A presentation of our NeuroImage paper "Does Parametric fMRI Analysis with SPM Yield Valid Results? - An Empirical Study of 1484 Rest Datasets"

Abstract The validity of parametric functional magnetic resonance imaging (fMRI) analysis has only been reported for simulated data. Recent advances in computer science and data sharing make it possible to analyze large amounts of real fMRI data. In this study, 1484 rest datasets have been analyzed in SPM8, to estimate true familywise error rates. For a familywise significance threshold of 5%, significant activity was found in 1%-70% of the 1484 rest datasets, depending on repetition time, paradigm and parameter settings. This means that parametric significance thresholds in SPM both can be conservative or very liberal. The main reason for the high familywise error rates seems to be that the global AR(1) auto correlation correction in SPM fails to model the spectra of the residuals, especially for short repetition times. The findings that are reported in this study cannot be generalized to parametric fMRI analysis in general, other software packages may give different results. By using the computational power of the graphics processing unit (GPU), the 1484 rest datasets were also analyzed with a random permutation test. Significant activity was then found in 1%-19% of the datasets. These findings speak to the need for a better model of temporal correlations in fMRI timeseries.

The included archive of .csv files has one .csv file for each time index. Each .csv file contains a single matrix, which has one column for each of the spatial coordinates x, y, z and one column for the fMRI signal amplitude at voxel (x, y, z) at that time index. This is normalized scan data for a single patient from our study, described in the paper, with an ACC (anterior cingulate cortex) mask applied. This is the data we used, together with our software implementation of our workflow (available at https://github.com/regalski/Wayne-State-TDA), to produce the persistence diagrams, vineyards, and loop locations pictured in the figures and described in the Workflow section of our paper. (ZIP)

SummaryData sets are from healthy women, from two studies, and are separated into four folders containing fMRI data from the brain (Brain1 and Brain2) and from the brainstem and cervical spinal cord (BSSC1 and BSSC2). The data have already been pre-processed by converting to NIfTI format, and applying co-registration, slice-timing correction, spatial normalization, and physiological noise removal, as detailed below. Each folder contains participant information in terms of the stimulation temperature and pain rating for each fMRI run, and the age of each participant.Details Functional MRI data sets are included from the healthy control groups in two previous studies of pain processing in women, conducted between 2013-2014 (referred to as ‘Study 1’), and between 2018-2019 (referred to as ‘Study 2’). Full details of the methods used for Study 1 have been previously published (11). All participants were free of previous neurological injury or disease, and were free of any contraindications for MRI. Both studies involved noxious hot stimulation of the right hand, with temperatures that were calibrated to elicit moderate pain. Also, both studies involved two sessions, one for brain fMRI, and the other for fMRI of the brainstem and cervical spinal cord. All methods were reviewed and approved by our institutional research ethics board. Study 1 consists of data from 15 females (age range = 21-55, average 39.1 ± 10.2 years (mean ± std)). Study 2 consists of data from 18 females (age range = 21-59, average 36 ± 11.3 years). Multiple fMRI runs were acquired from each participant. Participants in both studies were characterized by completing questionnaires to assess anxiety, depression, pain catastrophizing, social desirability, and health-related factors. Prior to fMRI data collection, each participant underwent a 1-hour training session, during which they were introduced to the experimental pain stimulus and study design, and were trained how to rate their pain using a standardized numerical pain intensity scale (NPS). The scale ranges from 0 to 100 in increments of 5, with verbal descriptors at increments of 10 (11, 43). In both studies, the stimulus consisted of heat applied briefly to the skin overlying the thenar eminence (base of the thumb) on the right hand. The stimulus devices, temperatures, and timings, were different for the two studies, as detailed below. Study 1 Heat stimuli were applied to the hand by means of an MRI-compatible Peltier thermode (Medoc, Ramat Yishai, Israel), which was attached to the participant’s right hand. During heat stimulation, the temperature was rapidly increased and decreased under computer control. Stimulation consisted of 11 heat spikes applied every 3 seconds in order to evoke temporal summation of second pain (TSSP). The stimulation period was preceded by a 52 second rest period and followed by a 65 second rest period. Participants viewed instructions on a rear-projection screen which notified them when a new scan was about to begin, when the application of the heat stimulus would begin, and when to report their ratings for the first and last heat contacts. Study 2 Heat stimuli were applied by means of an MRI-compatible Robotic Contact-Heat Thermal Heat Stimulator (RTS-2) which pneumatically raises and lowers a heated aluminum thermode to make contact with the participants’ skin, with software-controlled timing and temperature. The stimulation paradigm consisted of an initial 60 seconds of “baseline” scanning. This is one condition which was randomly interleaved with a condition without stimulation. Participants were then informed that stimulation would begin 1 minute later, at the 120 second mark, and then 10 heat contacts at the calibrated temperature were administered over 30 seconds. This 30 second stimulation period was followed by a 120 second rest period, for a total time of 4 minutes and 30 seconds. All image data were acquired using a 3 tesla whole-body MRI system (Siemens Magnetom Trio; Siemens, Erlangen, Germany). For all studies, participants were positioned supine and were supported by foam padding as needed to ensure comfort and minimize bulk body movement. Imaging methods were optimized for each region (brain, or BS/SC), due to the different imaging challenges, and were acquired with T2*-weighted imaging in the brain, and T2-weighted imaging in the brainstem and spinal cord, in order to provide an optimal balance of image quality and BOLD sensitivity in both regions (31, 44, 45). Study 1 Brain fMRI Functional images were acquired in 49 contiguous axial slices oriented parallel to the anterior commissure-posterior commissure (AC-PC) line using a T2*-weighted gradient-echo echo-planar imaging (GE-EPI) sequence (TR = 3 s, TE = 30 ms, Flip Angle = 90°, FOV = 192 mm x 192 mm, Matrix = 64 x 64, Resolution = 3 x 3 x 3 mm3). A 12-channel head coil was used for detection of the MRI signal, with a body coil for transmission of RF pulses. A total of 50 volumes were acquired for each imaging run. Five runs of the same type were combined for each fMRI data set. Study 2 Brain fMRI Functional images were acquired in 66 contiguous axial slices using a T2*-weighted GE-EPI sequence (TR = 2 s, TE = 30 ms, Flip Angle = 84°, Multiband = 3, 7/8 Partial Fourier, FOV = 180 mm x 180 mm, Matrix = 90 x 90, Resolution = 2 x 2 x 2 mm3). A 32-channel head coil was used for detection of the MR signal, with a body coil for transmission of RF pulses. A total of 135 volumes were acquired for each imaging run. Three to five runs of the same type were combined for each fMRI data set. Study 1 and Study 2, Brainstem and Spinal Cord fMRI Functional MRI data were acquired with a T2-weighted half-fourier single-shot fast spin-echo (HASTE) sequence. Data were acquired in 9 contiguous sagittal slices with a repetition time (TR) of 0.75 sec/slice, an echo time of 76 msec to optimize the T2-weighted BOLD sensitivity, and a 28 × 21 cm field-of-view with 1.5 × 1.5 × 2 mm3 resolution (45). The imaging volume spanned from the T1 vertebra to above the thalamus, and spanned the entire cervical spinal cord and brainstem left-to-right. Data were acquired using the upper elements of a spine receiver-array coil, a posterior neck coil, and the posterior half of a 12-channel head coil. The receiver elements were adjusted based on the participant’s size, as needed. A body coil was used for transmitting radio-frequency (RF) excitation pulses. In Study 1, a total of 138 volumes were acquired for each condition (over 6 repeated runs). In Study 2, a total of 200 volumes were acquired for each condition (over 5 repeated runs). The image quality was enhanced by means of spatial suppression pulses anterior to the spine to reduce motion artefacts caused by breathing, swallowing, etc, and motion compensating gradients in the head-foot direction.Data were pre-processed in MATLAB using SPM12 for brain data (available at https://www.fil.ion.ucl.ac.uk/spm/software/spm12/ ), and SpinalfMRI9 for brainstem and spinal cord data (available at https://www.queensu.ca/academia/stromanlab/home/fmri-analysis-software ).Data were converted to NIfTI format and pre-processed, including co-registration (i.e. motion correction), slice-timing correction, spatial normalization to standardized templates (MNI152 for brain, and combination of MNI152 and PAM50 for brainstem and cervical spinal cord), and noise removal.The data included in the repository have been pre-processed, and are in separate folders for Study 1 and Study 2, and for brain fMRI data, and brainstem and spinal cord (BSSC) data. Corresponding data of pain ratings and stimulation temperatures for each run are included.

