DOI: 10.5281/zenodo.13903557
title: A fMRI dataset in response to large number of short natural dynamic facial expression videos

description: #A fMRI dataset in response to large number of short natural dynamic facial expression videosNatural facial expressions dataset (NFED),a dataset of functional magnetic resonance imaging (fMRI) responses to 1,320 short (3s) facial expression video clips.NFED offers researchers fMRI data that enables them to investigate the neural mechanisms involved in processing emotional information communicated by facial expression videos in real-world environments.The dataset contains raw data, pre-processed volume data,pre-processed  surface data and suface-based analyzed data.To get more details, please refer to the paper at {website} and the dataset at https://openneuro.org/datasets/ds005047## Preprocess procedureThe MRI data were preprocessed by using Kay et al, combining code written in MATLAB and certain tools from FreeSurfer, SPM,and FSL(http://github.com/kendrickkay). We used FreeSurfer software (http://surfer.nmr.mgh.harvard.edu) to construct the pial and white surfaces of participants from the T1 volume. Additionally, we established an intermediate gray matter surface between the pial and the white surfaces for all participants.**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/volume_pre-process/**Detailed usage notes are available in codes, please read carefully and modify variables to satisfy your customed environment.## GLM of main experimentWe utilized a single-trial General Linear Model (GLMsingle) (https://github.com/cvnlab/GLMsingle) approach, an advanced denoising approach in MATLAB R2019a, to model the pre-processed fMRI data from main experiment. For single trials, the method of GLM was developed to offer estimations of BOLD response magnitudes ('betas'). GLMsingle requires only fMRI time series data and a design matrix as inputs, integrating three techniques to enhance the accuracy of experimental GLM beta estimates. Firstly, for each voxel, a custom HRF is identified from a library of candidate functions. Secondly, cross-validation is utilized to derive a set of noise regressors from voxels unrelated to the experimental paradigm. Thirdly, to improve the stability of beta estimates for closely spaced trials, ridge regression is employed on a voxel-wise basis to regularize the betas. In this study, three betas were calculated by analyzing the BOLD response corresponding to individual video onset ranging from 1 to 3 seconds with 1-second intervals. We produced individual GLMsingle models for each session (consisted of 4 training runs and 2 test runs). In general, for each video within the training set, 2 (repetitions) x 3 (seconds) betas were acquired. Similarly, for each video within the testing set, 10 (repetitions) x 3 (seconds) betas were acquired. The utilization of repetitions enabled us to acquire video-evoked responses with a high signal-to-noise ratio (SNR).The regressors in the GLMsingle toolbox mainly include the following categories:1.Experimental Design Matrix: This is constructed based on the experimental design, including experimental conditions, task events, etc., and is used to estimate the BOLD (Blood - Oxygen - Level - Dependent) signal response.2.Data-driven nuisance regressors: The data-driven nuisance regressors used in the GLMdenoise technique. These are identified by analyzing the data itself and are used to remove noise and improve the accuracy of beta estimation.3.Physiological noise regressors: May include indicators of physiological signals such as heart rate and respiration, and are used to correct the influence of physiological noise on the BOLD signal.4.Movement parameter regressors: Usually include head movement parameters, such as translation and rotation, and are used to correct signal changes caused by head movement.5.Polynomial regressors: Polynomial terms used to o characterize the baseline signal level.**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/GLMsingle-main-experiment/matlab/NFED_GLMsingle.m**#### retinotopic mappingThe fMRI data from the the population receptive field experiment were analyzed by a pRF model implemented in the analyzePRF toolbox (http://cvnlab.net/analyzePRF/) to characterize individual retinotopic representation. Make sure to download required software mentioned in the code.