Typically Developing Participants from Late Childhood to Adolescence

This dataset provides diffusion-weighted MRI and structural T1-weighted MRI from typically developing participants spanning late childhood to adolescence. It is intended to support studies of white matter maturation and normative neurodevelopment.

The diffusion data were processed using ExploreDTI, while the structural T1-weighted images are provided in unprocessed form.

Dataset Overview

• Title: Structural and diffusion-weighted images of the adolescent brain
• DOI: https://doi.org/10.6084/m9.figshare.6002273
• Version: 2
• Release date: 2018-03-27
• Author: Catherine Lebel
• Total size: ~5.63 GB

Modalities

• Diffusion-weighted imaging (DWI)
• T1-weighted structural MRI

Cohort

• Typically developing participants
• Age range: late childhood to adolescence

Data Contents

The dataset includes:

• DWI volumes for each subject
• Corresponding bval and bvec files
• T1-weighted structural images for each subject

Processing status

• Diffusion data: processed via ExploreDTI
• T1-weighted data: unprocessed

This combination makes the dataset useful both for downstream diffusion analysis and for custom structural preprocessing workflows.

Related Dataset

The subjects in this release are the same as those in the related dataset:

• NODDI measures of brain development
  https://figshare.com/articles/NODDI_measures_of_brain_development/3497357

This makes it possible to combine conventional diffusion analyses with neurite-orientation-based measures from the companion release.

Scientific Use

This dataset is suitable for:

• White matter development studies
• Adolescent brain maturation analyses
• DTI and tractography method development
• Comparison of diffusion-derived measures across development
• Validation against NODDI-based developmental findings
• Teaching and benchmarking for pediatric/adolescent diffusion MRI pipelines

Related Publication

Please also refer to the associated publication:

• https://doi.org/10.1371/journal.pone.0182340

This paper provides the scientific context and interpretation for the developmental imaging findings.

Keywords

• magnetic resonance imaging
• diffusion tensor imaging
• white matter
• adolescence
• neurodevelopment
• neuroscience
• radiology and organ imaging

Citation

Please cite the dataset:

Lebel, C.
Structural and diffusion-weighted images of the adolescent brain
Figshare, Version 2, 2018
https://doi.org/10.6084/m9.figshare.6002273

Please also cite the associated paper and the related NODDI dataset if used together.

Notes for This Repository

The data-others/adolescent-dwi release may include:

• Harmonized organization of subject folders
• Preorganized DWI + bval/bvec structure
• Optional DSI Studio derivatives (*.sz, *.fz)
• QC summaries when available

Users should consult the original Figshare dataset and linked publication for full acquisition details and cohort description.

Southwest University Longitudinal Imaging Multimodal (SLIM) Brain Data Repository: A Long-term Test-Retest Sample of Young Healthy Adults in Southwest China

The SLIM (Structural, Longitudinal, and Integrated Multimodal) dataset is a valuable resource in neuroimaging, acquired through multimodal magnetic resonance imaging (mMRI). With a three-and-a-half-year longitudinal design, it addresses gaps in test-retest reliability and age-span limitations. Featuring diverse mMRI scans and behavioral assessments, SLIM provides a comprehensive view of the human brain. This dataset, acquired meticulously, is available for researchers globally, fostering collaboration and contributing to reproducible human brain sciences in partnership with CoRR. Explore the SLIM dataset for insights into the complexities of the human brain.

License

License: Creative Commons License: Attribution - Non-Commercial

Reference: Liu W, Wei D, Chen Q, Yang W, Meng J, Wu G, Bi T, Zhang Q, Zuo XN, Qiu J. Longitudinal test-retest neuroimaging data from healthy young adults in southwest China. Scientific data. 2017 Feb 14;4(1):1-9. link

Official website: link

Download

Methods

A DTI diffusion scheme was used, and a total of 90 diffusion sampling directions were acquired. The b-value was 1000 s/mm². The in-plane resolution was 2 mm. The slice thickness was 2 mm. FSL eddy was used to correct for eddy current distortion. The correction was conducted through the integrated interface in DSI Studio ("Chen" release)(http://dsi-studio.labsolver.org). The diffusion MRI data were rotated to align with the AC-PC line at an isotropic resolution of 2.000000. The restricted diffusion was quantified using restricted diffusion imaging (Yeh et al., MRM, 77:603–612 (2017)). The diffusion data were reconstructed using generalized q-sampling imaging (Yeh et al., IEEE TMI, ;29(9):1626-35, 2010) with a diffusion sampling length ratio of 1.25. The tensor metrics were calculated using DWI with b-value lower than 1750 s/mm².

