<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>Women'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 (cQTL) 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.wachan.lnl.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.wachan.lnl.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>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/datast/ dbgap/ phs001416</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 (ECOCD)</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.wachan.lnl.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.wachan.lnl.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/ 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>Genotype-Tissue Expression (GTEx)</td><td>948 individuals, 20-70</td><td>USA</td><td>Cross-sectional</td><td>General medical history: HIV status, potential exposures, medical history, at the time of death, death circumstances, scatology results, smoking status</td><td>NA</td><td>NA</td><td>NA</td><td>WGS</td><td>Gene expression (bulk RNA-seq)</td><td>NA</td><td>NA</td><td>https://www.ncbi.nlm.nih.gov/projects/gap/cg-biu/danescu-egi-study/id=phob00424.8.p26phu=1609018kphu=39108kphu=&phat=27428kphu=&phat=&phaf=27428kphaf=&phaf=&phaf=&temp=1&temp=1</td><td>[222]</td><td>Postmortem samples</td></tr><tr><td>Developmental Genotype-Tissue Expression (dGTEx)</td><td>120 individuals, 0-18</td><td>USA</td><td>Cross-sectional</td><td>General medical history</td><td>NA</td><td>NA</td><td>NA</td><td>WGS</td><td>Gene expression (bulk RNA-seq)</td><td>NA</td><td>NA</td><td>https://dgtex.org/</td><td>NA</td><td>Expected data release by late 2022</td></tr><tr><td>Greater-Paris Area Hospital (AP-HP)</td><td>Several millions -130 K patients with brain MRI (~200 K images)</td><td>All ages</td><td>France</td><td>ICD10 codes for diagnoses, medical reports, and a wide range of physiological variables</td><td>All medical imaging modalities and all organs. MRI, PET, SPECT, CT, ultrasounds</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>https://cds.aphe.fr/</td><td>[155]</td><td>All patients visiting greater-Paris area hospital (AP-HP) the figures correspond to a specific project approved on the clinical data warehouse (CDW)</td></tr><tr><td>Swedish Total Population Register (TPR)</td><td>Nationwide coverage (N = 9,775,572 on April 30, 2015)</td><td>All ages</td><td>Sweden</td><td>Longitudinal (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.org/rapjantjer/bestal-data-och-statistik/bestalls-mikrodata/vilka-mikrodata-fins/indivregister/registret-over-totalbetid-kunnen-rtb/</td><td>[158]</td><td>Demographic 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)</td><td>Sweden</td><td>Longitudinal<br>(since 1990)</td><td>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>(SGR)</td><td>Over 11 million</td><td>All ages</td><td>Sweden</td><td>Longitudinal<br>(since 1961)</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>www.sch.se/cu/<br>finding-statistics/<br>statistic-by-subjec-<br>area/other/other/<br>other-publications/<br>non-statistical/<br>porte/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)</td><td>Nationwide inpatient<br>care since 1987<br>and outpatient<br>care since 2001</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>www.sch.se/cu/<br>finding-statistics/<br>statistic-by-subjec-<br>area/other/other/<br>other-publications/<br>non-statistical/<br>porte/publications/<br>multi-generation-<br>register-2016/</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</td><td>All ages</td><td>Sweden</td><td>Longitudinal<br>(since 1958)</td><td>Clinical diagnoses of<br>cancer</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>www.sch.se/cu/<br>finding-statistics-and-<br>data/registers/<br>register-<br>information/rhe-<br>national-patient-<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</td><td>Sweden</td><td>Longitudinal<br>(since 1973)</td><td>Yes</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>www.sch.se/cu/<br>finding-statistics-and-<br>data/registers/<br>register-<br>information/<br>swedish-cancer-<br>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</td><td>All ages</td><td>Sweden</td><td>Longitudinal<br>(since 1952)</td><td>Cause of death</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>www.sch.se/<br>finding-statistics-and-<br>data/registers/<br>register-<br>information/rhe-<br>swedish-medical-<br>birth-register/</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)</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>www.sch.se/<br>finding-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 reveal-
ing 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, <sup>18</sup>F-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 <sup>11</sup>C-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 <sup>18</sup>F-FDG because of its very short half-life. Other tracers are used to show the spatial concentration of specific biomarkers: for instance, <sup>18</sup>F-florbetapir (AV45), <sup>18</sup>F-flutemetamol (Flute), Pittsburgh compound B (PiB), and <sup>18</sup>F-florbetaben (FBB) are amyloid tracers used to highlight β-amyloid aggregation in the brain, which is a maker of Alzheimer's disease. Finally, <sup>11</sup>C-5-hydroxytryptamine (5-HT) neurotransmitter is used to expose the serotonergic transmitter system.

We have made a non-exhaustive list of publicly available data-
sets containing PET scans with different tracers. Most datasets
focused on neurodegenerative disorders and also collected brain
MRI (see previous section). The Alzheimer's Disease Neuroimag-
ing 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
<sup>18</sup>F-FDG [13].

In addition to those neurodegenerative datasets, PET is avail-
able 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 <sup>11</sup>C-5-HT which is rele-
vant 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 <sup>18</sup>F-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_269.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.

## 6.2 Gene Expression
Data: GTEx

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).

## References

1. UNESCO Recommendation on Open Science - UNESCO Digital Library. https://unesdoc.unesco.org/ark:/48223/pf0000379949 locale=en

2. Fry A, Littlejohns TJ, Sudlow C et al (2017) Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am J Epidemiol 186:1026-1034

3. Ben-Eghan C, Sun R, Hleap JS et al (2020) Don't ignore genetic data from minority populations. Nature 585:184-186

4. Yang H-C, Chen C-W, Lin Y-T et al (2021) Genetic ancestry plays a central role in population pharmacogenomics. Commun Biol 4: 1-14

5. Barton NTL Paul Resnick, and Genie (2019) Algorithmic bias detection and mitigation: Best practices and policies to reduce consumer harms. https://www.brookings.edu/research/algorithmic-bias-detection-and-mitigation-best-practices-and-policies-to-reduce-consumer-harms/

6. Courtland R (2018) Bias detectives: the researchers striving to make algorithms fair. Nature 558:357-360

7. Bustamante CD, De La Vega FM, Burchard EG (2011) Genomics for the world. Nature 475:163-165

8. Sirugo G, Williams SM, Tishkoff SA (2019) The missing diversity in human genetic studies. Cell 177:26-31
9. Herrick R, Horton W, Olsen T et al (2016) XNAT central: open sourcing imaging research data. NeuroImage 124:1093-1096

10. Routier A, Burgos N, Díaz M et al (2021) Clinica: an open source software platform for reproducible clinical neuroscience studies. Front Neuroinform 15:689675

11. Petersen RC, Aisen PS, Beckett LA et al (2010) Alzheimer's Disease Neuroimaging Initiative (ADNI): clinical characterization. Neurology 74:201-209

12. Ellis KA, Bush AI, Darby D et al (2009) The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer's disease. Int Psychogeriatr 21:672-687

13. LaMontagne PJ, Keefe S, Lauren W et al (2018) OASIS-3: longitudinal neuroimaging, clinical and cognitive dataset for normal aging and Alzheimer's disease. Alzheimers Dement J Alzheimers Assoc 14:P1097

14. Miller KL, Alfaro-Almagro F, Bangerter NK et al (2016) Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci 19:1523-1536

15. Alfaro-Almagro F, Jenkinson M, Bangerter NK et al (2018) Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank. NeuroImage 166:400-424

16. Casey BJ, Cannonier T, Conley MI et al (2018) The adolescent brain cognitive development (ABCD) study: imaging acquisition across 21 sites. Dev Cogn Neurosci 32:43-54

17. Barch DM, Albaugh MD, Avenevoli S et al (2018) Demographic, physical and mental health assessments in the adolescent brain and cognitive development study: rationale and description. Dev Cogn Neurosci 32:55-66

18. Schmaal L, Hibar DP, Sämann PG et al (2017) Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group. Mol Psychiatry 22:900-909

19. Hoogman M, Muetzel R, Guimaraes JP et al (2019) Brain imaging of the cortex in ADHD: a coordinated analysis of large-scale clinical and population-based samples. Am J Psychiatry 176:531-542

20. van Rooij D, Anagnostou E, Arango C et al (2018) Cortical and subcortical brain morphometry differences between patients with autism Spectrum disorder and healthy

individuals across the lifespan: results from the ENIGMA ASD Working Group. Am J Psychiatry 175:359-369

21. Logue MW, van Rooij SJH, Dennis EL et al (2018) Smaller hippocampal volume in post-traumatic stress disorder: a multisite ENIGMA-PGC study: subcortical volumetry results from posttraumatic stress disorder consortia. Biol Psychiatry 83:244-253

22. Boedhoe PSW, Schmaal L, Abe Y et al (2018) Cortical abnormalities associated with pediatric and adult obsessive-compulsive disorder: findings from the ENIGMA Obsessive-Compulsive Disorder Working Group. Am J Psychiatry 175:453-462

23. Mackey S, Allgaier N, Chaarani B et al (2019) Mega-analysis of gray matter volume in substance dependence: general and substance-specific regional effects. Am J Psychiatry 176:119-128

24. van Erp TGM, Walton E, Hibar DP et al (2018) Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 control subjects via the Enhancing Neuroimaging Genetics Through Meta-analysis (ENIGMA) Consortium. Biol Psychiatry 84:644-654

25. Hibar DP, Westlye LT, Doan NT et al (2018) Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group. Mol Psychiatry 23:932-942

26. Dima D, Modabbernia A, Papachristou E, et al (2021) Subcortical volumes across the lifespan: data from 18,605 healthy individuals aged 3-90 years. Hum Brain Mapp

27. Thompson PM, Jahanshad N, Ching CRK et al (2020) ENIGMA and global neuroscience: a decade of large-scale studies of the brain in health and disease across more than 40 countries. Transl Psychiatry 10:1-28

28. Kremen WS, Franz CE, Lyons MJ (2013) VETSA: the Vietnam era twin study of aging. Twin Res Hum Genet 16:399-402

29. Van Essen DC, Smith SM, Barch DM et al (2013) The WU-Minn Human Connectome Project: an overview. NeuroImage 80:62-79

30. Marek K, Chowdhury S, Siderowf A et al (2018) The Parkinson's progression markers initiative (PPMI) - establishing a PD biomarker cohort. Ann Clin Transl Neurol 5: 1460-1477

31. Dufouil C, Dubois B, Vellas B et al (2017) Cognitive and imaging markers in non-demented subjects attending a memory clinic: study design and baseline findings of the MEMENTO cohort. Alzheimers Res Ther 9:67
32. Ritchie CW, Muniz-Terrera G, Kivipelto M et al (2020) The European Prevention of Alzheimer's Dementia (EPAD) Longitudinal Cohort Study: Baseline Data Release V500.0. J Prev Alzheimers Dis 7:8-13

33. Di Martino A, Yan C-G, Li Q et al (2014) The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry 19: 659-667

34. Di Martino A, O'Connor D, Chen B et al (2017) Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Sci Data 4:170010

35. Sharp, PF, Welch, A (2005) Positron emission tomography. In: Sharp PF, Gemmell HG, Murray AD (eds) Practical Nuclear Medicine. Springer, London. https://doi.org/10.1007/1-84628-018-4_3

36. Herholz K (1995) FDG PET and differential diagnosis of dementia. Alzheimer Dis Assoc Disord 9:6-16

37. Sudre CH, Cardoso MJ, Modat M et al (2020) Chapter 15 - Imaging biomarkers in Alzheimer's disease. In: Zhou SK, Rueckert D, Fichtinger G (eds) Handbook of medical image computing and computer assisted intervention. Academic Press, pp 343-378

38. Knudsen GM, Jensen PS, Erritzoe D et al (2016) The Center for Integrated Molecular Brain Imaging (Cimbi) database. NeuroImage 124:1213-1219

39. Wang H, Tian Y, Liu Y et al (2021) Population-specific brain [18F]-FDG PET templates of Chinese subjects for statistical parametric mapping. Sci Data 8:305

40. Jackson AF, Bolger DJ (2014) The neurophysiological bases of EEG and EEG measurement: a review for the rest of us. Psychophysiology 51:1061-1071

