This data was converted using ezBIDS (https://brainlife.io/ezbids). Additional information regarding this dataset can be entered in this file.

ezbids

This dataset was converted to BIDS using ezBIDS (https://brainlife.io/ezbids)

This repository contains tutorial data for the ezBIDS (https://github.com/brainlife/ezbids) web-based BIDS conversion tool. Data is comprised of dcm2niix transformed data (i.e., DICOMs ---> NIfTI/JSON).

This data was converted using ezBIDS (https://brainlife.io/ezbids). Additional information regarding this dataset can be entered in this file.

ezbids

This dataset was converted to BIDS using ezBIDS (https://brainlife.io/ezbids)

Disarming emotional memories using Targeted Memory Reactivation during Rapid Eye Movement sleep

This dataset contains fMRI and EEG data from a study investigating the effects of Targeted Memory Reactivation (TMR) during REM sleep on emotional reactivity.

Study Design

Participants rated the arousal of 48 affective images paired with semantically matching sounds. Half of these sounds were cued during REM in the subsequent overnight sleep cycle. Participants rated the images in an MRI scanner with pulse oximetry 48 hours after encoding, and again two weeks later.

Sessions

1. Baseline: Initial arousal ratings and overnight sleep with TMR
2. Session 48-H: fMRI scanning and arousal ratings (48 hours after baseline)
3. Session 2-Wk: Online follow-up (2 weeks after baseline)

Data Acquisition

• fMRI: Acquired using a Siemens Magnetom Prisma 3T scanner with a 32-channel head coil
• Heart Rate: Recorded using pulse oximetry during the fMRI session3
• EEG data from the overnight sleep session

Dataset Contents

This initial upload contains: - T1-weighted structural images - Functional MRI data from Session 48-H - B0 field maps

Preprocessing

fMRI data were preprocessed using fMRIPrep 20.2.7. Details of the preprocessing pipeline can be found in the methods section of the associated publication.

T1-weighted structural scans were defaced using pydeface version 2.0.2 to ensure participant anonymity.

Additional Information

For more detailed information about the study design, methods, and results, please refer to the associated publication (citation to be added upon publication).

This dataset was initially converted to BIDS format using ezBIDS (https://brainlife.io/ezbids).

Contact

For questions about this dataset, please contact: Dr Tamas Foldes foldesta@cardiff.ac.uk

fMRI dataset accompanying the preprint "Brain bases for navigating acoustic features" (https://doi.org/10.1101/2025.02.10.636597).

Last update: 7 May 2025.

The following subjects’ data were not uploaded or analysed for reasons given in the manuscript: sub-06, sub-10, sub-17, sub-25, sub-28.

Event files contain trial onsets (secs), durations (secs) and conditions (MA=memory adjustment, MP=memory parity, NA=non-memory adjustment, NP=non-memory parity).

For further information please contact the first author, Alexander J. Billig, at a.billig@ucl.ac.uk or ajbillig@gmail.com.

This dataset was converted using ezBIDS (https://brainlife.io/ezbids).

Shanxi Musical-Enculturation Dataset References: Paulsen, Sean (2023). Self-Supervised Pretraining and Transfer Learning on fMRI Data with Transformers (PhD Thesis). https://digitalcommons.dartmouth.edu/dissertations/173/

Marion, Pelofi, Paulsen, Shamma, and Casey (TBP).

Overview: We measured brain activity using functional MRI while five participants (“sub-sid001401/2548/2564/2566/2589”) listened to music clips of Bach chorals and Chinese folk music from the region of Shanxi. All participants reported familiarity with western classical music and no familiarity with Chinese folk music (see participants.tsv).

Participants were scanned in two sessions, one week apart. Stimuli presentation varied among participants, but for each participant the first and second sessions had identical protocols. To examine enculturation of the Shanxi musical grammars, participants were instructed to listen to at least 30 minutes of Shanxi music clips at home each day between sessions. These clips were similar to but distinct from the clips heard during scanning. Actual daily listening amounts are given in participants.tsv.

