Overview

• The Naturalistic Neuroimaging Database (NNDb v2.0) contains datasets from 86 human participants doing the NIH Toolbox and then watching one of 10 full-length movies during functional magnetic resonance imaging (fMRI).The participants were all right-handed, native English speakers, with no history of neurological/psychiatric illnesses, with no hearing impairments, unimpaired or corrected vision and taking no medication. Each movie was stopped in 40-50 minute intervals or when participants asked for a break, resulting in 2-6 runs of BOLD-fMRI. A 10 minute high-resolution defaced T1-weighted anatomical MRI scan (MPRAGE) is also provided.
• The NNDb V2.0 is now on Neuroscout, a platform for fast and flexible re-analysis of (naturalistic) fMRI studies. See: https://neuroscout.org/

v2.0 Changes

• Overview
  • We have replaced our own preprocessing pipeline with that implemented in AFNI’s afni_proc.py, thus changing only the derivative files. This introduces a fix for an issue with our normalization (i.e., scaling) step and modernizes and standardizes the preprocessing applied to the NNDb derivative files. We have done a bit of testing and have found that results in both pipelines are quite similar in terms of the resulting spatial patterns of activity but with the benefit that the afni_proc.py results are 'cleaner' and statistically more robust.

• Normalization

  • Emily Finn and Clare Grall at Dartmouth and Rick Reynolds and Paul Taylor at AFNI, discovered and showed us that the normalization procedure we used for the derivative files was less than ideal for timeseries runs of varying lengths. Specifically, the 3dDetrend flag -normalize makes 'the sum-of-squares equal to 1'. We had not thought through that an implication of this is that the resulting normalized timeseries amplitudes will be affected by run length, increasing as run length decreases (and maybe this should go in 3dDetrend’s help text). To demonstrate this, I wrote a version of 3dDetrend’s -normalize for R so you can see for yourselves by running the following code:

  # Generate a resting state (rs) timeseries (ts)
  # Install / load package to make fake fMRI ts
  # install.packages("neuRosim")
  library(neuRosim)
  # Generate a ts
  ts.rs <- simTSrestingstate(nscan=2000, TR=1, SNR=1)
  # 3dDetrend -normalize
  # R command version for 3dDetrend -normalize -polort 0 which normalizes by making "the sum-of-squares equal to 1"
  # Do for the full timeseries
  ts.normalised.long <- (ts.rs-mean(ts.rs))/sqrt(sum((ts.rs-mean(ts.rs))^2));
  # Do this again for a shorter version of the same timeseries
  ts.shorter.length <- length(ts.normalised.long)/4
  ts.normalised.short <- (ts.rs[1:ts.shorter.length]- mean(ts.rs[1:ts.shorter.length]))/sqrt(sum((ts.rs[1:ts.shorter.length]- mean(ts.rs[1:ts.shorter.length]))^2));
  # By looking at the summaries, it can be seen that the median values become  larger
  summary(ts.normalised.long)
  summary(ts.normalised.short)
  # Plot results for the long and short ts
  # Truncate the longer ts for plotting only
  ts.normalised.long.made.shorter <- ts.normalised.long[1:ts.shorter.length]
  # Give the plot a title
  title <- "3dDetrend -normalize for long (blue) and short (red) timeseries";
  plot(x=0, y=0, main=title, xlab="", ylab="", xaxs='i', xlim=c(1,length(ts.normalised.short)), ylim=c(min(ts.normalised.short),max(ts.normalised.short)));
  # Add zero line
  lines(x=c(-1,ts.shorter.length), y=rep(0,2), col='grey');
  # 3dDetrend -normalize -polort 0 for long timeseries
  lines(ts.normalised.long.made.shorter, col='blue');
  # 3dDetrend -normalize -polort 0 for short timeseries
  lines(ts.normalised.short, col='red');
  

