Mind Data Hub

Stanford Translational AI Lab (STAI)

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Mind Data Hub

Overview

The Australian Imaging, Biomarkers & Lifestyle (AIBL) Study of Ageing is a longitudinal research initiative aimed at identifying biomarkers, imaging markers, and lifestyle factors that influence the development and progression of Alzheimer’s disease and other forms of dementia. The study includes neuroimaging, cognitive assessments, blood biomarkers, and lifestyle data from older adults, including both healthy controls and individuals with mild cognitive impairment (MCI) or Alzheimer’s disease (AD).

Official Website: aibl.csiro.au

Data Available in Mind Data Hub

Below, we summarize the modalities and number of subjects processed and available for request in Mind Data Hub:

Structural MRI (T1-weighted & T2-weighted)

Preprocessing Steps:

  • Conversion to NIfTI format (If needed)
  • Brain Extraction
  • Registration to MNI Space using ANTs (Rigid + Affine transformations)
  • Quality Control (QC)

Data Available:

  • T1-weighted scans: 1,293 records from 692 unique subjects
  • T2-weighted scans: 611 records from 692 unique subjects

Metadata:
Comprehensive demographic.

Metadata Source Files:

  • aibl_ptdemog_01-Jun-2018.csv — Demographics (862 records)
  • aibl_medhist_01-Jun-2018.csv — Medical history (smoking, conditions)
  • aibl_pdxconv_01-Jun-2018.csv — Diagnosis (1,688 records)
  • idaSearch_4_02_2025.csv — Accurate ages per session
  • aibl_mri3meta_01-Jun-2018.csv — MRI visit dates/protocols
  • aibl_mrimeta_01-Jun-2018.csv — Secondary MRI metadata
  • all_subjects_qc_results.csv — QC scores
Processed data are organized in a CSV file with the following columns:
Column Name Description
subjectId Subject identifier (RID)
sessionName Session directory name (YYYY-MM-DD_HH_MM_SS.S format)
MNI_Warped Path to MNI warped brain file
MNI_ZSCORE Path to z-score normalized file
MNI_Z_Cropped Path to cropped z-score file
age Age in years (61–103, mean: 80.7±7.1)
sex Sex encoded (1=Male, 2=Female)
smoking_history Smoking history (0=No, 1=Yes, -1=Unknown)
current_diagnosis Current diagnosis (Normal, MCI, AD, Unknown)
scanner_manufacturer Scanner manufacturer (Philips, GE, Siemens, Unknown)
Overall_qc_score SynthSeg QC score (0–1 scale, session-type specific)


Encoding Dictionaries:

  • Sex: 1 = Male, 2 = Female
  • Smoking History: 0 = No, 1 = Yes, -1 = Unknown
  • Diagnosis: Normal, MCI, AD, Unknown

Diagnosis Distribution (T1):

  • Normal: 976 (75.4%)
  • MCI: 195 (15.1%)
  • AD: 119 (9.2%)
  • Unknown: 3 (0.3%)

Diagnosis Distribution (T2):

  • Normal: 471 (77.1%)
  • MCI: 92 (15.1%)
  • AD: 46 (7.5%)
  • Unknown: 2 (0.3%)

Sex Distribution:

  • Female: 470 (54.5%)
  • Male: 392 (45.5%)

Scanner Manufacturer Distribution:

  • Unknown: ~65.6% of scans
  • Philips: ~60.8% of known scans
  • GE: ~5.1% of known scans


Download Data Date: May 2025

Functional MRI (Resting-state fMRI)

AIBL does not have enough FMRI subjects!

Diffusion Tensor Imaging (DTI)

AIBL does not have enough DTI subjects!

Request Access to AIBL Data

To request access to AIBL data, please fill out our internal request form. Requests will be reviewed by our data management team.

Citation and Acknowledgment

When using AIBL data, please cite the official AIBL project and acknowledge the Mind Data Hub preprocessing pipeline:

“Data used in the preparation of this article were obtained from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) database (aibl.csiro.aug). The preprocessing and harmonization of data were performed by Mind Data Hub at Stanford University.”