The NKI Rockland Sample

The NKI Rockland Sample Initiative, funded by the US National Institutes of Health and NY State Office of Mental Health, is a cutting-edge research program mapping the human brain's development and its connections to behavior. With over 1,500 participants from Rockland County and nearby areas, this initiative actively involves individuals of all ages in shaping our understanding of mental health. For researchers, the NKI-Rockland Sample offers a well-supported, rich neuroimaging and phenotypic resource, fostering collaborative efforts to advance knowledge on normative brain-behavior relationships across the lifespan.

License

NKI-Rockland Sample data are distributed using the Creative Commons-Attribution-Noncommercial license. For the high-dimensional phenotypic data, all terms specified by the DUA must be complied with.

Reference: Tobe, R. H., MacKay-Brandt, A., Lim, R., Kramer, M., Breland, M. M., Tu, L., ... & Milham, M. P. (2022). A longitudinal resource for studying connectome development and its psychiatric associations during childhood. Scientific Data, 9(1), 300.

• official website

• fcon website

Participants and Experiments

The enhanced Nathan Kline Institute-Rockland Sample I was an institutionally centered endeavor aimed at creating a large-scale community neuroimaging sample of participants across the lifespan (ages 6-85 years). Measures included a wide array of physiological and behavioral assessments, cognitive and psychiatric characterization, genetic information, and advanced neuroimaging. Anonymized data are publicly shared openly and were released prospectively (i.e., on a quarterly basis) as data were collected. The NKI Rockland Sample I data resource includes N = 1, 500 participants across the first set of NIH funded studies (2011-2020).

Phenotypic data

Phenotypic data can be accessed at COINS Data Exchange. Except for age, sex, and handedness, which are publicly available, NKI-RS phenotypic data are protected by a Data Usage Agreement (DUA). Investigators must complete the DUA and have it approved by an authorized institutional official before receiving access.

The NKI-Rockland phenotypic battery primarily focuses on dimensional mental health assessments, particularly psychiatric symptomatology detection (e.g., Conners ADHD Scale, Children's Depression Inventory). However, the reliance on deficit-only measures, common in the field, is questioned for its limitations in distinguishing behaviors among unaffected individuals. Recent efforts highlight this concern, emphasizing the need for measures assessing strengths as well as weaknesses. For ADHD assessment, both Conners (symptoms) and SWAN (strengths and weaknesses) are included, enabling exploration of this issue. While attempts were made to include measures emphasizing strength, standard clinical measures remain central due to the lack of assessments in psychiatric symptom characterization. The recommendation is to incorporate psychometric instruments producing dimensional distributions in non-clinical samples for community sample phenotyping.

• BAS1: BASELINE1

• BAS2: BASELINE2

• FLU1: FOLLOWUP1 (mid-point follow-up)

• FLU2: FOLLOWUP2 (final follow-up)

• TRT: RETEST BAS1 (usually acquired within 3 weeks of a baseline or a follow-up)

Automated release for ixi-hh_1

🧠 IXI Dataset Overview

📄 License and Citation

The IXI dataset is made available under the Creative Commons CC BY-SA 3.0 license.

If you use this dataset, please acknowledge the source as follows:

https://brain-development.org/ixi-dataset/

🧪 MRI Acquisition Protocol

The IXI project has collected nearly 600 MRI scans from normal, healthy subjects. Each subject underwent the following imaging protocols:

• T1-weighted, T2-weighted, and Proton Density (PD) images

• Magnetic Resonance Angiography (MRA)

• Diffusion-Weighted Imaging (DWI) with 15 diffusion directions

🏥 Scanning Sites and Equipment

MRI data were acquired from three London hospitals using different scanners:

• Hammersmith Hospital

  ▸ Philips 3T system

  ▸ (Scanner parameter details available)

• Guy’s Hospital

  ▸ Philips 1.5T system

  ▸ (Scanner parameter details available)

• Institute of Psychiatry

  ▸ GE 1.5T system

  ▸ (Scanner parameter details not currently available)

🔬 Project Background

This dataset was generated as part of the research project:

IXI – Information eXtraction from Images

(EPSRC Grant: GR/S21533/02)

📥 Download the Dataset

All images are provided in NIFTI format and can be downloaded below:

• 🔗 T1-weighted Images

• 🔗 T2-weighted Images

• 🔗 Proton Density (PD) Images

• 🔗 MRA Images

• 🔗 Diffusion Tensor Images (DTI)

