Abstract
Brain sciences have recently been argued to be the most data-rich speciality in medicine ( 1 Editorial The power of big data must be harnessed for medical progress. Nature. 2016; 539: 467-468 Crossref Scopus (14) Google Scholar ). The abundance of biomedical data is partly explained by neurosciences’ use of magnetic resonance imaging and microarray-type common variant information. For example, genomics and imaging genetics studies commonly investigate >1,000,000 genetic loci (single nucleotide polymorphisms). When individual subjects are characterized by detailed assessments of whole-genome profiles along with brain structure, function, and connectivity measurements, we depend on large participant sample sizes to draw defensible conclusions from our analytical procedures ( 2 Bzdok D. Nichols T.E. Smith S.M. Towards algorithmic analytics for large-scale datasets. Nat Mach Intell. 2019; 1: 296-306 Crossref PubMed Scopus (37) Google Scholar ). Thus, multimodal data initiatives emerged to respond to these calls by aggregating more extensive and phenotypically rich measurements from thousands of subjects. The UK Biobank ( 3 Sudlow C. Gallacher J. Allen N. Beral V. Burton P. Danesh J. et al. UK Biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLOS Med. 2015; 12e1001779 Crossref PubMed Scopus (3238) Google Scholar ) and Adolescent Brain Cognitive Development (ABCD) ( 4 Casey B.J. Cannonier T. Conley M.I. Cohen A.O. Barch D.M. Heitzeg M.M. et al. The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Dev Cogn Neurosci. 2018; 32: 43-54 Crossref PubMed Scopus (496) Google Scholar ) studies are currently the largest homogeneously acquired populations of old and young age, respectively. The recent advent of population datasets challenges the way we are used to quantitatively analyzing and drawing insights about psychiatric disorders, including autism spectrum disorder or schizophrenia. Because the biology of these disorders is intertwined with various aspects of population stratification, we need to aim for analyses that put forward major sources of population diversity. Specifically, capturing more multifaceted aspects of disease etiopathology bears the potential to improve diagnosis, risk detection, and treatment choice. Therefore, personalized disease-related predictions will be supported by analysis techniques enabling consideration of ethnicity, height, weight/body mass index, gender identity, handedness/language differentiation/hemisphere dominance, personality/risk aversion, race, or hormone metabolism.
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