# Main Existing Datasets for Open Brain Research on Humans

Baptiste Couvy-Duchesne, Simona Bottani, Etienne Camenen, Fang Fang, Mulusew Fikere, Juliana Gonzalez-Astudillo, Joshua Harvey, Ravi Hassanaly, Irfahan Kassam, Penelope Lind, et al.

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Baptiste Couvy-Duchesne, Simona Bottani, Etienne Camenen, Fang Fang, Mulusew Fikere, et al. Main Existing Datasets for Open Brain Research on Humans. Olivier Colliot. Machine Learning for Brain Disorders, Springer US, pp.753-804, 2023, 10.1007/978-1-0716-3195-9_24. hal-04254339

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**Chapter 24**

**Main Existing Datasets for Open Brain Research on Humans**

Baptiste Couvy-Duchesne ID, Simona Bottani, Etienne Camenen, Fang Fang, Mulusew Fikere ID, Juliana Gonzalez-Astudillo ID, Joshua Harvey ID, Ravi Hassanaly, Irfahan Kassam ID, Penelope A. Lind ID, Qianwei Liu, Yi Lu, Marta Nabais, Thibault Rolland, Julia Sidorenko, Lachlan Strike ID, and Margie Wright ID

**Abstract**

Recent advances in technology have made possible to quantify fine-grained individual differences at many levels, such as genetic, genomics, organ level, behavior, and clinical. The wealth of data becoming available raises great promises for research on brain disorders as well as normal brain function, to name a few, systematic and agnostic study of disease risk factors (e.g., genetic variants, brain regions), the use of natural experiments (e.g., evaluate the effect of a genetic variant in a human population), and unveiling disease mechanisms across several biological levels (e.g., genetics, cellular gene expression, organ structure and function). However, this data revolution raises many challenges such as data sharing and management, the need for novel analysis methods and software, storage, and computing.

Here, we sought to provide an overview of some of the main existing human datasets, all accessible to researchers. Our list is far from being exhaustive, and our objective is to publicize data sharing initiatives and help researchers find new data sources.

**Key words** Genetic, Methylation, Gene expression, Brain MRI, PET, EEG/MEG, Omics, Electronic health records, Wearables

# 1 Aims

We sought to provide an overview and short description of some of the main existing human datasets accessible to researchers. We hope this chapter will help publicize them as well as encourage the sharing of datasets for open science. As much as possible, we tried to provide practical aspects, such as data type, file size, sample demographics, study design, as well as links toward data use/transfer agreements. We hope this can help researchers study larger and more diverse data, in order to advance scientific discovery and improve reproducibility.
This chapter does not aim to provide an exhaustive list of the dataset and data types currently available. In addition, the interested readers may refer to the complementary chapters that focus on data processing, feature extraction, and existing methods for their analyses.

## 2 Introduction

The availability of data used in research is one of the cornerstones of open science, which contributes to improving the quality, reproducibility, and impact of the findings. In addition, data sharing increases openness and transparent and collaborative scientific practices. The global push for open science is exemplified by the recent publication of UNESCO guidelines [1], the engagement of many research institutions, and the requirements of some scientific journals to make data available upon publication. Finally, the sharing and re-use of data also maximizes the return on investment of the agencies (e.g., states, charities, associations) that fund the data collection.

In light of this, our chapter aims at providing a broad (albeit partial) overview of some of the human datasets publicly available to researchers. To assist researchers and data managers, we first describe the different file formats and the size of the different data types (see Table 1). As many of these data are high-dimensional, the size of the data can cause storage and computational challenges, which need to be anticipated before download and analysis. Of note, some datasets cannot be downloaded or analyzed outside of a dedicated system/server. This is the case of the UK Biobank (UKB) exome and whole genome sequencing, whose sheer size has led to the creation of a dedicated Research Analysis Platform, accessible (at some cost) by UKB-approved researchers. In addition, the Swedish registry data is only accessible via national dedicated servers due to the extreme sensitive nature of the data.

This chapter breaks down into sections that focus on each data type, although the same dataset may be mentioned in several sections. Beyond a practical writing advantage (each author or group of authors contributed a section), this also reflects the fact that most datasets are organized around a central data type. For example, the ADNI (Alzheimer's disease Neuroimaging Initiative) focuses on brain imaging and later included genotyping information. Another example is the UKB, which released genotyping data of the 500 K participants in 2017, is now collecting brain MRI (as well as cardiac and abdominal MRI, whole-body DXA, and carotid ultrasound), and has recently made available sequencing data. The different sections also discuss and present the specific data sharing tools and portals (e.g., LONI for brain imaging, GTEx for gene expression) or organization of the different fields (e.g., consortia in
<table><caption>**Table 1**
Overview of data types and sample size</caption><thead><tr><th>Data</th><th>Subtype</th><th>File type/extension</th><th>Description</th><th>Approx. size of one sample</th></tr></thead><tbody><tr><td rowspan="2">Clinical</td><td>Self-reports</td><td>Text</td><td rowspan="2"></td><td>~100 KB</td></tr><tr><td>EHR</td><td>Text</td><td>~100 KB</td></tr><tr><td rowspan="2">Neuroimaging</td><td>MRI</td><td>NIfTI (.nii/.nii.gz) or DICOM (.dcm)</td><td>3D image (.nii)<br>2D slices (.dcm) that compose the 3D volume</td><td>T1w ~50 MB<br>T2 FLAIR ~30 MB<br>SWI ~30 MB<br>DWI ~400 MB (highly variable depending on sequence)<br>fMRI ~500 MB (100 MB per minute of acquisition)</td></tr><tr><td></td><td></td><td></td><td>~1 GB after processing<br>~15 MB</td></tr><tr><td rowspan="2">PET</td><td></td><td>NIfTI (.nii / .nii.gz) or DICOM (.dcm)</td><td>3D image (.nii) 2D slices (.dcm) that compose the 3D volume</td><td>~15 MB</td></tr><tr><td>EEG</td><td>.edf, .gdf, .eeg, .csv, .mat</td><td>Raw EEG signal (.egg, .gdf, .edf – file formats), processed/formatted data (.csv, .mat)</td><td>~15 MB<br>~50 MB</td></tr><tr><td>MEG</td><td></td><td>.fif, .bin, .csv</td><td>Raw MEG signal (.fif, .bin) processed/formatted data (.csv, .mat)</td><td></td></tr><tr><td rowspan="5">Genetics</td><td>Twin/family sample</td><td>Text</td><td>Phenotypes and pedigree information</td><td>~10 KB</td></tr><tr><td>Genotyping</td><td>.bgen, .bed (.bim + .bed + .fam)</td><td>Binary files</td><td>~5 MB</td></tr><tr><td>GWAS</td><td>Text</td><td>Effect size, test statistic, p-value, effect allele from association testing</td><td>Not individual-level data, ~500 MB for a genome-wide association summary</td></tr><tr><td>summary statistics</td><td></td><td></td><td>~1 M</td></tr><tr><td>Exome sequencing</td><td>.cram, .bcf, .vcf</td><td>Each variant site with multiple sequence quality metrics and trained machine learning filters to identify and exclude inconsistencies. Followed by initial QC with various parameters and thresholds</td><td></td></tr><tr><td>Whole genome sequencing</td><td></td><td>.cram, .bcf, .vcf</td><td>Curated using TOPMed variant calling algorithm. All freezes are fully processed</td><td>~5 MB</td></tr></tbody></table>

(continued)
<table><caption>Table 1
(continued)</caption><thead><tr><th>Data</th><th>Subtype</th><th>File type/extension</th><th>Description</th><th>Approx. size of one sample</th></tr></thead><tbody><tr><td rowspan="3">Genomics</td><td>Methylation</td><td>.ids<br>.txt or .csv.</td><td>Raw binary intensity file (two per sample, one for green and red channels)<br>Processed, normalized, and filtered beta matrix</td><td>~16 MB (450 K)<br>~27 MB (EPIC)<br>~8 MB (450 K)<br>~14 MB (EPIC)</td></tr><tr><td>Expression</td><td>.bed (.bim + .bed + .fam)<br>Text</td><td>Fully processed, filtered, and normalized gene expression matrices (in BED format) for each tissue<br>Covariates used in eQTL analysis. Includes genotyping principal components and PEER factors</td><td>~1 MB per individual<br>~6 KB</td></tr><tr><td>Smartphone and sensors</td><td>Actigraphy<br>Varies with product (e.g., GENEActiv uses .bin)</td><td>Raw accelerator data</td><td>~500 MB (GENEActiv, 14 days of recording)</td></tr></tbody></table>

Size of one sample may vary based on the technology used to generate the data (e.g., MRI resolution, genotyping chip)
genetics). Every time, we have tried to include the largest dataset (s) available, as well as the commonly used ones, although the selection may be subjective and reflect the authors’ specific interests (e.g., age or disease groups).

All datasets are listed in a single table (see Table 2), which includes information about country of origin, design (e.g., cross-sectional, longitudinal, clinical, or population sample), and age range of the participants. Unless specified, the datasets presented include male and female participants, although the proportion may differ depending on the recruitment strategy and disease of interest. In addition, the table lists (and details) the different data types that have been collected on the participants. We have only focused on a handful of data types: genetic data (including twin/family samples, genotyping, and exome and whole genome sequencing), genomics (methylation and gene expression), brain imaging (MRI and PET), EEG/MEG, electronic health records (hospital data and national registry), as well as wearable and sensor data. However, we have included additional columns “Other omics” and “Specificities” that list other types of data being collected, such as proteomics, metabolomics, microRNA, single-cell sequencing, microbiome, and non-brain imaging.

Our main table (see Table 2) also includes the URLs to the dataset websites and data transfer/agreement. From our experience, data access can take between an hour and up to a few months. The agreements almost always require a review of the project and to acknowledge the data collection team and funding sources (e.g., under the form of a byline, a paragraph in the acknowledgment, and more rarely co-authorships). Standard restrictions of use include that the data cannot be redistributed and that the users do not attempt to identify participants. Specific clauses are often added depending on the nature of data and the specific laws and regulations of the countries it originates from.