  ▸ Includes bvecs.txt and bvals.txt

• 🔗 Demographic Information (Spreadsheet)

The Healthy Brain Network (HBN) dataset

Introduction

The Healthy Brain Network (HBN) is an ongoing initiative focused on building a biobank of data from 10,000 children and adolescents (ages 5-21) in the New York City area. This initiative adopts a community-referred recruitment model, where study advertisements target families concerned about one or more psychiatric symptoms in their child. The Healthy Brain Network Biobank contains a rich array of data, including psychiatric, behavioral, cognitive, and lifestyle (e.g., fitness, diet) information, as well as multimodal brain imaging, electroencephalography, digital voice and video recordings, genetics, and actigraphy. Beyond its primary aim of advancing transdiagnostic research, the Healthy Brain Network Biobank also holds promise for furthering biophysical modeling, voice and speech analysis, natural viewing fMRI and EEG, and methods optimization.

License

HBN requests that any usage of HBN Biobank data is recognized through appropriate citation of the Data Descriptor, which is available on Nature Scientific Data:

Reference: Alexander, Lindsay M., et al. "An open resource for transdiagnostic research in pediatric mental health and learning disorders." Scientific data 4.1 (2017): 1-26.

• official website

Participants and Experiments

Phase I: Implementation and testing (Participants 1–500; completed)

Established a prototype HBN Diagnostic Research Center in Staten Island, New York, and tested workflows for recruitment, evaluations, and assessments.

Phase II: Revision and hardening (Participants 501–1000; completed)

Balanced stable protocols with integrating new measures and optimizing protocols based on experiences and advances.

Phase III: Scale-up (Participants 1001–8500; in process)

Aims to enroll 7,500 participants, expanding capacity with more centers and MRI sites in NYC for a diverse sample.

Phase IV: Targeted recruitment (Participants 8501–10000)

Will use epidemiologic sampling to recruit an additional representative sample of 1,500 participants

Automated release for gsp_1

Large-Scale Human Neuroimaging, Behavioral, and Cognitive Dataset

The Brain Genomics Superstruct Project (GSP) is a large-scale open dataset designed to enable the study of relationships between brain structure, brain function, behavior, and genetics. It provides a carefully curated and quality-controlled dataset of over 1,500 healthy human participants, integrating multimodal MRI with extensive phenotypic data.

• Source: https://www.neuroinfo.org/gsp
• Primary reference: Holmes et al., 2015, Scientific Data :contentReference[oaicite:0]{index=0}

Dataset Overview

• Participants: 1,570 healthy individuals

• Age range: 18–35 years

• Population: Non-clinical (screened for neurological and psychiatric conditions)

• Modalities:

  • Structural MRI (T1-weighted)
  • Resting-state fMRI (1–2 runs per subject)

• Additional data:

  • Behavioral measures
  • Cognitive assessments
  • Personality traits
  • Demographics and health questionnaires
  • (Planned/partial) genetic data

Study Design

The GSP was designed as a high-throughput, large-scale aggregation effort, leveraging existing studies across the Boston research community:

• Standardized MRI protocol applied across multiple sites
• Short acquisition time (~15–30 minutes total)
• Integration with behavioral and cognitive testing
• Linked imaging with phenotypic and genetic data

This strategy enabled rapid accumulation of thousands of datasets within a few years :contentReference[oaicite:1]{index=1}

MRI Acquisition

Structural MRI

• Sequence: Multi-echo MPRAGE
• Resolution: ~1.2 mm isotropic
• Optimized for fast acquisition (~2 minutes)

Resting-State fMRI

• Sequence: Gradient-echo EPI (BOLD)
• TR: 3000 ms
• Volumes: 120 timepoints (after stabilization)
• Whole-brain coverage including cerebellum

Participants were instructed to:

• Keep eyes open
• Stay awake and still

Data Quality and Processing

• Automated quality control applied to all scans

• Exclusion criteria included:

  • Motion artifacts
  • Low temporal SNR
  • Anatomical abnormalities

• Provided quality metrics:

  • Temporal signal-to-noise ratio (tSNR)
  • Motion parameters
  • Functional data quality indices

• Data format:

  • Imaging: NIfTI
  • Phenotypic data: CSV

Behavioral and Cognitive Measures

A large subset of participants completed:

• Personality assessments (e.g., neuroticism, anxiety)
• Cognitive tasks (e.g., mental rotation, attention tasks)
• Self-report behavioral measures

These measures enable:

• Brain–behavior association studies
• Individual difference analyses
• Cognitive neuroscience modeling

Special Features

Large Sample Size

• Enables detection of small effect sizes in brain–behavior relationships

Test–Retest Subset

• 69 participants scanned twice
• Supports reliability and reproducibility studies

Precomputed Outputs

• Morphometric measures
• Functional connectivity matrices
• Quality metrics

Scientific Applications

The GSP dataset supports:

• Functional connectivity and network analysis
• Brain–behavior relationship studies
• Cognitive and personality neuroscience
• Method development and benchmarking
• Reliability and reproducibility research

Limitations

• Convenience sample (Boston area, highly educated population)
• Short scan duration limits some analyses
• Demographic distribution not fully representative

Usage Agreement

Data access requires agreement to the official terms:

• https://www.neuroinfo.org/s/GSP_DataUse_Terms_2014-04-22.pdf

Typical requirements include:

• Use for scientific research only
• Proper citation of the dataset
• Compliance with data use restrictions

Data Access

Available through:

• Harvard Dataverse
• LONI Image Data Archive

Users must request access and comply with data use terms.

Citation

Holmes AJ et al. (2015)
Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures
Scientific Data 2:150031

Diffusion MRI from 67 healthy controls (ages 7–63 years) to study white-matter maturation with NODDI (neurite orientation dispersion and density imaging), alongside standard DTI metrics. The release includes multi-shell HARDI (two shells), single-shell DTI, and T1-weighted anatomical images suitable for ROI- and voxel-wise analyses of neurite density (ND), orientation dispersion (OD), and conventional diffusion indices. Subsets include test-retest sessions for reliability assessment. Findings reported in the companion article show ND rising with a logarithmic pattern across childhood to adulthood, OD increasing exponentially later in life, and NODDI measures predicting age more specifically than DTI.

License

Use is governed by the dataset’s UCSF Datashare Data Use Agreement (see Dryad record).

Citation

Chang, Y., & Mukherjee, P. (2015). NODDI-PLOS-ONE-Chang et al 2015 [Data set]. Dryad. https://doi.org/10.7272/Q6D798BD

Primary article: PLOS ONE — Neurite orientation dispersion and density imaging of brain development across the lifespan (2015).

Source

https://doi.org/10.7272/Q6D798BD
Repository: Dryad (Published Mar 2, 2015)

Dataset details

• Participants: 67 healthy controls, 7–63 years; includes test-retest subsets.
• Modalities:
  • DTI: single-shell diffusion (b≈1000 s/mm²).
  • HARDI (×2): multi-shell diffusion for NODDI modeling.
  • Anatomical: T1-weighted MRI.
• Intended uses: lifespan modeling of ND/OD, age prediction, reliability benchmarking, comparison of NODDI vs. DTI.

Suggested acknowledgement

Please cite the Dryad dataset DOI above and the associated PLOS ONE article when using these data.

Keywords

NODDI • Diffusion MRI • DTI • Lifespan • White matter • Development • Test–retest

This dataset contains diffusion tensor imaging (DTI) data and corresponding behavioral learning measures from a study examining how white matter microstructure predicts success in learning Mandarin Chinese as a second language.
Each subject’s dataset includes nifti-formatted DWI volumes, b-values, and b-vectors, along with a separate CSV file containing demographic and learning performance information.

The dataset accompanies the publication:
Qi Z., Han M., Garel K., Chen E. S., & Gabrieli J. D. E. (2014). White-matter structure in the right hemisphere predicts Mandarin Chinese learning success. Journal of Neurolinguistics, 33, 14–28.

License

Creative Commons Attribution 4.0 International (CC BY 4.0)

Citation

Qi, Z. (2017).
DTI Predicts Mandarin Learning [Data set]. Zenodo.
https://doi.org/10.5281/zenodo.260007

Source

https://doi.org/10.5281/zenodo.260007
Contact: zhenghan.qi@unl.edu
Institution: University of Nebraska–Lincoln, Department of Special Education and Communication Disorders

Dataset Information

Purpose

This dataset supports research into the neurobiological basis of second language learning, with an emphasis on white matter connectivity and diffusion anisotropy as predictors of individual learning outcomes.
It is useful for validating DTI-based biomarkers of language aptitude and for developing new analytic frameworks linking microstructural brain features to language learning performance.

Keywords

Diffusion MRI • DTI • Language Learning • Mandarin Chinese • Second Language Acquisition • White Matter • Neuroplasticity • Neurolinguistics • FA • Tractography