41. Niedermeyer E and da Silva FHL (2005) Electroencephalography: basic principles, clinical applications, and related fields, Lippincott Williams & Wilkins

42. Nunez PL, Srinivasan R (2006) Electric fields of the brain: the neurophysics of EEG. Oxford University Press, New York

43. Engel AK, Moll CKE, Fried I et al (2005) Invasive recordings from the human brain: clinical insights and beyond. Nat Rev Neurosci 6:35-47

44. Cohen D (1972) Magnetoencephalography: detection of the brain's electrical activity with a superconducting magnetometer. Science 175:664-666

45. Hämäläinen M, Hari R, Ilmoniemi RJ et al (1993) Magnetoencephalography---theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys 65:413-497

46. Lopes da Silva F (2013) EEG and MEG: relevance to neuroscience. Neuron 80:1112-1128

47. Malmivuo J (2012) Comparison of the properties of EEG and MEG in detecting the electric activity of the brain. Brain Topogr 25:1-19

48. Bertrand O, Perrin F, Pernier J (1985) A theoretical justification of the average reference in topographic evoked potential studies. Electroencephalogr Clin Neurophysiol 62: 462-464

49. de Cheveigné A, Nelken I (2019) Filters: when, why, and how (not) to use them. Neuron 102:280-293

50. Michel CM, Brunet D (2019) EEG source imaging: a practical review of the analysis steps. Front Neurol 10

51. Subha DP, Joseph PK, Acharya UR et al (2010) EEG signal analysis: a survey. J Med Syst 34:195-212

52. Ang KK, Chin ZY, Zhang H, et al (2008) Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 2390-2397

53. Müller-Gerking J, Pfurtscheller G, Flyvbjerg H (1999) Designing optimal spatial filters for single-trial EEG classification in a movement task. Clin Neurophysiol 110:787-798

54. Pfurtscheller G, Lopes da Silva FH (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 110:1842-1857

55. Sur S, Sinha VK (2009) Event-related potential: an overview. Ind Psychiatry J 18:70-73

56. Neuper C, Wörtz M, Pfurtscheller G (2006) ERD/ERS patterns reflecting sensorimotor activation and deactivation. Prog Brain Res 159:211-222

57. Crone NE, Miglioretti DL, Gordon B et al (1998) Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. II. Event-related synchronization in the gamma band. Brain J Neurol 121(Pt 12):2301-2315

58. Bastos AM, Schoffelen J-M (2016) A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Front Syst Neurosci 9:175
59. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186-198

60. De Vico Fallani F, Richiardi J, Chavez M et al (2014) Graph analysis of functional brain networks: practical issues in translational neuroscience. Philos Trans R Soc Lond Ser B Biol Sci 369:20130521

61. Gonzalez-Astudillo J, Cattai T, Bassignana G et al (2021) Network-based brain-computer interfaces: principles and applications. J Neural Eng 18:011001

62. Mirowski P, Madhavan D, LeCun Y et al (2009) Classification of patterns of EEG synchronization for seizure prediction. Clin Neurophysiol 120:1927-1940

63. Siddiqui MK, Morales-Menendez R, Huang X et al (2020) A review of epileptic seizure detection using machine learning classifiers. Brain Inform 7:5

64. Smith SJM (2005) EEG in the diagnosis, classification, and management of patients with epilepsy. J Neurol Neurosurg Psychiatry 76: ii2-ii7

65. Dauwels J, Vialatte F, Cichocki A (2010) Diagnosis of Alzheimer's disease from EEG signals: where are we standing? Curr Alzheimer Res 7:487-505

66. Vecchio F, Babiloni C, Lizio R et al (2013) Resting state cortical EEG rhythms in Alzheimer's disease: toward EEG markers for clinical applications: a review. Suppl Clin Neurophysiol 62:223-236

67. Lotte F, Bougrain L, Cichocki A et al (2018) A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. J Neural Eng 15:031005

68. Oostenveld R, Fries P, Maris E et al (2011) FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electro-physiological data. Comput Intell Neurosci 2011:156869

69. Tadel F, Baillet S, Mosher JC et al (2011) Brainstorm: a user-friendly application for MEG/EEG analysis. Comput Intell Neurosci 2011:879716

70. Gramfort A, Luessi M, Larson E et al (2013) MEG and EEG data analysis with MNE-python. Front Neurosci 7:267

71. Jayaram V, Barachant A (2018) MOABB: trustworthy algorithm benchmarking for BCIs. J Neural Eng 15:066011

72. Carlin JB, Gurrin LC, Sterne JA et al (2005) Regression models for twin studies: a critical review. Int J Epidemiol 34:1089-1099

73. Sainani K (2010) The importance of accounting for correlated observations. PM&R 2: 858-861

74. Zagai U, Lichtenstein P, Pedersen NL et al (2019) The Swedish twin registry: content and management as a research infrastructure. Twin Res Hum Genet 22:672-680

75. Magnusson PKE, Almqvist C, Rahman I et al (2013) The Swedish Twin Registry: establishment of a biobank and other recent developments. Twin Res Hum Genet 16:317-329

76. Tomata Y, Li X, Karlsson IK et al (2020) Joint impact of common risk factors on incident dementia: a cohort study of the Swedish Twin Registry. J Intern Med 288:234-247

77. Wirdefeldt K, Gatz M, Pawitan Y et al (2005) Risk and protective factors for Parkinson's disease: a study in Swedish twins. Ann Neurol 57:27-33

78. Fang F, Kamel F, Lichtenstein P et al (2009) Familial aggregation of amyotrophic lateral sclerosis. Ann Neurol 66:94-99

79. Wright MJ, Martin NG (2004) Brisbane Adolescent Twin Study: outline of study methods and research projects. Aust J Psychol 56:65-78

80. Miranda-Dominguez O, Feczko E, Grayson DS et al (2018) Heritability of the human connectome: a connectotyping study. Netw Neurosci 2:175-199

81. Kochunov P, Jahanshad N, Marcus D et al (2015) Heritability of fractional anisotropy in human white matter: a comparison of Human Connectome Project and ENIGMA-DTI data. NeuroImage 111:300-311

82. Schmitt JE, Raznahan A, Liu S et al (2020) The genetics of cortical myelination in young adults and its relationships to cerebral surface area, cortical thickness, and intelligence: a magnetic resonance imaging study of twins and families. NeuroImage 206:116319

83. Kremen WS, Franz CE, Lyons MJ (2019) Current status of the Vietnam Era Twin Study of Aging (VETSA). Twin Res Hum Genet 22:783-787

84. Sachdev PS, Lammel A, Trollor JN et al (2009) A comprehensive neuropsychiatric study of elderly twins: the Older Australian Twins Study. Twin Res Hum Genet 12:573-582

85. Hur Y-M, Bogl LH, Ordoñana JR et al (2019) Twin family registries worldwide: an important resource for scientific research. Twin Res Hum Genet 22:427-437

86. Ligthart L, van Beijsterveldt CEM, Kevenaar ST et al (2019) The Netherlands twin register: longitudinal research based on twin and twin-
family designs. Twin Res Hum Genet 22: 623-636

87. Glahn DC, Winkler AM, Kochunov P et al (2010) Genetic control over the resting brain. Proc Natl Acad Sci 107:1223-1228

88. Raffield LM, Cox AJ, Hugenschmidt CE et al (2015) Heritability and genetic association analysis of neuroimaging measures in the Diabetes Heart Study. Neurobiol Aging 36:1602. e7-1602.15

89. Iacono WG, Heath AC, Hewitt JK et al (2018) The utility of twins in developmental cognitive neuroscience research: how twins strengthen the ABCD research design. Dev Cogn Neurosci 32:30-42

90. Brouwer RM, Schutte J, Janssen R et al (2021) The speed of development of adolescent brain age depends on sex and is genetically determined. Cereb Cortex 31:1296-1306

91. Cole JH, Poudel RPK, Tsagkrasoulis D et al (2017) Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. NeuroImage 163:115-124

92. Vandenbosch MMLJZ, van't Ent D, Boomsma DI et al (2019) EEG-based age--prediction models as stable and heritable indicators of brain maturational level in children and adolescents. Hum Brain Mapp 40:1919-1926

93. Chung MK, Lee H, DiChristofano A et al (2019) Exact topological inference of the resting-state brain networks in twins. Netw Neurosci 3:674-694

94. Yamin MA, Dayan M, Squarcina L, et al (2019) Investigating the impact of genetic background on brain dynamic functional connectivity through machine learning: a Twins Study. In: 2019 IEEE EMBS International Conference on Biomedical Health Informatics (BHI), pp. 1-4

95. Han Y, Adolphs R (2020) Estimating the heritability of psychological measures in the Human Connectome Project dataset. PLoS One 15:e0235860

96. Demeter DV, Engelhardt LE, Mallett R et al (2020) Functional connectivity fingerprints at rest are similar across youths and adults and vary with genetic similarity. iScience 23: 100801

97. Elliott LT, Sharp K, Alfaro-Almagro F et al (2018) Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 562:210-216

98. Pirruccello JP, Bick A, Wang M et al (2020) Analysis of cardiac magnetic resonance imaging in 36,000 individuals yields genetic

insights into dilated cardiomyopathy. Nat Commun 11:2254

99. Liu Y, Basty N, Whitcher B et al (2021) Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning. elife 10:e65554

100. Chua SYL, Dhillon B, Aslam T et al (2019) Associations with photoreceptor thickness measures in the UK Biobank. Sci Rep 9: 19440

101. Bycroft C, Freeman C, Petkova D et al (2018) The UK Biobank resource with deep phenotyping and genomic data. Nature 562:203-209

102. Wain LV, Shrine N, Miller S et al (2015) Novel insights into the genetics of smoking behaviour, lung function, and chronic obstructive pulmonary disease (UK BiLEVE): a genetic association study in UK Biobank. Lancet Respir Med 3:769-781

103. Van Hout CV, Tachmazidou I, Backman JD et al (2020) Exome sequencing and characterization of 49,960 individuals in the UK Biobank. Nature 586:749-756

104. Backman JD, Li AH, Marcketta A et al (2021) Exome sequencing and analysis of 454,787 UK Biobank participants. Nature 599:628-634

105. Wang Y, Guo J, Ni G et al (2020) Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations. Nat Commun 11:3865

106. Medland SE, Grasby KL, Jahanshad N, et al (2020) Ten years of enhancing neuro-imaging genetics through meta-analysis: an overview from the ENIGMA Genetics Working Group. Hum Brain Mapp

107. Adams HHH, Hibar DP, Chouraki V et al (2016) Novel genetic loci underlying human intracranial volume identified through genome-wide association. Nat Neurosci 19: 1569-1582

108. Hibar DP, Adams HHH, Jahanshad N et al (2017) Novel genetic loci associated with hippocampal volume. Nat Commun 8:13624

109. Stein JL, Medland SE, Vasquez AA et al (2012) Identification of common variants associated with human hippocampal and intracranial volumes. Nat Genet 44:552-561

110. Hibar DP, Stein JL, Renteria ME et al (2015) Common genetic variants influence human subcortical brain structures. Nature 520: 224-229

111. Satizabal CL, Adams HHH, Hibar DP et al (2019) Genetic architecture of subcortical brain structures in 38,851 individuals. Nat Genet 51:1624-1636
112. Grasby KL, Jahanshad N, Painter JN et al (2020) The genetic architecture of the human cerebral cortex. Science 367:eay6690

113. Smit DJA, Wright MJ, Meyers JL et al (2018) Genome-wide association analysis links multiple psychiatric liability genes to oscillatory brain activity. Hum Brain Mapp 39:4183-4195

114. Sønderby IE, Ching CRK, Thomopoulos SI, et al (2021) Effects of copy number variations on brain structure and risk for psychiatric illness: large-scale studies from the ENIGMA working groups on CNVs. Hum Brain Mapp

115. Peterson RE, Kuchenbaecker K, Walters RK et al (2019) Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. Cell 179:589-603

