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This is a human fMRI dataset that investigates coding of individual faces in the visual cortex of healthy human volunteers. We also include behavioral data from a similarity judgment task, and computational models that can be used to fit both data modalities. See the associated reference (Carlin & Kriegeskorte, in press, PLOS CB) for details about the experimental protocol. In this README we focus on technical details that may be useful for re-analysing the data. The behavioral and fMRI distance matrices as well as the computational modeling efforts have been shared previously at OSF (https://osf.io/5g9rv) and zenodo (https://doi.org/10.5281/zenodo.242666), so this may be an easier way to go if you don't want to re-run the entire fMRI preprocessing pipeline. DICOM TO BIDS CONVERSION DCM files were converted to nifti using dcm2niix v1.0.20170130 (https://github.com/rordenlab/dcm2niix), and dcm2bids (https://github.com/jooh/Dcm2Bids). Anatomical images were de-faced using pydeface (https://github.com/poldracklab/pydeface). DATA ANALYSIS SETUP We analysed data using Matlab R2013A, SPM, FSL, and various custom software developed in Matlab. The following packages (and their associated dependencies) are necessary to get the included analysis code to run: * Automatic Analysis 5 (AA, https://github.com/rhodricusack/automaticanalysis) * pilab (https://github.com/jooh/pilab) * pilab_aa (https://github.com/jooh/pilab_aa) * facedistid_analysis (https://github.com/jooh/facedistid_analysis - an up to date copy is available in the code folder) In general, the AA pipeline generates all fMRI results and figures (Figs 1-2, S3-S4 Figs in the manuscript). We then extracted fMRI distance matrices from cortical regions of interests for further computational modeling (remaining figures in the manuscript). KEY FILES facedist_aa_frombids.m The master function for running the AA fMRI analysis pipeline facedist_aa_frombids_tasklist.xml Specifies which AA modules to run - note that the roiroot flag specifies an absolute path that will need updating for your file system facedist_doit_facepairs The master function for running the behavioral similarity judgment analysis facedist_doit_modeling The master function for running the computational model fits (you will need to run through facedist_aa_frombids and facedist_doit_facepairs first to generate intermediate results) derivatives/aa/aap_prov.png Nice visualisation of fMRI result provenance derivatives/rois ROI masks for fROI analysis (if you want to re-define ROIs from the localiser data you can do so using https://github.com/jooh/roitools/blob/master/spm2roi.m) misc/data_perceptual_judgment_task.mat data from behavioral similarity judgment task misc/stimuli_mri.mat video stimuli used during MRI scanning misc/stimuli_perceptual_judgments.mat video stimuli used during behavioral task A NOTE ON REPRODUCIBILITY If you run the above pipeline you will obtain results that are very similar to those in the manuscript (which, again, are publicly available on OSF/Zenodo), but not identical. This is because of the following differences with regard to the analysis in the paper: * The paper analysis used SPM8, not 12 * The paper analysis used SPM dicom conversion, not dcm2niix * The paper analysis included a super-extraneous conversion of the floating point precision on the niftis during preprocessing, which did nothing but blow up file size it turns out. * The paper analysis used a bastardised version of AA4, not 5, which probably introduces lots of subtle differences in the preprocessing parameters (for this AA version, see https://github.com/jooh/automaticanalysis/tree/v4-master) * Lots of other dependencies (including pilab) continued to be developed and improved. Note that in particular, the ROI masks were generated using the old analysis, so the results could definitely be improved by re-running ROI definition, if someone has a few days to spare... But again, discrepancies are very small and do not qualitatively change any conclusions made in the paper. Exact reproducibility in neuroimaging is hard. If you want to inspect the AA analysis that is reported in the paper, please get in touch and we will see if there is a way to convince the MRC to let you have access to non-anonymous data. REFERENCE Carlin, J.D & Kriegeskorte, N. (in press). Adjudicating between face-coding models with individual-face fMRI responses. PLOS Computational Biology. See BioRXiv for a preprint (2017, original version 2015): https://doi.org/10.1101/029603 CONTACT Johan Carlin, MRC CBU, Cambridge, UK. johan.carlin@gmail.com ### Comments added by Openfmri Curators ### =========================================== General Comments ---------------- Defacing -------- Pydeface was used on all anatomical images to ensure deindentification of subjects. The code can be found at https://github.com/poldracklab/pydeface Quality Control --------------- Mriqc was run on the dataset. Results are located in derivatives/mriqc. Learn more about it here: https://mriqc.readthedocs.io/en/latest/ Where to discuss the dataset ---------------------------- 1) www.openfmri.org/dataset/ds000232/ See the comments section at the bottom of the dataset page. 2) www.neurostars.org Please tag any discussion topics with the tags openfmri and ds000232. 3) Send an email to submissions@openfmri.org. Please include the accession number in your email. Known Issues ------------ N/A Bids-validator Output --------------------- 1: Not all subjects/sessions/runs have the same scanning parameters. (code: 39 - INCONSISTENT_PARAMETERS) /sub-02/ses-01/anat/sub-02_ses-01_T1w.nii.gz /sub-02/ses-01/func/sub-02_ses-01_task-localizer_run-01_bold.nii.gz /sub-02/ses-01/func/sub-02_ses-01_task-localizer_run-02_bold.nii.gz /sub-02/ses-01/func/sub-02_ses-01_task-main_run-01_bold.nii.gz /sub-02/ses-01/func/sub-02_ses-01_task-main_run-02_bold.nii.gz /sub-10/ses-01/anat/sub-10_ses-01_T1w.nii.gz Summary: Available Tasks: Available Modalities: 1120 Files, 34.38GB localizer T1w 10 - Subjects main bold 4 - Sessions