There is a growing scientific and ethical discussion about the representativity of the datasets being used in research. Researchers should be aware of the biases present in some datasets (e.g., “healthy bias” in the UKB [2]), which should be taken into account in study design (e.g., analysis of diverse ancestry being collected in genetics [3]), when reporting results [2, 4] and evaluating algorithms [5, 6]. Overall, our (selected) list exemplifies the need for datasets from under-represented countries or groups of individuals (e.g., disease, age, ancestry, socioeconomic status) [7, 8]. Our main table (see Table 2) will be accessible online, in a user-friendly, searchable version. Finally, we will also make this table collaborative (via GitHub https://github.com/baptisteCD/ MainExistingDatasets) in order to grow this resource beyond this book chapter.
<table><caption>Table 2
Description of a selection of the main human datasets available for research</caption><thead><tr><th>Sample</th><th>N</th><th>Age range</th><th>Country</th><th>Cross-sectional/<br>longitudinal</th><th>Clinical</th><th>Neuroimaging<br>(MRI, PET, fMRI)</th><th>EEG/MEG</th><th>Genetics (genotyping,<br>exome, WGS,<br>tions)</th><th>Genomics</th><th>Smartphones<br>and sensors</th><th>Other omics</th><th>Website/Data<br>Transfer<br>Agreement</th><th>Reference<br>article(s)</th><th>Specificities</th></tr></thead><tbody><tr><td>Alzheimer's<br>Disease<br>Neurodegenerative<br>Initiative<br>(ADNI)</td><td>2215</td><td>55-90</td><td>USA and<br>Canada</td><td>Longitudinal (up<br>to 9 years of<br>follow-up,<br>ongoing)</td><td>Neurological<br>(Alzheimer's),<br>cognition, lumbar<br>puncture</td><td>MRI (T1w, T2, DWI,<br>stMRI)<br>PET ("F"-FDG,<br>FBB, AV45,<br>FB)</td><td>NA</td><td>Genotyping,<br>WGS</td><td>Methylation<br>(subset<br>of 653<br>individuals—<br>3 time<br>points)</td><td>NA</td><td>Transcriptomics,<br>CFN<br>proteomics,<br>metabolomics,<br>lipidomics</td><td>http://adl-loni.unc.edu/data-samples/access-data/</td><td>[11, 191]</td><td>Four waves<br>(ADNI 2,<br>GO, 3) with<br>different inclusion<br>and protocols</td></tr><tr><td>Australian<br>Imaging<br>Biomarkers<br>and Lifestyle<br>Study of Aging<br>(AIBL)</td><td>726</td><td>60+</td><td>Australia</td><td>Longitudinal (up<br>to 6 years of<br>follow-up)</td><td>Neurological<br>(Alzheimer's),<br>cognition, lumbar<br>puncture</td><td>MRI (T1w, T2,<br>DWI,<br>stMRI)<br>PET (FB, AV45,<br>Flare)</td><td>NA</td><td>Genotyping</td><td>Methylation</td><td>ActiGraph activity NA<br>(10% of<br>sample)</td><td>https://adl-loni.unc.edu/collaboration/access/appLicense.jsp<br>https://aibl.cairo.au/</td><td></td><td>[12]</td><td>Neuroimaging and<br>selected clinical<br>data available via<br>the LONI platform<br>Full sample (N = 1200),<br>including extended<br>clinical,<br>genotyping, or<br>methylation<br>available via<br>application to<br>CSIRO</td></tr><tr><td>Open Access<br>Series of<br>Imaging<br>Studies<sup>13</sup><br>(OASIS)</td><td>1096</td><td>42-95</td><td>USA</td><td>Longitudinal (up<br>to 12 years of<br>follow-up)</td><td>Neurological<br>(Alzheimer's),<br>cognition</td><td>MRI (T1w, T2,<br>FLAIR, ASL,<br>SWI, time<br>of flight,<br>stMRI, and<br>DWI)<br>PET (FB, AV45,<br>FG)</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>https://www.oasis-brains.org<br>https://www.oasis-brains.org/#access</td><td>[13]</td><td>Retrospective dataset<br>from imaging<br>projects collected<br>by WUSTL Knight<br>ADBRC over 30<br>years<br>Two other<br>(non-independent)<br>datasets available:<br>OASIS1 and<br>OASIS2</td></tr><tr><td>Adolescent<br>Brain<br>Cognitive<br>Development<br>(ABCD)</td><td>-11,878</td><td>9-12</td><td>USA</td><td>Longitudinal (up<br>to 2 years of<br>follow-up,<br>ongoing)</td><td>Self- and parental<br>rating. Substance<br>use, mental health<br>(psychiatry),<br>cognition,<br>physical health</td><td>MRI (T1w, T2,<br>stMRI,<br>tMRI)</td><td>NA</td><td>Genotyping,<br>pedigree<br>(twanning)</td><td>Methylation</td><td>iPAD tasks<br>and testing</td><td>NA</td><td>https://abcstudy.org<br>https://nda.nih.gov/<br>abcod/request-access</td><td>[16, 17]</td><td>Objective: 10 years of<br>follow-up</td></tr><tr><td>Enhancing<br>Neuroimaging<br>Grainitis<br>through Metagenomics<br>(ENIGMA)</td><td>>50,000<br>indiv.<br>incl.: 9572<br>(SCZ),<br>6503 (BD),<br>10,105<br>(MDD),<br>1868<br>(PTSD),<br>3240<br>(SUD),<br>3605<br>(OCD),<br>4180<br>(ADHD),<br>18,605 (life<br>span)</td><td>3-90 (no<br>restriction)</td><td>Worldwide<br>(43+<br>countries)</td><td>Cross-sectional<br>and<br>longitudinal</td><td>Psychiatry, neurology,<br>addiction,<br>suicidality,<br>brain injury, HIV,<br>antisocial<br>behavior</td><td>T1w, DWI,<br>stMRI,<br>tMRI</td><td>Resting-state<br>EEG</td><td>Genotyping, CNYs,<br>pedigree<br>(twanning)</td><td>Methylation</td><td>NA</td><td>NA</td><td>https://enigma.ini.unc.edu/poin/<br>http://enigma.ini.unc.edu/research/download-enigma-gwas-results/</td><td>Neuroimaging<br>projects<br>[27<br>[112, 114]</td><td>Consortium organized<br>around genetics<br>and/or disease/<br>trait working<br>groups as well as<br>non-clinical<br>working groups<br>with focus on sex,<br>healthy aging,<br>plasticity, etc.<br>Imaging and genetic<br>protocols and<br>genome-wide<br>association statistics<br>are available for<br>download on the<br>ENIGMA website</td></tr></tbody></table>
<table><tr><td>Chinose Brain PET 116<br>Template<br>(CNPET)</td><td>27-81</td><td>China</td><td>Cross-sectional</td><td>Healthy subjects</td><td>PET (¹³F-FDG)</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>https://www.wuaitrc.org/<br>doi/landing/page.<br>php?table=groups&amp;<br>id=14860&doi=<br>https://www.wuaitrc.org/<br>projects/cnpt</td><td>39</td><td>Data made to build a<br>Chinese-specific<br>SPM PET brain<br>template</td></tr><tr><td>Centre for<br>Integrated<br>Molecular<br>Brain Imaging<br>(CIMBI)</td><td>-2000</td><td>17-93</td><td>Denmark</td><td>Cross-sectional</td><td>Mean and physical<br>state, personality,<br>and background<br>Neuropsychological<br>measures<br>(memory,<br>language, etc.)</td><td>MRI (T1w, T2w,<br>DWI, fMRI)<br>PET (¹¹C-5-HT)</td><td>NA</td><td>NA</td><td>Genetic<br>polymorphisms<br>relevant for the<br>5-HT system</td><td>NA</td><td>NA</td><td>NA</td><td>https://www.cimbi.dk/<br>index.php</td><td>38</td><td>Data sharing only<br>within the<br>European Union<br>(EU). Visiting<br>access for non-EU<br>researchers</td></tr><tr><td>Moor Imagery<br>dataset from<br>Cho et al.<br>[192]</td><td>52</td><td>24.8 SD =<br>3.86 years old</td><td>South<br>Korea</td><td>Cross-sectional</td><td>Healthy subjects</td><td>NA</td><td>NA</td><td>EEG<br>(64<br>channels;<br>real hand<br>movements,<br>motor<br>imagination<br>hand<br>movement<br>tasks</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>http://noabb.<br>neurotoch.com/<br>docs/generated/<br>noabb generateds.<br>Cho.2017.<br>hami#noabb.<br>daets Cho.2017</td><td>192</td><td>BCI experiments</td></tr><tr><td>EEG Alpha Waves<br>dataset</td><td>20</td><td>19-44 years old</td><td>France</td><td>Cross-sectional</td><td>Healthy subjects</td><td>NA</td><td>NA</td><td>EEG<br>(16<br>channels;<br>resting state,<br>eyes open/<br>closed</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>https://xenodo.org/<br>record/2608110#<br>YIC154AZZIE</td><td>193</td><td>Alpha waves dataset.<br>BCI experiments</td></tr><tr><td>Multiper spectra<br>recorded<br>daring<br>GABAergic<br>anesthetic<br> unconsciousness</td><td>55</td><td></td><td>USA</td><td>Cross-sectional</td><td>Healthy participants<br>and patients<br>receiving an<br>anesthesia care in<br>an operating<br>room context</td><td>NA</td><td>NA</td><td>EEG (64 for<br>healthy<br>volunteers, 6<br>for patients<br>under<br>anesthesia)</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>https://physionet.org/<br>content/ccg-<br>power-<br>anesthesia/1.00/</td><td>194</td><td>Patients under<br>anesthesia<br>during surgery</td></tr><tr><td>A multi-subject,<br>multi-modal<br>human<br>neuroimaging<br>dataset</td><td>19 (16 for<br>MEG and<br>EEG, 3 for<br>fMRI)</td><td>23-37 years old</td><td>UK</td><td>Longitudinal (2<br>visits over 3<br>months)</td><td>Healthy subjects</td><td>fMRI</td><td>NA</td><td>MEG, EEG (70<br>channels;<br>resting state,<br>sensory-<br>motor tasks</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>https://legacy.opentinn.<br>org/dataset/<br>ds000117/</td><td>195</td><td>OpenMRI database,<br>accession number<br>ds000117</td></tr><tr><td>Moor imagery,<br>sunuc<br>classification<br>application</td><td>10</td><td>26-46 years old</td><td>Germany</td><td>Cross-sectional</td><td>Healthy subjects</td><td>NA</td><td>NA</td><td>EEG (59<br>channels;<br>hands, feet,<br>and tongue<br>motor<br>imagination<br>tasks</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>http://www.bbci.de/<br>competition/iv/<br>desc_1.html</td><td>196</td><td>Data used for a BCI<br>competition. Also<br>include simulated/<br>synthetic data</td></tr><tr><td>BCI Competition<br>2008: Goetz<br>data set A</td><td>9</td><td>NA</td><td>Austria</td><td>Cross-sectional</td><td>Healthy subjects</td><td>NA</td><td>NA</td><td>EEG (22<br>channels;<br>hands, feet,<br>and tongue<br>motor<br>imagination<br>tasks</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>http://www.bbci.de/<br>competition/iv/<br>desc_2a.pdf</td><td>197</td><td>Data used for a BCI<br>competition</td></tr></table>

(continued)
<table><caption>Table 2
(continued)</caption><thead><tr><th>Sample</th><th>N</th><th>Age range</th><th>Country</th><th>Cross-sectional/ longitudinal</th><th>Clinical</th><th>Neuroimaging (MRI, PET MRI)</th><th>EEG/MEG</th><th>Genetics (genotyping, exome, WES, twins)</th><th>Genomics</th><th>Smartphones and sensors</th><th>Other omics</th><th>Website/Data Transfer Agreement</th><th>Reference article(s)</th><th>Specificities</th></tr></thead><tbody><tr><td>BCI Competition 2008/2009: Genz data set B</td><td>9</td><td>NA</td><td>Austria</td><td>Cross-sectional</td><td>Healthy subjects</td><td>NA</td><td>EEG (3 channels); hands motor imagination tasks</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>http://www.bci.de/ competition/iv/ disc_2b.pdf</td><td>[197]</td><td>Data for a BCI competition</td></tr><tr><td>EEG data from Monnens et al. [198]</td><td>64 (34 MDD, 30 healthy) years old</td><td>Malaysia</td><td>Longitudinal (multiple visits to the clinic)</td><td>Case-control for major depressive disorder</td><td>NA</td><td>EEG (19 channels)</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>https://journals.plos.org/plosone/ article/10.1371/journal.pone.0171409</td><td>[198]</td><td>EEG (reasing task), MDD based on medical history of patients</td></tr><tr><td>EEG data for ADHD/control children</td><td>121</td><td>7-12 years old</td><td>South Korea</td><td>Cross-sectional</td><td>ADHD (61 children) and healthy controls [61]</td><td>NA</td><td>EEG (19 channels); visual attention tasks</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>https://icee-dataport.org/open-access/ ceg-data-adhd-control-children</td><td>[199]</td><td>Visual cognitive tests on children with ADHD, using videos</td></tr><tr><td>EEG and EOG data from Juramilla-Gonzales et al. [61]</td><td>NA</td><td>Germany</td><td>Longitudinal (2 to 10 visits)</td><td>Amytrophic lateral sclerosis, locked-in state</td><td>NA</td><td>EEG (116 channels)</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>https://doi.org/10. 6084/m9figharc. 13148762</td><td>[200]</td><td>Spelling task with eye movement-based answers</td></tr><tr><td>Temple University Hospital (TUH) EEG Corp</td><td>10,874</td><td>1-90</td><td>USA</td><td>Cross-sectional</td><td>Epilepsy, stroke, concussion, healthy</td><td>NA</td><td>EEG (20-41 channels)</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>https://sippi.coe press.com/projects/tuh_ ceg/html/ downloads.shtml</td><td>[201]</td><td>Extensive dataset of 30 K clinical EEG recordings collected at TUH from 2002 to today: Epilepsy subset: TUEP dataset</td></tr><tr><td>Bern-B Barcelona EEG database</td><td>5</td><td>NA</td><td>Spain</td><td>Longitudinal</td><td>Epilepsy</td><td>NA</td><td>EEG (64 channels); rest</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>https://repository.upf.edu/handle/ 10230/42829</td><td>[202]</td><td>Intracranial EEG samples before and after surgery</td></tr><tr><td>CHB-MIT Salp EEG Database</td><td>22</td><td>1.5-22</td><td>USA</td><td>Longitudinal</td><td>Seizures and intracranial seizures</td><td>NA</td><td>EEG (21 channels); resting state</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>https://www.physionet.org/content/chbmc/1.0.0/</td><td>[203]</td><td>Pediatric subjects with intracranial seizures</td></tr><tr><td>Brisa/Narul-Computer Intervention (BNC2) Horizon</td><td>2</td><td>NA</td><td>Austria</td><td>Longitudinal</td><td>Chronic stroke</td><td>NA</td><td>EEG (2 channels); eye tracking task</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>http://nчи- horizon-2020.ca/ database/data-sets</td><td>[204]</td><td>The BNC1 is an open-source project with several datasets. Stroke is 66. SCT training in stroke (006-2014)*</td></tr><tr><td>Queensland Twin Additive Brain (QZAB)</td><td>422 (baseline)</td><td>9-14 (baseline)</td><td>Australia</td><td>Longitudinal</td><td>Population sample: Parental and/or self-report mental health, cognition, and social behavior measures</td><td>MRI (T1w; T2w, FLAIR, DWI, stMRI, tMRI, ASL)</td><td>NA</td><td>Pedigree (twin/ sibling), genotyping</td><td>NA</td><td>Wrist-worn accelerometer</td><td>Glut microsome</td><td>https:// imaginggenomics. net.au/</td><td>[205]</td><td>Includes participants from Queensland Twin Registry and Twin Research Australia</td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>Queensland Twin Imaging Study (QTIM)</td><td>1200-1230</td><td>Australia</td><td>Cross-sectional</td><td>Population sample (as part of BATS). Self-report, mental health, cognition, substance use, and personality measures</td><td>MRI (T1w, DWI, stMRI, tMRI)</td><td>NA</td><td>Pedigree (twin/ sibling), genotyping</td><td>NA</td><td>NA</td><td>NA</td><td>https://imaginggenomics.net.au/</td><td>[206]</td><td>Include participants from Brisbane adolescent twin study (BATS)</td></tr><tr><td>Human Conscione Project, young adults (HECP-T2A)</td><td>-1200</td><td>22-35</td><td>USA</td><td>Population sample. Self-report, mental health, cognition, personality, and substance use measures</td><td>MRI (T1w, T2w, DWI, stMRI, tMRI)</td><td>MEG (n = 95)</td><td>Pedigree (twin/ sibling), Genotyping</td><td>NA</td><td>NA</td><td>NA</td><td>https://dth.humanconnect.cme.org</td><td>[29]</td><td>Expanded to development and aging projects (similar imaging protocols, but extensions do not include twins)</td></tr><tr><td>Veranum Era Twin Study of Aging (FEYNA)</td><td>1237 (baseline; 500+ with MRI)</td><td>51-60 (baseline)</td><td>USA</td><td>US veterans, males only</td><td>MRI (T1w, DWI, ASL (subset of sample))</td><td>NA</td><td>Pedigree (twin), Genotyping</td><td>NA</td><td>NA</td><td>NA</td><td>https://neuroschol.ac.edu/son/psychiatry/ research/ VETSA/ Researchers/ Page/ default.aspx</td><td>[83]</td><td>Subset of the Vietnam Era Twin Registry, males only</td></tr><tr><td>Older Australian Twin Study (OATS)</td><td>623 (baseline) >65</td><td>Australia</td><td>Longitudinal</td><td>Population sample. Self-report, medical and mental health and neurology, chological measures</td><td>MRI (T1w, DWI, tMRI; PET)</td><td>NA</td><td>Pedigree (twin/ sibling), Genotyping</td><td>NA</td><td>NA</td><td>NA</td><td>https://cheba.unsw.edu.au/research-programs/older-australian-twin-study</td><td>[84]</td><td>Recruited through the Australian Twin Registry</td></tr><tr><td>Swedish Twin Registry (STR)</td><td>87,000 twin pairs</td><td>All ages</td><td>Sweden</td><td>Longitudinal (since the late 1950s)</td><td>NA</td><td>NA</td><td>Genome-wide single-nucleotide polymorphism array genotyping</td><td>Methylation is not available in the STR yet but is available in the Swedish adoption/ twin study of aging (SAYSA), a sub-study of the STR</td><td>NA</td><td>NA</td><td>https://ki.ac/en/ research/the-swedish-twin-registry</td><td>[74]</td><td>Twin Registry</td></tr><tr><td>UK Rikbank (UKB)</td><td>-502,000 -50,000 (imaging subset) -100,000 (actigraphy subset)</td><td>37-73 44-82 (imaging) 40-69 (actigraphy)</td><td>UK</td><td>Longitudinal</td><td>Self-reported and EHR medical history (incl. cancer, neurology, COVID-19)</td><td>MRI (T1w, T2w, FLAIR, DWI, SWI, stMRI, tMRI)</td><td>NA</td><td>Genotyping, cme, WGS, pedigree</td><td>NA</td><td>Actigraphy</td><td>Microbiology (future release)</td><td>https://ibham.naphox.ac.uk/acm/researchapplications</td><td>Population based sample (volunteers), healthy bias, ongoing resource and data collection/ target MRI sample 100 K, retest 10 K. also available: Cardiac and abdominal MRI, whole-body DXA (dual-energy X-ray) absorptionometry.</td></tr></tbody></table>