116. Lam M, Awasthi S, Watson HJ et al (2020) RICOPILI: rapid imputation for COnsortias PIpeLine. Bioinforma 36:930-933

117. Sullivan PF, Kendler KS (2021) The state of the science in psychiatric genomics. Psychol Med 51:2145-2147

118. Stilp AM, Emery LS, Broome JG et al (2021) A system for phenotype harmonization in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) program. Am J Epidemiol 190: 1977-1992

119. Taliun D, Harris DN, Kessler MD et al (2021) Sequencing of 53,831 diverse genomes from the NHLBI TOPMed program. Nature 590:290-299

120. Patel RA, Musharoff SA, Spence JP, et al (2021) Effect sizes of causal variants for gene expression and complex traits differ between populations

121. Li Z, Li X, Zhou H, et al (2021) A framework for detecting noncoding rare variant associations of large-scale whole-genome sequencing studies

122. Schubert R, Geoffroy E, Gregga I, et al (2021) Protein prediction for trait mapping in diverse populations

123. Hindy G, Dornbos P, Chaffin MD, et al (2021) Rare coding variants in 35 genes associate with circulating lipid levels – a multi-ancestry analysis of 170,000 exomes

124. Selvaraj MS, Li X, Li Z, et al (2021) Whole genome sequence analysis of blood lipid levels in >66,000 individuals

125. Bibikova M, Barnes B, Tsan C et al (2011) High density DNA methylation array with single CpG site resolution. Genomics 98: 288-295

126. Yong W-S, Hsu F-M, Chen P-Y (2016) Profiling genome-wide DNA methylation. Epigenetics Chromatin 9:26

127. Ziller MJ, Gu H, Müller F et al (2013) Charting a dynamic DNA methylation landscape of the human genome. Nature 500:477-481

128. Maden SK, Thompson RF, Hansen KD et al (2021) Human methylome variation across Infinium 450K data on the gene expression omnibus. NAR Genom Bioinf 3:lqab025

129. Moran S, Arribas C, Esteller M (2016) Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences. Epigenomics 8:389-399

130. Sala C, Di Lena P, Fernandes Durso D et al (2020) Evaluation of pre-processing on the meta-analysis of DNA methylation data from the Illumina HumanMethylation450 BeadChip platform. PLoS One 15:e0229763

131. Barrett T, Wilhite SE, Ledoux P et al (2013) NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res 41: D991-D995

132. Wang T, Guan W, Lin J et al (2015) A systematic study of normalization methods for Infinium 450K methylation data using whole-genome bisulfite sequencing data. Epigenetics 10:662-669

133. Min JL, Hemani G, Davey Smith G et al (2018) Meffil: efficient normalization and analysis of very large DNA methylation datasets. Bioinforma 34:3983-3989

134. Birney E, Smith GD, Greally JM (2016) Epigenome-wide association studies and the Interpretation of Disease -Omics. PLoS Genet 12:e1006105

135. Michels KB, Binder AM (2018) Considerations for design and analysis of DNA methylation studies. Methods Mol Biol 1708:31-46

136. Mill J, Heijmans BT (2013) From promises to practical strategies in epigenetic epidemiology. Nat Rev Genet 14:585-594

137. Reed ZE, Suderman MJ, Relton CL et al (2020) The association of DNA methylation with body mass index: distinguishing between predictors and biomarkers. Clin Epigenetics 12:50

138. Mendelson MM, Marioni RE, Joehanes R et al (2017) Association of Body Mass Index with DNA methylation and gene expression in blood cells and relations to cardiometabolic disease: a Mendelian Randomization Approach. PLoS Med 14:e1002215

139. Min JL, Hemani G, Hannon E et al (2021) Genomic and phenotypic insights from an
atlas of genetic effects on DNA methylation. Nat Genet 53:1311-1321

140. Chadwick LH (2012) The NIH Roadmap Epigenomics Program data resource. Epigenomics 4:317-324

141. Fernández JM, de la Torre V, Richardson D et al (2016) The BLUEPRINT data analysis portal. Cell Syst 3:491-495.e5

142. Martens JHA, Stunnenberg HG (2013) BLUEPRINT: mapping human blood cell epigenomes. Haematologica 98:1487-1489

143. Tryka KA, Hao L, Sturcke A et al (2014) NCBI's database of genotypes and phenotypes: dbGaP. Nucleic Acids Res 42:D975-D979

144. Lonsdale J, Thomas J, Salvatore M et al (2013) The Genotype-Tissue Expression (GTEx) project. Nat Genet 45:580-585

145. Oliva M, Muñoz-Aguirre M, Kim-Hellmuth S et al (2020) The impact of sex on gene expression across human tissues. Science 369:eaba3066

146. Ferraro NM, Strober BJ, Einson J et al (2020) Transcriptomic signatures across human tissues identify functional rare genetic variation. Science 369:eaaz5900

147. Kim-Hellmuth S, Aguet F, Oliva M et al (2020) Cell type-specific genetic regulation of gene expression across human tissues. Science 369:eaaz8528

148. Demanelis K, Jasmine F, Chen LS et al (2020) Determinants of telomere length across human tissues. Science 369:eaaz6876

149. Burgos N, Colliot O (2020) Machine learning for classification and prediction of brain diseases: recent advances and upcoming challenges. Curr Opin Neurol 33:439-450

150. Koikkalainen J, Rhodius-Meester H, Tolonen A et al (2016) Differential diagnosis of neurodegenerative diseases using structural MRI data. NeuroImage Clin 11:435-449

151. Morin A, Samper-Gonzalez J, Bertrand A et al (2020) Accuracy of MRI classification algorithms in a tertiary memory center clinical routine cohort. J Alzheimers Dis 74:1157-1166

152. Rathore S, Habes M, Iftikhar MA et al (2017) A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages. NeuroImage 155:530-548

153. Samper-González J, Burgos N, Bottani S et al (2018) Reproducible evaluation of classification methods in Alzheimer's disease: framework and application to MRI and PET data. NeuroImage 183:504-521

154. Wen J, Thibeau-Sutre E, Diaz-Melo M et al (2020) Convolutional neural networks for classification of Alzheimer's disease: overview and reproducible evaluation. Med Image Anal 63:101694

155. Daniel C, Salamanca E (2020) Hospital Databases. In: Nordlinger B, Villani C, Rus D (eds) Healthcare and artificial intelligence. Springer International Publishing, Cham, pp 57-67

156. Bottani S, Burgos N, Maire A et al (2022) Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse. Med Image Anal 75:102219

157. Ludvigsson JF, Otterblad-Olausson P, Petersson BU et al (2009) The Swedish personal identity number: possibilities and pitfalls in healthcare and medical research. Eur J Epidemiol 24:659-667

158. Ludvigsson JF, Almqvist C, Bonamy A-KE et al (2016) Registers of the Swedish total population and their use in medical research. Eur J Epidemiol 31:125-136

159. Ludvigsson JF, Svedberg P, Olén O et al (2019) The longitudinal integrated database for health insurance and labour market studies (LISA) and its use in medical research. Eur J Epidemiol 34:423-437

160. Ekbom A (2011) The Swedish multi-generation register. Methods Mol Biol 675: 215-220

161. Ludvigsson JF, Andersson E, Ekbom A et al (2011) External review and validation of the Swedish national inpatient register. BMC Public Health 11:450

162. Song H, Sieurin J, Wirdefeldt K et al (2020) Association of stress-related disorders with subsequent neurodegenerative diseases. JAMA Neurol 77:700-709

163. Fang F, Zhan Y, Hammar N et al (2019) Lipids, apolipoproteins, and the risk of Parkinson Disease. Circ Res 125:643-652

164. Longinetti E, Mariosa D, Larsson H et al (2017) Neurodegenerative and psychiatric diseases among families with amyotrophic lateral sclerosis. Neurology 89:578-585

165. Barlow L, Westergren K, Holmberg L et al (2009) The completeness of the Swedish Cancer Register: a sample survey for year 1998. Acta Oncol 48:27-33

166. Tettamanti G, Ljung R, Ahlbom A et al (2019) Central nervous system tumor registration in the Swedish Cancer Register and Inpatient Register between 1990 and 2014. Clin Epidemiol 11:81-92
167. Källén B, Källén K (2003) The Swedish Medical Birth Register - a summary of content and quality. 2003-112-3

168. Persson M, Razaz N, Tedroff K et al (2018) Five and 10 minute Apgar scores and risks of cerebral palsy and epilepsy: population based cohort study in Sweden. BMJ 360:k207

169. Tettamanti G, Ljung R, Mathiesen T et al (2016) Maternal smoking during pregnancy and the risk of childhood brain tumors: results from a Swedish cohort study. Cancer Epidemiol 40:67-72

170. Brooke HL, Talbäck M, Hörnblad J et al (2017) The Swedish cause of death register. Eur J Epidemiol 32:765-773

171. Subic A, Zupanic E, von Euler M et al (2018) Stroke as a cause of death in death certificates of patients with dementia: a Cohort Study from the Swedish Dementia Registry. Curr Alzheimer Res 15:1322-1330

172. Wallerstedt SM, Wettermark B, Hoffmann M (2016) The first decade with the Swedish prescribed drug register - a systematic review of the output in the scientific literature. Basic Clin Pharmacol Toxicol 119:464-469

173. Wettermark B, Hammar N, Fored CM et al (2007) The new Swedish Prescribed Drug Register--opportunities for pharmacoepidemiological research and experience from the first six months. Pharmacoepidemiol Drug Saf 16:726-735

174. Cermakova P, Nelson M, Secnik J et al (2017) Living alone with Alzheimer's disease: data from SveDem, the Swedish Dementia Registry. J Alzheimers Dis 58:1265-1272

175. Haasum Y, Fastbom J, Johnell K (2016) Use of fall-risk inducing drugs in patients using Anti-Parkinson Drugs (APD): a Swedish Register-Based Study. PLoS One 11: e0161246

176. Religa D, Fereshtehnejad S-M, Cermakova P et al (2015) SveDem, the Swedish Dementia Registry - a tool for improving the quality of diagnostics, treatment and care of dementia patients in clinical practice. PLoS One 10: e0116538

177. Hillert J, Stawiarz L (2015) The Swedish MS registry - clinical support tool and scientific resource. Acta Neurol Scand 132:11-19

178. Longinetti E, Regodón Wallin A, Samuelsson K et al (2018) The Swedish motor neuron disease quality registry. Amyotroph Lateral Scler Front Degener 19:528-537

179. Asplund K, Hulter Åsberg K, Appelros P et al (2011) The Riks-stroke story: building a sustainable national register for quality assessment of stroke care. Int J Stroke 6:99-108

180. Shiffman S, Stone AA, Hufford MR (2008) Ecological momentary assessment. Annu Rev Clin Psychol 4:1-32

181. Hamer M, Sharma N, Batty GD (2018) Association of objectively measured physical activity with brain structure: UK Biobank study. J Intern Med 284:439-443

182. Lyall LM, Wyse CA, Graham N et al (2018) Association of disrupted circadian rhythmicity with mood disorders, subjective wellbeing, and cognitive function: a cross-sectional study of 91 105 participants from the UK Biobank. Lancet Psychiatry 5:507-514

183. Huang J, Zuber V, Matthews PM et al (2020) Sleep, major depressive disorder, and Alzheimer disease: a Mendelian randomization study. Neurology 95:e1963-e1970

184. Little MA (2021) Smartphones for remote symptom monitoring of Parkinson's disease. J Parkinsons Dis 11:S49-S53

185. Yim SJ, Lui LMW, Lee Y et al (2020) The utility of smartphone-based, ecological momentary assessment for depressive symptoms. J Affect Disord 274:602-609

186. Williamson JR, Telfer B, Mullany R et al (2021) Detecting Parkinson's disease from Wrist-Worn Accelerometry in the U.-K. Biokank. Sensors 21:2047

187. Chaibub Neto E, Bot BM, Perumal T et al (2016) Personalized hypothesis tests for detecting medication response in Parkinson disease patients using iPhone sensor data. Pac Symp Biocomput Pac Symp Biocomput 21:273-284

