./contents.txt = this file ./install.m = Matlab script for installing the repository. Run after having extracted with path the repository archive. ./example_code.m = example code for group-level analyses ./scripts = scripts repository ./stimuli contents: sound stimuli and acoustic representations ./stimuli/sound_stimuli.mat contents: Waveforms = sound stimuli. Matrix of dimensions: 1. time sample 2. stimulus = 1-39 3. speaker gender = [female,male] Waveforms_SamplingRate = 48000 Hz Waveforms_time = time in ms Morph_percent = percent morph of the un-morphed emotionally expressive phonemes considered to synthesize the morphed stimuli. Matrix of dimensions: 1. stimulus = 1-39 2. expressed emotion = [anger, disgust,fear, pleasure, neutral] 3. speaker gender = [female,male] Morph_percent_RDM = morph-based distance (same for both speaker genders). Matrix of dimensions: 1. stimulus pair (ordered as required by squareform.m to yield square distance matrix) Resort_order = order of stimuli (1-39) considered to create the RDM plots. Matrix of dimensions: 1. stimulus = 1-39 Apply as follows: >> input_RDM = input square 39x39 distance matrix >> resorted_RDM = input_RDM(Resort_order,Resort_order) ./sound_stimuli_mtf.mat contents: MTF = modulation transfer function of the sound stimuli. Matrix of dimensions: 1. time sample (defined in MTF_time measured in ms) 2. frequency (defined in MTF_frequency measured in Hz) 3. scale (defined in MTF_scale, measured in in cyc/oct) 4. rate (defined in MTF_rate, measured in Hz where negative = down) 5. stimulus = [1-39] 6. speaker gender = [female male] MTF_RDM = time-varying MTF distance. Matrix of dimensions: 1. stimulus pair (ordered as required by squareform.m to yield square distance matrix) 2. time sample 3. speaker gender = [female male] ./participants contents: one sub-directory for each experiment participant, each including four sub-directories: 1. anatomy: native-space analysis masks and native-to-dartel transformations 2. behaviour: behavioural data 3. fmri: fMRI data 4. meg: MEG data. ./participants/participant-xx/anatomy/ contents: native_fmri_mask.nii = binary volume for the native-space fMRI analysis mask native_meg_mask.nii = binary volume for the native-space MEG analysis mask native_to_dartel.nii = native-to-dartel deformation field ./participants/participant-xx/behaviour.mat contents: Emotion_Categorization = single-trial emotion categorization responses. Binary matrix of dimensions: 1. stimulus = [1-39] 2. recognized emotion = [anger disgust fear pleasure] 3. speaker gender = [female male] 4. trial Emotion_Ratings = single-trial emotion category ratings. Matrix of dimensions: 1. stimulus = [1-39] 2. emotion = [anger disgust fear pleasure] 3. speaker gender = [female male] 4. trial ValenceArousal_Ratings = single-trial valence/arousal ratings. Matrix of dimensions: 1. stimulus = [1-39] 2. dimension = [valence arousal] 3. speaker gender = [female male] 4. trial Dissimilarity_Ratings = dissimilarity ratings. Matrix of dimensions: 1. stimulus pair (ordered as required by squareform.m to yield square distance matrix) 2. speaker gender = [female male] ./participants/participant-xx/behaviour_rdms.mat contents: Emotion_Ratings_RDM = emotion ratings RDM (averaged across genders). Matrix of dimensions: 1. stimulus pair (ordered as required by squareform.m to yield square distance matrix) Valence_Ratings_RDM = valence ratings RDM Arousal_Ratings_RDM = arousal ratings RDM Dissimilarity_Ratings_RDM = dissimilarity ratings RDM ./participants/participant-xx/fmri/glm_denoise_fold-xx.mat: output of the GLMdenoise analysis of native-space fMRI data, one .mat file for each of five cross-validation folds. Contents: maskv = spm_vol structure for the analysis mask mask = binary analysis mask invox = indices to in-mask voxels. Note: >> invox = find(mask(:)>0) >> mask(invox) = 1 results = output structure from GLMdenoise analysis betas = beta coefficients for the GLMdenoise model. Matrix of dimensions: 1. in-mask voxel (invox) 2. stimulus = [1-39] intercept = intercept of the GLMdenoise model. Matrix of dimensions: 1. in-mask voxel (invox) residuals = residuals of GLMdenoise model. Matrix of dimensions: 1. in-mask voxel (invox) 2. fMRI volume ./participants/participant-xx/fmri/fmri_rdms.mat contents: maskv = spm_vol structure