(continued)
<table><caption>Table 2
(continued)</caption><thead><tr><th>Sample</th><th>N</th><th>Age range</th><th>Country</th><th>Cross-sectional/ longitudinal</th><th>Clinical</th><th>Neuroimaging (MRI, PET MRI)</th><th>EEG/MEG</th><th>Genetics (genotyping, exome, WGS, twins)</th><th>Genomics</th><th>Smartphones and sensors</th><th>Other omics</th><th>Website/Data Transfer Agreement</th><th>Reference article(s)</th><th>Specificities</th></tr></thead><tbody><tr><td>Avon Longitudinal Study of Parents and Children (ALSPAC): Accessible Resource for Integrated Epigenomic Studies (ARIES)</td><td>2044</td><td>Average age: Mothers (antenatal = 28.7, follow-up = 47.5), offspring (birth = 40 weeks, childhood = 7.5, adolescence = 17.1)</td><td>UK</td><td>Longitudinal (2 time points for mother, 3 offspring)</td><td>Clinical evaluations, obstric data, cognition, questionnaires</td><td>MRI (T1w, DWI, mDESPOT (subset of offspring cohort))</td><td>NA</td><td>Genotyping</td><td>Methylation</td><td></td><td>Transcriptomics</td><td>http://www.ariesepigenomics.org/ak/https://github.com/MRCIEU/aries</td><td>[207]</td><td>General population study (health and development); following 1022 mother-offspring pairs; 2 time points for mother, 3 for offspring</td></tr><tr><td>Boombank-based Integrative Omics Studies (The BIOS Consortium)</td><td>-4000</td><td>18-87</td><td>Netherlands</td><td>Longitudinal</td><td>Clinical information and bioassays (depending on the sub-cohort)</td><td>NA</td><td>NA</td><td>Genotyping, pedigree</td><td>Methylation</td><td></td><td>Transcriptomics, metabolomics</td><td>https://www.bhuni.nl/ node/24</td><td>[208]</td><td>Population study including various sub-cohorts, covering differing research designs (lifecycles, Leiden Longevity study, Netherlands twin registry, Rotterdam study, CODAM, and the prospective ALS study Netherlands); access via European genome-phenome archive (EGA) and SURFsaera high performance computing cloud</td></tr><tr><td>Frankfurtam Heart Study</td><td>4241</td><td>Offspring cohort mean age = 66, third-generation cohort = 45</td><td>USA</td><td>Cross-sectional (multi-generational)</td><td>Extensive clinical evaluations and bioassays, original focus on cardiovascular diseases</td><td>MRI (T2w)</td><td>NA</td><td>Genotyping, WGS</td><td>Methylation</td><td></td><td>Transcriptomics, metabolomics</td><td>https://www.wachn.alnl.nhl.gov/projects/gap/cgi-bin/study/cgi-study.id=phu000724.v9.p13</td><td>[209, 210]</td><td>Data collected over two generations of individuals; cardiovascular MRI A subset of individuals are used to identify rare variants influencing brain imaging phenotypes. Data included in NHLBI TOPMed</td></tr></tbody></table>
<table><thead><tr><th>Women's Health Initiative</th><th>2129</th><th>50-79</th><th>USA</th><th>Cross-sectional</th><th>Clinical evaluations and bioassays; cardiovascular diseases, women only</th><th>NA</th><th>NA</th><th>Genotyping</th><th>Methylation</th><th>NA</th><th>Transcriptomics, metabolomics, miRNA</th><th>https://www.wadi.nlm.nih.gov/projects/gap/cgibin/study/cgistudy_id=phs001335.v2.p3</th><th>[211]</th><th>Women only</th></tr></thead><tbody><tr><td>Sporadic ALS<br>Australia<br>Systems<br>Genomics<br>Consortium<br>(SALSA-SGC)</td><td>1395</td><td>Predicted mean age (from DNA methylation, 60.4 (controls), 62.9 (cases)</td><td>Australia</td><td>Cross-sectional</td><td>Cae control of amyotrophic lateral sclerosis</td><td>NA</td><td>NA</td><td>Genotyping</td><td>Methylation</td><td>NA</td><td>NA</td><td>https://www.wadi.nlm.nih.gov/projects/gap/cgibin/study/cgistudy_id=phs002068.v1.p1</td><td>[212]</td><td></td></tr><tr><td>System Genomics of Parkinson's Disease (SGPD)</td><td>2333</td><td>Predicted mean age (from DNA methylation), mean 70.05, range 26-93</td><td>Australia and New Zealand</td><td>Cross-sectional</td><td>Cae control of Parkinson's disease</td><td>NA</td><td>NA</td><td>Genotyping</td><td>Methylation</td><td>NA</td><td>NA</td><td>https://www.wadi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE145361</td><td>[213]</td><td></td></tr><tr><td>Genetics of DNA Methylation Consortium (nDMC)</td><td>32,851</td><td>0-91</td><td>Worldwide</td><td>Cross-sectional</td><td>Multiple diseases, but also healthy aging cohorts</td><td>NA</td><td>NA</td><td>Genotyping</td><td>Methylation</td><td>NA</td><td>NA</td><td>http://www.godmc.org.uk/coborts.html</td><td>[79]</td><td>Consortium gathering 38 independent studies, data access to be obtained from each sub-sample</td></tr><tr><td>Pediatric Genetics Consortium (FGC)</td><td>By 2025, -2.5 million cases of psychiatric disorders</td><td>All ages</td><td>Worldwide</td><td>Cross-sectional</td><td>Psychiatric disorders, substance use disorders, and neurology: Major depressive disorder, cannabis use disorder, alcohol use disorder, schizophrenia, anorexia, bipolar, ADHD, Alzheimer's</td><td>NA</td><td>NA</td><td>Genotyping and sequencing</td><td>Expression and methylation data available in some working groups</td><td>NA</td><td>NA</td><td>https://www.wadnc.edu/Pge/shareMethods/open-source-philsophyry/</td><td>[214, 215]</td><td>Consortium organized around disease/ trait working groups</td></tr><tr><td>The genetic epidemiology of asthma in Costa Rica</td><td>4347</td><td>6-12</td><td>Costa Rica</td><td>Cross-sectional</td><td>Asthma cases and controls</td><td>NA</td><td>NA</td><td>WGS</td><td>NA</td><td>NA</td><td>NA</td><td>https://www.wadi.nlm.nih.gov/biproject/?term=PRINA295246</td><td>[216]</td><td>A subset of individuals from this sample/ accession is used in Pharmacogenetic drug response in racially diverse children with asthma. Data included in NHLBI TOPMed</td></tr><tr><td>Coronary Artery Risk Development in Tonsil Adults (CARDIA)</td><td>3425</td><td>18-30</td><td>USA</td><td>Longitudinal</td><td>Coronary Artery Risk</td><td>NA</td><td>NA</td><td>WGS</td><td>NA</td><td>NA</td><td>NA</td><td>https://anlvproject.org/ncrp/data/studies/phs00101612</td><td>[217]</td><td>A subset of longitudinal data of 30% self-identified Black and White participants from the CARDIA study were used to study multi-ethnic polygenic risk score</td></tr></tbody></table>

(continued)
<table><caption>Table 2
(continued)</caption><thead><tr><th>Sample</th><th>N</th><th>Age range</th><th>Country</th><th>Cross-sectional/Longitudinal</th><th>Clinical</th><th>Neuroimaging (MRI, PET/MRI)</th><th>EEG/MEG</th><th>Genetics (genotyping, exome, WGS, tumors)</th><th>Genomics</th><th>Smartphones and sensors</th><th>Other omics</th><th>Website/Data Transfer Agreement</th><th>Reference article(s)</th><th>Specifications</th></tr></thead><tbody><tr><td>Genetic Epidemiology Network of Arteriopathy (GENOA)</td><td>1854</td><td>>60</td><td>USA</td><td>Longitudinal</td><td>Elucidate the genetics of target organ complications of hypertension</td><td>NA</td><td>NA</td><td>WGS</td><td>NA</td><td>NA</td><td>NA</td><td>https://www.wada.alm.nih.gov/projects/gap/cgibin/study/cgistudy.id=phs001345.v3.p1</td><td>[217]</td><td>Study was used to study multi-ethnic polygenic risk score associated with hypertension prevalence and progression</td></tr><tr><td>Hispanic Community Health Study/Study of Latinos (HCHS/SOL)</td><td>8093</td><td>18-74</td><td>USA</td><td>Longitudinal</td><td>A multicenter prospective cohort study for asthma patients</td><td>NA</td><td>NA</td><td>WGS</td><td>NA</td><td>NA</td><td>NA</td><td>https://www.wada.alm.nih.gov/projects/gap/cgibin/study/cgistudy.id=phs001395.v1.p1</td><td>[217]</td><td>Study was used to study multi-ethnic polygenic risk score associated with hypertension prevalence and progression</td></tr><tr><td>Woman's Health Initiative (WHI)</td><td>11,357</td><td>>65</td><td>USA</td><td>Longitudinal</td><td>Women's Health Initiative cohort involved study on ischemic stroke, 900 cases of hemorrhagic stroke</td><td>NA</td><td>NA</td><td>WGS</td><td>NA</td><td>NA</td><td>NA</td><td>https://www.wada.alm.nih.gov/projects/gap/cgibin/study/cgistudy.id=phs00200.v12.p3</td><td>[217]</td><td>Study was used to study multi-ethnic polygenic risk score associated with hypertension prevalence and progression</td></tr><tr><td>Adrenocortical Risk in Communities (ARCIC)</td><td>8975</td><td>45-64</td><td>USA</td><td>Cross-sectional/longitudinal</td><td>Real blood cell phenotype</td><td>NA</td><td>NA</td><td>WGS</td><td>NA</td><td>NA</td><td>NA</td><td>https://anilproject.org/data/studies/phs001211</td><td>[218]</td><td>WGS association study of red blood cell phenotypes, GWAS statistics available. Data included in NHLBI TOPMed</td></tr><tr><td>Rare Variants for Hypertension in Taiwan Chinese (THRI)</td><td>2159</td><td>>35</td><td>Taiwan/China, Japan</td><td>Longitudinal</td><td>Insulin resistant cases and controls</td><td>NA</td><td>NA</td><td>WGS</td><td>NA</td><td>NA</td><td>NA</td><td>https://anilproject.org/rnsp/data/studies/phs001387</td><td>[219]</td><td>Clustering and heritability of insulin resistance in Chinese and Japanese hypertensive families. Data included in NHLBI TOPMed</td></tr><tr><td>My Life, Our Future initiative (MLOF)</td><td>7482</td><td>>18</td><td>USA</td><td>Cross-sectional</td><td>Hemophilia cases and controls</td><td>NA</td><td>NA</td><td>WGS</td><td>NA</td><td>NA</td><td>NA</td><td>https://athin.org/what-we-do/national-projects/mlof-research-repository.html</td><td>[220]</td><td>Summary statistics, different types of DNA variants detected in hemophilia. Data included in NHLBI TOPMed</td></tr></tbody></table>
<table><tbody><tr><td>Genetic Epidemiology of COPD (COPTDgene)</td><td>19,996</td><td>45-80</td><td>USA</td><td>Cross-sectional</td><td>Pulmonary functions</td><td>NA</td><td>NA</td><td>WGS</td><td>NA</td><td>NA</td><td>Gene expression (eQTL) and methylation (mQTL): eQTLs in 48 issues from GTEx v7</td><td>https://anlproject.org/data/studies/ phs001211</td><td>[221]</td><td>Multi-omic data from GTEx and TOPMed identify potential molecular mechanisms underlying 4 of the 22 novel loci. Data included in NHLBI TOPMed</td></tr><tr><td>Cardiovascular Health Study (CHS)</td><td>4877</td><td>&gt;65</td><td>USA</td><td>Longitudinal</td><td>Cardiovascular loads</td><td>NA</td><td>NA</td><td>WGS</td><td>NA</td><td>NA</td><td>NA</td><td>https://ega.archive.org/data/studies/ phs001268</td><td>[221]</td><td>Potential molecular mechanisms underlying 4 of the 22 novel loci. Data included in NHLBI TOPMed</td></tr><tr><td>Cleveland Family Study (CFS)</td><td>3576</td><td>&gt;65</td><td>USA</td><td>Longitudinal</td><td>Epidemiological data on genetic and non-genetic risk factors for sleep disordered breathing</td><td>NA</td><td>NA</td><td>WGS</td><td>NA</td><td>NA</td><td>NA</td><td>https://ega.archive.org/data/studies/ phs000954</td><td>[221]</td><td>Potential molecular mechanisms underlying 4 of the 22 novel loci. Data included in NHLBI TOPMed</td></tr><tr><td>Framingham Heart Study (FHS)</td><td>4241</td><td>&lt;65</td><td>USA</td><td>Longitudinal</td><td>Assess risk of cardiovascular disease study</td><td>NA</td><td>NA</td><td>WGS</td><td>NA</td><td>NA</td><td>NA</td><td>https://www.wach.aacr.org/nih.gov/projects/gap/cg1-bin/study/cg1study.id=</td><td>[221]</td><td>Potential molecular mechanisms underlying 4 of the 22 novel loci. Data included in NHLBI TOPMed</td></tr><tr><td>Jackson Heart Study (JHS)</td><td>3596</td><td>&gt;55</td><td>USA</td><td>Longitudinal</td><td>Assess cardiovascular disease</td><td>NA</td><td>NA</td><td>WGS</td><td>NA</td><td>NA</td><td>NA</td><td>https://www.wach.aacr.org/data/study/dbgap/pha001416</td><td>[221]</td><td>Potential molecular mechanisms underlying 4 of the 22 novel loci. Data included in NHLBI TOPMed</td></tr><tr><td>Multi-Ethnic Study of Atherosclerosis (MESA)</td><td>6814</td><td>45-84</td><td>USA</td><td>Longitudinal</td><td>Assess cardiovascular disease</td><td>NA</td><td>NA</td><td>WGS</td><td>NA</td><td>NA</td><td>NA</td><td>https://www.omicdata.org/data/study/dbgap/pha001416</td><td>[221]</td><td>Potential molecular mechanisms underlying 4 of the 22 novel loci. Data included in NHLBI TOPMed</td></tr><tr><td>Boston Early-Childhood (ECOCDT)</td><td>80</td><td>&lt;53</td><td>USA</td><td>Longitudinal</td><td>Chronic obstructive pulmonary disease (COPD)</td><td>NA</td><td>NA</td><td>WGS</td><td>NA</td><td>NA</td><td>NA</td><td>https://www.wach.aacr.org/nih.gov/projects/gap/cg1-bin/study/cg1study.id=</td><td>[221]</td><td>Potential molecular mechanisms underlying 4 of the 22 novel loci. Data included in NHLBI TOPMed</td></tr><tr><td>Genetic Epidemiology of COPD (COPTDgene)</td><td>10,647</td><td>45-80</td><td>USA</td><td>Longitudinal</td><td>Chronic obstructive pulmonary disease (COPD)</td><td>NA</td><td>NA</td><td>WGS</td><td>NA</td><td>NA</td><td>NA</td><td>https://www.wach.aacr.org/nih.gov/projects/gap/cg1-bin/study/cg1study.id=</td><td>[221]</td><td>Potential molecular mechanisms underlying 4 of the 22 novel loci. Data included in NHLBI TOPMed</td></tr></tbody></table>