• Standardization/modernization

  • The above individuals also encouraged us to implement the afni_proc.py script over our own pipeline. It introduces at least three additional improvements: First, we now use Bob’s @SSwarper to align our anatomical files with an MNI template (now MNI152_2009_template_SSW.nii.gz) and this, in turn, integrates nicely into the afni_proc.py pipeline. This seems to result in a generally better or more consistent alignment, though this is only a qualitative observation. Second, all the transformations / interpolations and detrending are now done in fewers steps compared to our pipeline. This is preferable because, e.g., there is less chance of inadvertently reintroducing noise back into the timeseries (see Lindquist, Geuter, Wager, & Caffo 2019). Finally, many groups are advocating using tools like fMRIPrep or afni_proc.py to increase standardization of analyses practices in our neuroimaging community. This presumably results in less error, less heterogeneity and more interpretability of results across studies. Along these lines, the quality control (‘QC’) html pages generated by afni_proc.py are a real help in assessing data quality and almost a joy to use.

• New afni_proc.py command line

  • The following is the afni_proc.py command line that we used to generate blurred and censored timeseries files. The afni_proc.py tool comes with extensive help and examples. As such, you can quickly understand our preprocessing decisions by scrutinising the below. Specifically, the following command is most similar to Example 11 for ‘Resting state analysis’ in the help file (see https://afni.nimh.nih.gov/pub/dist/doc/program_help/afni_proc.py.html): afni_proc.py \ -subj_id "$sub_id_name_1" \ -blocks despike tshift align tlrc volreg mask blur scale regress \ -radial_correlate_blocks tcat volreg \ -copy_anat anatomical_warped/anatSS.1.nii.gz \ -anat_has_skull no \ -anat_follower anat_w_skull anat anatomical_warped/anatU.1.nii.gz \ -anat_follower_ROI aaseg anat freesurfer/SUMA/aparc.a2009s+aseg.nii.gz \ -anat_follower_ROI aeseg epi freesurfer/SUMA/aparc.a2009s+aseg.nii.gz \ -anat_follower_ROI fsvent epi freesurfer/SUMA/fs_ap_latvent.nii.gz \ -anat_follower_ROI fswm epi freesurfer/SUMA/fs_ap_wm.nii.gz \ -anat_follower_ROI fsgm epi freesurfer/SUMA/fs_ap_gm.nii.gz \ -anat_follower_erode fsvent fswm \ -dsets media_?.nii.gz \ -tcat_remove_first_trs 8 \ -tshift_opts_ts -tpattern alt+z2 \ -align_opts_aea -cost lpc+ZZ -giant_move -check_flip \ -tlrc_base "$basedset" \ -tlrc_NL_warp \ -tlrc_NL_warped_dsets \ anatomical_warped/anatQQ.1.nii.gz \ anatomical_warped/anatQQ.1.aff12.1D \ anatomical_warped/anatQQ.1_WARP.nii.gz \ -volreg_align_to MIN_OUTLIER \ -volreg_post_vr_allin yes \ -volreg_pvra_base_index MIN_OUTLIER \ -volreg_align_e2a \ -volreg_tlrc_warp \ -mask_opts_automask -clfrac 0.10 \ -mask_epi_anat yes \ -blur_to_fwhm -blur_size $blur \ -regress_motion_per_run \ -regress_ROI_PC fsvent 3 \ -regress_ROI_PC_per_run fsvent \ -regress_make_corr_vols aeseg fsvent \ -regress_anaticor_fast \ -regress_anaticor_label fswm \ -regress_censor_motion 0.3 \ -regress_censor_outliers 0.1 \ -regress_apply_mot_types demean deriv \ -regress_est_blur_epits \ -regress_est_blur_errts \ -regress_run_clustsim no \ -regress_polort 2 \ -regress_bandpass 0.01 1 \ -html_review_style pythonic We used similar command lines to generate ‘blurred and not censored’ and the ‘not blurred and not censored’ timeseries files (described more fully below). We will provide the code used to make all derivative files available on our github site (https://github.com/lab-lab/nndb).