Note: This dataset is the combination of four related datasets that were originally hosted on OpenfMRI.org: ds000113, ds000113b, ds000113c and ds000113d. The combined dataset is now in BIDS format and is simply referred to as ds000113 on OpenNeuro.org.

For more information about the project visit: http://studyforrest.org

This dataset contains high-resolution functional magnetic resonance (fMRI) data from 20 participants recorded at high field strength (7 Tesla) during prolonged stimulation with an auditory feature film ("Forrest Gump''). In addition, a comprehensive set of auxiliary data (T1w, T2w, DTI, susceptibility-weighted image, angiography) as well as measurements to assess technical and physiological noise components have been acquired. An initial analysis confirms that these data can be used to study common and idiosyncratic brain response pattern to complex auditory stimulation. Among the potential uses of this dataset is the study of auditory attention and cognition, language and music perception as well as social perception. The auxiliary measurements enable a large variety of additional analysis strategies that relate functional response patterns to structural properties of the brain. Alongside the acquired data, we provide source code and detailed information on all employed procedures — from stimulus creation to data analysis. (https://www.nature.com/articles/sdata20143)

The dataset also contains data from the same twenty participants while being repeatedly stimulated with a total of 25 music clips, with and without speech content, from five different genres using a slow event-related paradigm. It also includes raw fMRI data, as well as pre-computed structural alignments for within-subject and group analysis.

Additionally, seven of the twenty subjects participated in another study: empirical ultra high-field fMRI data recorded at four spatial resolutions (0.8 mm, 1.4 mm, 2 mm, and 3 mm isotropic voxel size) for orientation decoding in visual cortex — in order to test hypotheses on the strength and spatial scale of orientation discriminating signals. (https://www.sciencedirect.com/science/article/pii/S2352340917302056)

Finally, there are additional acquisitions for fifteen of the the twenty participants: retinotopic mapping, a localizer paradigm for higher visual areas (FFA, EBA, PPA), and another 2 hour movie recording with 3T full-brain BOLD fMRI with simultaneous 1000 Hz eyetracking.

For more information about the project visit: http://studyforrest.org

Dataset content overview

Stimulus material and protocol descriptions

./sourcedata/acquisition_protocols/04-sT1W_3D_TFE_TR2300_TI900_0_7iso_FS.txt ./sourcedata/acquisition_protocols/05-sT2W_3D_TSE_32chSHC_0_7iso.txt ./sourcedata/acquisition_protocols/06-VEN_BOLD_HR_32chSHC.txt ./sourcedata/acquisition_protocols/07-DTI_high_2iso.txt ./sourcedata/acquisition_protocols/08-field_map.txt Philips-specific MRI acquisition parameters dumps (plain text) for structural MRI (T1w, T2w, SWI, DTI, fieldmap -- in this order)

./sourcedata/acquisition_protocols/task01_fmri_session1.pdf ./sourcedata/acquisition_protocols/task01_fmri_session2.pdf ./sourcedata/acquisition_protocols/angio_session.pdf Siemens-specific MRI acquisition parameters dumps (PDF format) for functional MRI and angiography.

