fMRI Toolbox

Various tools to perform analyses of fMRI data.

Requirements

• SPM
• rsatoolbox
• the decoding toolbox

Preprocessing

fMRI preprocessing routines. allows you to avoid the SPM GUI entirely. Parameters are defined in fmri_preproc_setParams().
To run all steps defined in the params file in one go, call fmri_preproc_runner()

Table of Contents:

.
├── fmri_preproc_coregistration.m
├── fmri_preproc_dicomImport.m
├── fmri_preproc_normalisation.m
├── fmri_preproc_realignUnwarp.m
├── fmri_preproc_runner.m
├── fmri_preproc_segmentation.m
├── fmri_preproc_setParams.m
├── fmri_preproc_slicetimeCorr.m
├── fmri_preproc_smooth.m
├── fmri_preproc_structureData.sh
└── fmri_spm_preproc_prefixes.md

GLM

Generalised Linear Model estimation, interfaces SPM and operates mostly on nifti (or whichever format you prefer) images.

Features:

• define conditions, number of nuisance regressors and runs, automatically generate design for whole session.
• estimate the model
• perform 1st level contrasts (of as many contrast as you wish, in one go)
• perform 2nd level inference, generates group-level T-Map.
• do above in leave-one-subject-out manner, for functional ROIs

Again, all parameters adjusted set in one file, fmri_glm_setParams()

Table of Contents

.
├── fmri_glm_contrast_1stLevel.m
├── fmri_glm_contrast_2ndLevel_LOSO.m
├── fmri_glm_contrast_2ndLevel.m
├── fmri_glm_contrast_generate.m
├── fmri_glm_designMatrix.m
├── fmri_glm_estimate.m
└── fmri_glm_setParams.m

RSA

Representational Similarity Analysis. Mostly custom code which operates directly on matlab matrices.

Features

• ROI-based RSA: Specify a few masks (Nifti volumes) and let the toolbox do the rest

• Searchlight RSA: Spherical Searchlight for whole-brain analyses

• different distance measures supported

  • Euclidean distance
  • Correlation distance
  • Cosine distance
  • Mahalanobis distance
  • Crossvalidated Mahalanobis distance (leave-one-run-out)

• whitening can be combined with any distance measure, as residuals for the covariance matrix are computed on the fly

• Large between-run-dissimilarity matrices also supported (within run comparison automatically set to NaN)

• batch correlation with model RDMs

  • optional recursive Gram-Schmid Orthogonalisation
  • different coefficients supported
    • spearman's rho
    • Kendall's taua
    • Pearson's r
    • bonus: simple linear regression (of models onto brain)

• batch computation of RDMs for set of ROIs

• computation of the lower and upper bounds of the noise ceiling

  • using Spearman's rank correlation
  • using Pearson correlation
  • using Kendall's taua (only approximate for LB)

• Multi-Dimensional Scaling (wrapper), T-SNE (wrapper)

• 2nd level inference

  • Signed-Rank test
  • T-test (with optional Inverse Hyperbolic tangent of coefficients)
  • export as thresholded (1-p)-images and t-images (Nifit format)

• Visualisation of single-subject/group average RDMs

• Visualisation of MDS/T-SNE projections

• Visualisation of whole-brain maps

• within ROI bar-graphs of correlation coefficients (with optional noise ceiling)

Table of Contents

.
├── compute
│   ├── fmri_rsa_compute_performRSA_ROI.m
│   ├── fmri_rsa_compute_performRSA_searchlight.m
│   ├── fmri_rsa_compute_rdmSet_avg.m
│   ├── fmri_rsa_compute_rdmSet_cval.m
│   └── fmri_rsa_compute_setParams.m
├── convert
│   ├── fmri_rsa_convert_mni2rdm.m
│   └── fmri_rsa_convert_struct2mat.m
├── disp
│   ├── fmri_rsa_disp_rdmReplicabilityBehav.m
│   ├── fmri_rsa_disp_rdmReplicability.m
│   ├── fmri_rsa_disp_showCorrs_MultipleROIs.m
│   ├── fmri_rsa_disp_showCorrs_ROI.m
│   ├── fmri_rsa_disp_showMDS.m
│   ├── fmri_rsa_disp_showRDM.m
│   ├── fmri_rsa_disp_showRDMs.m
│   ├── fmri_rsa_disp_showRewardMDS.m
│   └── fmri_rsa_disp_showTSNE.m
├── helper
│   ├── fmri_rsa_helper_betasInRewSpace.m
│   ├── fmri_rsa_helper_genImageStruct.m
│   ├── fmri_rsa_helper_getBetas.m
│   ├── fmri_rsa_helper_getResiduals.m
│   ├── fmri_rsa_helper_rdmReplicabilityBehav.m
│   ├── fmri_rsa_helper_rdmReplicability.m
│   └── fmri_rsa_helper_whiten.m
├── mds
│   └── fmri_rsa_mds_rdmToND.m
├── modelcorrs
│   ├── fmri_rsa_corrs_corrBrainRDMs_ROI.m
│   ├── fmri_rsa_corrs_corrBrainRDMs_Searchlight.m
│   ├── fmri_rsa_corrs_genCorrImagesAndMaskedCorrs.m
│   ├── fmri_rsa_corrs_genModelRDMs_cval.m
│   ├── fmri_rsa_corrs_genModelRDMs.m
│   ├── fmri_rsa_corrs_genSigImages_Searchlight.m
│   ├── fmri_rsa_corrs_noiseCeiling.m
│   ├── fmri_rsa_corrs_setParams.m
│   ├── fmri_rsa_corrs_sigtest_ROI.m
│   └── fmri_rsa_corrs_sigtest_Searchlight.m
├── tsne
│   └── fmri_rsa_tsne_rdmTo2D.m
└── wrappers
    ├── wrapper_rsa_computeRDMs_roi.m
    ├── wrapper_rsa_corr_roi.m
    ├── wrapper_rsa_makeCorrFigure_MultipleROIs.m
    ├── wrapper_rsa_makeCorrFigure_ROI.m
    ├── wrapper_rsa_makeMDSfigure.m
    ├── wrapper_rsa_makeRDMfigure.m
    ├── wrapper_rsa_makeSingleSubsRDMFigures.m
    ├── wrapper_rsa_noiseCeiling_roi.m
    └── wrapper_rsa_sigtest_roi.m

MVPA

mostly code skeletons to interface "The Decoding Toolbox"

.
├── fmri_tdt_compute2ndlevelContrast.m
├── fmri_tdt_normalisation.m
├── fmri_tdt_runSearchlightMVPA.m
├── fmri_tdt_setParamsMVPA.m
├── fmri_tdt_setParamsNormalisation.m
├── fmri_tdt_setParamsSmoothing.m
└── fmri_tdt_smooth.m

IO

helper functions to convert image files to MATLAB matrices and vice versa

.
├── fmri_io_mat2nifti.m
├── fmri_io_nifti2mat.m
├── fmri_io_reslice.m
└── fmri_io_setParams.m

Mask

generate and apply masks

.
├── fmri_mask_genGroupMask.m
├── fmri_mask_genSphericalMask.m
├── fmri_mask_mask2ind.m
├── fmri_mask_MDparcellation.m
└── fmri_mask_mni2roi.m

Helper

various auxiliary functions

.
├── fmri_helper_changeSPMpaths.m
├── fmri_helper_dispContrastVectors.m
├── fmri_helper_genConImgNames.m
├── fmri_helper_genContrastVector.m
|── fmri_helper_genVolume.m
├── fmri_helper_genWeightedContrastVector.m
├── fmri_helper_set_fileName.m
└── plot_progbar_cli.m