(continued)
<table><caption>Table 2
(continued)</caption><thead><tr><th>Sample</th><th>N</th><th>Age range</th><th>Country</th><th>Cross-sectional/<br>longitudinal</th><th>Clinical</th><th>Neuroimaging<br>(MRI, PET/MRI)</th><th>EEG/MEG</th><th>Genetics (genotyping,<br>exome, WGS,<br>twins)</th><th>Genomics</th><th>Smartphones<br>and sensors</th><th>Other omics</th><th>Website/Data<br>Transfer<br>Agreement</th><th>Reference<br>article(s)</th><th>Specificities</th></tr></thead><tbody><tr><td>Genotype-Tissue<br>Expression<br>(GTEx)</td><td>948 individuals, 20-70</td><td>USA</td><td></td><td>Cross-sectional</td><td>General medical<br>history: HIV<br>status, potential<br>exposures,<br>medical history, at<br>the time of death,<br>death<br>circumstances,<br>scrology results,<br>smoking status</td><td>NA</td><td>NA</td><td>WGS</td><td>Gene expression<br>(bulk<br>RNA-seq)</td><td>NA</td><td>NA</td><td>https://www.ncbi.nlm.<br>nih.gov/projects/<br>gap/cg-biu/<br>dancer-egi-study,<br>id=phobio424.8,<br>p26phr=1609018k,<br>phl=39108k,<br>phs=&amp;phfr=27428k,<br>phvl=&amp;phdf=&amp;<br>phaf=&amp;phrf=&amp;<br>dspp=1k&amp;<br>consent=&amp;temp=1</td><td>[222]</td><td>Postmortem samples</td></tr><tr><td>Developmental<br>Genotype-Tissue<br>Expression<br>(dGTEx)</td><td>120 individuals, 0-18</td><td>USA</td><td></td><td>Cross-sectional</td><td>General medical<br>history</td><td>NA</td><td>NA</td><td>WGS</td><td>Gene expression<br>(bulk<br>RNA-seq)</td><td>NA</td><td>NA</td><td>https://dgtex.org/<br>aphr/</td><td>NA</td><td>Expected data release by<br>late 2022</td></tr><tr><td>Greater Paris Area<br>Hospital<br>(AP-HP)</td><td>Several millions<br>-130 K patients<br>with brain<br>MRI (-200<br>K images)</td><td>France</td><td></td><td>Longitudinal</td><td>ICD10 codes for<br>diagnoses,<br>medical reports,<br>and a wide range<br>of physiological<br>variables</td><td>All medical<br>imaging<br>modifications<br>and all<br>organs. MRI,<br>PET,<br>SPECT, CT,<br>ultrasounds</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>https://cds.<br>aphr.fr/</td><td>[155]</td><td>All patients visiting<br>greater-Paris<br>area hospital<br>(AP-HP) the figures<br>correspond to a<br>specific<br>project approved<br>on the clinical<br>data warehouse<br>(CDW)</td></tr><tr><td>Swedish Total<br>Population<br>Register (TPR)</td><td>Nationwide<br>coverage<br>(N =<br>9,775,572<br>on April 30,<br>2015)</td><td>All ages</td><td>Sweden</td><td>Longitudinal<br>(since 1968)</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>www.sctc/
<br>tajister/bestall-<br>data-<br>och-statistik/<br>bestalls-mikrodata/<br>vilka-mikrodata-<br>fins/<br>indivregister/<br>registret-over-<br>totalbefield<br>kanngen-rtb/</td><td>[158]</td><td>Demographic<br>information</td></tr></tbody></table>
<table><tbody><tr><td>Swedish<br>Longitudinal<br>Integrated<br>Database for<br>Health<br>Insurance and<br>Labour Market<br>Studies (LISA)</td><td>All individuals<br>≥16 years<br>in Sweden</td><td>≥16 years (15<br>since 2010)<br>Sweden</td><td>Longitudinal<br>(since 1990)<br>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>www.sch.se/lisa</td><td>[159]</td><td>Demographic and<br>socioeconomic<br>information</td></tr><tr><td>Swedish Multi-<br>Generation<br>Register<br>(MGR)</td><td>Over 11 million<br>All ages</td><td>Sweden</td><td>Longitudinal<br>(since 1961)<br>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>www.sch.se/cn/<br>finding-statistics/<br>statistics-by-subject-<br>areas/other/other/<br>other-publications/<br>non-statistical/<br>ports/publications/<br>multi-generation-<br>register-2016/</td><td>[160]</td><td>Family relation</td></tr><tr><td>Swedish National<br>Patent<br>Register<br>(NPR)</td><td>Nationwide<br>coverage</td><td>All ages</td><td>Sweden</td><td>Longitudinal<br>(since 1964)<br>Nationwide inpatient<br>case since 1987<br>and outpatient<br>case since 2001</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>www.sch.se/cn/<br>statistics-and-<br>data/registers/<br>register-<br>information/rhe-<br>national-patient-<br>register/</td><td>[161]</td><td>Clinical diagnoses from<br>inpatient and<br>outpatient care</td></tr><tr><td>Swedish Cancer<br>Register (SCR)</td><td>Around 60,000<br>All ages</td><td>Sweden</td><td>Longitudinal<br>(since 1958)<br>Clinical diagnoses of<br>cancer</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>www.sch.se/cn/<br>statistics-and-<br>data/registers/<br>register-<br>information/<br>swedish-cancer-<br>register/</td><td>[165]</td><td></td></tr><tr><td>Swedish Medical<br>Birth Register<br>(MBR)</td><td>Around<br>80,000-<br>120,000<br>deliveries<br>annually</td><td>Infants at birth<br>Sweden</td><td>Longitudinal<br>(since 1973)<br>Yes</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>https://www.<br>socialstyrelsen.se/<br>cn/statistics-and-<br>data/registers/<br>register-<br>information/rhe-<br>swedish-medical-<br>birth-register/</td><td>[167]</td><td>Register of medical<br>birth</td></tr><tr><td>Swedish Cancer of<br>Death Register<br>(CDR)</td><td>Nearly 100,000<br>deaths<br>annually</td><td>All ages</td><td>Sweden</td><td>Longitudinal<br>(since 1952)<br>Cause of death</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>https://www.<br>socialstyrelsen.se/<br>cn/statistics-and-<br>data/registers/<br>register/<br>register/<br>dodsona<br>korggjorter/</td><td>[170]</td><td>Register of cause of<br>death</td></tr><tr><td>Swedish Prescribed<br>Drug Register<br>(FDR)</td><td>More than 100<br>million<br>records<br>annually</td><td>All ages</td><td>Sweden</td><td>Longitudinal<br>(since July<br>2005)</td><td>Prescribed drug(s)<br>NA</td><td>NA</td><td>NA</td><td>NA</td><td>https://www.<br>socialstyrelsen.se/<br>cn/statistics-and-<br>data/registers/<br>register-<br>information/rhe-<br>swedish-prescribed-<br>drug-register/</td><td>[172]</td><td>Register of prescribed<br>drug</td></tr></tbody></table>

(continued)
<table><caption>Table 2
(continued)</caption><thead><tr><th>Sample</th><th>N</th><th>Age range</th><th>Country</th><th>Cross-sectional/longitudinal</th><th>Clinical</th><th>Neuroimaging (MRI, PET, MRI)</th><th>EEG/MEG</th><th>Genetics (genotyping, exome, WGS, twins)</th><th>Genomics</th><th>Smartphones and sensors</th><th>Other omics</th><th>Website/Data Transfer Agreement</th><th>Reference article(s)</th><th>Specificities</th></tr></thead><tbody><tr><td>Swedish Dementia Registry (SDR)</td><td>More than 100,000</td><td>27-103 (between 2007 and 2012)</td><td>Sweden</td><td>Longitudinal (since 2007)</td><td>Dementia</td><td>MRI, PET, CT, EEG</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>www.svddem.se</td><td>[176]</td><td>Register of dementia</td></tr><tr><td>Swedish NeuroRegister (SNR)</td><td>Around 16,000 multiple sclerosis, over 1500 Parkinson's disease, and over 600 myasthenia gravis (numbers from 2015)</td><td>All age</td><td>Sweden</td><td>Longitudinal (since 2004)</td><td>Motor neuron disease, MS</td><td>MRI, PET, and CT</td><td>MEG</td><td>Genotyping</td><td>NA</td><td>NA</td><td>NA</td><td>www.neuroreg.se</td><td>[177]</td><td>Register of MIND (especially MS)</td></tr><tr><td>Swedish Stroke Register (RiStroke)</td><td>Around 29,000 Mean age 75 for Sweden (around 21,000 stroke and 8000 TIA) annually (statistics in 2020)</td><td>Mean age 75 for Sweden (between 2007 and 2012)</td><td>Australia</td><td>Longitudinal (since 1994)</td><td>Stroke and transient ischemic attack (TIA)</td><td>MRI and CT scan</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>www.riskstroke.org/</td><td>[179]</td><td>Register of stroke and TIA, includes electrocardiograms</td></tr><tr><td>Brisbane Alzheimer's Brain Sample (BATS)</td><td>4000</td><td>>11</td><td>Australia</td><td>Cross-sectional and longitudinal</td><td>Population sample. Self-report mental health, cognition, substance use, and personality measures</td><td>NA</td><td>EEG (15 channels, eyes closed, resting, N ~ 1000)</td><td>Pedigree (twin/ sibling, genotyping)</td><td>NA</td><td>Wrist-worn accelerometer (N ~ 130)</td><td>NA</td><td>https://imaginggenomics.net.au/</td><td>[79, 223-225]</td><td>Also known as the Brisbane Longitudinal Twin Study (BLTS). Includes participants from the Queensland Twin Registry</td></tr><tr><td>mPower</td><td>-8000</td><td>>18</td><td>USA</td><td>Longitudinal</td><td>Parkinson's disease (subsample self-identified professional diagnosis)</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>iPhone application</td><td>NA</td><td>https://parkinsonpower.org/teamhttps://www.synapse.org/#SynapseSynth993293/wik/247860</td><td>[226]</td><td>Sample size varies across surveys and tasks completed</td></tr></tbody></table>
We hope this overview could be useful to the readers wanting
to replicate findings, maximize sample size and statistical power,
develop and apply methods that utilize multi-level data, or even
select the most relevant dataset to tackle a research question. We
also hope this encourages the collection of new data shared with the
community while ensuring interoperability with the existing
datasets.

3 Neuroimaging

3.1 Magnetic
Resonance
Imaging (MRI)

Brain magnetic resonance images are 3D images that measure brain structure (T1w, T2w, FLAIR, DWI, SWI) or function (fMRI). The different MRI sequences (or modalities) can characterize different aspects of the brain. For example, T1w and T2w offer the maximal contrast between tissue types (white matter, gray matter, and cerebrospinal fluid), which can yield structural/shape/volume measurements. They can also be used in conjunction with an injection of a contrast agent (e.g., gadolinium) for detecting and characterizing various types of lesions. FLAIR is also useful for detecting a wide range of lesions (e.g., multiple sclerosis, leukoaraiosis, etc.). SWI focuses on the neurovascular system, while DWI allows measuring the integrity of the white matter tracts. Functional MRI measures BOLD (blood oxygen level dependent) signal, which is thought to measure dynamic oxygen consumption in the different brain regions. Of note, fMRI consists of a series of 3D images acquired over time (typically 5–10 min).

Brain MRI is available as a series of DICOM files (brain slices, traditional format of the MRI machines) or a single NIfTI (single 3D image) format (see Table 1). The two formats are roughly equivalent, and most image processing pipelines allow both data sources as input. MR images are composed of voxels (3D pixel), and their size (e.g., 1 × 1 × 1 mm) corresponds to the image resolution.

In practice, most MR images are archived and shared via
web-based applications and more rarely using specific software
(e.g., UKB). The two major web platforms are XNAT (eXtensible
Neuroimaging Archive Toolkit) [9], an open-source platform
developed by the Neuroinformatics Research Group of the
Washington University School of Medicine (Missouri, (1, 2)), and
IDA (Image and Data Archive) created by the Laboratory of Neu-
roImaging of the University of South California (LONI, https://
loni.usc.edu/). Of note, XNAT also allows to perform some image
processing [9].

The neuroimaging community has developed BIDS (Brain
Imaging Data Structure), a standard for MR image organization
to accommodate multimodal acquisitions and facilitate processing.
In practice, few datasets come in BIDS format, and tools have been
developed to assist with download and conversion (e.g., https://
clinica.run) [10].

We have listed a handful of datasets (see Table 1), which is far
from being exhaustive but aims at summarizing some of the largest
and/or most used samples. Our selection aims at presenting diverse
and complementary samples in terms of age range, populations,
and country of origin.

First, we have described three clinical elderly samples from the
USA and Australia, with a focus on Alzheimer's disease and cogni-
tive disorders. The Alzheimer's Disease Neuroimaging Initiative
(ADNI) was launched in 2004 and funded by a partnership
between private companies, foundations, the National Institute of
Health, and the National Institute for Aging. ADNI is a longitudi-
nal study, with data collected across 63 sites in the USA and
Canada. To date, four phases of the study have been funded,
which makes ADNI one of the largest clinical neuroimaging sam-
ples to study Alzheimer's disease and cognitive impairment in
aging. ADNI collected a wide range of clinical, neuropsychological,
cognitive scales as well as biomarkers, in addition to multimodal
imaging and genotyping data [11]. Sites contribute data to the
LONI, which is automatically shared with approved researchers
without embargo. The breadth of data available and its accessibility
have made ADNI one of the most used neuroimaging samples, with
more than 1000 scientific articles published to date.