188. LeMoyne R, Mastroianni T, Whiting D et al (2019) Assessment of machine learning classification strategies for the differentiation of deep brain stimulation "on" and "off" status for Parkinson's disease using a smartphone as a wearable and wireless inertial sensor for quantified feedback. In: LeMoyne R, Mastroianni T, Whiting D et al (eds) Wearable and Wireless Systems for Healthcare II: movement disorder evaluation and deep brain stimulation systems. Springer, Singapore, pp 113-126

189. Behar J, Roebuck A, Shahid M et al (2015) SleepAp: an automated obstructive sleep apnoea screening application for smartphones. IEEE J Biomed Health Inform 19: 325-331

190. Shah RV, Grennan G, Zafar-Khan M et al (2021) Personalized machine learning of depressed mood using wearables. Transl Psychiatry 11:1-18

191. Vasanthakumar A, Davis JW, Idler K et al (2020) Harnessing peripheral DNA methylation differences in the Alzheimer's Disease
Neuroimaging Initiative (ADNI) to reveal novel biomarkers of disease. Clin Epigenetics 12:84

192. Cho H, Ahn M, Ahn S et al (2017) EEG datasets for motor imagery brain-computer interface. GigaScience 6:1–8

193. Cattan G, Rodrigues PLC, Congedo M (2018) EEG Alpha Waves Dataset. https://hal.archives-ouvertes.fr/hal-02086581

194. Abel JH, Badgeley MA, Meschede-Krasa B et al (2021) Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia. PLoS One 16:e0246165

195. Wakeman DG, Henson RN (2015) A multi-subject, multi-modal human neuroimaging dataset. Sci Data 2:150001

196. Blankertz B, Dornhege G, Krauledat M et al (2007) The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects. NeuroImage 37:539–550

197. Tangermann M, Müller K-R, Aertsen A et al (2012) Review of the BCI Competition IV. Front Neurosci 6:55

198. Mumtaz W, Xia L, Yasin MAM et al (2017) A wavelet-based technique to predict treatment outcome for Major Depressive Disorder. PLoS One 12:e0171409

199. Mohammadi MR, Khaleghi A, Nasrabadi AM et al (2016) EEG classification of ADHD and normal children using non-linear features and neural network. Biomed Eng Lett 6:66–73

200. Jaramillo-Gonzalez A, Wu S, Tonin A et al (2021) A dataset of EEG and EOG from an auditory EOG-based communication system for patients in locked-in state. Sci Data 8:8

201. Shah V, von Weltin E, Lopez S et al (2018) The temple university hospital seizure detection corpus. Front Neuroinformatics 12:83

202. Andrzejak RG, Schindler K, Rummel C (2012) Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys Rev E Stat Nonlinear Soft Matter Phys 86:046206

203. Goldberger AL, Amaral LA, Glass L et al (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101:E215–E220

204. Brunner C, Birbaumer N, Blankertz B et al (2015) BNCI Horizon 2020: towards a roadmap for the BCI community. Brain-Comput Interfaces 2:1–10

205. O’Callaghan VS, Hansell NK, Guo W et al (2021) Genetic and environmental influences

on sleep-wake behaviours in adolescence, vol 2. SLEEP Adv, p zpab018

206. de Zubicaray GI, Chiang M-C, McMahon KL et al (2008) Meeting the challenges of neuroimaging genetics. Brain Imaging Behav 2: 258–263

207. Relton CL, Gaunt T, McArdle W et al (2015) Data resource profile: Accessible Resource for Integrated Epigenomic Studies (ARIES). Int J Epidemiol 44:1181–1190

208. Bonder MJ, Luijk R, Zhernakova DV et al (2017) Disease variants alter transcription factor levels and methylation of their binding sites. Nat Genet 49:131–138

209. Huan T, Joehanes R, Song C et al (2019) Genome-wide identification of DNA methylation QTLs in whole blood highlights pathways for cardiovascular disease. Nat Commun 10:4267

210. Sarnowski C, Satizabal CL, DeCarli C et al (2018) Whole genome sequence analyses of brain imaging measures in the Framingham study. Neurology 90:e188–e196

211. Westerman K, Sebastiani P, Jacques P et al (2019) DNA methylation modules associate with incident cardiovascular disease and cumulative risk factor exposure. Clin Epigenetics 11:142

212. Nabais MF, Lin T, Benyamin B et al (2020) Significant out-of-sample classification from methylation profile scoring for amyotrophic lateral sclerosis. Npj Genomic Med 5:1–9

213. Vallerga CL, Zhang F, Fowdar J et al (2020) Analysis of DNA methylation associates the cystine–glutamate antiporter SLC7A11 with risk of Parkinson’s disease. Nat Commun 11: 1238

214. Sullivan PF (2010) The psychiatric GWAS consortium: big science comes to psychiatry. Neuron 68:182–186

215. Sullivan PF, Agrawal A, Bulik CM et al (2018) Psychiatric genomics: an update and an agenda. Am J Psychiatry 175:15–27

216. Mak ACY, White MJ, Eckalbar WL et al (2018) Whole-genome sequencing of pharmacogenetic drug response in racially diverse children with asthma. Am J Respir Crit Care Med 197:1552–1564

217. Kurniansyah N, Goodman MO, Kelly T, et al (2021) A multi-ethnic polygenic risk score is associated with hypertension prevalence and progression throughout adulthood, medRxiv

218. Hu Y, Stilp AM, McHugh CP et al (2021) Whole-genome sequencing association analysis of quantitative red blood cell phenotypes: the NHLBI TOPMed program. Am J Hum Genet 108:874–893
219. Wu K-D, Hsiao C-F, Ho L-T et al (2002) Clustering and heritability of insulin resistance in Chinese and Japanese hypertensive families: a Stanford-Asian Pacific Program in Hypertension and Insulin Resistance sibling study. Hypertens Res 25:529-536

220. Johnsen JM, Fletcher SN, Huston H et al (2017) Novel approach to genetic analysis and results in 3000 hemophilia patients enrolled in the my life, our future initiative. Blood Adv 1:824-834

221. Zhao X, Qiao D, Yang C et al (2020) Whole genome sequence analysis of pulmonary function and COPD in 19,996 multi-ethnic participants. Nat Commun 11:5182

222. GTEx Consortium (2020) The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369:1318-1330

223. Sletten TL, Rajaratnam SMW, Wright MJ et al (2013) Genetic and environmental contributions to sleep-wake behavior in 12-year-old twins. Sleep 36:1715-1722

224. Mitchell BL, Campos AI, Rentería ME et al (2019) Twenty-five and up (25Up) study: a new wave of the Brisbane Longitudinal Twin Study. Twin Res Hum Genet 22:154-163

225. Zietsch BP, Hansen JL, Hansell NK et al (2007) Common and specific genetic influences on EEG power bands delta, theta, alpha, and beta. Biol Psychol 75:154-164

226. Bot BM, Suver C, Neto EC et al (2016) The mPower study, Parkinson disease mobile data collected using ResearchKit. Sci Data 3: 160011

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# Part V

## Disorders
# Machine Learning for Alzheimer's Disease and Related Dementias

Marc Modat, David M. Cash, Liane Dos Santos Canas, Martina Bocchetta, and Sébastien Ourselin

## Abstract

Dementia denotes the condition that affects people suffering from cognitive and behavioral impairments due to brain damage. Common causes of dementia include Alzheimer's disease, vascular dementia, or frontotemporal dementia, among others. The onset of these pathologies often occurs at least a decade before any clinical symptoms are perceived. Several biomarkers have been developed to gain a better insight into disease progression, both in the prodromal and the symptomatic phases. Those markers are commonly derived from genetic information, biofluid, medical images, or clinical and cognitive assessments. Information is nowadays also captured using smart devices to further understand how patients are affected. In the last two to three decades, the research community has made a great effort to capture and share for research a large amount of data from many sources. As a result, many approaches using machine learning have been proposed in the scientific literature. Those include dedicated tools for data harmonization, extraction of biomarkers that act as disease progression proxy, classification tools, or creation of focused modeling tools that mimic and help predict disease progression. To date, however, very few methods have been translated to clinical care, and many challenges still need addressing.

**Key words** Dementia, Alzheimer's disease, Cognitive impairment, Machine learning, Data harmonization, Biomarkers, Imaging, Classification, Disease progression modeling

## 1 Introduction

Dementia is a progressive condition which affects over 55 million people worldwide, with nearly 10 million new cases every year [1]. The term “dementia” indicates not a single disease, but rather a spectrum of different conditions with different clinical phenotypes, which can be caused by a multitude of pathologies that cause changes in the structure and chemistry of the brain. While the most common cause of dementia-related symptoms is a neurodegenerative disease, other causes do exist (e.g., chronic inflammatory disease, alcoholism...). The exact pathological cascade of events which causes the development of symptoms is still unknown, but overall it
is thought that a combination of genetic and environmental factors results in the abnormal accumulation of misfolded, toxic proteins in the brain, which then triggers both chemical imbalance and neurological loss in the brain (a process called atrophy), ultimately leading to the hallmark clinical symptoms that eventually impair the daily functioning of affected individuals. An important distinction to make is between the concept of “dementia” as a collection of clinical syndromes and as qualitative and quantitative clinical expressions of the disease, and “disease” as the underlying pathological processes of the syndromes.

Thanks to the increased insight into disease pathophysiology, there has been a revision of the clinical diagnostic criteria, moving from considering the observable clinical signs and symptoms and implying a close and consistent correspondence between clinical symptoms and the underlying pathology, to including biomarkers of the underlying disease state in the clinical diagnosis. For example, the 1984 NINCDS-ADRDA<fnref n="1" /> criteria were the benchmark for a clinical diagnosis of Alzheimer’s disease, which was defined as “a progressive, dementing disorder, usually of middle or late life” [2]. These criteria were revised in 2011 [3], to include biomarkers to support the clinical diagnosis and to account for the “pre-dementia” stages and the slow pathological changes occurring over many years before the manifestation of clinical symptoms [4].

Despite different pathological origins, many forms of dementia can have similar symptoms, which typically include memory loss, language difficulties, disorientation, and behavioral changes. However, at an individual level, the symptoms can vary with regard to their nature, presentation, rate of progression, and severity. Such heterogeneity between and within forms of dementia is typically related to the area (or areas) of the brain affected by the underlying pathology and by the etiological cause of the disease itself.

**1.1 Alzheimer's Disease (AD)**

AD is the most common form of dementia, accounting for 60–65% of all cases. It typically presents in individuals aged 65 or older, with the initial and most prominent cognitive deficits being memory loss, with additional cognitive impairments in the language, visuospatial, and executive functions [3]. The distinguishing feature of AD is the buildup of amyloid-β plaques and neurofibrillary tangles of tau proteins. The amyloid plaques tend to be diffuse throughout the brain, while tau pathology tends to start in the mediotemporal lobe, and in particular in the hippocampus and entorhinal cortex, and spread to prefrontal and temporoparietal cortex in the moderate stages of the disease. There are numerous genetic factors that have different levels of risk and prevalence in the population. The

<footnote n="1">¹National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorders Association.</footnote>
greatest risk comes from the nearly fully penetrant autosomal dominant mutations in the amyloid precursor protein (APP), presenilin 1 (PSEN1), or presenilin 2 (PSEN2) genes. However, the prevalence of these mutations is extremely low, comprising less than 0.5% of all AD cases. The age at onset of autosomal dominant AD is relatively similar between generations [5] and within individual mutations [6], typically resulting in an early-onset form of AD (below the age of 60 years). The most prominent risk factor gene in terms of both hazard and prevalence is apolipoprotein E (APOE). Carriers of a single copy (roughly 25% of the population) of the ε4 allele are roughly two to three times more likely to develop AD [7], and they tend to have an earlier age of disease onset. Homozygotic ε4 carriers represent 2–3% of the general population, with a dose-dependent increase in risk. There have been some suggestions that carrying an ε2 form of APOE can infer some protection to individuals compared to the most common ε3 allele [7, 8].