./stimuli/annotations/german_audio_description.csv

Audio-description transcript

This transcript contains all information on the audio-movie content that cannot be inferred from the DVD release — in a plain text, comma-separated-value table. Start and end time stamp, as well as the spoken text are provided for each continuous audio description segment.

./stimuli/annotations/scenes.csv

Movie scenes

A plain text, comma-separated-value table with start and end time for all 198 scenes in the presented movie cut. In addition, each table row contains whether a scene takes place indoors or outdoors.

./stimuli/generate/generate_melt_cmds.py Python script to generate commands for stimuli generation

./stimuli/psychopy/buttons.csv ./stimuli/psychopy/forrest_gump.psyexp ./stimuli/psychopy/segment_cfg.csv Source code of the stimuli presentation in PsychoPy

Functional imaging - Forrest Gump Task

Prolonged quasi-natural auditory stimulation (Forrest Gump audio movie)

Eight approximately 15 min long recording runs, together comprising the entire duration of a two-hour presentation of an audio-only version of the Hollywood feature film "Forrest Gump" made for a visually impaired audience (German dubbing).

For each run, there are 4D volumetric images (160x160x36)in NIfTI format , one volume recorded every 2 s, obtain from a Siemens MR scanner at 7 Tesla using a T2*-weighted gradient-echo EPI sequence (1.4 mm isotropic voxel size). These images have partial brain coverage — centered on the auditory cortices in both brain hemispheres and include frontal and posterior portions of the brain. There is no coverage for the upper portion of the brain (e.g. large parts of motor and somato-sensory cortices).

Several flavors of raw and preprocessed data are available:

Raw BOLD functional MRI ~~~~~~~~~~~~~~~~~~~~~~~

These raw data suffer from severe geometric distortions.

Filename examples for subject 01 and run 01

./sub-01/ses-forrestgump/func/sub-01_ses-forrestgump_task-forrestgump_acq-raw_run-01_bold.nii.gz BOLD data

./sourcedata/dicominfo/sub-01/ses-forrestgump/func/sub-01_ses-forrestgump_task-forrestgump_acq-raw_run-01_bold_dicominfo.txt Image property dump from DICOM conversion

Raw BOLD functional MRI (with applied distortion correction) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Identical to raw BOLD data, but with a scanner-side correction for geometric distortions applied (also include correction for participant motion). These data are most suitable for analysis of individual brains.

Filename examples for subject 01 and run 01

./sub-01/ses-forrestgump/func/sub-01_ses-forrestgump_task-forrestgump_acq-dico_run-01_bold.nii.gz BOLD data

./derivatives/motion/sub-01/ses-forrestgump/func/sub-01_ses-forrestgump_task-forrestgump_acq-dico_run-01_moco_ref.nii.gz Reference volume used for motion correction. Only runs 1 and 5 (first runs in each session)

./sourcedata/dicominfo/sub-01/ses-forrestgump/func/sub-01_ses-forrestgump_task-forrestgump_acq-dico_run-01_bold_dicominfo.txt Image property dump from DICOM conversion

Raw BOLD functional MRI (linear anatomical alignment) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

These images are motion and distortion corrected and have been anatomically aligned to a BOLD group template image that was generated from the entire group of participants.

Alignment procedure was linear (image projection using an affine transformation). These data are most suitable for group-analyses and inter-individual comparisons.

Filename examples for subject 01 and run 01

./derivatives/linear_anatomical_alignment/sub-01/ses-forrestgump/func/sub-01_ses-forrestgump_task-forrestgump_rec-dico7Tad2grpbold7Tad_run-01_bold.nii.gz BOLD data

./derivatives/linear_anatomical_alignment/sub-01/ses-forrestgump/func/sub-01_ses-forrestgump_task-forrestgump_rec-dico7Tad2grpbold7TadBrainMask_run-01_bold.nii.gz Matching brain mask volume

./derivatives/linear_anatomical_alignment/sub-01/ses-forrestgump/func/sub-01_ses-forrestgump_task-forrestgump_rec-XFMdico7Tad2grpbold7Tad_run-01_bold.mat 4x4 affine transformation matrix (plain text format)

Raw BOLD functional MRI (non-linear anatomical alignment) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

These images are motion and distortion corrected and have been anatomically aligned to a BOLD group template image that was generated from the entire group of participants.

Alignment procedure was non-linear (image projection using an affine transformation with additional transformation by non-linear warpfields). These data are most suitable for group-analyses and inter-individual comparisons.

Filename examples for subject 01 and run 01

./derivatives/non-linear_anatomical_alignment/sub-01/ses-forrestgump/func/sub-01_ses-forrestgump_task-forrestgump_rec-dico7Tad2grpbold7TadNL_run-01_bold.nii.gz BOLD data

./derivatives/non-linear_anatomical_alignment/sub-01/ses-forrestgump/func/sub-01_ses-forrestgump_task-forrestgump_rec-dico7Tad2grpbold7TadBrainMaskNLBrainMask_run-01_bold.nii.gz Matching brain mask volume

./derivatives/non-linear_anatomical_alignment/sub-01/ses-forrestgump/func/sub-01_ses-forrestgump_task-forrestgump_rec-dico7Tad2grpbold7TadNLWarp_run-01_bold.nii.gz Warpfield (associated affine transformation is identical with "linear" alignment

Functional imaging - Auditory Perception Session

Participants were repeatedly stimulated with a total of 25 music clips, with and without speech content, from five different genres using a slow event-related paradigm.