The top-level folder of this dataset consists of participant-wise subfolders (“sub-sid001401”,…). Each participant’s folder contains two subfolders, corresponding to the two sessions ("ses-A005515","ses-A005552",...). The greater of the two numbers indicates the second session. Each session folder contains two subfolders: 1) anat: T1-weighted structural images 2) func: functional signals (multi-band echo-planar images) and events files

Each session consisted of 8 functional runs, each of which had 4 blocks, with 3 trials in each block. Each block was either all Bach or all Shanxi trials. Each trial lasted 45s with no time in between trials, and presented one music clip. Half of all blocks for each participant were Bach and the other half Shanxi. The arrangement of blocks was randomized for each participant. After each block the participant was asked to rate the pleasure they felt during the preceding block. The functional data for each run are named as follows: sub-sid*_ses-A00*_task-enculture_acq-mb8_run-*_bold.nii.gz

Each *_event.tsv file contains following information: 1) Onset: stimulus onset 2) Duration: Each clip was constructed to be approximately 30 seconds 3) Trial_type: Either Bach or Chinese 4) Value: The track number of this trial's music clip, e.g value 49 with trial_type Bach indicates Bach_49.mp3 was played.

Further details of the protocol design can be found in Section 3.2.2 of Paulsen (2023). All music clips used in the collection of this dataset were created by Marion and Barbarot (TBP).

Caution: This dataset can be used for research purposes only. The data were anonymized and de-faced with pydeface, and users shall not perform analyses to re-identify individual participants.

Contributors: Sean Paulsen Department of Computer Science, Dartmouth College, USA

Guilhem Marion Laboratoire des Systèmes Perceptifs, Département d’Étude Cognitive, École Normale Supérieure, PSL, Paris, France

Claire Pelofi Center for Language, Music and Emotion, New York University, Max Planck Institute for Empirical Aesthetics

Michael A. Casey Department of Computer Science & Department of Music, Dartmouth College, USA

Shihab Shamma Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland College Park, USA & Laboratoire des Systèmes Perceptifs, Département d’Étude Cognitive, École Normale Supérieure, PSL, Paris, France

Camille Barbarot Centre PsyCLÉ, Université Aix-Marseille, Aix-en-Provence, Provence-Alpes-Côte d’Azur, France

ezbids

This dataset was converted to BIDS using ezBIDS (https://brainlife.io/ezbids)

San Diego State University Traveling Subjects (SDSU-TS) Dataset

The San Diego State University Traveling Subjects (SDSU-TS) dataset was designed to facilitate the harmonization of MRI data acquired across autism neuroimaging studies conducted at San Diego State University (SDSU). The dataset includes MRI scans from 9 healthy adult participants (aged 22-55 years) collected at 2 imaging sites: SDSU Imaging Center (SDSU-MRI) and Center for Functional MRI, UC San Diego (CFMRI). Each participant was scanned at least once at both sites, with rescan data available for 5 participants at SDSU-MRI and 6 participants at CFMRI. The average interval between scan sessions within a participant was 7 days (range: 1-19 days).

The scan protocols we collected reflect those used in SDSU autism MRI studies over the last 10-15 years and include the following:

• 5 diffusion-weighted (3 at CFMRI, 2 at SDSU-MRI)
• 3 T1-weighted (2 at CFMRI, 1 at SDSU-MRI)
• 2 T2-weighted (1 at each site)

Additionally, diffusion-weighted MRI protocols matching the publicly available Adolescent Brain Cognitive Development (ABCD) and Lifespan Human Connectome Project (HCP) studies were collected.

Scan Protocols

The MRI protocols acquired at each site are shown below:

SDSU-MRI (GE Discovery MR750 3T)

CFMRI (Siemens Prisma 3T)

Note: - TE = Echo time (ms); TR = Repetition time (s); Res = spatial resolution. - Reversed phase-encoding fieldmaps were acquired for all protocols. - All data were acquired using a 32-channel head coil, except for the legacy sequences at CFMRI, which were acquired using an 8-channel head coil.

The unprocessed MRI data are provided along with anonymized participant demographic data.