Besides the typical presentations of AD in which episodic memory deficits are prominent, there are other variants with atypical presentations. Posterior cortical atrophy (PCA) [9] is characterized by visual and spatial impairments, but memory and language abilities are preserved in the early stages, with atrophy localized in the parietal and occipital lobe. Logopenic variant of the primary progressive aphasia (lvPPA), also called logopenic progressive aphasia (LPA), is characterized by impairments in the language domain (i.e., word-finding difficulty, impaired repetition of sentences and phrases) and atrophy in the left temporoparietal junction [10]. Despite presenting with different symptoms and neuroanatomical features, both PCA and lvPPA typically share the same forms of pathology, amyloid plaques, and neurofibrillary tangles, with the typical forms of AD. Besides these pathological hallmarks, accumulation of the TAR DNA-binding protein 43 (TDP-43) [11] is another form of pathology often observed in AD, particularly in cases with older onset of symptoms, resulting in increased rates of atrophy. The limbic-predominant age-related TDP-43 encephalopathy dementia (LATE) is a related condition found in older elderly adults (above 80 years of age), presenting with a slow progression of amnestic symptoms and hippocampal sclerosis.

## 1.2 Vascular Dementia (VaD)

As the second most common form of dementia (accounting for 10–15% of all dementia cases), VaD is an umbrella term for a number of syndromes due to a clear primary cause: the decreased blood flow due to damage in the blood supply (large or small vessels), which leads to brain tissue damage. The vascular origin is clearly seen on magnetic resonance imaging (MRI) as the presence of extensive periventricular white matter lesions, or multiple lacunes in the basal ganglia and/or white matter [12]. Symptoms tend to accumulate in a step-wise fashion, rather than gradually
worsening, and they greatly vary based on which vessel is involved: ranging from memory loss, and difficulties in executive functions, to language and motor impairments. Different syndromes include multi-infarct dementia (or vascular cognitive impairment), when a series of small strokes damage multiple areas of the brain, typically in the cortex; strategic infarct dementia, when symptoms are caused by a focal ischemic lesion; subcortical vascular dementia (or subcortical leukoencephalopathy), caused by occlusions in small vessels, resulting in multiple lacunes in the subcortical structures; and mixed dementia, when symptoms of both vascular dementia and AD are present.

Generally, strategic infarct dementia and multi-infarct dementia involving the cortex are due to occlusion in one of the major cerebral arteries, and therefore the insult in the brain usually results in a large area affected; they have a definable time of onset and specific deficits related to the region affected. When the occlusion involves small vessels, the dementia symptoms have a more insidious onset and less defined deficits in the executive function domain.

Risk factors typically include age, hypertension, high cholesterol, obesity, smoking, and other cardiovascular diseases (family history of stroke, heart disease, or diabetes). Mutations in the Notch3 gene have been associated to the cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), which is a genetic disorder showing recurrent stroke, resulting in lacunar infarcts [13].

**1.3 Frontotemporal Dementia (FTD)**

FTD describes a very heterogeneous group of neurodegenerative disorders with multiple genetic and pathological causes. However, there is sufficient overlap in terms of both clinical (behavioral and/or language symptoms) and anatomical presentation (frontal and temporal lobe atrophy and hypometabolism) that the conditions are commonly considered together as one group. While representing 5-10% of all dementia cases, the FTD disorders constitute a more common cause of early onset dementia, approximately equal in frequency to AD in people under the age of 65. The only confirmed risk factors are genetic, and about a 30-50% of cases are due to an autosomal dominant mutation, primarily found in the microtubule-associated protein tau (MAPT), progranulin (GRN), or chromosome 9 open reading frame 72 (C9orf72) genes [14]. The age at onset is extremely variable within and between genetic forms, including within families, and therefore hard to predict [15].

Clinically, behavioral variant FTD (bvFTD) is the most common presentation, with impaired social conduct and personality changes, often misdiagnosed as psychiatric illness at the onset [16]. It could be caused by tau, TDP-43, or fused-in-sarcoma pathology [17, 18] and associated with extremely variable pattern
of atrophy between patients, with a predominant involvement of
the frontal and temporal cortex (often asymmetrical), but also
insula, anterior cingulate, and subcortical structures [19–21].

Less frequently, patients present with progressive decline in
speech and language functions, a collection of disorders and var-
iants referred to as primary progressive aphasia, PPA. There are
multiple variants of PPA, with different language deficits and
brain regions involved. These are semantic variant (svPPA), with a
breakdown of semantic memory, and associated atrophy in the left
antero-inferior temporal lobe [22, 23]; non-fluent variant
(nfvPPA), characterized by agrammatism and speech apraxia, and
associated atrophy in the left inferior frontal, superior temporal,
and insular cortex [22]; and lvPPA, though as mentioned previ-
ously is more often linked with AD pathology [10].

Around 15% of people on the FTD spectrum can also develop
motor features consistent with either amyotrophic lateral sclerosis
(ALS) (or motor neurone disease, MND) or parkinsonism (includ-
ing progressive supranuclear palsy, PSP, or corticobasal syndrome,
CBS) [24].

There is a distinct differential brain involvement across the
genetic forms of FTD, evident up to 15 years before the estimated
symptoms onset [25, 26]: MAPT mutations cause focal symmetric
atrophy in the anterior temporal and orbitofrontal cortex, includ-
ing hippocampus and amygdala; GRN mutations usually cause
asymmetric atrophy in the temporal, inferior frontal, and inferior
parietal lobes and striatum; while C9orf72 repeated expansions
showed wider symmetric atrophy, predominantly involving the
dorsolateral and medial frontal and orbitofrontal cortex, as well as
the thalamus and cerebellum [25, 27, 28]. Despite these common
patterns, there is still large variability even within the same genetic
group, potentially due to the specific mutations, clinical presenta-
tions, or genetic and environmental factors [29, 30].

1.4 Dementia with Lewy Bodies (DLB)

Around 10–15% of dementia cases have a diagnosis of DLB [31]. Symptoms tend to have an insidious onset, usually at the age of 65 years or older, and disease duration has an average of 5 to 8 years from diagnosis, but it can range from 2 to 20 years. Symptoms change greatly from person to person but typically include fluctuating cognition, pronounced alterations in attention, alertness and executive functions, visual hallucinations, and motor features of parkinsonism. Early signs also include rapid eye movement (REM) sleep behavior disorder, while memory and hippocampal volume are relatively preserved in the initial stages, but they become impaired later during the course of the disease. Alongside relatively preserved mediotemporal lobe volumes, typical biomarkers are reduced dopamine transporter (DAT) uptake in the basal ganglia on single-photon emission computed tomography (SPECT) or positron emission tomography (PET) imaging, and polysomnographic recordings, showing REM sleep without atonia.
DLB is considered a sporadic disease; however, mutations in genes encoding α-synuclein (SNCA) and β-synuclein (SNCB) proteins have been associated with DLB [32].

Pathologically, DLB is characterized by the presence of α-synuclein proteins which abnormally aggregate in the brain to form Lewy bodies.

Lewy bodies are also found in the brain of individuals affected by Parkinson's disease and Parkinson's disease dementia (PDD). DLB and PDD are often difficult to distinguish, and the “1-year rule” is used for differential diagnosis: if the parkinsonian motor symptoms are experienced for a year or more before the onset of the cognitive impairments, then the condition of PDD is diagnosed, while if the cognitive problems start before or within 1 year after the movement difficulties, then a diagnosis of DLB is likely to be given.

**Box 1: Different Diseases Causing Dementia**

Dementia is not a disease but a spectrum of disorders defined by different pathologies, the most common being the following.

* Alzheimer's disease is the most prevalent, with hallmark pathologies of amyloid-β and neurofibrillary tau tangles. Memory is the most common symptom, but there are visual, language, and behavioral variants.

* Vascular dementia is caused by various types of vascular insults.

* Frontotemporal dementia is more common in those with younger ages of onset and is more associated with behavioral and language forms.

* Dementia with Lewy bodies (DLB) shares pathology with parkinsonian disorders and often has visual fluctuations and hallucinations as symptoms.

## 2 Features and Markers of Dementia

As previously mentioned, the most prevalent forms of dementia are multi-factorial processes that typically occur over a very long time period, from the silent buildup of pathology through to the onset and progression of the clinical syndrome. As such, there will be numerous types of assessment that can help identify individuals at risk, underlying pathology burden, and severity of the disease. These range from classic clinical workups, cognitive assessments of memory and other brain functions, fluid-based biomarkers, and
medical imaging-based assessments. The utility and sensitivity of
these investigations will highly depend on the stage of the disease
that the patient is experiencing.

## 2.1 Genetic Markers

Despite the numerous forms of pathology that can ultimately lead to dementia, many genetic risk factors have been identified for AD and related disorders; the overall heritability of AD has been characterized to be between 60 and 80% [33]. This risk of heritability however is spread out over a wide range of locations that vary in terms of prevalence and impact. Identifying genetic risk factors and the associated pathways that these genes are involved in have led to a better understanding of various forms of dementia [34].

As mentioned in Subheading 1, the genetic variants with the strongest penetrance are the autosomal dominant forms of dementia. What these rare autosomal dominant forms provide is an opportunity to study a “purer” form of dementia, as the age of disease onset tends to be in the 30s through 50s, when there should be a far lower likelihood of commodities. It also provides a chance to study pre-symptomatic changes in individuals who are nearly certain to become affected by the disease. Thus, these cohorts are an ideal population for clinical trials of new therapies, in part to prove that the target engagement is successful and whether it provides any evidence that supports the underlying hypothesis around the disease start and spread.

Outside of the autosomal dominant mutations, the gene most linked with risk for AD is *APOE*. There is not an equivalent gene in terms of risk and prevalence to *APOE* yet discovered for other forms of dementia, in part because these forms of dementia are rarer and it is thus more difficult to include the number of subjects needed for a well-powered GWAS (genome-wide association study). However, there are some suggestions, such as the *TREM2* variant in FTD [35].

Rather than trying to identify single target genes and their associated risks, many researchers have looked to generate a polygenic risk score, i.e., a sum of the risks conferred by each associated variant across the genome. Polygenic risk scores (PRS) have been developed for multiple diseases to better account for the amalgamated risk that the entire genetic profile provides [36]. For AD, however, *APOE* confers a far greater risk to individuals, with the PRS scores able to slightly improve predictive accuracy and explain additional risks beyond *APOE* [37, 38].

## 2.2 Clinical and Cognitive Assessment

Given that various forms of dementia have historically been defined by their clinical phenotype, and that clinical and cognitive assessments tend to be the cheapest and most widely available, they often are paramount in terms of initial diagnostic workup of an individual, as well as their subsequent patient management.
Clinical vital signs, such as blood pressure [39, 40] and body mass index (BMI) [41], may suggest causes of cognitive impairment other than a neurodegenerative disorder or indications of an at-risk profile that would result in a more aggressive disease. The Clinical Dementia Rating (CDR) [42] is a semi-structured interview that examines several aspects of physical and mental well-being which is summarized under six subdomains. For each subdomain, a score of 0 (no impairment), 0.5 (mild/questionable impairment), 1, 2, or 3 is given. Both the sum of these subdomains, referred to as the CDR Sum of Boxes (CDR-SB), and a global summary score are often used, with CDR-SB now commonly used as a primary endpoint in trials. Other clinical workups may help identify non-memory symptoms that would not be picked up via cognitive assessments, such as anxiety, depression, and quality of daily activities.

Cognitive assessments look at numerous domains of brain function, including executive function, language, visuospatial functions, and behavior. However, given that memory is the most common primary complaint from individuals with AD, assessments of various aspects of an individual's memory is one of the most important and typically included in both clinical and research settings. Numerous tests have been developed and validated for use in the clinic as well, and they often serve as a primary outcome measure in clinical trials of subjects with mild to moderate AD. Standard clinical and cognitive assessments include the Mini Mental State Exam (MMSE) [43], the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-COG) [44], and the Montreal Cognitive Assessment (MoCA) [45].