**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/Functional-localizer-experiment-analysis/s4a_analysis_prf.m**#### fLoc experimentWe used GLMdenoise,a data-driven denoising method,to analyze the pre-processed fMRI data from the fLoc experiment.We used a "condition-split" strategy to code the 10 stimulus categories, splitting the trials related to each category into individual conditions in each run. Six response estimates (beta values) for each category were produced by using six condition-splits.To quantify selectivity for various categories and domains,we computed t-values using the GLM beta values after fitting the GLM.The regions of interest with category selectivity for each participant were defined by using the resulting maps.**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/Functional-localizer-experiment-analysis/s4a_analysis_floc.m**## Validation### Basic quality controlThe fundamental quality control suggests that the data displays good quality. To evaluate the quality of the structural data obtained from NFED, we employed four crucial metrics:Coefficient of Joint Variation(CJV), Contrast-to-Noise Ratio (CNR), Signal-to-Noise Ratio in Grey Matter (SNR_GM), and Signal-to-Noise Ratio in White Matter (SNR_WM). Specifically, the CJV is between white matter(WM) and grey matter(GM).The CNR assesses the relationship between the contrast of GM and WM with the noise present in the image. The SNR assesses the connection between the mean signal measurements and the noise present in the image. The SNR assessment is conducted individually for GM and WM.For quality control of the NFED’s functional scans, we assessed the amount of head motion for each participant and the temporal signal-to-noise ratio (tSNR) of the time-series data, separately.**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/T1_Image-quality-metrics/T1data/noiseeval/cal_SNRindex.m****code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/FD/FD.py****code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/tSNR/tSNR.py**### The visual cortex exhibits reliable BOLD responses to natural facial expression videos stimuli in main experiment.In the main experiment, there were 5 participants who accomplished 10 sessions, each consisting of of 4 training runs and 2 test runs, making a total of 60 runs(40 training and 20 test runs) in all. For each run, 30 (videos)*2 (repetitions) x 3 (seconds) betas were acquired. Z-score normalization was performed on the raw betas at each voxel for each run.The betas were then averaged across stimulus repetitions to generate a vector of betas. In general, 44 runs (40 training runs and 4 test runs)*90 betas were acquired. Hence, the test-retest reliability of responses to these videos in main experiment were evaluated through computing the Pearson correlation between the 90 betas obtained from the even runs and odd runs on each vertex.**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/validation/reliability_face/reliability_face.py**### noise cellingThe code are available at  https://openneuro.org/datasets/ds005047/validation/code/noise_celling/sub-xx" store the intermediate files required for running the program**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/noise_celling/Noise_Ceiling.py**### Correspondence between human brain and DCNNThe code are available at  https://openneuro.org/datasets/ds005047. We combined the data from main experiment and functional localizer experiments to build an encoding model to replicate the hierarchical correspondences of representation between the brain and the DCNN. The encoding models were built to map artificial representations from each layer of the pre-trained VideoMAEv2 to neural representations from each area of the human visual cortex as defined in the multimodal parcellation atlas.**code: https://openneuro.org/datasets/ds005047/derivatives/validation/code/dnnbrain/**### Semantic metadata of action and expression labels reveal that NFED can encode temporal and spatial stimuli features in the brainThe code are available at  https://openneuro.org/datasets/ds005047/validation/code/semantic_metadata/xx_xx_semantic_metadata" store the intermediate files required for running the program.**code:https://openneuro.org/datasets/ds005047/derivatives/validation/code/semantic_metadata/**## results The results can be viewed at  "https://openneuro.org/datasets/ds005047/derivatives/validation/results/brain_map_individual".## Whole-brain mappingThe whole-brain data mapped to the cerebral cortex as obtained from the technical validation.**code: https://openneuro.org/datasets/ds005047/derivatives/validation/results/show_results_allbrain/Showresults.m**## Mannually prepared environmentWe provide the *requirements.txt* to install python packages used in these codes. However, some packages like *GLM* and *pre-processing* require external dependecies and we have provided the packages in the corresponding file.## stimuliThe video stimuli used in the NFED experiment are saved in the "stimuli" folders.