Filename examples for subject 01 and run 01

./sub-01/ses-auditoryperception/func/sub-01_ses-auditoryperception_task-auditoryperception_run-01_bold.nii.gz ./sub-01/ses-auditoryperception/func/sub-01_ses-auditoryperception_task-auditoryperception_run-01_events.tsv

Functional imaging - Localizer Session

Filename examples for subject 01 and run

In this study, subjects performed motor execution or motor imagery of their left hand, right hand, or right foot, in which EEG and fMRI were recorded simultaneously. Seventeen participants completed a single EEG-fMRI session. The dataset includes the preprocessed fMRI recordings and the preprocessed EEG recordings after MR-induced artifact removal, including gradient artifact (GA) and ballistocardiogram (BCG) artifact correction, for each subject. The detailed description of the study can be found in the following publication:Bondi, E., Ding, Y., Zhang, Y., Maggioni, E., & He, B. (2025). Investigating the Neurovascular Coupling Across Multiple Motor Execution and Imagery Conditions: A Whole-Brain EEG-Informed fMRI Analysis. NeuroImage, 121311. https://doi.org/10.1016/j.neuroimage.2025.121311If you use a part of this dataset in your work, please cite the above publication.This dataset was collected under support from the National Institutes of Health via grants NS124564, NS131069, NS127849, and NS096761 to Dr. Bin He.Correspondence about the dataset: Dr. Bin He, Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, PA 15213. E-mail: bhe1@andrew.cmu.edu

Haptic three-dimensional curved surface exploration fMRI dataset

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.biorxiv.org/content/10.1101/2020.08.04.235275v1.

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). **

Summary

Facial expression is among the most natural methods for human beings to convey their emotional information in daily life. Although the neural mechanism of facial expression has been extensively studied employing lab-controlled images and a small number of lab-controlled video stimuli, how the human brain processes natural facial expressions still needs to be investigated. To our knowledge, this type of data specifically on large number of natural facial expression videos is currently missing. We describe here the natural Facial Expressions Dataset (NFED), a fMRI dataset including responses to 1,320 short (3-second) natural facial expression video clips. These video clips is annotated with three types of labels: emotion, gender, and ethnicity, along with accompanying metadata. We validate that the dataset has good quality within and across participants and, notably, can capture temporal and spatial stimuli features. NFED provides researchers with fMRI data for understanding of the visual processing of large number of natural facial expression videos.

Data Records

The data, which were structured following the BIDS format53, were accessible at https://openneuro.org/datasets/ds00504754. The “sub-

Stimulus. Distinct folders store the stimuli for distinct fMRI experiments: "stimuli/face-video", "stimuli/floc", and "stimuli/prf" (Fig. 2b). The category labels and metadata corresponding to video stimuli are stored in the "videos-stimuli_category_metadata.tsv”. The “videos-stimuli_description.json” file describes category and metadata information of video stimuli(Fig. 2b).

Raw MRI data. Each participant's folder is comprised of 11 session folders: “sub-

Volume data from pre-processing. The pre-processed volume-based fMRI data were in the folder named “pre-processed_volume_data/sub-

Surface data from pre-processing. The pre-processed surface-based data were stored in a file named “volumetosurface/sub-

FreeSurfer recon-all. The results of reconstructing the cortical surface were saved as “recon-all-FreeSurfer/sub-

Surface-based GLM analysis data. We have conducted GLMsingle on the data of the main experiment. There is a file named “sub--

Validation. The code of technical validation was saved in the “derivatives/validation/code” folder. The results of technical validation were saved in the “derivatives/validation/results” folder (Fig. 2h). “README.md” describes the detailed information of code and results.

EEG and fMRI data collected from a single healthy adult performing an auditory oddball task

A geometric shape regularity effect in the human brain: fMRI dataset

Authors:

• Mathias Sablé-Meyer*
• Lucas Benjamin
• Cassandra Potier Watkins
• Chenxi He
• Maxence Pajot
• Théo Morfoisse
• Fosca Al Roumi
• Stanislas Dehaene

*Corresponding author: mathias.sable-meyer@ucl.ac.uk

Abstract

The perception and production of regular geometric shapes is a characteristic trait of human cultures since prehistory, whose neural mechanisms are unknown. Behavioral studies suggest that humans are attuned to discrete regularities such as symmetries and parallelism, and rely on their combinations to encode regular geometric shapes in a compressed form. To identify the relevant brain systems and their dynamics, we collected functional MRI and magnetoencephalography data in both adults and six-year-olds during the perception of simple shapes such as hexagons, triangles and quadrilaterals. The results revealed that geometric shapes, relative to other visual categories, induce a hypoactivation of ventral visual areas and an overactivation of the intraparietal and inferior temporal regions also involved in mathematical processing, whose activation is modulated by geometric regularity. While convolutional neural networks captured the early visual activity evoked by geometric shapes, they failed to account for subsequent dorsal parietal and prefrontal signals, which could only be captured by discrete geometric features or by more advanced transformer models of vision. We propose that the perception of abstract geometric regularities engages an additional symbolic mode of visual perception.

Notes about this dataset

We separately share the MEG dataset at https://openneuro.org/datasets/ds006012. Below are some notes about the fMRI dataset of N=20 adult participants (sub-2xx, numbers between 204 and 223), and N=22 children (sub-3xx, numbers between 301 and 325).