Useful Links

 

Mind Data Hub Home Page >>

Mind Data Hub

Overview

The Stanford Alzheimer’s Disease Research Center (ADRC) is part of the U.S. NIA-funded Alzheimer’s Disease Centers Program, dedicated to advancing research on Alzheimer’s disease and related dementias. The center integrates clinical, cognitive, neuroimaging, genetic, and biomarker data to study the progression, risk factors, and potential interventions for Alzheimer’s and related disorders.

Official Website: med.stanford.edu/adrc

Data Available in Mind Data Hub

Processing Status: Data ingestion completed — preprocessing is currently in progress. Once completed, available modalities will include:

Structural MRI (T1-weighted & T2-weighted)

Preprocessing Steps:

  • Conversion to NIfTI format (If needed)
  • Brain Extraction
  • Registration to MNI Space using ANTs (Rigid + Affine transformations)
  • Quality Control (QC)

Download Data Date: Aug 2025

Functional MRI (Resting-state fMRI)

  • Conversion to NIfTI (BIDS format)
  • Slice-timing and motion correction
  • Brain extraction and normalization to MNI
  • Denoising and smoothing 
  • Quality Control (QC) and time-series extraction

Download Data Date: Aug 2025.

Diffusion Tensor Imaging (DTI)

Preprocessing Steps:

  • Convert DICOM to NIfTI
  • Topup Correction (Using a top-up tool from FSL)
  • Eddy Correction (Using eddy tool from FSL)
  • Brain Extraction (Using Bet tool from FSL)
  • Merging of Acquisitions (If the session is split into multiple scans)
  • DTI Model Fitting(Using the Dtifit tool from FSL)
  • Registration to MNI Space using ANTs (Rigid + Affine transformations)
  • QC 

Download Data Date: Aug 2025

Request Access to ADRC Data

To request access to ADRC data, please fill out our internal request form. Requests will be reviewed by our data management team.

Citation and Acknowledgment

When using ADRC data, please cite the official ADRC project and acknowledge the Mind Data Hub preprocessing pipeline:

"Data used in the preparation of this article were obtained from the Stanford Alzheimer’s Disease Research Center (ADRC) database (med.stanford.edu/adrc.html). The preprocessing and harmonization of data were performed by Mind Data Hub at Stanford University."

Useful Links

 

Mind Data Hub Home Page >>

Mind Data Hub

Overview

The OpenNeuro platform hosts a wide variety of open-access neuroimaging datasets from multiple studies and research projects. From this collection, Mind Data Hub has currently selected and processed only the dataset ds004215 for inclusion in our repository.

Official Website: openneuro.org

Data Available in Mind Data Hub

Below, we summarize the modalities and number of subjects processed and available for request in Mind Data Hub:

Structural MRI (T1-weighted & T2-weighted)

Preprocessing Steps:

  • Conversion to NIfTI format (If needed)
  • Brain Extraction
  • Registration to MNI Space using ANTs (Rigid + Affine transformations)
  • Quality Control (QC)

Data Available:

  • T1-weighted scans: 183 records from 252 unique subjects
  • T2-weighted scans: 182 record sfrom 182 unique subjects

Metadata:
Comprehensive demographic.

Metadata Source Files (1 main file):

  • Primary Demographics Source: metadata.csv
Processed data are organized in a CSV file with the following columns:
Column Name Description
subjectId Subject identifier (e.g., sub-ON99943)
MNI_Warped Path to MNI warped brain image
MNI_ZSCORE Path to Z-score normalized brain image
MNI_Z_Cropped Path to Z-score normalized and cropped brain image
age Subject age (string, may contain 'n/a')
sex Encoded sex (0 = n/a, 1 = male, 2 = female)
handedness Encoded handedness (0 = n/a, 1 = right, 2 = left, 3 = ambidextrous)


Encoding Dictionaries:

  • Sex: 1 = Male, 2 = Female, 0 = Unknown
  • Handedness: 0 = Unknown, 1 = Right, 2 = Left, 3 = Ambidextrous

Download Data Date: Oct 2024

Functional MRI (Resting-state fMRI)

Preprocessing Steps (fMRIPrep):

  • Conversion to NIfTI (BIDS format)
  • Slice-timing and motion correction
  • Brain extraction and normalization to MNI
  • Denoising and smoothing 
  • Quality Control (QC) and time-series extraction

Data Available:

  • fMRI scans processed using fMRIPrep: 219 Subjects.
  • Comprehensive QC files.