The imaging data was converted to BIDS using ezBIDS (https://brainlife.io/ezbids) and custom scripts. All anatomical scans have been defaced using the Quickshear method in ezBIDS.

Data Notes

Please note the following:

• The 1shell61dir data in the cfmri1 session of subjects ts001, ts002, and ts004 were acquired with a variant spatial resolution (0.94x0.94mm instead of 1.875x1.875mm in-plane resolution).
• The 3shell45dir data in the cfmri1 session of subject ts002 was acquired with a variant spatial resolution (2mm instead of 2.5mm slice thickness).
• The 1shell61dir data in the cfmri1 session of subject ts010 was acquired with a variant TE (0.0843 instead of 0.0818s).

Reference

Hau, J., Scarlett, S., & Arantes de Oliveira Campos, G. (2025). A traveling subjects dataset for diffusion MRI harmonization benchmarking [Poster presentation]. International Society for Magnetic Resonance in Medicine Workshop on 40 Years of Diffusion: Past, Present & Future Perspectives, Kyoto, Japan.

San Diego State University Traveling Subjects Diffusion MRI (SDSU-TS) Dataset

The San Diego State University Traveling Subjects Diffusion MRI (SDSU-TS) dataset was designed to facilitate the harmonization of MRI data acquired across autism neuroimaging studies conducted at San Diego State University (SDSU). The dataset includes MRI scans from 9 healthy adult participants (aged 22-55 years) collected at 2 imaging sites: SDSU Imaging Center (SDSU-MRI) and Center for Functional MRI, UC San Diego (CFMRI). Each participant was scanned at least once at both sites, with rescan data available for 5 participants at SDSU-MRI and 6 participants at CFMRI. The average interval between scan sessions within a participant was 7 days (range: 1-19 days).

The scan protocols we collected reflect those used in SDSU autism MRI studies over the last 10-15 years and include the following:

• 5 diffusion-weighted (3 at CFMRI, 2 at SDSU-MRI)
• 3 T1-weighted (2 at CFMRI, 1 at SDSU-MRI)
• 2 T2-weighted (1 at each site)

Additionally, diffusion-weighted MRI protocols matching the publicly available Adolescent Brain Cognitive Development (ABCD) and Lifespan Human Connectome Project (HCP) studies were collected.

Scan Protocols

The MRI protocols acquired at each site are shown below:

SDSU-MRI (GE Discovery MR750 3T)

CFMRI (Siemens Prisma 3T)

Note: - TE = Echo time (ms); TR = Repetition time (s); Res = spatial resolution. - Reversed phase-encoding fieldmaps were acquired for all protocols. - All data were acquired using a 32-channel head coil, except for the legacy sequences at CFMRI, which were acquired using an 8-channel head coil.

The unprocessed MRI data are provided along with anonymized participant demographic data.

The imaging data was converted to BIDS using ezBIDS (https://brainlife.io/ezbids) and custom scripts. All anatomical scans have been defaced using the Quickshear method in ezBIDS.

Data Notes

Please note the following:

• The 1shell61dir data in the cfmri1 session of subjects ts001, ts002, and ts004 were acquired with a variant spatial resolution (0.94x0.94mm instead of 1.875x1.875mm in-plane resolution).
• The 3shell45dir data in the cfmri1 session of subject ts002 was acquired with a variant spatial resolution (2mm instead of 2.5mm slice thickness).
• The 1shell61dir data in the cfmri1 session of subject ts010 was acquired with a variant TE (0.0843 instead of 0.0818s).
• The sdsu1 session hcplifespan protocol for subjects ts002 and ts003 was not acquired with reversed phase-encoding directions (needed for distortion correction) and are not included in the dataset but are available upon request.

Reference

Hau, J., Scarlett, S., & Arantes de Oliveira Campos, G. (2025). A traveling subjects dataset for diffusion MRI harmonization benchmarking [Poster presentation]. International Society for Magnetic Resonance in Medicine Workshop on 40 Years of Diffusion: Past, Present & Future Perspectives, Kyoto, Japan.