  We made one choice above that is different enough from our original pipeline that it is worth mentioning here. Specifically, we have quite long runs, with the average being ~40 minutes but this number can be variable (thus leading to the above issue with 3dDetrend’s -normalise). A discussion on the AFNI message board with one of our team (starting here, https://afni.nimh.nih.gov/afni/community/board/read.php?1,165243,165256#msg-165256), led to the suggestion that '-regress_polort 2' with '-regress_bandpass 0.01 1' be used for long runs. We had previously used only a variable polort with the suggested 1 + int(D/150) approach. Our new polort 2 + bandpass approach has the added benefit of working well with afni_proc.py.

  Which timeseries file you use is up to you but I have been encouraged by Rick and Paul to include a sort of PSA about this. In Paul’s own words: * Blurred data should not be used for ROI-based analyses (and potentially not for ICA? I am not certain about standard practice). * Unblurred data for ISC might be pretty noisy for voxelwise analyses, since blurring should effectively boost the SNR of active regions (and even good alignment won't be perfect everywhere). * For uncensored data, one should be concerned about motion effects being left in the data (e.g., spikes in the data). * For censored data: * Performing ISC requires the users to unionize the censoring patterns during the correlation calculation. * If wanting to calculate power spectra or spectral parameters like ALFF/fALFF/RSFA etc. (which some people might do for naturalistic tasks still), then standard FT-based methods can't be used because sampling is no longer uniform. Instead, people could use something like 3dLombScargle+3dAmpToRSFC, which calculates power spectra (and RSFC params) based on a generalization of the FT that can handle non-uniform sampling, as long as the censoring pattern is mostly random and, say, only up to about 10-15% of the data. In sum, think very carefully about which files you use. If you find you need a file we have not provided, we can happily generate different versions of the timeseries upon request and can generally do so in a week or less.

• Effect on results

  • From numerous tests on our own analyses, we have qualitatively found that results using our old vs the new afni_proc.py preprocessing pipeline do not change all that much in terms of general spatial patterns. There is, however, an

Image acquisition

MRI scans were performed on each participant using a GE Discovery MR750 3T MRI scanner (GE Healthcare, Chicago, IL). Each scanning session consisted of acquiring the following fMRI datasets: an individual finger somatotopic mapping run that was 10 min (240 volumes) in duration, followed by three or four haptic task runs that were each 11 min in duration (265 volumes). Standard T2*-weighted echo planar imaging (EPI) sequence parameters were used to obtain the functional images and ten reverse-blip volumes with the following parameters: repetition time (TR) = 2500 ms, echo time (TE) = 30 ms, phase encoding = A to P, flip angle = 75°, matrix = 77 × 77, axial slices = 42, in-plane field of view = 186 × 186 mm^2, in-plane resolution = 2.58 × 2.58 mm^2, and slice thickness = 3.0 mm (whole-brain coverage). After the fMRI acquisition, a T1-weighted magnetization prepared rapid gradient echo (MPRAGE) high-resolution anatomical volume was obtained with the following parameters: voxel size = 1.0 × 1.0 × 1.0 mm^3, TR = 7040 ms, TE = 3480 ms, matrix = 256 × 256 × 172, and duration = 5 min.

FMRI tasks

Each participant was asked to perform four fMRI task runs that focused on roughness estimation (RE), curve estimation (CE) and hand motion and visual control (HMVC). Each fMRI task run consisted of 48 trials (16 trials × 3 tasks), which were pseudorandomly presented. Participants were informed that a series of surfaces would be presented. Their task was to estimate the roughness or curve of each stimulus or to move their fingers. For detail, please reach the paper https://www.sciencedirect.com/science/article/pii/S1053811921000318.