Dedicated scripts are available for both BIDS conversion and parcellation:

Download Data Date: Oct 2024.

Diffusion Tensor Imaging (DTI)

Preprocessing Steps:

  • Convert DICOM to NIfTI
  • Topup Correction (Using a top-up tool from FSL)
  • Eddy Correction (Using eddy tool from FSL)
  • Brain Extraction (Using Bet tool from FSL)
  • Merging of Acquisitions (If the session is split into multiple scans)
  • DTI Model Fitting(Using the Dtifit tool from FSL)
  • Registration to MNI Space using ANTs (Rigid + Affine transformations)
  • QC 

Data Available:

  • fMRI scans processed using fMRIPrep: 238 Subjects.
  • Comprehensive QC files.

Download Data Date: Oct 2024

Request Access to OpenNeuro Data

To request access to OpenNeuro data, please fill out our internal request form. Requests will be reviewed by our data management team.

Citation and Acknowledgment

When using OpenNeuro data, please cite the official OpenNeuro project and acknowledge the Mind Data Hub preprocessing pipeline:

“Data used in the preparation of this article were obtained from the OpenNeuro (dataset ds004215) database (openneuro.org). The preprocessing and harmonization of data were performed by Mind Data Hub at Stanford University.”

Useful Links

 

Mind Data Hub Home Page >>

Mind Data Hub

Overview

The Adolescent Brain Cognitive Development (ABCD) dataset aims to map the structural and functional connectivity of the human brain in healthy young adults using high-resolution neuroimaging and behavioral assessments.

Official Website: abcdstudy.org

Data Available in Mind Data Hub

Below, we summarize the modalities and number of subjects processed and available for request in Mind Data Hub:

Structural MRI (T1-weighted & T2-weighted)

Preprocessing Steps:

  • Conversion to NIfTI format (If needed)
  • Brain Extraction
  • Registration to MNI Space using ANTs (Rigid + Affine transformations)
  • Quality Control (QC)

Data Available:

  • T1-weighted scans: 20,988 records from 11039 unique subjects
  • T2-weighted scans: 14,845 records from 11039 unique subjects

Metadata:
Comprehensive demographic.

Metadata Source Files (1 main file):

  • Primary Demographics Source: /simurgh/group/BWM/DataSets/ABCD/metadata/demo.csv
  • 48,807 total records - Comprehensive demographics including age, sex, race, ethnicity, socioeconomic factors
Key Features:
  • Age provided as continuous numeric values (birth date based)
  • Subject IDs in BIDS format (sub-NDARINV...)
  • Rich demographic data, including race, ethnicity, and parental information
  • Multiple timepoints supported (baseline, 1-year, 2-year, etc.)
Processed data are organized in a CSV file with the following columns:
Column Name Description
subjectId Subject identifier in BIDS format (e.g., sub-NDARINV003RTV85)
sessionName Session identifier (e.g., ses-baselineYear1Arm1, ses-followUpYear2Arm1, etc.)
MNI_Warped Path to MNI warped brain image
MNI_ZSCORE Path to Z-score normalized brain image
MNI_Z_Cropped Path to cropped Z-score normalized brain image
age Child’s age at the time of scan (continuous numeric value)
sex Sex assigned at birth (1 = Male, 2 = Female, 3 = Intersex-male, 4 = Intersex-female)
gender_identity Gender identity (1 = Male, 2 = Female, 3 = Trans male, 4 = Trans female, 5 = Non-binary, 6 = Other)
grade_level Current school grade level (e.g., 5.0 = 5th grade)
hispanic_ethnicity Hispanic/Latino ethnicity (1 = Yes, 2 = No)
race Race (1 = White, 2 = Black, 3 = Asian, 4 = Native, 5 = Pacific Islander, 6 = Multiple, 0 = Missing)
primary_caregiver Role of the primary caregiver (1 = Bio mother, 2 = Bio father, etc.)
parent_age Age of the primary caregiver
parent_income Household income category
language_spanish Parent survey language (0 = English, 1 = Spanish)
partner_education Education level of the partner (other parent or caregiver)
partner_employment Employment status of the partner