The Australian Imaging Biomarkers and Lifestyle Study of Ageing (AIBL) started in 2006 and has since recruited about 1100 participants over 60 years of age, who have been followed over several years (see Table 1) [12]. AIBL collected data across the different Australian states and, similar to ADNI, consisted in an in-depth assessment of individual cognition, clinical status, genetics, genomics, as well as multimodal brain imaging [12]. In 2010, AIBL partnered with ADNI to release the AIBL imaging subset and selected clinical data via the LONI platform. Having the same MRI protocols and similar fields collected, AIBL represents a great addition to the ADNI study, by boosting statistical power or allowing for replication. The full clinical information as well as genetics, genomics, and wearable data (actigraphy watches) are not available via the LONI and require a direct application to the Commonwealth Scientific and Industrial Research Organisation (CSIRO) (see Table 1).

The Open Access Series of Imaging Studies v3 (OASIS3) is
another longitudinal sample comprising almost 1100 adult partici-
pants (see Table 1) [13]. Its main focus is around aging and neuro-
logical disorders, and the application/approval process is extremely
fast (typically a couple of days). OASIS3 is hosted on XNAT and is
the third dataset to be made available by the Washington University
in Saint Louis (WUSTL) Knight Alzheimer's Disease Research
Center (ADRC), although the three datasets are not independent and cannot be analyzed together. Contrary to ADNI and AIBL, OASIS3 is a retrospective study that aggregates several research studies conducted by the WUSTL over the past 30 years. As a result, the data collected may vary from one individual to the next, with a variable time window between visits. In that sense, OASIS3 resembles data from clinical practice, with individual specific care/assessment pathways.

The UK Biobank (UKB) imaging study [14] is the largest brain imaging study to date, with around 50,000 individuals already imaged (target of 100,000). The imaging wave complements the wealth of data already collected in the previous waves (see Table 1; see also Subheading 5 for a description of the full dataset). Considering the sheer size of the data, the biobank shares raw and processed images as well as structured data (measurements of regions of interest) [15]. Data is accessible upon request by all bona fide researchers, with certified profiles. Data access requires payment of a fee, which only aims to cover the biobank functioning costs. The UKB has developed proprietary tools for a secure download and data management (https://biobank.ndph.ox.ac.uk/showcase/download.cgi).

The Adolescent Brain Cognitive Development (ABCD) is an ongoing longitudinal study of younger individuals, recruited aged 9–10 years and who will be followed over a decade [16, 17]. The ABCD focuses on cognition, behavior, and physical and mental health (e.g., substance use, autism, ADHD) of adolescents. It includes self- and parental rating of the adolescents as well as a description of the familial environment [17]. ABCD data is hosted on the NIMH data archive and requires obtaining and maintaining an NDA Data Use Certification, which requires action from a signing official (SO) from the researcher institution, as defined in the NIH eRA Commons (https://era.nih.gov/files/eRA-Commons-Roles-10-2019.pdf).

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) disease working groups have stemmed from the ENIGMA genetics project (see Subheading 5.3) to perform worldwide neuroimaging studies for a wide range of disorders (e.g., major depressive disorder [18], attention-deficit hyperactivity disorder [19], autism [20], post-traumatic stress disorder [21], obsessive-compulsive disorder [22], substance dependence [23], schizophrenia [24], bipolar disorder [25]) as well as traits of interest (e.g., sex, healthy variation [26]); see [27] for a review. Each working group may conduct simultaneously several research projects, proposed and led by its members. Each site of the working group chooses the project(s) they contribute to and performs the analyses. Of note, most ENIGMA working groups still rely on a meta-analytic framework, even if recent projects (e.g., machine learning) now require sharing data onto a central server. Interested
researchers can contribute new data and propose analyses or new image processing pipelines to the different working groups. The ENIGMA samples typically comprise thousands of participants (controls and/or cases; see Table 1), and data are inherently heterogeneous, each site having specific recruitment and protocols.

Other neuroimaging MRI datasets have focused on twins and siblings (see Subheading 5.1) and include the Queensland Twin Imaging (QTIM) study, the Queensland Twin Adolescent Brain Project, the Vietnam Era Twin Study of Aging (VETSA) [28], and the Human Connectome Project (HCP) [29] (see Subheading 5.1). In addition, there are many more datasets available on neurological disorders, which may be explored via XNAT, LONI, or the Demen- tias Platform UK (DPUK), to name a few, PPMI (Parkinson’ Progression Markers Initiative) [30], MEMENTO (deterMinants and Evolution of AlzheiMer’s disEase aNd relaTed disOrder) [31], EPAD (European Prevention of Alzheimer’s Dementia) [32], and ABIDE (Autism Brain Imaging Data Exchange) [33, 34].

## 3.2 Positron Emission Tomography (PET-MRI)

Positron emission tomography (PET) images are 3D images that highlight the concentration of a radioactive tracer administered to the patient. Here, we will focus on brain PET images, although other parts of the body may also be imaged. The different tracers allow to measure several aspects of brain metabolism (e.g., glucose) or spatial distribution of a molecule of interest (e.g., amyloid).

PET relies on the nuclear properties of radioactive materials that are injected in the patient intravenously. When the radioactive isotope disintegrates, it emits a photon that will be detected by the scanner. This signal is used to find the position of the emitted positrons which allow us to reconstruct the concentration map of the molecule we are tracing [35].

As for MRI, PET images are available as a series of DICOM files or a single NIfTI format. They are composed of voxels (3D pixel), and their size (e.g., 1 × 1 × 1 mm) corresponds to the image resolution. A BIDS extension has also been developed for positron emission tomography, in order to standardize data organization for research purposes.

PET is considered invasive due to the injection of the tracer, which results in very small risk of potential tissue damage. Overall, the quantity of radioactive isotope remains small enough to make it safe for most people, but this limits its widespread acquisition in research, especially on healthy subjects or in children. Moreover, PET requires to have a high-cost cyclotron to produce the radio-tracers nearby because the half-life of the radioisotopes is typically short (between a few minutes to few hours).

Several tracers are used for brain PET imaging, one of the most common ones being the ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG). ¹⁸F-FDG concentrates in areas that consume a lot of glucose and will thus highlight brain metabolism. In practice, ¹⁸F-FDG PET
images are often used to study neurodegenerative disease by revealing hypometabolism that characterizes some dementia [36, 37]. Other diseases such as epilepsy and multiple sclerosis can be studied through this modality, but since it is not part of clinical routine, data are rare, and we are not aware of publicly available datasets.

In whole-body PET scans, ¹⁸F-FDG is used to detect tumors, which consumes a lot of glucose. However, the brain consumes a lot of glucose as part of its normal functioning, and brain tumors are not noticeable using this tracer. Instead, clinicians would use ¹¹C-choline that will also accumulate in the tumor area but is not specifically used by the brain otherwise. In addition to glycemic radiotracers, oxygen-15 is also used to measure blood flow in the brain, which is thought to be correlated with brain activity. In practice, this tracer is less used than ¹⁸F-FDG because of its very short half-life. Other tracers are used to show the spatial concentration of specific biomarkers: for instance, ¹⁸F-florbetapir (AV45), ¹⁸F-flutemetamol (Flute), Pittsburgh compound B (PiB), and ¹⁸F-florbetaben (FBB) are amyloid tracers used to highlight β-amyloid aggregation in the brain, which is a maker of Alzheimer's disease. Finally, ¹¹C-5-hydroxytryptamine (5-HT) neurotransmitter is used to expose the serotonergic transmitter system.

We have made a non-exhaustive list of publicly available datasets containing PET scans with different tracers. Most datasets focused on neurodegenerative disorders and also collected brain MRI (see previous section). The Alzheimer's Disease Neuroimaging Initiative (ADNI) is one of the largest datasets with PET images for Alzheimer's disease [11]. ADNI used F-FDG-PET as well as PET amyloid tracers: FBB, AV45, and PiB. The Australian Imaging Biomarkers and Lifestyle Study of Ageing (AIBL) only collected amyloid tracers of PET images: PiB, AV45, and Flute [12]. The Open Access Series of Imaging Studies v3 (OASIS3) includes PET imaging from three different tracers, PIB, AV45, and ¹⁸F-FDG [13].

In addition to those neurodegenerative datasets, PET is available in the Lundbeck Foundation Centre for Integrated Molecular Brain Imaging (CIMBI) database and biobank established in 2008 in Copenhagen, Denmark [38]. CIMBI shares structural MRI, PET, genetic, biochemical, and clinical data from 2000 persons (around 1600 healthy subjects and almost 400 patients with various pathologies). Tracer used for PET is the ¹¹C-5-HT which is relevant to study the serotonergic transmitter system. Applications to access the data can be made on their website by completing a form (see Table 2).

The ChiNese brain PET Template (CNPET) dataset has been developed by the Medical Imaging Research Group (https://biomedimg-dlut-edu.cn/), from Dalian University of Technology (China) [39]. The database contains 116 records of ¹⁸F-FDG-PET
from healthy patients, which has been used to make a Chinese population-specific statistical parametric mapping (SPM, i.e., average template used for PET processing). The data used to build the PET brain template have been released and are available on NeuroImaging Tools and Resources Collaboratory (NITRC, https://www.nitrc.org/) platform.

## 4 EEG/MEG

Electroencephalography (EEG) measures the electrical activity of the brain [40–42]. Signals are captured through sensors distributed over the scalp (noninvasive) or by directly placing the electrodes on the brain surface, a procedure that requires a surgical intervention [43]. This technique is characterized by its high temporal resolution, enabling the study of dynamic processes such as cognition or the diagnosis of conditions such as epilepsy. Yet, EEG signals are nonstationary and have a non-linear nature, which makes it difficult to get useful information directly in the time domain. Nonetheless, specific patterns can be extracted using advanced signal processing techniques.

Another technique that captures brain activity is magnetoencephalography (MEG). This technology maps the magnetic fields induced above the scalp surface. Similar to EEG, MEG provides high time resolution, but it is preferentially sensitive to tangential fields from superficial sources [44, 45]. This could be considered as an advantage, since magnetic fields are less sensitive to tissue conductivities, facilitating source localization. However, MEG instrumentation is more expensive and not portable [46, 47].

During signal recording, undesirable potential coming from sources other than the brain may alter the quality of the signals. These artifacts should be detected and removed in order to improve pattern recognition. Multiple methods could be applied depending on the artifact to be eliminated: re-referencing with common average reference (CAR), ICA decomposition to remove other physiological sources as eye movements or cardiac components, notch filter to get rid of power line noise, and pass-band filtering to keep the physiological rhythms of interest, among others [48–51]. Other spatial filters such as common spatial pattern (CSP) for channel selection or filter bank CSP (FBCSP) for band elimination are largely used in motor decoding [52, 53].

Other signal processing tools allow the user to extract features describing relevant information contained in the signals. Subsequently, those patterns may be used as input for a classification pipeline. The target features vary according to the condition under study. Generally, the domain of clinical diagnostics focuses either on event-related potentials (ERP) or on spectral content of the signal [54, 55]. The first refers to voltage fluctuations associated
with specific sensory stimuli (e.g., P300 wave) or task, like motor preparation and execution, covert mental states, or other cognitive processes. The amplitude, latency, and spatial location of the resulting waveform activity reveal the underlying mental state [56]. On the other side, spectral analysis refers to the computation of the energy distribution of the signals in the frequency domain. Most spectral estimates are based on Fourier transform; this is the case of non-parametric methods, such as Welch periodogram estimation, which based their computation on data windowing [57].

Another approach is to study the interactions across sources (inferring connection between two electrodes by means of temporal dependency between the registered signals), which is known as functional connectivity. Multiple connectivity estimators have been developed to quantify this interaction [58]. Through these functional interactions, complex network analysis can also be implemented, where sensors are modeled as nodes and connectivity interactions as links [59-61].

EEG and MEG are essential to evaluate several types of brain disorders. One of the most documented is epilepsy, based on seizure detection and prediction [62-64]. Other neurological conditions can be characterized like Alzheimer's disease, associated with changes in signal synchrony [65, 66]. Furthermore, motor task decoding in brain-computer interfaces (BCI) offers a promising tool in rehabilitation [67]. This type of data, from healthy to clinical cases, can be found on multiple open-access repositories, such as Zenodo (https://zenodo.org) or PhysioNet ((1)), as well as via collaborative projects such as the BNCI Horizon 2020 (http://bnci-horizon-2020.eu), which gathers a collection of BCI datasets (see Table 2). These repositories are also valuable in that they contribute to establishing harmonization procedures in processing and recording. All dataset-collected informed consent and data were anonymized to protect the participants' privacy. Moreover, regulations may vary from one country to another, which require, for example, studies to be approved by ethics committees. Additionally, licensing (that define copyrights of the dataset) must be considered depending on the intended use of the open-access datasets.

Data come in different formats according to the acquisition system or the preprocessing software. The most common formats for EEG are .edf, .gdf, .eeg, .csv, or .mat files. For MEG, it is very often .fif and .bin (see Table 1). The different formats can create challenges when working with multiple datasets. Luckily, some tools have been developed to handle this problem, for example, FieldTrip [68] or Brainstorm [69] implemented in MATLAB, or the Python modules mne [70] and moabb [71]. Of note, these tools also contain sets of algorithms and utility functions for analysis and visualization.
# 5 Genetics

## 5.1 Twin Samples

Twins provide a powerful method to estimate the importance of genetic and environmental influences on variation in complex traits. Monozygotic (MZ, aka identical) twins develop from a single zygote and are (nearly) genetically identical. In contrast, dizygotic (DZ, aka fraternal) twins develop from two zygotes and are, on average, no more genetically related than non-twin siblings. In the classical twin design, the degree of similarity between MZ and DZ twin pairs on a measured trait reveals the importance of genetic or environmental influences on variation in the trait. Twin studies often collect several different data types, including brain MRI scans, assessments of cognition and behavior, self-reported measures of mental health and wellbeing, as well as biological samples (e.g., saliva, blood, hair, urine). Datasets derived from twin studies are text-based and include phenotypic data and background variables (e.g., individual and family IDs, sex, zygosity, age). Notably, the correlated nature of twin data (i.e., the non-independence of participants) should be considered during analysis as it may violate statistical test assumptions [72, 73].

Raw data is typically stored locally by the data owner, with de-identified data available upon request. In larger studies, data is stored and distributed through online repositories. Recently, the sharing of publicly available de-identified data with accompanying publications has become commonplace.

Several extensive twin studies combine imaging, behavioral, or biological data (see Table 2). These studies cover the whole life span (STR) as well as specific age periods, for example, children/adolescents (QTAB), young (QTIM, HCP-YA), middle-aged (VETSA), or older (OATS) adults.