While cheap and readily available, these assessments do come with some disadvantages. These are often pencil and paper tests which are administered and scored by a trained rater. As such, there is a level of subjectivity in many of these assessments that tend to result in high variability. Often these tests repeat the same questions and tasks over and over again, which leads to practice effects. It also is often difficult to build these assessments such that their dynamic range can simultaneously cover both the early subtle signs of dementia pre-symptomatically and the full decline once the individuals have experienced symptoms. This results in some tests having substantial ceiling effects (i.e., being easy enough that there is limited distinction between healthy individuals and those experiencing the subtle initial symptoms) and floor effects (i.e., the tests are so difficult that many with a cognitive impairment cannot perform them). There are also the cultural and lingual artifacts that may produce bias when translating one of these tests over from one language to another. As a result, there is a trend to formulate cognitive assessments in a more objective, computational format to reduce issues around subjectivity, language differences, and learning effects. They may reduce the variability compared to
standard paper and pen tests, which may be of key benefit in
assessing therapeutic effects in clinical trials [46, 47]. This includes
multiple trials of the test run during a single assessment and collec-
tion of dense information about the task in addition to more
summary metrics as number of items correct or mean reaction
time. The rich set of detailed, repeated measures is ideal for further
exploration with machine learning algorithms.

## 2.3 Biofluid Assessments

The most widely studied fluid-based biomarkers in AD and related
disorders come from samples of cerebrospinal fluid extracted from
an individual's spine. Measures of primary AD-related pathology
(Aβ₁⁻₄₂, tau, p-tau) can be obtained from these samples, as well as
peripheral information on downstream mechanisms, such as neu-
roinflammation, synaptic dysfunction, and neuronal injury
[48]. Fluid-based biomarkers are very effective in terms of being a
“state” biomarker, i.e., whether an individual has a normal or
abnormal level. They are often much cheaper than imaging assess-
ments in providing this status and thus are more likely to be used
for screening of individuals at risk for dementia. At the same time,
their ability to track change in the disease over time is currently
limited. They are in general more noisy measurements, likely due to
a number of factors including consistency of extraction, storage,
and analysis methods [49]. Even when these have been held
extremely consistent, their variability is still much higher in terms
of measurement of change over time compared to cognitive and
imaging measures [50]. Despite the procedure being very safe and
continuing to improve, there is still a set of individuals who will not
wish to participate in studies involving these assessments. A far less
invasive and cheaper procedure is to extract similar measures from
the plasma. While plasma-based biomarkers have been actively
pursued for a lengthy time, it is only very recently that they have
produced the level of accuracy and precision needed to compete in
terms of performance to other established measurements
[51, 52]. There have been plasma-based assessments of amyloid-
β, different tau isoforms, and nonspecific markers of neurodegen-
eration (such as levels of the neurofilament light chain, NfL) which
show promise for detecting changes in the preclinical stage of
AD [53].

## 2.4 Imaging Dementia

The primary use of brain imaging in clinical settings is to exclude
non-neurodegenerative causes, such as normal pressure hydroceph-
alus, tumors, and chronic hemorrhages, together with absence of
atrophy, all features that can be visualized on T1-weighted MRI or
computerized tomography (CT) scans. Nevertheless, three-
dimensional tomographic medical imaging modalities, particularly
PET and MR imaging, provide high-precision measurements of
spatiotemporal patterns of disease burden that have proven
extremely valuable for research and also currently contribute to
the positive diagnosis.
These modalities have been employed primarily in clinical research settings, where longer advanced imaging protocols and novel radiotracers can be implemented. Due to costs, time, and availability of imaging resources, they have been slow to translate to the clinical setting itself, but they are beginning to make impact there as well.

### 2.4.1 Imaging Primary Pathology

Radiotracers that preferentially bind to the primary pathologies associated with AD allow the detection and tracking of the slow progressive buildup of the amyloid plaques and neurofibrillary tangles, which can occur decades before the onset of symptoms [54, 55]. The original amyloid tracer was ¹¹C Pittsburgh Compound B [PIB], and it identified individuals who were amyloid positive but showed no symptoms [56]. These individuals tended to progress to mild cognitive impairment and subsequently AD at much higher rates than those who were amyloid negative [57]. Since the introduction of PIB, there have been numerous ¹⁸F tracers which have been developed and approved for use in humans [58, 59]. As ¹⁸F-based tracers have a longer half-life than ¹¹C, it has enabled a much larger group of research centers access to this technology. Tracers specifically related to tau-based pathology have come much later. The most widely used has been flortaucipir, with second-generation tracers now available that have overcome some of the challenges of imaging with the early tau tracers [60]. Findings from tau PET studies suggest that the landmark postmortem staging of tau pathology seeding and spread according to Braak [61] is the most common spatiotemporal pattern observed in individuals [62, 63]. However, other subtypes of different distributions have been observed [64, 65]. Elevated tau PET uptake often happens much later than elevation of amyloid PET [66], especially in autosomal dominant cases of AD [67, 68]. Tau PET is also far more strongly linked regionally with subsequent evidence of neurodegeneration, while amyloid PET tends to elevate in a similar manner across multiple regions at the same time [69]. Examples of amyloid and tau PET images from both patients with various forms of AD and controls can be seen in Fig. 1. Despite many forms of FTD being some form of tauopathy, the available PET tracers have been primarily optimized to the specific form of tau pathology that is primarily observed in AD, namely, the mix of 3-Repeat/4-Repeat species observed in neurofibrillary tangles. Since there are many different forms of tau pathology within FTD, the level of tau PET uptake in these individuals is varied [70–72]. In other forms of dementia, amyloid PET can be used to rule out AD pathology if an individual with symptoms has an amyloid negative scan<fnref n="2" /> and tau

<footnote n="2">² https://www.accessdata.fda.gov/drugsatfda_docs/nda/2012/202008_Florbetapir_Orig1s000TOC.cfm.</footnote>
<figure><img src="image_270.png" /><figcaption>**Fig. 1** Example amyloid (left column) and tau (right column) PET scans from controls and patients with different forms of dementia (AD and PCA). PET images are presented as standardized uptake value ratio (SUVR) images, where the amyloid PET have been normalized to subcortical WM, an area of high nonspecific binding (mainly in myelin) for both healthy controls and patients with Alzheimer's disease. The tau PET images have been normalized to inferior cerebellar gray matter. While amyloid PET tends to show diffuse cortical uptake across the brain, tau PET tends to be more focal in the areas where neurodegeneration is occurring</figcaption></figure>

PET has now been approved to estimate the density and distribution of neurofibrillary tangles in individuals.<fnref n="3" />

### 2.4.2 Imaging Neurodegeneration

As the pathology continues to build over time during the pre-symptomatic period, it often leads to an insidious process of neuronal dysfunction and ultimately to degeneration in all forms of dementia. This is evidenced by atrophy visible in the structural T1-weighted MRI scans (Fig. 2) and decreased metabolism on fluorodeoxyglucose (FDG)-PET (Fig. 3). These forms of imaging start to be altered around the time when tau pathology is present and then provide close tracking with disease severity as symptoms become apparent. These modalities often tend to be the most widely available of imaging techniques within research settings, with MRI tending to be less costly than PET. Structural imaging, due to its high resolution (1 mm), signal-to-noise ratio, and contrast between tissues, lends itself to high-precision measurements of change over time. The spatial pattern of the neurodegeneration, whether it is hypometabolism or atrophy, can provide useful information for differential diagnosis between different dementia [73–75]. Parallel to neurodegeneration, changes in the white matter of individuals with dementia also show evidence of disease-related

<footnote n="3">³ https://www.accessdata.fda.gov/drugsatfda_docs/label/2020/212123s000lbl.pdf.</footnote>
<figure><img src="image_271.png" /><figcaption>**Fig. 2** Example T1-weighted MRI scans from a healthy control and individuals with different variants of Alzheimer's disease. For each variant, atrophy can be observed in areas of cortical GM which are known to cause the cognitive deficits typically linked to the clinical phenotype (see white arrows)</figcaption></figure>

insult. White matter lesions, suggestive of damage due to vascular insult/insufficiency or demyelination, are visible as hypointensities on T1-weighted imaging, while they present as hyperintense on other forms of structural MRI imaging known as T2-weighted or fluid attenuated inversion recovery (FLAIR) (Fig. 4). Other forms of changes in the WM observed in dementia include microbleeds,
<figure><img src="image_272.png" /><figcaption>**Fig. 3** Example FDG PET scans from a healthy control and multiple variants of Alzheimer's disease. For each variant, hypometabolism (denoted by cooler colors) observed in areas of cortical GM which are known to cause the cognitive deficits typically linked to the clinical phenotype. Red arrows have been added to highlight focal areas of hypometabolism in each variant</figcaption></figure>

lacunes, and perivascular spaces [76]. Whether these are separate processes or linked to the underlying disease cascade is actively being researched [77, 78]. There is evidence that they contribute equally and additively in individuals where no obvious impairment is present. In addition, individuals with heavy white matter burden tend to have more aggressive forms of the disease than those with limited or no signal of change in the white matter.

Advanced forms of MRI acquisitions are leading to better understanding of the diseases at different scales, from inferences made of the underlying tissue microstructure to how these forms of dementia disrupt the natural networks of the brain. Diffusion-
<figure><img src="image_273.png" /><figcaption>**Fig. 4** Example T1-weighted (left column) and FLAIR (right column) images of minimal, moderate, and prevalent lesions in the white matter, often referred to as white matter hyperintensities (WMH), as they appear bright on FLAIR acquisitions. These lesions also often show up hypointense on T1-weighted scans, but FLAIR tends to be more sensitive and provide more contrast, particularly around deep gray matter areas</figcaption></figure>

weighted imaging (DWI) provides measurements of both the magnitude and direction of the movement of water within a voxel. In white matter, the tissue consists of long fiber bundles that restrict the motion primarily along the direction of the fibers. In the cases of dementia, the integrity of these white matter bundles, whether through demyelination or some other form of neuronal dysfunction, tends to be less restrictive of water crossing boundaries, suggesting loss of microstructural integrity [79–82].
On a larger scale, connected brain networks can be identified with these techniques, either by tracing the diffusion profiles from one gray matter region to another using diffusion weighted imaging or by observing correlating patterns of deoxygenation of hemoglobin in the brain regions using functional MRI (fMRI) as a proxy for brain activity. These networks can become disrupted, primarily in regard to within network communication [83]. The direction of this disruption may depend on the stage of the disease. There is more evidence that the later stages of disease cause reduced connectivity and a disconnection from key seed regions to other areas. However, there may be an earlier stage where subjects compensate for increased pathology burden with hyperconnectivity [84, 85].

**2.5 Advances in**
**Novel Biomarkers**

Novel assessments and biomarkers for all forms of dementia are highly active areas of research, from new fluid-based biomarkers to better computerized psychometric batteries to new imaging tracers and MR sequences to track additional aspects of the disease. While big data for machine learning in dementia has often meant assessing a large number of individuals, each with a small handful of measures, there are new forms of data collection that provide a rich set of data on single individuals. This could include not only the new epigenetic markers like single-cell RNA sequencing [86, 87] but also wearable devices that produce lots of data about individuals' daily activities and spatial navigation.

**3 Challenges for Machine Learning**

Researchers are nowadays focusing on two main aspects when it comes to AD and other forms of dementia. First, they aim to gain a better understanding of the disease process, including why individuals with similar underlying primary pathology result in different areas of the brain being affected, and thus have different clinical presentations. This is currently being investigated using many approaches ranging from molecular biology studies in wet labs to large epidemiological studies involving several thousands of participants. Second, they are developing tools to better assist clinicians with treatment management at the level of an individual. This includes, for example, the design of effective computational pipelines dedicated to patient diagnosis and prognosis.

Any research relying on machine learning methodologies must address the specific challenges presented by the disease. The first challenge comes from the large variability of diseases that makes it difficult to differentiate them, especially in the early stages. Additionally, mixed dementia, where patients have several diseases, is quite common. Indeed, the AD phenotype often coexists with vascular dementia or DLB. To partially address this issue, data from individuals with autosomal dominant forms of these diseases
are collected by international multicenter studies, as they often have earlier onset and usually “purer” forms of the disease. The Dominantly Inherited Alzheimer Network (DIAN)<fnref n="4" /> and the Genetic Frontotemporal Dementia Initiative (GENFI)<fnref n="5" /> are two studies collecting data from patients and relatives with familial AD and genetic FTD, respectively. While these studies have many benefits (see Subheading 2.1), there can be substantial differences between the genetic and the more widespread sporadic forms of these diseases, the most notable being younger disease onset and fewer comorbidities. Thus, there is a crucial need for ML methods that can disentangle the full complexity of sporadic forms of dementia. Finally, the variability comes not only from the presentation of the disease and comorbidities but also from the age at which the disease starts and the pace at which it progresses.