• The code for the analyses is provided at https://github.com/mathias-sm/AGeometricShapeRegularityEffectHumanBrain
  However, the analyses work from already preprocessed data. Since there is no custom code per se for the preprocessing, I have not included it in the repository. To preprocess the data as was done in the published article, here is the command and software information:
  • fMRIPrep version: 20.0.5
  • fMRIPrep command: /usr/local/miniconda/bin/fmriprep /data /out participant --participant-label <label> --output-spaces MNI152NLin6Asym:res-2 MNI152NLin2009cAsym:res-2
• Defacing has been performed with bidsonym running the pydeface masking, and nobrainer brain registraction pipeline.
  The published analyses have been performed on the non-defaced data. I have checked for data quality on all participants after defacing. In specific cases, I may be able to request the permission to share the original, non-defaced dataset.
• sub-325 was acquired by a different experimenter and defaced before being shared with the rest of the research team, hence why the slightly different defacing mask. That participant was also preprocessed separately, and using a more recent fMRIPrep version: 20.2.6.
• The data associated with the children has a few missing files. Notably:
  1. sub-313 and sub-316 are missing one run of the localizer each
  2. sub-316 has no data at all for the geometry
  3. sub-308 has eno useable data for the intruder task Since all of these still have some data to contribute to either task, all available files were kept on this dataset. The analysis code reflects these inconsistencies where required with specific exceptions.

Collection description

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

single-subject

Cognitive paradigm (task)

relational processing fMRI task paradigm

Map type

T

Collection description

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

single-subject

Cognitive paradigm (task)

gambling fMRI task paradigm

Map type

T

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 25, 2013 Public curated repository of peer reviewed fMRI studies and their underlying data. This Web-accessible database has data mining capabilities and the means to deliver requested data to the user (via Web, CD, or digital tape). Datasets available: 107 NOTE: The fMRIDC is down temporarily while it moves to a new home at UCLA. Check back again in late Jan 2013! The goal of the Center is to help speed the progress and the understanding of cognitive processes and the neural substrates that underlie them by: * Providing a publicly accessible repository of peer-reviewed fMRI studies. * Providing all data necessary to interpret, analyze, and replicate these fMRI studies. * Provide training for both the academic and professional communities. The Center will accept data from those researchers who are publishing fMRI imaging articles in peer-reviewed journals. The goal is to serve the entire fMRI community.

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach “Task-related Edge Density” (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.

Version 1.0 of the Naturalistic-Data.org educational course. Naturalistic-Data.org is an open access online educational resource that provides an introduction to analyzing naturalistic functional neuroimaging datasets using Python. Naturalistic-Data.org is built using Jupyter-Book and provides interactive tutorials for introducing advanced analytic techniques . This includes functional alignment, inter-subject correlations, inter-subject representational similarity analysis, inter-subject functional connectivity, event segmentation, natural language processing, hidden semi-markov models, automated annotation extraction, and visualizing high dimensional data. The tutorials focus on practical applications using open access data, short open access video lectures, and interactive Jupyter notebooks. All of the tutorials use open source packages from the python scientific computing community (e.g., numpy, pandas, scipy, matplotlib, scikit-learn, networkx, nibabel, nilearn, brainiak, hypertoos, timecorr, pliers, statesegmentation, and nltools). The course is designed to be useful for varying levels of experience, including individuals with minimal experience with programming, Python, and statistics.

MIND: Multilingual Imaging Neuro Dataset

This repository contains structural and functional MRI data of 126 monolingual and bilingual participants with varying language backgrounds and proficiencies.

This README is organized into two sections:

1. Usage describes how one can go about recreating data derivatives and brain measures from start to finish.
2. Directories gives information on the file structure of the dataset.

If you just want access to the processed brain and language data, go to Quick Start.

Usage

There are two ways that one can go about this dataset. If you want to jump immediately into analyzing participants and their language profiles, then go to Quick Start. If instead you are looking to go from low-level MRI data to cleaned CSVs with various brain measure types, either to learn the process or double check our work, then go to Data Replication.

Quick Start

If you just want access to cleaned brain measure and language history data of 126 participants, they can be found in the following folders:

• Brain Data: processing_output
• Language Data: language_background

Each folder has a metadata.xlsx file that gives more information on the files and their fields. Have fun, go nuts.

Data Replication

If you are looking to go through the steps required to create the data from start to finish, we first start with the raw structural and functional MRI data, which can be found in ./sub-EBE{XXXX}. Information on the data in this folder, which follows BIDS, can be found here.

The data in ./sub-EBE{XXXX} is then inputted into various processing pipelines, the versions for which can be found at Dependency versions. The following processing pipelines are used:

• fMRIprep

  fMRIprep is a neuroimaging procesing tool used for task-based and resting-state fMRI data. fMRIprep is not used directly to create brain measure CSVs used in analysis, but instead creates processed fMRI data used in the CONN toolbox. For more information on fMRIprep and how to use it, click here.

• CAT12

  We use the CAT12 toolbox, which stands for Computational Anatomy Toolbox, to calculate brain region volumes using voxel-based morphometry (VBM). CAT12 works through SPM12 and Matlab, and requires that both be installed. We have included the Matlab scripts used to create the files in ./derivatives/CAT12 in preprocessing_scripts/cat12. To use it, install necessary dependencies (CAT12, SPM12, and Matlab) and run preprocessing_scripts/cat12/CAT12_segmentation_n2.m in Matlab. You will also need to update for your local path to Matlab on lines 12, 24, and 41. For more information on CAT12 and how to use it to calculate brain region volumes using VBM, click here.

• CONN

  CONN is a functional connectivity toolbox, which we used to generate participant brain connectivity measures. CONN requires first that you run the fMRIprep pipeline, as it uses some of fMRIprep's outputs as input. Like CAT12, CONN works through SPM12 and Matlab and requires that both be installed. For more information on CONN and how to use it, click here.

• FDT

  We used FMRIB's Diffusion Toolbox (FDT) for extracting values from diffusion weighted images. For more information on FDT and how to use it, click here.