Data analysis

FMRI data were analyzed using afni_proc.py” with the AFNI/SUMA in the original paper. The full afni_proc.py command used to generate the processing stream, and quality control is provided in the Supplementary material of the paper. For further information, please contact the corresponding author **(J. Yang: yang (at) okayama-u.ac.jp). **

Overview

• The Magic, Memory, Curiosity (MMC) dataset contains data from 50 healthy human adults incidentally encoding 36 videos of magic tricks inside the MRI scanner across three runs.
• Before and after incidental learning, a 10-min resting-state scan was acquired.
• The MMC dataset includes contextual incentive manipulation, curiosity ratings for the magic tricks, as well as incidental memory performance tested a week later using a surprise cued recall and recognition test .
• Working memory and constructs potentially relevant in the context of motivated learning (e.g., need for cognition, fear of failure) were additionally assessed.

Stimuli

The stimuli used here were short videos of magic tricks taken from a validated stimulus set (MagicCATs, Ozono et al., 2021) specifically created for the usage in fMRI studies. All final stimuli are available upon request. The request procedure is outlined in the Open Science Framework repository associated with the MagicCATs stimulus set (https://osf.io/ad6uc/).

Participant responses

Participants’ responses to demographic questions, questionnaires, and performance in the working memory assessment as well as both tasks are available in comma-separated value (CSV) files. Demographic (MMC_demographics.csv), raw questionnaire (MMC_raw_quest_data.csv) and other score data (MMC_scores.csv) as well as other information (MMC_other_information.csv) are structured as one line per participant with questions and/or scores as columns. Explicit wordings and naming of variables can be found in the supplementary information. Participant scan summaries (MMC_scan_subj_sum.csv) contain descriptives of brain coverage, TSNR, and framewise displacement (one row per participant) averaged first within acquisitions and then within participants. Participants’ responses and reaction times in the magic trick watching and memory task (MMC_experimental_data.csv) are stored as one row per trial per participant.

Preprocessing

Data was preprocessed using the AFNI (version 21.2.03) software suite. As a first step, the EPI timeseries were distortion-corrected along the encoding axis (P>>A) using the phase difference map (‘epi_b0_correct.py’). The resulting distortion-corrected EPIs were then processed separately for each task, but scans from the same task were processed together. The same blocks were applied to both task and resting-state distortion-corrected EPI data using afni_proc.py (see below): despiking, slice-timing and head-motion correction, intrasubject alignment between anatomy and EPI, intersubject registration to MNI, masking, smoothing, scaling, and denoising. For more details, please refer to the data descriptor (LINK) or the Github repository (https://github.com/stefaniemeliss/MMC_dataset).

afni_proc.py -subj_id "${subjstr}" \
  -blocks despike tshift align tlrc volreg mask blur scale regress \
  -radial_correlate_blocks tcat volreg \
  -copy_anat $derivindir/$anatSS \
  -anat_has_skull no \
  -anat_follower anat_w_skull anat $derivindir/$anatUAC \
  -anat_follower_ROI aaseg anat $sswindir/$fsparc \
  -anat_follower_ROI aeseg epi $sswindir/$fsparc \
  -anat_follower_ROI FSvent epi $sswindir/$fsvent \
  -anat_follower_ROI FSWMe epi $sswindir/$fswm \
  -anat_follower_ROI FSGMe epi $sswindir/$fsgm \
  -anat_follower_erode FSvent FSWMe \
  -dsets $epi_dpattern \
  -outlier_polort $POLORT \
  -tcat_remove_first_trs 0 \
  -tshift_opts_ts -tpattern altplus \
  -align_opts_aea -cost lpc+ZZ -giant_move -check_flip \
  -align_epi_strip_method 3dSkullStrip \
  -tlrc_base MNI152_2009_template_SSW.nii.gz \
  -tlrc_NL_warp \
  -tlrc_NL_warped_dsets $sswindir/$anatQQ $sswindir/$matrix $sswindir/$warp \
  -volreg_base_ind 1 $min_out_first_run \
  -volreg_post_vr_allin yes \
  -volreg_pvra_base_index MIN_OUTLIER \
  -volreg_align_e2a \
  -volreg_tlrc_warp \
  -volreg_no_extent_mask \
  -mask_dilate 8 \
  -mask_epi_anat yes \
  -blur_to_fwhm -blur_size 8 \
  -regress_motion_per_run \
  -regress_ROI_PC FSvent 3 \
  -regress_ROI_PC_per_run FSvent \
  -regress_make_corr_vols aeseg FSvent \
  -regress_anaticor_fast \
  -regress_anaticor_label FSWMe \
  -regress_censor_motion 0.3 \
  -regress_censor_outliers 0.1 \
  -regress_apply_mot_types demean deriv \
  -regress_est_blur_epits \
  -regress_est_blur_errts \
  -regress_run_clustsim no \
  -regress_polort 2 \
  -regress_bandpass 0.01 1 \
  -html_review_style pythonic

