Big data in epilepsy: Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy

Abstract

Epilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches [Correction added on Aug 30, 2020, after first online publication: Affiliation of author "Satya S. Sahoo" was changed and the rest of the affiliations were renumbered accordingly.] 1870 | LHATOO eT AL. 1 | INTRODUCTION Big data is an intuitive, colloquially used term 1-first in business, and latterly in science and health care. MetaGroup's 2014 definition describes big data as high-volume, high-velocity, and high-variety information assets that demand costeffective, innovative forms of information processing for enhanced insight and decision-making. In addition to these "3 V's," 2 the fourth "V" of data veracity is particularly pertinent, because suspect data produce suspect conclusions (Figure 1). In an era of unprecedented collaboration and resource pooling, big data's promise is both inviting and challenging, especially in epilepsy due to its inherent heterogeneity and the vast array of scientific disciplines it involves. This review examines big data aspects specific to epilepsy and describes the current state of the art as well as future directions. 2 | THE MEANING OF BIG DATA In epilepsy, a plethora of disparate data drive variety (phenotype, genotype, video-electroencephalographic [EEG], extracranial and intracranial physiological signal, structural to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data.

Modeling seizures in the Human Phenotype Ontology according to contemporary ILAE concepts makes big phenotypic data tractable

“…1,2 To fully exploit these data, information must be harmonized to allow reliable comparison of individuals to diagnostic criteria, or to each other. 3 Interpretation of phenotypic data is challenging in epilepsy, particularly because the central clinical features-seizures-are diverse, dynamic, and can occur in different combinations and in different developmental and comorbidity contexts. [4][5][6][7][8][9] Crucially, the same seizure can be classified at various levels of detail according to clinical need, or to accommodate incomplete information.…”

“…The need for rapid and accurate automated phenotypic harmonization of large multicenter data sets is increasingly important as diagnostic services and research studies grow to analyze data from tens of thousands of individuals. 1,3,[13][14][15][16][17][18][19] Ontologies formally model the concepts and relationships that exist in a particular expert domain. 20 The Human Phenotype Ontology (HPO) is a tool that has become the lingua franca for clinical and research geneticists for concise communication and harmonization of clinical features (human-phenotype-ontology.org).…”

“…Information about the clinical features of research participants is essential for diagnostic interpretation of genetic findings and discovery research in epilepsy genetics 1,2 . To fully exploit these data, information must be harmonized to allow reliable comparison of individuals to diagnostic criteria, or to each other 3 . Interpretation of phenotypic data is challenging in epilepsy, particularly because the central clinical features—seizures—are diverse, dynamic, and can occur in different combinations and in different developmental and comorbidity contexts 4–9 .…”

“…In addition, the format of clinical data collected for one study may not map onto the variables required for another study, limiting the ease and yield of reuse. The need for rapid and accurate automated phenotypic harmonization of large multicenter data sets is increasingly important as diagnostic services and research studies grow to analyze data from tens of thousands of individuals 1,3,13–19 …”

Bridging the gap between analytical methods and their clinical interpretation

“…Finally, with the advent of this century, studies featuring "big data," whole brain connectivity and multivariate analysis have become common in all branches of neurology including epilepsy, [3][4][5][6][7][8] and their applications are only expected to increase in near future. The purpose of this letter therefore is not only to critique this exemplary work undertaken by Martire et al, but also to generate a discussion regarding the up and coming use of advanced methods of analysis such as PLS, connectomics, and so on, and the best way to equip the clinicians with these new tools.…”