Encoding Dictionaries:

  • Sex: 1 = Male, 2 = Female, 0 = Unknown
  • Ethnicity: 0 = Unknown, 1 = Non-Hispanic, 2 = Hispanic
  • Race: 0 = Unknown, 1 = White, 2 = Black, 3 = Asian, 4 = Pacific Islander, 6 = Multiple, 7 = Native American
Encoding Dictionaries:
  • Sex Encoding: 1=Male, 2=Female, 0=Unknown
Age Distribution:
  • Mean age: ~10 years (baseline cohort)
  • Age range: 9-11 years at baseline
  • Longitudinal follow-up: 1-year, 2-year, 3-year, 4-year timepoints
Sex Distribution:
  • Male: ~52% of subjects
  • Female: ~48% of subjects
Race Distribution:
  • White: Majority population
  • Black/African American: Significant representation
  • Asian: Moderate representation
  • Other categories: Native American, Pacific Islander


Download Data Date: March 2025

Functional MRI (Resting-state fMRI)

Preprocessing Steps (fMRIPrep):

  • Conversion to NIfTI (BIDS format)
  • Slice-timing and motion correction
  • Brain extraction and normalization to MNI
  • Denoising and smoothing 
  • Quality Control (QC) and time-series extraction

Data Available:

  • fMRI scans processed using fMRIPrep: 497 Subjects.
  • Comprehensive QC files.

Dedicated scripts are available for both BIDS conversion and parcellation:

Download Data Date: April 2024.

Diffusion Tensor Imaging (DTI)

Preprocessing Steps:

  • Convert DICOM to NIfTI
  • Topup Correction (Using a top-up tool from FSL)
  • Eddy Correction (Using eddy tool from FSL)
  • Brain Extraction (Using Bet tool from FSL)
  • Merging of Acquisitions (If the session is split into multiple scans)
  • DTI Model Fitting(Using the Dtifit tool from FSL)
  • Registration to MNI Space using ANTs (Rigid + Affine transformations)
  • QC 

 

Download Data Date: March 2025

Request Access to ABCD Data

To request access to ABCD data, please fill out our internal request form. Requests will be reviewed by our data management team.

Citation and Acknowledgment

When using ABCD data, please cite the official ABCD project and acknowledge the Mind Data Hub preprocessing pipeline:

“Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) database (abcdstudy.org). The preprocessing and harmonization of data were performed by Mind Data Hub at Stanford University.”

Useful Links

 

Mind Data Hub Home Page >>

Mind Data Hub

Overview

The Human Connectome Project – Young Adult (HCP-YA) dataset aims to map the structural and functional connectivity of the human brain in healthy young adults using high-resolution neuroimaging and behavioral assessments.

Official Website: humanconnectome.org/study/hcp-young-adult

Data Available in Mind Data Hub

Below, we summarize the modalities and number of subjects processed and available for request in Mind Data Hub:

Structural MRI (T1-weighted & T2-weighted)

Preprocessing Steps:

  • Conversion to NIfTI format (If needed)
  • Brain Extraction
  • Registration to MNI Space using ANTs (Rigid + Affine transformations)
  • Quality Control (QC)

Data Available:

  • T1-weighted scans: 1,113 MPR1 records 670 MPR2 records from 1113 unique subjects
  • T2-weighted scans: 1,113 SPC1 records 484 SPC2 records from 1113 unique subjects

What are SPC1, 2, and MPR1, 2?

The HCP-YA dataset includes two T1-weighted scans (MPR1 and MPR2) and two T2-weighted scans (SPC1 and SPC2) per subject. These duplicates improve signal-to-noise ratio and ensure high-quality structural imaging. More information

Metadata:
Comprehensive demographic.

Metadata Source Files:

Primary Demographics Source: md.csv
  • 1,206 total records - Demographics: age, sex/gender
  • Key Innovation: Age provided in age ranges (22-25, 26-30, 31-35, 36+)
  • Subject IDs are in integer format (100206, 100307, etc.)

We have accumulated for getting advance metadata, and we will update it once we received it.