Derivatives

The anat folder contains derivatives associated with the anatomical scan. The skull-stripped image created using @SSwarper is available in original and ICBM 2009c Nonlinear Asymmetric Template space as sub-[group][ID]_space-[space]_desc-skullstripped_T1w.nii.gz together with the corresponding affine matrix (sub-[group][ID]_aff12.1D) and incremental warp (sub-[group][ID]_warp.nii.gz). Output generated using @SUMA_Make_Spec_FS (defaced anatomical image, whole brain and tissue masks, as well as FreeSurfer discrete segmentations based on the Desikan-Killiany cortical atlas and the Destrieux cortical atlas) are also available as sub-[group][ID]_space-orig_desc-surfvol_T1w.nii.gz, sub-[group][ID]_space-orig_label-[label]_mask.nii.gz, and sub-[group][ID]_space-orig_desc-[atlas]_dseg.nii.gz, respectively.

The func folder contains derivatives associated with the functional scans. To enhance re-usability, the fully preprocessed and denoised files are shared as sub-[group][ID]_task-[task]_desc-fullpreproc_bold.nii.gz. Additionally, partially preprocessed files (distortion corrected, despiked, slice-timing/head-motion corrected, aligned to anatomy and template space) are uploaded as sub-[group][ID]_task-[task]_run-[1-3]_desc-MNIaligned_bold.nii.gz together with slightly dilated brain mask in EPI resolution and template space where white matter and lateral ventricle were removed (sub-[group][ID]_task-[task]_space-MNI152NLin2009cAsym_label-dilatedGM_mask.nii.gz) as well as tissue masks in EPI resolution and template space (sub-[group][ID]_task-[task]_space-MNI152NLin2009cAsym_label-[tissue]_mask.nii.gz).

The regressors folder contains nuisance regressors stemming from the output of the full afni_proc.py preprocessing pipeline. They are provided as space-delimited text values where each row represents one volume concatenated across all runs for each task separately. Those estimates that are provided per run contain the data for the volumes of one run and zeros for the volumes of other runs. This allows them to be regressed out separately for each run. The motion estimates show rotation (degree counterclockwise) in roll, pitch, and yaw and displacement (mm) in superior, left, and posterior direction. In addition to the motion parameters with respect to the base volume (sub-[group][ID]_task-[task]_label-mot_regressor.1D), motion derivatives (sub-[group][ID]_task-[task]_run[1-3]_label-motderiv_regressor.1D) and demeaned motion parameters (sub-[group][ID]_task-[task]_run[1-3]_label-motdemean_regressor.1D) are also available for each run separately. The sub-[group][ID]_task-[task]_run[1-3]_label-ventriclePC_regressor.1D files contain time course of the first three PCs of the lateral ventricle per run. Additionally, outlier fractions for each volume are provided (sub-[group][ID]_task-[task]_label-outlierfrac_regressor.1D) and sub-[group][ID]_task-[task]_label-censorTRs_regressor.1D shows which volumes were censored because motion or outlier fraction exceeded the limits specified. The voxelwise time course of local WM regressors created using fast ANATICOR is shared as sub-[group][ID]_task-[task]_label-localWM_regressor.nii.gz.