for the analysis mask mask = binary analysis mask invox = indices to in-mask voxels. Note: >> invox = find(mask(:)>0) >> mask(invox) = 1 RDMs = fMRI RDMs. Matrix of dimensions: 1. stimulus pair (ordered as required by squareform.m to yield square distance matrix) 2. in-mask voxel (invox) ./participants/participant-xx/fmri/fmri_rdms_ranked.mat contents: maskv = spm_vol structure for the analysis mask mask = binary analysis mask invox = indices to in-mask voxels. Note: >> invox = find(mask(:)>0) >> mask(invox) = 1 RDMs = ranked fMRI RDMs. Matrix of dimensions: 1. stimulus pair (ordered as required by squareform.m to yield square distance matrix) 2. in-mask voxel (invox) ./participants/participant-xx/meg/meg_preprocessed.mat contents: meg = single-trial preprocessed sensor-space MEG data. Matrix of dimensions: 1. MEG sensor 2. time sample 3. stimulus = [1-40] (40th stimulus = silence) 4. block of trials meg_labels = 4D MEG scanner sensor labels. Matrix of dimensions: 1. MEG sensor time = time to sound onset (in ms; negative = before sound onset). Matrix of dimensions: 1. time sample session_block = MEG session and within-session block identifiers. Matrix of dimensions: 1. block of trials 2. identifier = [session within-session block] speaker_gender = speaker gender for each block of trials (female = 1). Matrix of dimensions: 1. block of trials ./participants/participant-xx/meg/beamforming_grid.mat contents: maskv = spm_vol structure for the analysis mask mask = binary analysis mask invox = indices to in-mask voxels. Note: >> invox = find(mask(:)>0) >> mask(invox) = 1 grid = Fieldtrip structure describing the beamforming grid ./participants/participant-xx/meg/meg_beamforming_filter.mat contents: maskv = spm_vol structure for the analysis mask mask = binary analysis mask invox = indices to in-mask voxels. Note: >> invox = find(mask(:)>0) >> mask(invox) = 1 beamforming_filter = across-blocks SVD-based beamforming filter. Matrix of dimensions: 1. SVD component (note: first SVD component = optimal filter). 2. MEG sensor 3. beamforming grid voxel 4. block of trials Note: beamforming_filter usage. Use the following code to beamform the sensor-space MEG data at a given time sample = time_sample using the optimal filter: >> optim_filter = beamforming_filter(1,:,:,:); >> time_sample = 1; >> meg_selected = meg(:,time_sample,:,:); >> beam_meg = mtimesx(optim_filter,permute(meg_selected,[1 3 2 4])); where beam_meg = optimal filter beamforming. Matrix of dimensions: 1. time sample (= 1 in the example); 2. stimulus = [1-40] (40th stimulus = silence) 3. beamforming grid voxel 4. block of trials ./participants/participant-xx/meg/meg_rdms.mat contents: maskv = spm_vol structure for the analysis mask mask = binary analysis mask invox = indices to in-mask voxels. Note: >> invox = find(mask(:)>0) >> mask(invox) = 1 RDMs = MEG RDMs. Matrix of dimensions: 1. stimulus pair (ordered as required by squareform.m to yield square distance matrix) 2. in-mask voxel (invox) 3. time sample time = time to sound onset (in ms; negative = before sound onset). Matrix of dimensions: 1. time sample ./participants/participant-xx/meg/meg_rdms_ranked.mat contents: maskv = spm_vol structure for the analysis mask mask = binary analysis mask invox = indices to in-mask voxels. Note: >> invox = find(mask(:)>0) >> mask(invox) = 1 RDMs = ranked MEG RDMs. Matrix of dimensions: 1. stimulus pair (ordered as required by squareform.m to yield square distance matrix) 2. in-mask voxel (invox) 3. time sample time = time to sound onset (in ms; negative = before sound onset). Matrix of dimensions: 1. time sample ./group/anatomy contents: dartel_template.nii = dartel template dartel_template_mask = mask for considered dartel template voxels dartel_template_fmri_mask.nii = dartel-space fMRI analysis mask dartel_template_meg_mask.nii = dartel-space MEG analysis mask dartel_template_mni.nii = MNI-normalized dartel template y_dartel_to_mni.nii = dartel-to-MNI deformation field icbm152_nlin_sym_09c_no_cerebellum.nii = ICBM152 template without cerebellum ./group/fmri contents: group-level T statistics for behaviour/fMRI RDM correlations in dartel and MNI space (see example_code.m) ./group/meg contents: group-level T statistics for behaviour/MEG RDM correlations in dartel and MNI space (see example_code.m)