Processed data are organized in a CSV file with the following columns:

Column Name Description
subjectId Subject identifier (6-digit integer format, e.g., 100206)
sessionName Session identifier in the format subjectId_modality (e.g., 100206_MPR1)
MNI_Warped Path to the MNI-space warped brain image
MNI_ZSCORE Path to the Z-score normalized brain image
MNI_Z_Cropped Path to the cropped Z-score normalized brain image
age Age range category (22–25, 26–30, 31–35, 36+)
sex Encoded biological sex (1 = Male, 2 = Female, 0 = Unknown)


Encoding Dictionaries:

  • Sex: 1 = Male, 2 = Female, 0 = Unknown
Encoding Dictionaries:
  • Sex Encoding: 1=Male, 2=Female, 0=Unknown
Data Coverage:
  • Age Coverage: 1,113/1,113 records (100%)
  • Sex Coverage: 1,113/1,113 records (100%)
Age Distribution:
  • 26-30 years: 486 subjects (43.7%)
  • 31-35 years: 383 subjects (34.4%)
  • 22-25 years: 231 subjects (20.8%)
  • 36+ years: 13 subjects (1.2%)
Sex Distribution:
  • Female: 606 subjects (54.4%)
  • Male: 507 subjects (45.6%)


Download Data Date: May 2025

Functional MRI (Resting-state fMRI)

Preprocessing Steps (fMRIPrep):

  • Conversion to NIfTI (BIDS format)
  • Slice-timing and motion correction
  • Brain extraction and normalization to MNI
  • Denoising and smoothing 
  • Quality Control (QC) and time-series extraction

Data Available:

  • fMRI scans processed using fMRIPrep: 1056 Subjects.
  • Comprehensive QC files.

Dedicated scripts are available for both BIDS conversion and parcellation:

Download Data Date: March 2025.

Diffusion Tensor Imaging (DTI)

Preprocessing Steps:

  • Convert DICOM to NIfTI
  • Topup Correction (Using a top-up tool from FSL)
  • Eddy Correction (Using eddy tool from FSL)
  • Brain Extraction (Using Bet tool from FSL)
  • Merging of Acquisitions (If the session is split into multiple scans)
  • DTI Model Fitting(Using the Dtifit tool from FSL)
  • Registration to MNI Space using ANTs (Rigid + Affine transformations)
  • QC 

Download Data Date: March 2025

Request Access to HCP Young Adult Data

To request access to HCP Young Adult data, please fill out our internal request form. Requests will be reviewed by our data management team.

Citation and Acknowledgment

When using HCP Young Adult data, please cite the official HCP Young Adult project and acknowledge the Mind Data Hub preprocessing pipeline:

“Data used in the preparation of this article were obtained from the Human Connectome Project – Young Adult (HCP YA) database (humanconnectome.org). The preprocessing and harmonization of data were performed by Mind Data Hub at Stanford University.”

Useful Links

 

Mind Data Hub Home Page >>

Mind Data Hub

Overview

The Human Connectome Project – Development (HCP Development) dataset aims to understand brain development and connectivity during adolescence through multimodal neuroimaging and behavioral data.

Official Website: humanconnectome.org/study/hcp-lifespan-development

Data Available in Mind Data Hub

Below, we summarize the modalities and number of subjects processed and available for request in Mind Data Hub:

Structural MRI (T1-weighted & T2-weighted)

Preprocessing Steps:

  • Conversion to NIfTI format (If needed)
  • Brain Extraction
  • Registration to MNI Space using ANTs (Rigid + Affine transformations)
  • Quality Control (QC)

Data Available:

  • T1-weighted scans: 652 records from 652 unique subjects (1 session per subject)
  • T2-weighted scans: 652 records from 652 unique subjects (1 session per subject)

Metadata:
Comprehensive demographic.