• Freesurfer

  FreeSurfer is a software package for the analysis and visualization of structural and functional neuroimaging data, which we use to extract region volumes and cortical thickness through surface-based morphometry (SBM). For more information on Freesurfer and how to use it, click here.

The results from these pipelines, which use the data in ./sub-EBE{XXXX} as input, are then outputted into folders in ./derivatives. For information on which folder stores each pipeline result, see Directories.

After running these pipelines, we can take their outputs and convert them into CSVs for analysis. To do this, we use preprocessing_scripts/brain_data_preprocessing.ipynb. This Python notebook takes the data in ./derivatives as input and outputs CSVs to processing_output. Outputted from this notebook are CSVs containing brain volumes, cortical thicknesses, fractional anisotropy values, and connectivity measures. Information on the outputted CSVs can be found at processing_output/metadata.xlsx.

Dependency versions

1. MATLAB v. R2023a
2. SPM12
3. CAT12 v8.2
4. CONN v22a
5. FSL v6.0.2
6. Freesurfer v7.4.1
7. fMRIprep v23.0.2

Chen, Salvadore, & Blanco-Elorrieta Paper Replication

Also included in this dataset is code used in the analyses of Chen, Salvaore, & Blanco-Elorrieta (submitted). If you are interested in running analyses used in that paper, see the README in chen_salvadore_elorrieta/code.

Directories

• participants.tsv: Subject demographic information.

• participants.json: Describes participants.tsv.

• sub-EBE

  Each of these directories contain the BIDS formatted anatomical and functional MRI data, with the name of the directory corresponding to the subject's unique identifier. For more information on the subfolders, see BIDS information here.

• derivatives

  This directory contains outputs of common processing pipelines run on the raw MRI data from ./sub-EBE{XXXX}.

  • CAT12

    Results of the CAT12 toolbox, which stands for Computational Anatomy Toolbox, and is used to calculate brain region volumes using voxel-based morphometry (VBM).

  • conn

    Results of the CONN toolbox, used to generate data on functional connectivity from brain fMRI sequences.

  • fdt

    Results of the FMRIB's Diffusion Toolbox (FDT), used for extracting values from diffusion weighted images.

  • fMRIprep

    Results from fMRIprep, a preprocessing pipeline for task-based and resting-state functional MRI data.

  • freesurfer

    Results from FreeSurfer, a software package for the analysis and visualization of structural and functional neuroimaging data.

• language_background

  Participant information is kept on the first level of the dataset and includes information on language history, demographics, and their composite multilingualism score. Below is a list of all participant information files.

  • language_background.csv: Full subject language information and history.

  • metadata.xlsx: Metadata on each file in this directory.

  • multilingual_measure.csv: Each participant’s composite multilingualism score specified in Chen & Blanco-Elorrieta (in review).

• processing_output

  This directory contains processed brain measure data for brain volumes, cortical thickness, FA, and connectivity. The CSVs are created from scripts in the directory processing_scripts using files in the derivatives directory as input. Descriptions of each file can be found below.

  • connectivity_network.csv: Contains 36 Network-to-Network connectivity values for each participant.

  • connectivity_roi.csv: Contains 13,336 ROI-to-ROI connectivity values for each participant.

  • dti.csv: Contains averaged white matter FA values for 76 brain regions for each participant based on Diffusion tensor imaging.

  • metadata.xlsx: Metadata on each file in this directory.

  • sbm_thickness.csv: Contains cortical thickness values for 68 brain regions for each participant based on Surface-based morphometry.

  • sbm_volume.csv: Contains volume values for 165 brain regions for each participant based on Surface-based morphometry.

  • tiv.csv: Contains two total intracranial volumes for each subject, calculated using SBM and VBM respectively

  • vbm_volume.csv: Contains volume values for 153 brain regions for each participant based on Voxel-based morphometry. `

• preprocessing_scripts

  Code involved in processing raw MRI data.

  • brain_data_preprocessing.ipynb Python notebook used to create CSVs with brain measure values used in analyses. For more information on the code and how to use it, read Data Replication.
  • ### raw_mri_preprocessing Scripts used to create files some files in ./derviatives folder from raw MRI data in ./sub-EBE{XXXX}. For more information on the scripts, read Data Replication.
  • ### toolbox_outputs Intermediary files created and used by analysis/processing_scripts/brain_data_preprocessing.ipynb.