The second challenge is related to the duration of the disease, which often spans two decades and includes a yearslong prodromal phase. This makes it difficult to acquire data from individual patients that cover the full disease duration, especially as it is extremely challenging to identify with certainty who will develop the disease in the general population. While the previously mentioned studies of autosomal dominant forms of dementia can address this issue, it is not yet clear how much their findings can be translated to the far more common sporadic forms of these diseases. The Alzheimer's Disease Neuroimaging Initiative (ADNI)<fnref n="6" /> is a large multicenter study that focuses on the acquisition of data from elderly individuals, consisting of those that are cognitively normal, those labeled as having mild cognitive impairment (MCI), and individuals diagnosed with probable AD [88]. These individuals are followed over several years, providing extremely valuable information for researchers. UK Biobank<fnref n="7" /> is another relevant initiative, as it aims to acquire in-depth phenotyping of half a million UK participants. Due to the large prevalence of AD and other related diseases in the population, it is anticipated that many individuals are in the pre-symptomatic phase of the diseases.

As aforementioned in the previous section, many markers of dementia are used to track the diseases, some being more relevant than others at specific times in the illness progression. For example, while amyloid PET-derived imaging biomarkers are valuable in the early stages of the disease, they are unable to quantify the progression toward the final stages. On the opposite end, clinical assessments, while being ineffective prior to symptomatic onset, enable monitoring of symptomatic evolution over time. This is a challenge

<footnote n="4">⁴ https://dian.wustl.edu/.</footnote>

<footnote n="5">⁵ https://www.genfi.org/.</footnote>

<footnote n="6">⁶ https://adni.loni.usc.edu.</footnote>

<footnote n="7">⁷ https://www.ukbiobank.ac.uk.</footnote>
as it requires to use the most relevant marker for the correct stage. In practice, this often leads to the use of complex models to handle large amount of multimodal data (imaging, clinical, genetic, demographic, ...). Additionally, each marker often suffers from its own variability, which can be intra-patient, inter-patient, and inter-center. For example, clinical assessments potentially differ based on the rater. MRI acquisitions will differ due to pulse sequence properties or scanner characteristics, as well as normal physiological variance such as hydration and caffeine intake, among others.

# 4 Machine Learning Developments

Machine learning has been used in multiple applications related to Alzheimer's disease and related dementia. As mentioned in the above section, dementia research has leveraged worldwide, multi-center studies in order to obtain enough data to characterize early changes and heterogeneity within the disease process. This has, in turn, propelled the development of dementia-focused ML applications. In this section, we review four main tasks in which extensive ML research has been performed.

*Biomarker extraction* from imaging data was originally done with manual assessments, which were time-consuming and subject to high inter-rater variability. Machine learning approaches that recreate these measurements with reduced time and variability have been a large effort that has served not only ADRD but many neurological disorders and neurodegenerative diseases. Given the numerous measurements that are now available on the datasets and the different aspects of the phenotype that they reflect, *disease classification and prediction* techniques have been used to identify consistent multivariate signatures between both healthy and disease groups, for differential diagnosis and for predicting the future state of patients. *Disease progression models* have been developed to determine the ordering of how markers go from normal to abnormal and to reconstruct the trajectories followed by these biomarkers, leading to advances in disease understanding and prognostication. *Data harmonization* to characterize variation caused by changes in scanning equipment and software across sites must be accounted for in order to obtain more accurate estimates of the biological changes.

## 4.1 Machine Learning-Derived Biomarkers

The largest area of machine learning research in relation to Alzheimer's disease and related disorders is to extract measurements from the different datasets. These biomarkers tend to reflect an aspect of function or integrity of the individual that will gain a better understanding of a disease. Changes in these biomarkers from normal values to abnormal provide a proxy for disease progression. Note that most of this research can usually be useful for other brain disorders.
Medical imaging provides valuable insights into an individual's brain and is key to noninvasively assessing phenotypes due to the neurodegeneration process. Structural imaging, especially T1-weighted MRI, is commonly acquired in neurodegenerative studies as it enables quantifying key information such as atrophy in particular brain regions and thinning of the cortex or localized brain lesions. These features are often used as imaging biomarkers of disease progression, and many approaches relying on machine learning have been developed to extract them in the last three decades.

Brain segmentation and parcellation relate respectively to the classification of voxel into tissue types (e.g., gray matter, white matter, CSF) and the delineation of identified brain regions (e.g., whole brain, hippocampus, ...).

The most popular open-source implementation (FSL<sup>8</sup> [89], SPM<sup>9</sup> [90]) for tissue segmentation relied on Gaussian mixture model optimized using expectation maximization [91, 92]. They enable to classify voxels based on their intensity but as well to accommodate with intensity inhomogeneity as well as noise via explicit modeling of the intensity bias field [91] and the use of Markov random field regularization, respectively [92]. With the advance of deep learning in the last decade, many techniques using convolutional neural networks have been proposed. Kumar et al. presented a U-Net based approach achieving close to 90% average Dice score coefficient on the segmentation for gray matter, white matter, and CSF on their dataset [93].

Classical approaches for brain parcellation rely on the concept of segmentation propagation and label fusion. In short, a set of template images, consisting of original images and associated labels, are aligned through medical image registration to a new image. The template labels are then warped into the shape of the new image's brain and fused into a consensus segmentation. Popular approaches are HAMMER<sup>10</sup> [94], FreeSurfer<sup>11</sup> [95], or Geodesic Information Flows (GIF) [96], among others. Using neural networks, de Brébisson et al. proposed a dedicated architecture concurrently using 2D and 3D patches and used iteratively to refine their results [97]. More recently, FastSurfer [98] was introduced, which is a deep learning-based method that aims to reproduce FreeSurfer's results while considerably reducing processing time. While it is rapidly attracting users, further validation is needed to ensure that

<footnote n="8"><sup>8</sup> https://fsl.fmrib.ox.ac.uk/fsl/fslwiki.</footnote>

<footnote n="9"><sup>9</sup> https://www.fil.ion.ucl.ac.uk/spm/.</footnote>

<footnote n="10"><sup>10</sup> https://www.nitrc.org/projects/hammer/.</footnote>

<footnote n="11"><sup>11</sup> https://surfer.nmr.mgh.harvard.edu/.</footnote>
it is as robust as FreeSurfer across all disease types and severities. Finally, a current trend is to train from vast amounts of synthetic data with the hope of easing generalization to other sequences and/or resolutions. This is, for example, the approach taken in SynthSeg [99].

Other approaches have been proposed to segment individual regions of the brain that are particularly relevant to the study of dementia. For example, hippocampal segmentation has been an active area of research, with many studies including extensive validation in AD [100–104]. In the past years, the focus has turned to the segmentation of hippocampal subfields rather than the whole hippocampus [105, 106]. In particular, Manjon et al. used a U-Net approach combined with a deep supervision approach for training where their loss function optimizes segmentation accuracy at different image scales [107].

The identification of abnormalities (in particular those of vascular origin) is also a key step in the study of dementia and particularly for differential diagnosis. These abnormalities include hyper- or hypo-intensity lesions, micro-bleeds, perivascular spaces, or lacunes. Various approaches to segment T2/FLAIR white matter hyperintensities have been proposed (see [108] for a comparison of seven of them), while there have been fewer works on micro-bleeds or lacunes. Sudre et al. proposed a Gaussian mixture model approach with automated detection of classes number to accurately segment brain tissue classes, as well as abnormalities [109]. More recently, deep learning approaches have also been developed. For example, Boutinaud et al. used a U-Net, which parameters were pre-trained using an autoencoder, to automatically segment perivascular spaces from T1-weighted MRI scans [110]. Wu et al. also used a U-Net architecture for segmentation hyperintensities from T1-weighted and FLAIR MR images [111].

## 4.2 Disease Classification and Prediction

Machine learning is a powerful tool when it comes to disease diagnosis and prognosis. As a result, many approaches have been proposed for disease classifications, to identify the current stage of a disease within an individual or to predict their future state (e.g., transition to dementia in patients with MCI).

For over a decade, dozens (if not hundreds) of papers have proposed classification techniques to distinguish patients diagnosed with AD versus age-matched controls (e.g., [112–119]) or patients suffering from mild cognitive impairment who are staying stable in time versus those who will progress to a diagnosis of AD (e.g., [115, 117–124]). The latter task can contribute to prognosis which, when it comes to dementia-related diseases, often consists of classifying patients who are likely going to convert from mild cognitive impairment to symptomatic AD within a given time
interval, typically 3 years,<fnref n="12" /> from those who are going to stay stable. Several literature reviews on dementia classification and prediction have been published [125–130]. In particular, recent reviews by Jo et al. [126] and Ansart et al. [130] have covered the topic of prognosis.

The proposed methodologies have been extremely varied not only in terms of ML algorithms but also of input modalities and extracted features. Initial ML methods often used support vector machines (e.g., [112, 114]), while subsequent works used more recent techniques such as random forest [117] and Gaussian processes [121, 131]. Many recent works have used deep learning classification techniques [127, 129]. However, so far, deep learning has not outperformed classical ML for AD classification and prediction [129, 130, 132]. Furthermore, a review on convolutional neural networks for AD diagnosis from T1-weighted MRI [129] has identified that more than half of these deep learning studies may have been contaminated by data leakage, which is particularly worrisome. In terms of features, some studies use the whole brain as input, either using directly the raw image or computing voxel-wise (or vertex-wise when considering the cortical surface) measures [125]. Others parcellate the brain into regions of interest, within which features are computed. In particular, researchers have combined segmentation approaches with disease classification techniques. This has the advantage of limiting the search space of the machine learning approach via the use of prior knowledge. For example, Coupe et al. used a patch-based approach to classify voxels from the hippocampus as it is known to be a vulnerable structure in patients with dementia [116]. The input modalities have also been extremely varied. While earlier works often focused on T1-weighted MRI only [112–114], subsequent studies have included other imaging modalities, in particular FDG-PET [121, 123] Other researchers have combined tailored features extracted from images and non-images features such as fluid biomarkers [121], cognitive tests [124, 133], APOE genotype [121], or genome-wide genotyping data [134, 135]. Through deep learning, researchers are avoiding the need to craft features and can use traditional deep learning approaches to directly infer disease status from raw data: imaging or non-imaging. However, to date, there has been less interest in this area than in biomarker extraction, and thus fewer innovative solutions have been proposed. Popular architectures include conventional neural network, autoencoder, and recurrent neural networks, among others [127]. Training strategies mostly relied on supervised approaches, where some groups have relied on pre-trained networks to compensate for relatively

<footnote n="12">¹² While 3 years is certainly a relevant time frame to provide useful information to patients and relatives, it is likely that the focus of research on such time frame was largely driven by the typical follow-up which is available for most patients in large publicly available databases such as ADNI.</footnote>
small training databases. However, as mentioned above, it has not
been demonstrated so far that deep learning outperforms conven-
tional ML for dementia classification.

A large portion of the literature has relied on the ADNI data-
base. While having such a rich and large database has propelled the
development of algorithms, one can wonder if the proposed
method will generalize well to other datasets, a problem which
has less often been addressed. Another worrisome aspect is that
many of the papers based on ADNI are difficult to reproduce
because they lack a description of the subjects used and because
the code is not often available [136]. They are also difficult to
compare. In particular, since different preprocessing tools are
used, it is often difficult to know whether improvements in perfor-
mance come from the innovation in ML or from the preprocessing.
Standardized datasets have been created in ADNI [137] to address
the former issue. Whenever possible, one of these datasets should
be used, or authors should provide a list of subjects/scans included
in the study for the purpose of reproducibility.

Researchers have organized challenges that can provide an
objective comparison of algorithms. One can cite in particular the
CADDementia challenge for classification [138] and the TAD-
POLE challenge for prognosis [132]. Such challenges provide
very important and useful information on the respective merits of
different approaches. However, more challenges, in particular
using more diverse data, would be needed.