Processed data are organized in a CSV file with the following columns:

Column Name Description
subjectId Subject identifier (e.g., HCD0001305)
MNI_Warped Path to MNI warped brain image
MNI_ZSCORE Path to z-score normalized brain image
MNI_Z_Cropped Path to z-score normalized and cropped brain image
age Age in years (converted from months)
sex Encoded sex (0=Unknown, 1=Male, 2=Female)
handedness Encoded handedness (0=Unknown, 1=Right, 2=Left, 3=Ambidextrous)
race Encoded race (0=Unknown, 1=White, 2=Black, 3=Asian, 4=Native, 5=Pacific, 6=Multiracial)
ethnicity Encoded ethnicity (0=Unknown, 1=Non-Hispanic, 2=Hispanic)
weight Body weight
height Height
education Background education level
job Job/occupation category
family_income Annual family income
mother_age Mother's age at time of pregnancy
father_age Father's age at time of pregnancy
birth_country_dad Father's birth country
country_origin Country of origin
father_education Father's education level
mother_education Mother's education level


Encoding Dictionaries:

  • Sex: 1 = Male, 2 = Female, 0 = Unknown
  • Handedness: 0 = Unknown, 1 = Right, 2 = Left, 3 = Ambidextrous
  • Ethnicity: 0 = Unknown, 1 = Non-Hispanic, 2 = Hispanic
  • Race: 0 = Unknown, 1 = White, 2 = Black, 3 = Asian, 4 = American Indian/Alaska Native, 5 = Hawaiian/Pacific Islander, 6 = Multiple races.
Data Coverage Results:
  • Total subjects: 652
  • Coverage: 100% for core demographics (age, sex, race, ethnicity, country_origin)
  • Variable coverage for socioeconomic variables (23-24% for parent education)
  • Age range: 5.6-21.9 years


Download Data Date: March 2025

Functional MRI (Resting-state fMRI)

Preprocessing Steps (fMRIPrep):

  • Conversion to NIfTI (BIDS format)
  • Slice-timing and motion correction
  • Brain extraction and normalization to MNI
  • Denoising and smoothing 
  • Quality Control (QC) and time-series extraction

Data Available:

  • fMRI scans processed using fMRIPrep: 566 Subjects.
  • Comprehensive QC files.

Dedicated scripts are available for both BIDS conversion and parcellation:

Download Data Date: March 2025.

Diffusion Tensor Imaging (DTI)

Preprocessing Steps:

  • Convert DICOM to NIfTI
  • Topup Correction (Using a top-up tool from FSL)
  • Eddy Correction (Using eddy tool from FSL)
  • Brain Extraction (Using Bet tool from FSL)
  • Merging of Acquisitions (If the session is split into multiple scans)
  • DTI Model Fitting(Using the Dtifit tool from FSL)
  • Registration to MNI Space using ANTs (Rigid + Affine transformations)
  • QC 

Download Data Date: March 2025

Request Access to HCP Development Data

To request access to HCP Development data, please fill out our internal request form. Requests will be reviewed by our data management team.

Citation and Acknowledgment

When using HCP Development data, please cite the official HCP Development project and acknowledge the Mind Data Hub preprocessing pipeline:

“Data used in the preparation of this article were obtained from the Human Connectome Project – Aging (HCP Development) database (humanconnectome.org). The preprocessing and harmonization of data were performed by Mind Data Hub at Stanford University.”

Useful Links

 

Mind Data Hub Home Page >>

Mind Data Hub

Overview

The Human Connectome Project – Aging (HCP Aging) dataset aims to characterize brain connectivity patterns and their changes across the lifespan, focusing specifically on aging populations through multimodal neuroimaging and behavioral assessments.

Official Website: humanconnectome.org/study/hcp-lifespan-aging

Data Available in Mind Data Hub

Below, we summarize the modalities and number of subjects processed and available for request in Mind Data Hub:

Structural MRI (T1-weighted & T2-weighted)

Preprocessing Steps:

  • Conversion to NIfTI format (If needed)
  • Brain Extraction
  • Registration to MNI Space using ANTs (Rigid + Affine transformations)
  • Quality Control (QC)

Data Available:

  • T1-weighted scans: 725 records from 725 unique subjects (1 session per subject)
  • T2-weighted scans: 725 records from 725 unique subjects (1 session per subject)

Metadata:
Comprehensive demographic.