• ##

(Note: Part of the content of this post was adapted from the original DIRECT Psychoradiology paper (https://academic.oup.com/psyrad/article/2/1/32/6604754) and REST-meta-MDD PNAS paper (http://www.pnas.org/cgi/doi/10.1073/pnas.1900390116) under CC BY-NC-ND license.)Major Depressive Disorder (MDD) is the second leading cause of health burden worldwide (1). Unfortunately, objective biomarkers to assist in diagnosis are still lacking, and current first-line treatments are only modestly effective (2, 3), reflecting our incomplete understanding of the pathophysiology of MDD. Characterizing the neurobiological basis of MDD promises to support developing more effective diagnostic approaches and treatments.An increasingly used approach to reveal neurobiological substrates of clinical conditions is termed resting-state functional magnetic resonance imaging (R-fMRI) (4). Despite intensive efforts to characterize the pathophysiology of MDD with R-fMRI, clinical imaging markers of diagnosis and predictors of treatment outcomes have yet to be identified. Previous reports have been inconsistent, sometimes contradictory, impeding the endeavor to translate them into clinical practice (5). One reason for inconsistent results is low statistical power from small sample size studies (6). Low-powered studies are more prone to produce false positive results, reducing the reproducibility of findings in a given field (7, 8). Of note, one recent study demonstrate that sample size of thousands of subjects may be needed to identify reproducible brain-wide association findings (9), calling for larger datasets to boost effect size. Another reason could be the high analytic flexibility (10). Recently, Botvinik-Nezer and colleagues (11) demonstrated the divergence in results when independent research teams applied different workflows to process an identical fMRI dataset, highlighting the effects of “researcher degrees of freedom” (i.e., heterogeneity in (pre-)processing methods) in producing disparate fMRI findings.To address these critical issues, we initiated the Depression Imaging REsearch ConsorTium (DIRECT) in 2017. Through a series of meetings, a group of 17 participating hospitals in China agreed to establish the first project of the DIRECT consortium, the REST-meta-MDD Project, and share 25 study cohorts, including R-fMRI data from 1300 MDD patients and 1128 normal controls. Based on prior work, a standardized preprocessing pipeline adapted from Data Processing Assistant for Resting-State fMRI (DPARSF) (12, 13) was implemented at each local participating site to minimize heterogeneity in preprocessing methods. R-fMRI metrics can be vulnerable to physiological confounds such as head motion (14, 15). Based on our previous work examination of head motion impact on R-fMRI FC connectomes (16) and other recent benchmarking studies (15, 17), DPARSF implements a regression model (Friston-24 model) on the participant-level and group-level correction for mean frame displacements (FD) as the default setting.In the REST-meta-MDD Project of the DIRECT consortium, 25 research groups from 17 hospitals in China agreed to share final R-fMRI indices from patients with MDD and matched normal controls (see Supplementary Table; henceforth “site” refers to each cohort for convenience) from studies approved by local Institutional Review Boards. The consortium contributed 2428 previously collected datasets (1300 MDDs and 1128 NCs). On average, each site contributed 52.0±52.4 patients with MDD (range 13-282) and 45.1±46.9 NCs (range 6-251). Most MDD patients were female (826 vs. 474 males), as expected. The 562 patients with first episode MDD included 318 first episode drug-naïve (FEDN) MDD and 160 scanned while receiving antidepressants (medication status unavailable for 84). Of 282 with recurrent MDD, 121 were scanned while receiving antidepressants and 76 were not being treated with medication (medication status unavailable for 85). Episodicity (first or recurrent) and medication status were unavailable for 456 patients.To improve transparency and reproducibility, our analysis code has been openly shared at https://github.com/Chaogan-Yan/PaperScripts/tree/master/Yan_2019_PNAS. In addition, we would like to share the R-fMRI indices of the 1300 MDD patients and 1128 NCs through the R-fMRI Maps Project (http://rfmri.org/REST-meta-MDD). These data derivatives will allow replication, secondary analyses and discovery efforts while protecting participant privacy and confidentiality.According to the agreement of the REST-meta-MDD consortium, there would be 2 phases for sharing the brain imaging data and phenotypic data of the 1300 MDD patients and 1128 NCs. 1) Phase 1: coordinated sharing, before January 1, 2020. To reduce conflict of the researchers, the consortium will review and coordinate the proposals submitted by interested researchers. The interested researchers first send a letter of intent to rfmrilab@gmail.com. Then the consortium will send all the approved proposals to the applicant. The applicant should submit a new innovative proposal while avoiding conflict with approved proposals. This proposal would be reviewed and approved by the consortium if no conflict. Once approved, this proposal would enter the pool of approved proposals and prevent future conflict. 2) Phase 2: unrestricted sharing, after January 1, 2020. The researchers can perform any analyses of interest while not violating ethics.The REST-meta-MDD data entered unrestricted sharing phase since January 1, 2020. The researchers can perform any analyses of interest while not violating ethics. Please visit Psychological Science Data Bank to download the data, and then sign the Data Use Agreement and email the scanned signed copy to rfmrilab@gmail.com to get unzip password and phenotypic information. ACKNOWLEDGEMENTSThis work was supported by the National Key R&D Program of China (2017YFC1309902), the National Natural Science Foundation of China (81671774, 81630031, 81471740 and 81371488), the Hundred Talents Program and the 13th Five-year Informatization Plan (XXH13505) of Chinese Academy of Sciences, Beijing Municipal Science & Technology Commission (Z161100000216152, Z171100000117016, Z161100002616023 and Z171100000117012), Department of Science and Technology, Zhejiang Province (2015C03037) and the National Basic Research (973) Program (2015CB351702). REFERENCES1. A. J. Ferrari et al., Burden of Depressive Disorders by Country, Sex, Age, and Year: Findings from the Global Burden of Disease Study 2010. PLOS Medicine 10, e1001547 (2013).2. L. M. Williams et al., International Study to Predict Optimized Treatment for Depression (iSPOT-D), a randomized clinical trial: rationale and protocol. Trials 12, 4 (2011).3. S. J. Borowsky et al., Who is at risk of nondetection of mental health problems in primary care? J Gen Intern Med 15, 381-388 (2000).4. B. B. Biswal, Resting state fMRI: a personal history. Neuroimage 62, 938-944 (2012).5. C. G. Yan et al., Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. Proc Natl Acad Sci U S A 116, 9078-9083 (2019).6. K. S. Button et al., Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci 14, 365-376 (2013).7. J. P. A. Ioannidis, Why Most Published Research Findings Are False. PLOS Medicine 2, e124 (2005).8. R. A. Poldrack et al., Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat Rev Neurosci 10.1038/nrn.2016.167 (2017).9. S. Marek et al., Reproducible brain-wide association studies require thousands of individuals. Nature 603, 654-660 (2022).10. J. Carp, On the Plurality of (Methodological) Worlds: Estimating the Analytic Flexibility of fMRI Experiments. Frontiers in Neuroscience 6, 149 (2012).11. R. Botvinik-Nezer et al., Variability in the analysis of a single neuroimaging dataset by many teams. Nature 10.1038/s41586-020-2314-9 (2020).12. C.-G. Yan, X.-D. Wang, X.-N. Zuo, Y.-F. Zang, DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. Neuroinformatics 14, 339-351 (2016).13. C.-G. Yan, Y.-F. Zang, DPARSF: A MATLAB Toolbox for "Pipeline" Data Analysis of Resting-State fMRI. Frontiers in systems neuroscience 4, 13 (2010).14. R. Ciric et al., Mitigating head motion artifact in functional connectivity MRI. Nature protocols 13, 2801-2826 (2018).15. R. Ciric et al., Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. NeuroImage 154, 174-187 (2017).16. C.-G. Yan et al., A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. NeuroImage 76, 183-201 (2013).17. L. Parkes, B. Fulcher, M. Yücel, A. Fornito, An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. NeuroImage 171, 415-436 (2018).18. L. Wang et al., Interhemispheric functional connectivity and its relationships with clinical characteristics in major depressive disorder: a resting state fMRI study. PLoS One 8, e60191 (2013).19. L. Wang et al., The effects of antidepressant treatment on resting-state functional brain networks in patients with major depressive disorder. Hum Brain Mapp 36, 768-778 (2015).20. Y. Liu et al., Regional homogeneity associated with overgeneral autobiographical memory of first-episode treatment-naive patients with major depressive disorder in the orbitofrontal cortex: A resting-state fMRI study. J Affect Disord 209, 163-168 (2017).21. X. Zhu et al., Evidence of a dissociation pattern in resting-state default mode network connectivity in first-episode, treatment-naive major depression patients. Biological psychiatry 71, 611-617 (2012).22. W. Guo et al., Abnormal default-mode