The Swedish Twin Registry (STR) was established in the late 1950s with the primary aim to explore the effect of environmental factors (e.g., smoking and alcohol) on disorders [74]. Data were first collected through questionnaires and interviews with the twins and their parents. Later, the STR incorporated data from biobanks, clinical blood chemistry assessments, genotyping, health checkups, and linkages to various Swedish national population and health registers [74]. The STR is now one of the largest twin registers in the world [75] with information on more than 87,000 twin pairs (https://ki.se/en/research/the-swedish-twin-registry). It has been used extensively for the research of health and illness, including various neurological disorders, including dementia [76], Parkinson's disease [77], and motor neuron disease [78].

The Queensland Twin Adolescent Brain (QTAB, 2015-present) was enabled through funding from the Australian National Health and Medical Research Council (NHMRC). It focuses on the period of late childhood/early adolescence, with brain imaging,
cognition, mental health, and social behavior data collected over
two waves (age 9–14 years at baseline, N = 427). A primary
objective is to chart brain changes and the emergence of depressive
symptoms throughout adolescence. Biological samples (blood,
saliva), sleep (self-report), and motor activity measures (see
Section 8) were also collected. Data is available from the project
owners upon request.

The Queensland Twin IMaging (QTIM, 2007–2012) study,
funded through the National Institutes of Health (NIH) and
NHMRC, was a collaborative project between researchers from
QIMR Berghofer Medical Research Institute, the University of
Queensland, and the University of Southern California, Los
Angeles. Brain imaging was collected in a large genetically informa-
tive population sample of young adults (18–30 years, N > 1200)
for whom a range of behavioral traits, including cognitive function,
were already characterized (as a component of the Brisbane Ado-
lescent Twin Study, QIMR Berghofer Medical Research Institute
[79]). Notably, the dataset includes a test–retest neuroimaging
subsample (n = 75) to estimate measurement reliability. Data is
available from the project owners upon request.

The Human Connectome Project Young Adult (HCP-YA,
2010–2015) study, funded by the NIH, is based at Washington
University, University of Minnesota, and Oxford University. Inves-
tigators spent 2 years developing state-of-the-art imaging methods
[29] before collecting high-quality neuroimaging, behavioral, and
genotype data in ~1200 healthy young adult twins and non-twin
siblings (22–35 years). HCP-YA data has been used widely in twin-
based analyses, examining genetic influences on network connec-
tivity [80], white matter integrity [81], and cortical surface area/
thickness [82]. Open-access HCP-YA data is available from the
Connectome Coordination Facility following registration
(https://db.humanconnectome.org), with additional data use
terms applicable for restricted data (e.g., family structure, age by
year, handedness).

The Vietnam Era Twin Study of Aging (VETSA, 2003–pres-
ent), funded by the NIH, started as a study of cognitive and brain
aging but has since pivoted to the early identification of risk factors
for mild cognitive impairment and Alzheimer’s disease [28]. In
addition to neuroimaging and cognitive data, the VETSA study
includes health, psychosocial, and neuroendocrine data collected
across three waves (baseline mean age 56 years, follow-up waves
every 5–6 years) [83]. VETSA data is available following registra-
tion (https://medschool.ucsd.edu/som/psychiatry/research/
VETSA/Researchers/Pages/default.aspx).

The Older Australian Twins Study (OATS, 2007–present) [84], funded by the NHMRC and Australian Research Council, is a longitudinal study of genetic and environmental contributions to brain aging and dementia. The project includes neuroimaging and
cognitive data collected across four waves (baseline mean age 71 years, follow-up waves every 2 years). OATS was expanded in wave 2 to include positron emission tomography (PET) scans to investigate the deposition of amyloid plaques in the brain. Data is available from the project owners upon request.

There is a wealth of twin studies worldwide in addition to those mentioned here (see [85] for an overview). Foremost is the Netherlands Twin Registry [86], a substantial data resource with dedicated projects investigating neuropsychological, biomarker, and behavioral traits. In addition, several extensive family/pedigree imaging studies exist, including the Genetics of Brain Structure and Function study [87] and the Diabetes Heart Study-Mind Cohort [88]. Further, the previously mentioned ABCD study [89] includes embedded twin subsamples.

Twin datasets have been used to estimate the heritability (the proportion of observed variance in a phenotype attributed to genetic variance) of phenotypes derived through machine learning, such as brain aging [90–92] and brain network connectivity [93]. Further, machine learning models have been trained to discriminate between MZ and DZ twins based on dynamic functional connectivity [94] and psychological measures [95]. In addition, machine learning has been used to predict co-twin pairs based on functional connectivity data [96].

## 5.2 Molecular Genetics

### 5.2.1 The UK Biobank

The UK Biobank (UKB) is one of the largest population-based cohorts, comprising nearly half a million adult participants (aged over 40 years at the time of recruitment), recruited across over 20 assessment centers in the UK. The UKB resource is accessible to the research community through application (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access) and, as of the end of 2021, counted more than 28,000 registered approved researchers worldwide. In 2021, UKB launched a cloud-based Research Analysis Platform (RAP), which provides computational tools for data visualization and analysis, thereby aiming to democratize access for researchers lacking such infrastructure. The associated fees for using the UKB resource include the yearly tier-based access fees, depending on the type of data accessed, as well as the cost of running the analyses and storing the generated data, while the storage of the UKB dataset itself is provided free of charge. Certain emerging datasets (e.g., whole exome and genome sequences) will be only available for analysis through the platform, both due the enormous size and tighter regulation around those datasets. Upon publication, researchers are required to return their results, including the methodology and any essential derived data fields, back to the UKB, which are subsequently incorporated into the resource in order to promote reproducible research.
The cohort is deeply phenotyped with thousands of traits measured across multiple assessments. The initial assessment visit took place from 2006 to 2010, where ~502,000 participants consented to participate (each keeping the right to withdraw their consent and be removed from the study at any time), completed the interview, filled questionnaires, underwent multiple measurements, and donated blood urine and saliva samples (see https://biobank.ndph.ox.ac.uk/showcase/ukb/docs/Reception.pdf).

The first repeat assessment was conducted in 2012–2013 and included approximately 120,000 participants. Next, the participants were invited to attend the imaging visits: the initial (2014+) and the first repeat imaging visit (2019+). So far, 50,000 initial imaging visits have been conducted, with a target to image 100,000 participants (10,000 repeat). The imaging data includes brain [14, 97], heart [98], and abdominal MRI scans [99], with both bulk images and image-derived measures available for analysis, as well as retinal OCT images, whole body MRI, and carotid ultrasound [100]. Finally, follow-up information from the linked health and medical records is regularly collected and updated in the resource, including data for COVID-19 research. The showcase of the available anonymous summary information is available at https://biobank.ndph.ox.ac.uk/showcase/.

The interim release of the genotyping data comprised ~150,000 samples and was released in 2015, followed by the full release of 488,000 genotypes in the middle of 2017. The available genotype data included variant calls from UK BiLEVE and UK Biobank Axiom arrays (autosomes, sex chromosomes, and mitochondrial DNA) as well as phased haplotype values and imputation to a combined panel of Haplotype Reference Consortium (HRC) and the merged UK10K and 1000 Genomes phase 3 reference panels [101], also known as v2 release. Subsequently, the v2 imputation was replaced by imputation to HRC and UK10K haplotype resource only (v3), after a problem was discovered for the set imputed to UK10K + 1000 Genomes panel (https://biobank.ndph.ox.ac.uk/showcase/label.cgi?id=100319). The genotypes of approximately 3% of the participants remained not assayed due to DNA processing issues. To note, ~50,000 individuals included in the interim genotype release were involved in the UK Biobank Lung Exome Variant Evaluation (UK BiLEVE) project, and their genotypes were assayed on a different but very closely related array than the rest of the participants (https://biobank.ctsu.ox.ac.uk/crystal/ukb/docs/genotyping_qc.pdf). The UK BiLEVE focused on genetics of respiratory health, and the participants were selected based on lung function and smoking behavior [102].

Whole exome sequencing (WES) and whole genome sequencing (WGS) have been funded through the collaboration between the UK Biobank and biotechnology companies Regeneron and GlaxoSmithKline (GSK). The first UKB release of WES data
included 50,000 participants, prioritized based on the availability of MRI data, baseline measurements, and linked hospital and primary care records and enriched in patients diagnosed with asthma [103]. Recently (November 2021), the new data release included N = 200,000 WGS and N = 450,000 WES [104]. WGS for the remaining participants is currently underway. For all the past and future timelines, see https://biobank.ctsu.ox.ac.uk/showcase/exinfo.cgi?src=timelines_all.

Most of the UKB participants reported their ethnic background as White British/Irish or any other white background (~94%), which was coherent with the observed genetic ancestries [101]. For example, the ancestries identified from genetic markers showed a predominant European ancestry (N ~ 464,000), followed by South Asian (~12,000), African (~9000), and East Asian ancestry (~2500) [105]. As a population-based cohort, the UKB mostly comprises unrelated participants. While the pedigree information was not collected as a part of assessment, the genetic analysis has identified approximately 100,000 pairs of close relatives (third degree or closer, including 22,000 sibling pairs and 6000 parent-offspring pairs) [101]. This amount of relatedness is, however, larger than expected for a random sample from a population and reflects a participation bias toward the relatives of the participants. Moreover, the UKB sample is, on average, healthier, more educated, and less deprived than the general UK population [2].

**5.3 Genetic Consortia**

**5.3.1 ENIGMA**
*Consortium*

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium was formed in 2009 with the goal of conducting large-scale neuroimaging genetic studies of human brain structure, function, and disease [27]. Currently, more than 2000 scientists from 400 institutions around the world with neuroimaging (including structural and functional MRI) and electroencephalography (EEG) data have joined the consortium and formed 50 working groups that focus on different psychiatric and neurological disorders as well as healthy variation, method development, and genomics [27].

To date, the ENIGMA Genetics Working Group (for an overview, see [106]) have conducted genome-wide association meta-analyses for hippocampal and intracranial volume [107–109], subcortical volume [110, 111], and cortical surface area and thickness [112]. The ENIGMA Genetics Working Group provides researchers imaging and genetic protocols to enable each group to conduct their own association analyses before contributing summary statistics to the meta-analysis. While these genome-wide association studies have focused on structural phenotypes and the analysis of common single nucleotide polymorphisms (SNPs), the ENIGMA EEG Working Group have recently conducted a genome-wide association meta-analysis for oscillatory brain activity [113], and the ENIGMA Copy Number Variant (CNV) Working Group,
which formed in 2015, is currently investigating the impact of rare CNVs beyond the 22q11.2 locus on cognitive, neurodevelopmen- tal, and neuropsychiatric traits [114].

The sample sizes of the ENIGMA Genetics and CNV Working Groups continuously increase as new cohorts with MRI and genetic data join the consortium. As of 2020, the CNV Working Group sample comprises of 38 ENIGMA cohorts [114], while the latest Genetics Working Group genome-wide association meta-analysis [112] consisted of a discovery sample of 49 ENIGMA cohorts and the UK Biobank (N = 33,992 individuals of European ancestry), a replication sample of 2 European ancestry cohorts (N = 14,729 participants), and 8 ENIGMA cohorts of non-European ancestry (N = 2994 participants). This meta-analysis identified 199 genome-wide significant variants that were associated with either the surface area or thickness of the whole human cortex and 34 cortical regions with known functional specializations. They also found evidence that the genetic variants that influence brain structure also influence brain function, such as general cogni- tive function, Parkinson's disease, depression, neuroticism, ADHD, and insomnia [112].

Importantly, all imaging, EEG, and genetic (imputation and association analysis) protocols are freely available from the ENIGMA website (http://enigma.ini.usc.edu/). However, to access the summary statistics for each published genome-wide asso- ciation meta-analysis, researchers need to complete an online Data Access Request Form (http://enigma.ini.usc.edu/research/down load-enigma-gwas-results/). If a researcher wants to propose new genetic analyses that cannot be conducted with these publicly available summary statistics, they need to become a member of ENIGMA. Researchers can join the consortium by (a) contributing a cohort with MRI and genetic data, (b) collaborating with another research group that does have MRI and genetic data, or (c) contributing their expertise in geno- mic or methodological areas that are inadequately addressed by other consortium members. Of note, since storage of the MRI and genetic data is not centralized, each ENIGMA cohort can choose to contribute or not to new proposed analyses.

The Psychiatric Genomics Consortium (PGC) began in 2007. The central idea of the PGC is to use a global cooperative network to advance genetic discovery in psychiatric disorders in order to iden- tify biologically, clinically, and therapeutically meaningful insights. To date, the PGC is one of the largest, most innovative, and productive consortia in the history of psychiatry. The Consortium now consists of workgroups on 11 major psychiatric disorders, a Cross-Disorder Workgroup, and a Copy-Number Variant Work- group. In addition, the PGC provides centralized support to the PGC researchers with a Statistical Analysis Group, Data Access
<figure><img src="image_2.png" /><figcaption>Fig. 1 PGC discoveries over time</figcaption></figure>

Committee, and Dissemination and Outreach Committee. To increase ancestral diversity, the Consortium established the Cross-Population Workgroup in 2017 for outreach and developing/ deploying trans-ancestry analysis methods [115]. The Consortium outreach expands ancestry diversity by adding non-European cases and controls. The PGC continues to unify the field and attract outstanding scientists to its central mission (800+ investigators from 150+ institutions in 40+ countries). PGC work has led to *320 papers*, many in high-profile journals (*Nature* 3, *Cell* 5, *Science* 2, *Nat Genet* 27, *Nat Neurosci* 9, *Mol Psych* 37, *Biol Psych* 25, *JAMA Psych* 12). The full results from all PGC papers are freely available, and the findings have fueled analyses by non-PGC investigators (sample sizes and findings for eight major psychiatric disorders are summarized in Fig. 1)

Computation and data warehousing for the PGC are non-trivial. The PGC uses the Netherlands “LISA” computing cluster. LISA compute cluster in Amsterdam which is used for most analyses (occasional analyses are done on other clusters, but 90% of PGC computation is done on LISA). The core software is the RICOPILI data analytic pipeline [116]. This pipeline has explicit written protocols for uploading data to the cluster in the Netherlands that one uses for quality control, imputation, analysis, meta-analysis, and bioinformatics. The actual mega-analyses are conducted by PGC analysts under the direction of a senior statistical geneticist, geneticist, or highly experienced analyst.
The PGC has a proven commitment to open-source, rapid progress science. All PGC results are made freely available as soon as a primary paper is accepted (GWAS summary statistics available at https://www.med.unc.edu/pgc/download-results/). The researchers can obtain access to the individual-level data either through controlled-access repositories (e.g., the Database of Genotypes and Phenotypes, dbGaP, or the European Genome-phenome Archive) or via the PGC streamlined process for secondary data analyses (https://www.med.unc.edu/pgc/shared-methods/data-access-portal/) [117].