Metadata Source Files:

  • Subject Info: ndar_subject01.txt (age, sex, race, ethnicity)
  • Handedness: edinburgh_hand01.txt (writing hand preference)
  • Vitals: vitals01.txt (weight_std, vtl007)
  • Sociodemographics: ssaga_cover_demo01.txt (education, income, marital status)

Processed data are organized in a CSV file with the following columns:

Column Name Description
subjectId Subject identifier
MNI_Warped Path to MNI warped file
MNI_ZSCORE Path to MNI z-score normalized file
MNI_Z_Cropped Path to MNI z-score cropped file
age Age in years (converted from months)
sex Sex (encoded: 1=Male, 2=Female, 0=Unknown)
handedness Handedness preference (encoded)
race Race category (encoded)
ethnicity Ethnicity (encoded)
weight Weight in kg
height Height
education Education level in years
family_income Annual family income
marital_status Marital status (encoded)


Encoding Dictionaries:

  • Sex: 1 = Male, 2 = Female, 0 = Unknown
  • Handedness: 0 = Unknown, 1 = Right, 2 = Left, 3 = Ambidextrous
  • Ethnicity: 0 = Unknown, 1 = Non-Hispanic, 2 = Hispanic
  • Race: 0 = Unknown, 1 = White, 2 = Black, 3 = Asian, 4 = American Indian/Alaska Native, 5 = Hawaiian/Pacific Islander, 6 = Multiple races.
  • Marital Status: 1 = Married, 2 = Never Married/Single, 3 = Divorced, 4 = Widowed, 5 = Living as Married/Cohabiting, 6 = Separated, 0 = Unknown
Data Coverage Results:
  • Age: 725 subjects (100.0%)
  • Sex: 725 subjects (100.0%)
  • Weight: 723 subjects (99.7%)
  • Height: 724 subjects (99.9%)
  • Education: 713 subjects (98.3%)
  • Family Income: 534 subjects (73.6%)
  • Marital Status: 712 subjects (98.2%)


Download Data Date: May 2025

Functional MRI (Resting-state fMRI)

Preprocessing Steps (fMRIPrep):

  • Conversion to NIfTI (BIDS format)
  • Slice-timing and motion correction
  • Brain extraction and normalization to MNI
  • Denoising and smoothing 
  • Quality Control (QC) and time-series extraction

Data Available:

  • fMRI scans processed using fMRIPrep: 632 Subjects.
  • Comprehensive QC files.

Dedicated scripts are available for both BIDS conversion and parcellation:

Download Data Date: March 2025.

Diffusion Tensor Imaging (DTI)

Preprocessing Steps:

  • Convert DICOM to NIfTI
  • Topup Correction (Using a top-up tool from FSL)
  • Eddy Correction (Using eddy tool from FSL)
  • Brain Extraction (Using Bet tool from FSL)
  • Merging of Acquisitions (If the session is split into multiple scans)
  • DTI Model Fitting(Using the Dtifit tool from FSL)
  • Registration to MNI Space using ANTs (Rigid + Affine transformations)
  • QC 

Download Data Date: March 2025

Request Access to HCP Aging Data

To request access to HCP Aging data, please fill out our internal request form. Requests will be reviewed by our data management team.

Citation and Acknowledgment

When using HCP Aging data, please cite the official HCP Aging project and acknowledge the Mind Data Hub preprocessing pipeline:

“Data used in the preparation of this article were obtained from the Human Connectome Project – Aging (HCP Aging) database (humanconnectome.org). The preprocessing and harmonization of data were performed by Mind Data Hub at Stanford University.”

Useful Links

 

Mind Data Hub Home Page >>

Mind Data Hub

Overview

The Parkinson’s Progression Markers Initiative (PPMI) is an observational clinical study designed to identify biomarkers of Parkinson’s Disease (PD) progression through comprehensive imaging, clinical assessments, genetics, and biospecimen collection.

Official Website: ppmi-info.org

Data Available in Mind Data Hub

Below, we summarize the modalities and number of subjects processed and available for request in Mind Data Hub:

Structural MRI (T1-weighted & T2-weighted)

Preprocessing Steps:

  • Conversion to NIfTI format (If needed)
  • Brain Extraction
  • Registration to MNI Space using ANTs (Rigid + Affine transformations)
  • Quality Control (QC)

Data Available:

  • T1-weighted scans: 835 records from 356 unique subjects (~2.35 sessions per subject)
  • T2-weighted scans: 835 records from 356 unique subjects (~2.35 sessions per subject)

Metadata:
Comprehensive demographic, clinical, and ...