Dataset

Dataset accompanying the manuscript "Feedback contribution to surface motion perception in the human early visual cortex" (biorxiv).

Description

fMRI data are arrange by subject (following BIDS convention). For each subject, there are subfolders for anatomical and functional MRI data.

├── sub-01
│ ├── anat
│ │ └── ...
│ ├── func
│ │ └── ...
│ ├── func_se
│ │ └── ...
│ └── func_se_op
│ └── ...

The subfolder 'anat' contains four images from the MP2RAGE sequence (among these, T1 and proton-density weighted images). The subfolder 'func' contains the functional data (GE EPI, T2* weighted) from the main experiment (i.e. the data from which the haemodynamic response was estimated, and on which statistical analysis was performed). The subfolders 'func_se' and 'func_se_op' contain SE EPI images with opposite phase encode polarity that were used for distortion correction. Moreover, for each image/timeseries there is a json file with metadata.

Anatomical images have been masked anteriorly (defaced). Functional images are in coronal oblique orientation, covering early visual cortex.

The folder 'stimuli' contains information on the stimuli used for retinotopic mapping, including timecourse models used for population receptive field mapping. (These files are included here because of their relatively large file size, which would make distribution via a git repository impractical.) The software used for the presentation of retinotopic mapping stimuli (and for the corresponding analysis) is available on github.

For example videos of the main experimental stimuli, see zenodo.2583017. If you would like to reproduce the experimental stimuli, the respective PsychoPy code can be found on github.

The exact timing of events during the experiments (rest & stimulus blocks, target events) can be found in FSL-style design matrices ("3 column format") on github.com/ingo-m/PacMan/tree/master/analysis/FSL_MRI_Metadata.

Analysis

The analysis pipeline makes use of several MRI software packages (such as SPM and FSL for preprocessing, and CBS tools for cortical depth sampling). In order to facilitate reproducibility, the entire analysis was containerised using docker. Because of licensing issues, the docker images with the third-party software cannot be directly made available. However, the docker files and detailed instructions for the creation of the docker images are available on github.

If you would like to reproduce the analysis, the first step will be to create the docker images (which provide an exact copy of the system environment that was used to conduct the published analysis). There are two docker images, one for the main analysis (motion correction, distortion correction, GLM fitting; named "dockerimage_pacman_jessie"), and another one for the depth sampling (named "dockerimage_cbs"). Detailed instructions on how to create the docker images can be found here and here.

Once you set up the docker images, the analysis can be run automatically. For each subject, there is one parent script for the main analysis (e.g. ~/analysis/20180118/metascript_01.sh for subject 20180118) and a separate script for the depth sampling (e.g. ~/analysis/20180118/metascript_03.sh). The only manual adjustments you should have to perform to reproduce the analysis is to change the file paths in the first section of these scripts ('pacman_anly_path' is the parent directory containing the analysis code, i.e. the git repository, and 'pacman_data_path' is the parent directory containing the MRI data). The main analysis (metascript_01.sh) should take about 24 h per subject on a workstation with 12 cores, and the depth sampling (metascript_02.sh) about 2 h. The analysis can be run on consumer-grade hardware, but some parts of the analysis may not run with less than 16 GB of RAM (recommended: 32 GB).

Visualisations (e.g. cortical depth profiles and signal timecourses) and group-level statistical tests are implemented in py_depthsampling.

Further resources

Please refer to the research paper for more details: https://doi.org/10.1101/653626

The analysis pipeline can be found on https://github.com/ingo-m/PacMan

A separate repository contains the code used for visualisation of depth-sampling results: https://github.com/ingo-m/py_depthsampling/tree/PacMan

Free & open source software package for population receptive field mapping: https://github.com/ingo-m/pyprf

Collection description

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

single-subject

Cognitive paradigm (task)

motor fMRI task paradigm

Map type

T

Some materials for a brain imaging course. Referenced in notebooks in git@github.com:tknapen/brainimaging_VU.git

Collection description

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

single-subject

Cognitive paradigm (task)

motor fMRI task paradigm

Map type

T