PGC analyses have always been characterized by exceptional rigor and transparency. PGC analysts will enhance this by publishing markdown notebooks for all papers on the PGC GitHub site (https://github.com/psychiatric-genomics-consortium) to enable precise reproduction of all analyses (containing code, documentation of QC decisions, analyses, etc.).

## 5.4 Exome and Whole Genome Sequencing: Trans-Omics for Precision Medicine (TOPMed)

The Trans-Omics for Precision Medicine (TOPMed) program, sponsored by the National Institutes of Health (NIH) National Heart, Lung, and Blood Institute (https://topmed.nhlbi.nih.gov), is part of a broader Precision Medicine Initiative, which aims to provide disease treatments tailored to an individual's unique genes and environment. TOPMed contributes to this Initiative through the integration of whole genome sequencing (WGS) and other omics data. The initial phases of the program focused on whole genome sequencing of individuals with rich phenotypic data and diverse backgrounds. The WGS of the TOPMed samples was performed over multiple studies, years, and sequencing centers [118, 119]. Available data are processed periodically to produce genotype data “freezes.” Individual-level data is accessible to researchers with an approved dbGaP data access request (https://topmed.nhlbi.nih.gov/data-sets), via Google and Amazon cloud services. More information about data availability and how to access it can be found on the dataset page (https://topmed.nhlbi.nih.gov/data-sets).

As of September 2021, TOPMed consists of ~180 K participants from >85 different studies with varying designs. Prospective cohorts provide large numbers of disease risk factors, subclinical disease measures, and incident disease cases; case-control studies provide improved power to detect rare variant effects. Most of the TOPMed studies focus on HLBS (heart, lung, blood, and sleep) phenotypes, which leads to 62 K (~35%) participants with heart phenotype, 50 K (~28%) with lung data, 19 K (~11%) with blood, 4 K (~2%) with sleep, and 43 K (~24%) for multi-phenotype cohort studies. TOPMed participants' diversity is assessed using a combination of self-identified or ascriptive race/ethnicity categories and observed genetics. Currently, 60% of the 180 K sequenced
participants are of non-European ancestry (i.e., 29% African ancestry, 19% Hispanic/Latino, 8% Asian ancestry, 4% other/multiple/unknown).

Whole genome sequencing is performed by several sequencing centers to a median depth of 30× using DNA from blood, PCR-free library construction, and Illumina HiSeq X technology (https://topmed.nhlbi.nih.gov/group/sequencing-centers). Randomly selected samples from freeze 8 were used for whole exome sequence using Illumina v4 HiSeq 2500 at an average 36.4× depth. A trained machine learning algorithm with known variants and Mendelian inconsistent variants is applied by the Informatics Research Centre for joint genotype calling across all samples to produce genotype data “freezes” (https://topmed.nhlbi.nih.gov/group/irc). In TOPMed data freeze 8 (N ~ 180 K) (https://topmed.nhlbi.nih.gov/data-sets), variant discovery identified 811 million single nucleotide variants and 66 million short insertion/deletion variants. In the latest data freeze 9 (https://topmed.nhlbi.nih.gov/data-sets), variant discovery was initially made on ~206 K samples including data from Centers for Common Disease Genomics (CCDG). This data was re-subset to ~158,470 TOPMed samples plus 2504 from 1000 Genomes samples were used for variant re-discovery. Then, a total of 781 million single nucleotide variants and 62 million short insertion/deletion variants were identified and passed variant quality controls. These variant counts in freeze 9 are slightly smaller than that of freeze 8 due to monomorphic sites in TOPMed samples. A series of data freezes is being made available to the scientific community as genotypes and phenotypes via dbGaP (https://www.ncbi.nlm.nih.gov/gap/); read alignments are available via the Sequence Read Archive (SRA) and variant summary information via the Bravo variant server (https://bravo.sph.umich.edu/freeze8/hg38/) and dbSNP (https://www.ncbi.nlm.nih.gov/snp/).

TOPMed studies provide unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. For instance, [119] used 53,831 samples from freeze 5 (https://topmed.nhlbi.nih.gov/data-sets) to investigate the role of rare variants into mutational processes and recent human evolutionary history. The recent TOPMed freeze 8 were used (together with WGS from the UK Biobank) to assess effect size of casual variants for gene expression using 72 K African American and ~298 K European American [120]. Similarly, a large set of multi-ethnic samples from freeze 5, 8, and 9 were used to develop comprehensive tools such as the STAAR and SCANG pipelines, which are used to identify noncoding rare variants [121] and to build predictive models for protein abundances [122] and discovery of causal genetic variants for different phenotypes [123, 124]. Overall, the Trans-Omics for Precision Medicine (TOPMed) program has the potential to help in improving
diagnosis, treatment, and prevention of major diseases by adding
WGS and other “omics” data to existing studies with deep
phenotyping.

# 6 Genomics

## 6.1 Methylation

DNA methylation (DNAm) is a covalent molecular modification by which methyl groups (CH₃) are added to the DNA. In vertebrates—and eukaryotes in general—the most common methylation modification occurs at the fifth carbon of the pyrimidine ring (5mC) at cytosine–guanine dinucleotides (CpG). Most bulk genomic methylation patterns are stable across cell types and throughout life, changing only in localized contexts, for example, due to disease-associated processes.

There are numerous ways of measuring DNAm at a genome-wide level, with bisulfite conversion-based methods being the most popular in the field of epidemiological epigenetics. These methods consist of bisulfite-induced modifications of genomic DNA, which results in unmodified cytosine nucleotides being converted to uracil, while 5mC remain unaffected. Of all these bisulfite conversion-based technologies—including sequencing-based methods—hybridization arrays are the most widely used, primarily due to their low cost and high-throughput nature.

The current Illumina Infinium® HumanMethylation450 (or 450 K) and Illumina Infinium® HumanMethylation850 (or E-PIC) arrays assess around 450,000 and 850,000 methylation sites across the genome, respectively, covering 96% of the CpG islands (i.e., genomic regions with high CpG frequency), 92% of the CpG islands' shores [125, 126] (<2 kb flanking CpG Islands), and 86% of the CpG islands' shelves (<2 kb flanking outward from a CpG shore), which have been shown to be more dynamic than CpG islands [127]. Although most current studies have used the 450 K array [128], the EPIC array covers >90% of the 450 K sites plus additional CpG sites in the enhancer regions identified by the ENCODE and FANTOM5 projects [129].

After probe hybridization and extension steps, the array is scanned, and the intensities of the unmethylated and methylated bead types are measured. DNAm values are then represented by the ratio of the intensity of the methylated bead type to the combined locus intensity. These are known as *beta* (β) values and are continuous variables between 0 and 1 (Equation 1), although a value of 1 is impossible to achieve in practice, due to the addition of a stabilizing α offset (to handle low-intensity signals):
**Equation 1 DNA methylation β values as measured by the Illumina Infinium® methylation arrays** M = methylated intensity, U = unmethylated intensity, α = arbitrary offset to handle signals with low readings (usually 100)

$$ \beta = \frac{M}{M + U + \alpha} \qquad (1) $$

These raw intensities are then stored in binary IDAT files (one for each of the red and green channels). The bulk of each file consists of four fields: the ID of each bead type on the array, the mean and standard deviation of their intensities, and the number of beads of each type, generated per sample. This raw data format allows for flexible use, including differing preprocessing strategies [130]. However, these files are usually not readily available in public repositories (e.g., Gene Expression Omnibus [131] or GEO), due to their large size. For example, a compressed .tar file of IDATs for a sample size of around 700 individuals, measured with EPIC arrays, is about 10 Gb. Instead, researchers usually upload the processed DNAm β values (following normalization) as compressed .txt or .csv files with columns representing samples and rows the measured loci. This can be a problem for reproducibility, as different research groups tend to prefer their own preprocessing or normalization methods—and there are many [132]! On this note, there has been a recent push in the field, for standardization of DNAm array preprocessing pipelines, including the user-friendly Meffil pipeline [133].

Reproducibility and interpretation of DNAm studies are subject to additional factors outside of data processing methods. For comparison, genetic data is (mostly) germline determined and can be assumed to be randomly assigned with respect to characteristics of individuals. Thus, a case-control (or cross-sectional) design has an inference of association through causality and can convey information of liability to disease. This contrasts with DNAm data which is a reversible process influenced by a large range of biological, technical, and environmental factors (e.g., medication and complications of the disease itself) and is thus more susceptible to spurious cryptic association or reverse causation [134, 135]. DNAm studies will therefore benefit from longitudinal designs, both for biomarker discovery and mechanistic insights [134, 136].

Reed et al. [137] provide one good example of this. Briefly, the authors generated a DNAm score for body mass index (BMI) within the ARIES subsample of the Avon Longitudinal Study of Parents and Children birth cohort (ALSPAC), using effect sizes of 135 CpG sites from a published meta-analysis of DNAm and BMI [138]. Using multiple time points for matched mothers and children using linear and cross-lagged models to explore the causal relationship between phenotypic BMI and the DNAm scores, they
found a strong linear association within time points [137]. How-
ever, when testing for temporal associations, DNAm scores at
earlier time points showed no association with future BMI, indicat-
ing that a DNAm score generated from a reference cross-sectional
study performs better as a biomarker of extant BMI, but poorly as a
predictor for future BMI.

In Table 2, we have compiled a list of the largest and/or most
used DNAm array datasets—including the Genetics of DNA Meth-
ylation Consortium (goDMC), an international collaboration of
human epidemiological studies that comprises >30,000 study par-
ticipants with genetic and DNAm array data [139]. These samples
are usually integrated in larger genetic/epidemiological studies,
except for perhaps the NIH Roadmap Epigenomics Mapping Con-
sortium [140], which was launched with the goal of producing a
public resource of human epigenomic data to catalyze basic biology
and disease-oriented research, and the BLUEPRINT project
[141, 142], which aims to generate at least 100 reference epigen-
omes of distinct types of hematopoietic cells from healthy indivi-
duals and of their malignant leukemic counterparts. Lastly, in
contrast to genetic data, the de-identified DNAm data—either
raw or preprocessed—is typically open access in public repositories
such as GEO [131], or dbGAP [143], or the web portals provided
by the respective projects. However, access to accompanying phe-
notypic data may require additional approval by the managing
committees of each individual project.

Launched in 2010, the Genotype-Tissue Expression (GTEx) project is an ongoing effort that aims to characterize the genetic determinants of tissue-specific gene expression [144]. It is a resource database available to the scientific community, which is comprised of multi-tissue RNA sequencing (RNA-seq: gene expression) and whole genome sequence (WGS) data collected in 17,382 samples across 54 tissue types from 948 postmortem donors (version 8 release). Sample size per tissue ranges from *n* = 4 in kidney (medulla) to *n* = 803 in skeletal muscle. The majority of donors are of European ancestry (84.6%) and male (67.1%) with ages ranging from 20–70 years old. The primary cause of death for donors 20–39 years old was traumatic injury (46.4%) and heart disease for donors 60–70 years (40.9%).

Data is constantly being added to the database using sample
data from the GTEx Biobank. For example, recent efforts have
focused on gene expression profiling at the single-cell level to
achieve a higher resolution understanding of tissue-specific gene
expression and within tissue heterogeneity. As a result, single-cell
RNA-seq (scRNA-seq) data was generated in 8 tissues from
25 archived, frozen tissue samples collected on 16 donors. Further,
the Developmental Genotype-Tissue Expression (dGTEx) project
(https://dgtex.org/) is a relatively new extension of GTEx that was
launched in 2021 that aims to understand the role of gene expres-
sion at four developmental time points: postnatal (0–2 years of
age), early childhood (2–8 years of age), pre-pubertal
(8–12.5 years of age), and post-pubertal (12.5–18 years of age).
It is expected that molecular profiling (including WGS, bulk
RNA-seq, and, for a subset of samples, scRNA-seq) will be per-
formed on 120 relatively healthy donors (approximately 30 donors
per age group) in 30 tissues. Data from this study would provide,
for example, a baseline for gene expression patterns in normal
development for comparison against individuals with disease.

GTEx provides extensive documentation on sample collection, laboratory protocols, quality control and standardization, and analytical methods on their website (https://gtexportal.org/home/). This allows for replication of their protocols and procedures in other cohorts to aid in study design and for researchers to further interrogate the GTEx data to answer more specific scientific questions. Processed individual-level gene expression data is made freely available on the GTEx website for download, while controlled access to individual-level raw genotype and RNA sequencing data are available on the AnVIL repository following approval via the National Center for Biotechnology Information's database of Genotypes and Phenotypes (dbGAP, dbGaP accession phs000424), a data archive website that stores and distributes data and results investigating the relationship between genotype and phenotype (https://www.ncbi.nlm.nih.gov/gap/). Clinical data collected for each donor is categorized into donor-level (demographics, media-tion use, medical history, laboratory test results, death circum-stances, etc.) and sample-level (tissue type, ischemic time, batch ID, etc.) data and is also available through dbGAP.

Over the many years, data from the GTEx project has provided
unprecedented insight into the role genetic variation plays in reg-
ulating gene expression and its contribution to complex trait and
disease variation in the population. The latest version 8 release from
GTEx comes with a comprehensive catalogue of variants associated
with gene expression, or eQTLs (expression quantitative trait loci),
across 49 tissues or cell lines (derived from 15,201 samples and
838 donors) (GTEx Consortium, 2020). This analysis has demon-
strated that gene expression is a highly heritable trait, with millions
of genetic variants affecting the expression of thousands of genes
across the genome. These pairwise gene variant associations can be
classified as either *cis*- or *trans*-eQTLs, which describes proximal
(i.e., within a predefined window of the target gene) or distal (i.e.,
beyond the predefined window or on a different chromosome from
the target gene) genetic control, respectively. Indeed, it has been
shown that 94.7% of all protein-coding genes have at least one *cis*-eQTL.
In addition, 43% of genetic variants (minor allele fre-
quency > 1%) have been found to affect gene expression in at
least one tissue, and the majority of *cis*-eQTLs appear to be shared
across the sexes and ancestries (GTEx Consortium, 2020). Rela-
tively few *trans*-eQTLs have been identified due to limitations in
sample sizes; however, these typically affect gene expression in one
or very few tissues, with about a third of *trans*-eQTLs mediated by
*cis*-eQTLs [144]. Importantly, GTEx provides full eQTL summary
statistics for download and an interactive portal (https://
gtexportal.org/home/) for quick searches. As most trait-associated
loci identified in genome-wide association studies (GWAS) are in
noncoding regions of the genome, the eQTL data generated by
GTEx has been leveraged to provide insight into the genetic and
molecular mechanisms that underlie complex traits and diseases.
Indeed, GWAS trait-associated variants are enriched for *cis*-eQTLs,
and genetic variants that affect multiple genes in multiple tissues are
found to also affect many complex traits (GTEx Consortium,
2020). This indicates that *cis*-eQTLs have a high degree of pleiot-
ropy and exert their effect on complex traits and diseases by reg-
ulating proximal gene expression.