Metadata Source Files:

  • Primary Source: diagnosis.csv
  • Main data (Age, sex, weight, research_group, visit, study_date, and ...): Demographics_31Jan2025.csv
  • Supplementary data (handedness, hispanic, race variables, Education Source, and ...) : Socio-Economics_31Jan2025.csv
  • Education Source - Online Socioeconomics: Socioeconomic_Status__Online__31Jan2025.csv

Processed data are organized in a CSV file with the following columns:

Column Name Description
subjectId Subject identifier (PATNO)
sessionName Session directory name (date-time format)
MNI_Warped Path to warped MNI file
MNI_ZSCORE Path to z-score normalized file
MNI_Z_Cropped Path to cropped z-score file
age Age in years (e.g., 71.1, 68.9, 55.4)
sex Sex encoded (1=male, 2=female)
weight Weight in kilograms
research_group Research group classification (PD, Control, Prodromal, SWEDD)
handedness Hand dominance (1=right, 2=left, 3=ambidextrous, 0=unknown)
Hispanic Hispanic/Latino ethnicity (1=no, 2=yes, 0=unknown)
race_combined Combined race variable (1=white, 2=black, 3=asian, 6=mixed, 7=other, 0=unknown)
education_years Years of education (range: 10-24 years)


Encoding Dictionaries:

  • Sex: 1 = Male, 2 = Female, 0 = Unknown
  • Handedness: 0 = Unknown, 1 = Right, 2 = Left, 3 = Ambidextrous
  • Ethnicity: 0 = Unknown, 1 = Non-Hispanic, 2 = Hispanic
  • Race: 0 = Unknown, 1 = White, 2 = Black, 3 = Asian, 7 = Other

Research Group Distribution:

  • Parkinson's Disease (PD): 542 records (64.9%)
  • Healthy Controls (Control): 135 records (16.2%)
  • Prodromal Stage: 82 records (9.8%)
  • Scan Without Evidence of Dopaminergic Deficit (SWEDD): 76 records (9.1%)

Sex Distribution:

  • Male (1): 556 records (66.6%)
  • Female (2): 279 records (33.4%)

Education Years Distribution (Top 10):

  • 16 years: 205 subjects (most common – Bachelor's degree)
  • 18 years: 144 subjects (Master's degree level)
  • 12 years: 80 subjects (High school)
  • 14 years: 76 subjects (Some college)
  • 20 years: 47 subjects (Doctoral level)
  • 13 years: 47 subjects
  • 15 years: 46 subjects
  • 11 years: 39 subjects
  • 17 years: 34 subjects
  • 19 years: 24 subjects


Download Data Date: May 2025

Functional MRI (Resting-state fMRI)

Preprocessing Steps (fMRIPrep):

  • Conversion to NIfTI (BIDS format)
  • Slice-timing and motion correction
  • Brain extraction and normalization to MNI
  • Denoising and smoothing 
  • Quality Control (QC) and time-series extraction

Data Available:

  • fMRI scans processed using fMRIPrep: 330 Subjects.
  • Comprehensive QC files.

Dedicated scripts are available for both BIDS conversion and parcellation:

Download Data Date: July 2024.

Diffusion Tensor Imaging (DTI)

Preprocessing Steps:

  • Convert DICOM to NIfTI
  • Topup Correction (Using a top-up tool from FSL)
  • Eddy Correction (Using eddy tool from FSL)
  • Brain Extraction (Using Bet tool from FSL)
  • Merging of Acquisitions (If the session is split into multiple scans)
  • DTI Model Fitting(Using the Dtifit tool from FSL)
  • Registration to MNI Space using ANTs (Rigid + Affine transformations)
  • QC 

Download Data Date: Sep 2025

Request Access to PPMI Data

To request access to PPMI data, please fill out our internal request form. Requests will be reviewed by our data management team.

Citation and Acknowledgment

When using PPMI data, please cite the official PPMI project and acknowledge the Mind Data Hub preprocessing pipeline:

“Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (ppmi-info.org). The preprocessing and harmonization of data were performed by Mind Data Hub at Stanford University.”

Useful Links

 

Mind Data Hub Home Page >>

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    Palo Alto, CA 94304-5990

  • staiadmin@stanford.edu

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