPM.Sess(i).U.name], {SPM.Sess(i).U.ons}, {SPM.Sess(i).U.dur}); | ||
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| % There's a need to pull apart the SPM design matrix for every run | ||
| % Column of regressors + covariates for each session + intercept | ||
| % X=SPM.xX.X(SPM.Sess(i).row, [SPM.Sess(i).col, SPM.xX.iB(i)]); | ||
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| % Trouble is, X is not filtered | ||
| % X=spm_filter(SPM.xX.K(i), SPM.xX.X(SPM.Sess(i).row, [SPM.Sess(i).col, SPM.xX.iB(i)])); | ||
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| % Trouble here is, task regressors may have to be re-generated for | ||
| % FIR and sFIR if the original design matrix is cHRF, so check the | ||
| % basis function | ||
| if strcmpi(method, 'FIR') && ~contains(SPM.xBF.name, 'FIR') | ||
| disp('Passed in SPM structure is not FIR. Reconstructing task-regressors for FIR fit'); | ||
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| len = numel(SPM.Sess(i).row); | ||
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| % Task regressors for each run can be found here: | ||
| numstim=numel(SPM.Sess(i).U); | ||
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| % Make the design matrix: | ||
| DX_all = cell(1, numstim); % Store DX matrices for each condition | ||
| tlen_all = zeros(1, numstim); % Store tlen for each condition | ||
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| for s=1:numstim | ||
| t = 1:TR:T{i}(s); | ||
| tlen_all(s) = length(t); | ||
| DX_all{s} = tor_make_deconv_mtx3(Runc(:,s), tlen_all(s), 1); | ||
| end | ||
| DX = horzcat(DX_all{:}); | ||
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| % due to horzcat, we will have multiple intercepts in this design matrix | ||
| % therefore, we'll find the intercepts, drop them, and add an intercept at the end of the matrix | ||
| intercept_idx = find(sum(DX)==len); | ||
| copyDX = DX; | ||
| copyDX(:,intercept_idx) = []; | ||
| DX = [copyDX ones(len,1)]; | ||
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| % Covariate Design Matrix for each session (without intercept): | ||
| NX=[SPM.Sess(i).C.C]; | ||
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| % Concatenate the Task regressors with Covariates | ||
| X=[DX, NX]; | ||
| % Filter | ||
| X=spm_filter(SPM.xX.K(i), X); | ||
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| else | ||
| % Otherwise set X to be the filtered design matrix of that | ||
| % section. | ||
| X=spm_filter(SPM.xX.K(i), SPM.xX.X(SPM.Sess(i).row, [SPM.Sess(i).col, SPM.xX.iB(i)])); | ||
| end | ||
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| [params_obj{i}, hrf_obj{i}, params_obj_dat{i}, hrf_obj_dat{i}] = hrf_fit(d{i},TR,Runc,T{i},method,mode,X); | ||
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| info{i}.X=X; | ||
| info{i}.names=[SPM.Sess(i).U.name]'; | ||
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| end | ||
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| end | ||
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| end |
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