In addition to the comprehensive catalogue of multi-tissue
eQTLs to understand gene regulation, additional flagship GTEx
studies include understanding sex-biased gene expression across
tissues [145], functional rare genetic variation [146], cell type-
specific gene regulation [147], and predictors of telomere length
across tissues [148].

The extensive publicly available data generated by the GTEx
project is a valuable resource to the scientific community and will
allow for further data interrogation for many years to come.

7 Electronic Health Records

7.1 **Clinical Data**
**Warehouse: Example**
**from the Parisian**
**Hospitals (APHP)**

Clinical data warehouses (CDW) gather electronic health records (EHR), which can gather demographic data, results from biological tests, prescribed medications, and images acquired in clinical routine, sometimes for millions of patients from multiple sites. CDW can allow for large-scale epidemiological studies, but they may also be used to train and/or validate machine learning (ML) and deep learning (DL) algorithms in a clinical context. For example, several computer-aided diagnosis tools have been developed for the classification of neurodegenerative diseases. One of their main limitations is that they are typically trained and validated using research data or on a limited number of clinical images [149–154]. It is still unclear how these algorithms would perform on large clinical dataset, which would include participants with multiple diagnoses and more generally heterogeneous data (e.g., multiple scanners, hospitals, populations).

One of the first CDW in France was launched in 2017 by the AP-HP (Assistance Publique – Hôpitaux de Paris), which gathers most of the Parisian hospitals [155]. They obtained the
authorization of the CNIL (Commission Nationale de l'informa-
tique et des Libertés, the French regulatory body for data collection
and management) to share data for research purposes. The aim is to
develop decision support algorithms, to support clinical trials, and
to promote multicenter studies. The AP-HP CDW keeps patients
updated about the different research projects through a portal
(as authorized by CNIL), but, according to French regulation,
active consent was not required as these data were acquired as
part of the routine clinical care of the patients.

Accessing the data is possible with the following procedure. A
detailed project must be submitted to the Scientific and Ethics
Board of the AP-HP. If the project holders are external to the
AP-HP, they have to sign a contract with the Clinical Research
and Innovation Board (Direction de la Recherche Clinique et de
l'Innovation). Once the project is approved, data are extracted and
pseudo-anonymized by the research team of the AP-HP. Data are
then made available in a specific workstation via the Big Data
Platform, which is internal to the AP-HP. The Big Data Platform
supports several research environments (e.g., JupyterLab
Environment, R, MATLAB) and provides computational power
(CPUs and GPUs) to analyze the data.

An example of the research possible using such CDW is the
APPRIMAGE project, led by the ARAMIS team at the Paris Brain
Institute. The project was approved by the Scientific and Ethics
Board of the AP-HP in 2018. It aims to develop or validate algo-
rithms that predict neurodegenerative diseases from structural
brain MRI, using a very large-scale clinical dataset. The dataset
provided by the AP-HP gathers all T1w brain MRI of patients
aged more than 18 years old, collected since 1980. It therefore
consists of around 130,000 patients and 200,000 MRI which were
made available via the Big Data Platform of the AP-HP. Of note,
clinical data was available for only 30% of the imaged participants
(>30,000 patients) as it relies on the ORBIS Clinical Information
System (Agfa HealthCare), installed more recently in the hospitals.
The sheer size of the data poses obvious computational challenges,
but other difficulties include harmonizing clinical reports collected
in the different hospitals or handling the general heterogeneity of
the data (e.g., hospitals, acquisition software, populations). To
tackle this issue, we have developed a pipeline for the quality
control of the MR images [156].

7.2 **Swedish National Registries**

In Sweden, a unique 10-digit personal identification number has been assigned to each individual at birth or migration since 1947, which allows linkages across different Swedish population and health registers with almost 100% coverage [157]. The Swedish Total Population Register (TPR) was established in 1968 and is maintained by Statistics Sweden to obtain data on major life events, such as birth, vital status, migration, and civil status [158]. TPR is a
key source to provide basic information in medical and social
research in Sweden. The Swedish Population and Housing Cen-
suses (1960–1990) and the Swedish Longitudinal Integrated Data-
base for Health Insurance and Labour Market Studies (Swedish
acronym LISA) (since 1990) provide information on demographic
and socioeconomic status for the Swedish population, including
the highest attained educational level and household income
[159]. The Swedish Multi-Generation Register (MGR) provides
information on familial links for individuals born since 1932
onward in Sweden [160], which makes it possible to perform family
studies to investigate familial risk of different health outcomes and
control for familial confounding when needed.

The Swedish National Patient Register (NPR) is a valuable
source for medical research, which has since 1964 collected data
on inpatient care (nationwide coverage since 1987) and outpatient
care (more than 85% of the entire country since 2001) [161]. Diag-
noses are according to the Swedish revisions of the International
Classification of Disease codes (ICD codes). The positive predictive
value of the diagnoses is high, ranging from 85% to 95%, in NPR
[161]. NPR has been used in studies of different diseases including
many neurological disorders such as Alzheimer's disease [162],
Parkinson's disease [163], and amyotrophic lateral sclerosis
[164]. The Swedish Cancer Register (SCR) has been used exten-
sively in Swedish cancer research, especially cancer epidemiology.
SCR was established in 1958 and includes data on all newly diag-
nosed malignant and benign tumors, including different kinds of
brain tumors [165, 166]. The Swedish Medical Birth Register
(MBR) was established in 1973 and contains information on almost
all deliveries (from prenatal to postnatal) in Sweden [167]. MBR
has contributed mainly to the reproductive epidemiologic research
in Sweden and has also been used in epidemiological studies of
diseases later in life including different neurological disorders
[168, 169]. The Swedish Causes of Death Register (CDR) includes
information on virtually all deaths in Sweden since 1952 [170] and
has been used to identify various causes of death in medical
research, including deaths due to neurological disorders
[171]. The Swedish Prescribed Drug Register (PDR) was founded
in July 2005 and provides information on all prescription drugs
dispensed from pharmacies in Sweden [172, 173]. PDR has been
used to study patterns of use as well as consequences of medication
use, including memantine [174] and dopaminergic anti-Parkinson
drug [175].

In addition to these general health registers, there are also
hundreds of disease quality registers that are used for patient care
and research in Sweden. For instance, the Swedish Dementia Reg-
istry (SDR) was established in 2007 to achieve high quality of
diagnostics and care for patients with dementia [176]. The Swedish
Neuro-Register (SNR) was founded in 2001 (web-based since
2004, originally named as the Swedish Multiple Sclerosis Quality Registry) with the primary aim to improve care of patients with different neurological disorders including multiple sclerosis, Parkinson's disease, severe neurovascular headache, myasthenia gravis, narcolepsy, epilepsy, inflammatory polyneuropathy, as well as amyotrophic lateral sclerosis in Sweden [177, 178]. The Swedish Stroke Register is one of the world's largest stroke registers, which was established in 1994 and has included data from almost all hospitals that admit acute stroke patients in Sweden [179].

In Sweden, individual-level data in public registers are strictly protected by several laws, including the Ethics Review Act, the General Data Protection Regulation (GDPR), and the Public Access to Information and Secrecy Act (OSL). The Swedish Ethical Review Authority (Etikprövningsmyndigheten in Swedish) assesses projects according to the Ethics Review Act and requires a Swedish responsible person (Forskningshuvudman in Swedish) for the research. In addition to ethical approval, the Statistics Sweden (SCB) and the National Board of Health and Welfare (Socialstyrelsen in Swedish) also need to make an assessment according to GDPR and OSL, to determine whether individual-level data can be made available for potential research purposes. It generally takes around 1–6 months from contact person assignment to delivery of microdata in the SCB (www.scb.se/en/services/ordering-data-and-statistics/ordering-microdata/) and around 3–6 months to process applications for individual-level data in the Socialstyrelsen (www.socialstyrelsen.se/en/statistics-and-data/statistics/). According to standard legal provisions and procedures, the SCB and Socialstyrelsen only provide data to researchers working in Sweden, and researchers in other countries need to cooperate with Swedish colleagues to apply for the data.

According to the General Data Protection Regulation (GDPR), online access (e.g., through virtual machines) or transfer of individual-level data is allowed in countries of the European Union (EU) or European Economic Area (EEA), after proper legal agreements. Online access or transfer of individual-level data to an external partner in a third country outside EU/EEA is also permitted, if the third country has been approved by the European Commission and the external partner signs and complies with legal agreements that include requirements for how data must be protected, including Data Transfer Agreement (DTA), Data Processing Agreement (DPA), Material Transfer Agreement (MTA), as well as Research Collaboration Agreements.
# 8 Smartphone and Sensors

Smartphones and sensors allow for the unobtrusive collection of behavioral and physiological data. For instance, smartphones are commonly used in ecological momentary assessment (EMA) studies [180], resulting in continuous, real-time assessment of participant behavior, symptoms, and experiences. In addition, the built-in microphone and touchscreen of smartphones/tablets can record speech and motor movement. Recent advances in smartwatch technology has enabled many commercial devices (e.g., Fitbit, Garmin, Apple) to track physiological metrics (e.g., heart rate variability, pulse oximetry, temperature) in addition to traditional physical activity data (e.g., step count, Global Positioning System, exercise tracking). Sensors are also commonly used to collect data without requiring participant interaction. Wearable sensor devices (e.g., wrist-worn accelerometers) can collect data on sleep, activity, and physiology without burdening participants or influencing their behavior. Datasets derived from smartphone and sensor studies are typically text-based, though raw data may be proprietary. The analysis of smartphone and sensor data typically requires complex algorithms/machine learning approaches due to the complexity of data collected (in the frequency of hundreds of observations per second, from many different sensors collecting data simultaneously). Raw data is typically stored locally by the data owner, with de-identified data available upon request. In more extensive studies, data is stored and distributed through online repositories.

Several studies have collected real-world behavioral and physiological data using smartphone and sensor devices (see Table 2), including community twin studies (BATS, QTAB), large-scale biomedical databases (UK Biobank), and studies focusing on specific disorders (mPower).

The Brisbane Adolescent Twin Study (BATS) and the Queensland Twin Adolescent Brain (QTAB) projects are twin studies sourced from the Queensland Twin Registry (QTwin). The BATS project, enabled through funding from the NHMRC, was a longitudinal study of adolescent twins, which collected accelerometry data over three waves between 2014 and 2018 (ages 12, 14, and 16 years). The Queensland Twin Adolescent Brain study (QTAB, 2015-present), previously discussed in Subheading 5.1, collected accelerometry data over two waves (age 9–14 years at baseline). In both studies, participants wore a wrist-mounted accelerometry recording device for 2 weeks (day and night, removed only for bathing) and completed a daily sleep diary. Raw accelerometry data were processed and consolidated with sleep diary data to produce sleep onset, wake, and sleep duration estimates. The BATS and QTAB datasets include behavioral and psychological measures (e.g., assessments of cognition and behavior, self-
reported mental health and well-being) for further investigation of
accelerometry measures. BATS and QTAB data is available from the
project owners upon request.

The UK Biobank, previously discussed in Subheading 5.2,
collected accelerometry data in 100,000 participants between
2013 and 2016. Participants wore a wrist-mounted activity moni-
tor to capture physical activity and sleep patterns for 7 days. Since
2018, repeat measures have been collected for a subset of partici-
pants every quarter to examine seasonal influences on measure-
ments. Data is available in raw (measured every 5 s) and average
(by day and hour) acceleration formats. The deep phenotyping of
the UK Biobank has allowed for accelerometry-based measures to
be examined alongside several other measures, including brain
structure [181], mood disorders [182], and Alzheimer's disease
[183]. UK Biobank data is available online following registration
(https://bbams.ndph.ox.ac.uk/ams/).

The mPower study (2015–present), sponsored by Sage Bionet-
works with funding from the Robert Wood Johnson Foundation,
aims to establish the baseline variability of real-world activity mea-
surements of individuals with Parkinson’s disease. Data is collected
through an iPhone application, with minimal interruption to the
daily life of participants. The initial data release (collected over
6 months) included health survey and sensor-based activity (e.g.,
gait and balance) data for ~8000 participants (with ~1000 self-
identified as having a professional diagnosis of Parkinson’s disease).
In addition, approximately 900 participants contributed at least five
separate days’ worth of data. mPower data is accessible through the
data sharing service Synapse (https://www.synapse.org/mpower).

A recent review [184] provides an overview of studies using
smartphones to monitor symptoms of Parkinson's disease and
in-depth descriptions of the methodology involved in these types
of studies. Additionally, studies have used smartphone-based EMA
to detect or treat mood disorders (see [185] for a review). Further,
the Mobile Motor Activity Research Consortium for Health
(MMARCH; http://mmarch.org/) is a collaborative international
network working to standardize the analysis of actigraphy data in
studies investigating motor activity, mood, and related disorders.

Machine learning approaches have been widely applied to data
collected from smartphone and sensor devices, most notably in
studies of Parkinson's disease. For example [186], used machine
learning classifiers applied to accelerometry data from the UK
Biobank to classify individuals with Parkinson's disease with an
area under the curve of 0.85 (based on gait and low movement
data). Another study [187] used data from the mPower study to
detect dopaminergic medication response by applying machine
learning techniques to the tapping task performance (measured
via the mPower smartphone application) of Parkinson's disease
patients before and after medication. Further, classifiers have been
used to detect states of deep brain stimulation (i.e., distinguishing between “On” and “Off” settings) in Parkinson’s disease patients using accelerometer and gyroscope signals from smartphones [188]. Machine learning approaches have also shown promise for other disorders. For instance, machine learning algorithms within a smartphone application have helped identify individuals with obstructive sleep apnea, using actigraphy, body position assessment, and audio recordings [189]. Lastly, some developed a pipeline for personalized modeling of depressed mood (based on EMA) and smartwatch-derived sleep and physical activity measures [190].

## Acknowledgments and Fundings

This research was supported by the Australian National Health and Medical Research Council (1,078,037, 1,078,901, 1,113,400, 1,161,356, and 1,107,258), the Australian Research Council (FT180100186 and FL180100072), the Sylvia and Charles Viertel Charitable Foundation, the program “Investissements d’avenir” ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6) and reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute), the European Union H2020 program (project EuroPOND, grant number 666992), the joint NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience” (project HIPLAY7, grant number ANR-16-NEUC-0001-01), the ICM Big Brain Theory Program (project DYNAMO, project PredictICD), and the Abeona Foundation (project Brain@Scale). BCD is supported by a CJ Martin Fellowship (APP1161356).

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