To a lesser extent, differential diagnosis has also been
addressed. Earlier works focused on classifying patients diagnosed
with AD versus patients diagnosed with frontotemporal dementia
[112, 139]. More recent studies have considered classifying
between various types of dementia [140, 141].

Overall, there has been considerable research in AD classification and prediction. The easiest task, which is the classification of patients diagnosed with AD versus normal controls, can be considered as solved with accuracy typically above 90%, at least when using data which quality is comparable to that of research studies. However, this task has little clinical utility. For the more interesting task of prediction progression to AD in MCI patients, the performance has increased over the years, with AUC now above 80% [130]. Interestingly, it has been shown that studies which include cognitive tests and FDG-PET tend to have better results than those using T1-weighted MRI only [130]. It is particularly noteworthy that cognitive tests tend to be overlooked, given that they are relatively cheap to perform and widely available. This probably reflects the fact that much of these works have arisen in the medical image computing community. Finally, other tasks are still short of becoming clinically useful, such as the development of a multi-pathology differential diagnostic classifier. This is possibly due to the lack of tailored methodologies leveraging all available information. This will be further discussed in the conclusions of this chapter.
## 4.3 Disease Progression Modeling

Following the release of a hypothetical model of disease progression by Jack et al. [142], researchers have tried to create data-driven models that would accurately describe the various components of the underlying disease process. These so-called *disease progression models* have been developed to understand the ordering of phenotypic events (such as a marker becoming abnormal), to model the variability of ordering or trajectories within a population and to be able to distinguish between different disease subtypes. The reader may refer to Chap. 17 for a detailed description of the methodology underlying data-driven disease progression models. Event-based models (EBM) [143] have been created to learn from a curated population and across different modalities the different hidden states of a disease. EBMs are able to order all input features from the one that will most likely become abnormal first to the one that becomes abnormal last [143]. The application of EBM in dementia has been very successful, including studies in familial AD [143, 144], sporadic AD [145, 146], posterior cortical atrophy [147], and genetic FTD [148]. An extended approach called Sub-type and Stage Inference (SuStaIn) was developed by Young et al., which incorporates clustering to characterize disease subtypes. In particular, it allowed uncovering the symptomatic profiles of different variants of genetic FTD as well as AD subtypes [29]. EBMs have the advantage of being applicable to cross-sectional data but only provide an ordering of events with no temporal scale as to when they become abnormal. In most EBMs, there is also an assumption of a monotonic biomarker trajectory, an assumption which has been questioned in the early stages of AD [149]. Other works have leveraged longitudinal data to build continuous trajectories. Jedynak et al. used a disease progression model to derive a progression score on a linear scale for every individual in AD [150]. Schiratti et al. proposed a general nonlinear mixed effects model that can handle not only scalar biomarker data but also images or shapes [151]. Applied to AD, the approach uncovered trajectories of progression for different variables, including cognitive tests, PET-derived hypometabolism, and local hippocampal atrophy [152]. In 2021, Wijeratne and Alexander proposed an approach that can, using longitudinal data, infer both discrete event ordering (as in EBMs) and continuous trajectories [153]. Lastly, Abi Nader et al. proposed SimulAD, which enables the setup of in silico interventional trial, where patient prognosis can be assessed against several possible therapies (drug type or timing of intervention) [154].

## 4.4 Data Harmonization

Data heterogeneity inducing bias arises from many sources, including differences in acquisition protocols, acquisition devices, or populations under study. This is true in large multicenter studies such as ADNI, DIAN, and GENFI. However, such research studies use harmonized protocols for data acquisition and perform strict
quality control. It becomes even worse when one is using clinical
routine data which is not acquired with harmonized protocols and
can be of extremely varied quality.

Data heterogeneity may have various impacts on ML algo-
rithms. For example, for any classification task, one wants to ensure
the model focuses on the diseases’ features rather than on any
differences caused by the acquisition sites. The ability to harmonize
data from any origin is also critical to translate any analytical tool in
clinical practice where acquisition protocols are rarely standardized.

This is especially true for imaging biomarkers where scans often
contain so-called scanner signatures. As a result, images are often
preprocessed prior to being used with machine learning algorithms.
The preprocessing steps involve intensity normalization in the
shape of image filtering, for denoising, or intensity histogram nor-
malization. For example, Erus et al. [155] proposed a framework to
achieve consistent segmentation of brain structures across multiple
sites. Their approach relies on the creation of site-specific atlases
while ensuring consistency between all available atlases. Their eval-
uation shows that they reduce the variability associated with sites on
volumetric measurements, key to track the process of brain atrophy,
derived from structural images. Another example is the work of Jog
et al. [156], who used image synthesis via contrast learning to
harmonize images acquired with different pulse sequences. The
Removal of Artificial Voxel Effect by Linear regression (RAVEL)
approach by Fortin et al. [157] is another exemplar application of
data harmonization. It consists of a voxel-wise intensity normaliza-
tion technique, where they apply singular value decomposition
(SVD) of the control voxels to estimate factors of unwanted varia-
tion. The control voxels are those unaffected by the pathology, such
as those in the cerebrospinal fluid (CSF). The unwanted factors are
then estimated using linear regression for every voxel of the brain,
and the residuals are taken as the RAVEL-corrected intensities. This
model has then been further extended [158] to include the model-
ing of site-specific scaling factors on summary measures derived
from the images. Using empirical Bayes to improve the estimation
of the site, this model can be used to correct several imaging
modalities while associating relevant clinical and demographic
information. It was originally developed to correct gene expression
microarray data [159], being later extended to correct DTI maps
[158], cortical thickness measurements [160], or structural MRI
[161]. Additional extensions include longitudinal data [162], site
effects due to covariance [163], and a generalized additive model in
order to handle nonlinear trajectories over the life span
[164]. Prado et al. [165] proposed Dementia ConnEEGtom to
harmonize neurophysiological data. They propose a whole analyti-
cal pipeline involving many steps, including denoising, artifact
removal, and spatial normalization, to promote a standardized
processing of EEG data, thereby enabling their use in machine
learning while minimizing the bias that could be induced by varia-
bility in data handling across sites.

Acquired clinical scales also need to be standardized and har-
monized between centers, especially when used jointly in a single
machine learning approach. Costa et al. [166] provide recommen-
dations on how this should be achieved and which scales should be
acquired for the neuropsychological assessment in neurodegenera-
tive diseases. Even when using the same scales across different
multicenter studies, it is important to understand that the prove-
nance and contextual information of each study must be consid-
ered, since that might introduce bias in the training of the
models [167].

5 What Is Next?

This chapter has illustrated that dementia is a complex, multifacto-
rial, heterogeneous set of pathologies and syndromes, sometimes
occurring in parallel. As a result, a wide variety of clinical, genetic,
cognitive, imaging, and biofluid data have been collected to char-
acterize these disease processes over a large number of different
cohorts, from both those individuals suffering from various forms
of dementia, as well as those at risk of developing a form of demen-
tia due to their genetic/environmental/pathophysiological risk
profile. Despite the wealth of data on dementia that is available to
machine learning researchers, there are still limitations, both in
terms of the data available and in the current thinking about how
to apply machine learning, that must be addressed in order for
machine learning to reach its true potential in terms of making an
impact on these conditions. Key to this validation will be open
science initiatives that allow for reproducibility and replication of
results, so that the value can be demonstrated in independent
cohorts.

Careful thought must be given to how to incorporate the
myriad different data types that are available to researchers. Despite
the wealth of multimodality information and big data available,
much of the machine learning-based research in dementia has
only considered a subset of the available information. This is the
case even within a single community such as the imaging one where
each modality or pulse sequence is most often analyzed individually.
Machine learning approaches are however able to extract highly
nonlinear information, which should enable the development of
truly multi-data frameworks able to capture the complexity of the
diseases. At the same time, when multiple features across different
data acquisition domains are combined into a single analysis, par-
ticularly in those individuals who already show evidence of
impairment, there is a higher likelihood of missing or corrupted
information. The simplest solution to the problem of missing data, and often the most implemented, is to perform complete case analyses, discarding any observations where any of the variables are missing. This practice results in the time donated by patients, carers, and volunteers willing to support research efforts through extensive and often onerous data collection being squandered and the full potential of the resulting data not being realized. While it is common to have 30% of data being discarded in some large studies, we could benefit hugely from further research in this direction. Multiple imputation techniques could be used to address this issue and ensure all available data can be used.

A related question is how to address the limitations of cross-sectional snapshots of data in a decades-long disease process and how best to use relatively short-term follow-up data. While inference on cross-sectional measures would be ideal in terms of providing information expediently to a patient, there are many confounds and covariates that can contribute to added variability, making classification tasks, particularly in the early stages of the disease, less accurate. Longitudinal data, particularly in imaging modalities, tends to reduce the influence of these confounds, and the within-subject change can be more sensitive to identifying the prognosis of individual trajectories. However, requiring longitudinal data for a classification task is undesirable for patients, who would be provided with no information until they come back for additional testing in a year or two. Thus, machine learning approaches could investigate whether a hybrid approach might be more powerful: triaging first with the cross-sectional data and only requiring longitudinal data in cases where inference cannot be made at the baseline assessment with a high degree of confidence.

A second challenge is to extend machine learning approaches to datasets that are more reflective of standard clinical settings. This refers not only to the type of data that is collected but also to the conditions under which the data is collected and to the populations within which data is acquired. Clinical research studies conducted at research institutions often include advanced data acquisitions that are costly and time-consuming, making them intractable for translation into wider communities and developing countries. There is thus a mismatch between the quality of the data acquired in research settings compared to the data acquired in day-to-day clinical environments. For example, only a subset of the patients suspected of having dementia undergo medical imaging and from this subset only a fraction of these individuals are offered MRI scans. In the majority of cases, CT are acquired, and they are mostly used to rule out other causes for impairment, such as space-occupying lesions. As a result, a large amount of CT scans are collected, and they could potentially be an important resource to develop computer-assisted tools able to reach a larger population [168]. Even when the same data type is acquired in clinical and
research settings, there can be a considerable mismatch in terms of data quality and homogeneity. For instance, clinical routine MRI is of extremely variable quality and usually acquired using non-harmonized protocols. Similarly, with the current democrati-zation of wearable and smartphone collection data which is prime to be processed with machine learning, there are opportunities to develop novel frameworks assisting patients, carers, and clini-cians.<fnref n="13" />,<fnref n="14" /> Another aspect of this challenge to improve widespread translation is that clinical research studies typically involve a dispro-portionate amount of affluent Caucasian individuals of European descent, meaning that we do not yet have enough data to fully quantify the heterogeneity that is observed in other ethnic groups. While large-scale cohort studies are looking to address this issue, focusing on assembling appropriate testing and training sets to represent the diversity of the population will be an important element for improving machine learning performance in the future.

Effective partnerships with the clinicians who are using the data are another key challenge in terms of incorporating machine learning in a wider clinical setting [169, 170]. Clinicians must believe in the added value that novel machine learning approaches can provide in order to incorporate them as part of their clinical workup and decision-making. One approach to achieving this buy-in from clinicians is a push toward “explainable AI,” such that the results from machine learning algorithms make intuitive sense and that the clinician can better understand how the algo-rithm came to that decision. While there are certainly concerns around the opaqueness of some algorithms that could lead to over-fitting or spurious results, an insistence on explainable AI may also restrict the development of better algorithms that can provide more value, and in some cases, it may result in reducing a complex multivariate pattern down to a summary measure that can be understood, throwing away valuable information in the process. What is likely more important is that best practices are followed in terms of training, testing, model development, and validation of the algorithm such that clinicians may not necessarily understand how the algorithm achieved a specific result but that they are convinced by the evidence of the value it provides.

The final, and likely most significant, challenge is how best to characterize heterogeneity and mixed pathology. Classification of well-characterized cohorts of clear cases of AD and normal controls provides little benefit, in particular given the rise of accurate and increasingly cheaper and accessible plasma tests. Dementia covers a wide range of symptoms caused by a myriad of pathologies and etiologies occurring in a decades-long process, and correctly

<footnote n="13">¹³ https://edon-initiative.org.</footnote>

<footnote n="14">¹⁴ https://www.ftdtalk.org/research/our-projects/digital/.</footnote>