A Study of Novel Exploratory Tools, Digital Technologies, and Central Nervous System Biomarkers to Characterize Unipolar Depression

Bibliographic Data

ID15523824
AuthorsOleksandr Sverdlov (0000-0002-1626-2588, Novartis (United States), corresponding author), Jelena Curcic (0000-0001-9647-5972, Novartis Institutes for BioMedical Research), Kristín Hannesdóttir (0000-0003-4496-0110, Novartis (United States)), Liangke Gou (0000-0002-5767-9603, Novartis (United States)), Valéria De Luca (0000-0003-3875-5786, Novartis Institutes for BioMedical Research), Francesco Ambrosetti (Novartis (Switzerland)), Bingsong Zhang (0000-0002-4904-6961, Georgetown University), Jens Præstgaard (0000-0001-5122-7190, Novartis (United States)), Vanessa Vallejo (0000-0001-9117-8186, Novartis (Switzerland)), Andrew J Dolman (Novartis (United States)), Andrew Dolman, Baltazar Gomez‐Mancilla (0009-0000-2427-9884, Novartis (Switzerland)), Baltazar Gomez-Mancilla, Konstantinos Biliouris (Novartis (United States)), Mark Deurinck (0000-0002-8842-0944, Novartis Institutes for BioMedical Research), Francesca Cormack (0000-0002-4413-177X, Cambridge Cognition (United Kingdom)), John J Anderson, John J B Anderson (0000-0001-7823-9378, Neurotrack Technologies (United States)), Nicholas T Bott (Stanford University), Ziv Peremen (0000-0001-7492-3471, Hewlett-Packard (Israel)), Gil Issachar (0000-0002-6546-4637, Hewlett-Packard (Israel)), Offir Laufer (0000-0002-5948-1633, Hewlett-Packard (Israel)), Dale Joachim, Raj Jagesar (0000-0002-5320-3341, University of Groningen), Raj R Jagesar, Niels Jongs (0000-0002-0882-3656, University of Groningen), Martien J Kas (0000-0002-4471-8618, University of Groningen), Ahnjili Zhuparris (0000-0002-1413-1648, Centre for Human Drug Research), Rob Zuiker (0000-0001-5604-0157, Centre for Human Drug Research), Kasper Recourt (0000-0001-9282-9307, Centre for Human Drug Research), Zoë Zuilhof (Centre for Human Drug Research), Jang‐Ho Cha (0000-0002-0458-3931, Novartis (United States)), Jang-Ho Cha, Gabriël E Jacobs (0000-0002-5140-9450, Leiden University Medical Center)
Year2021
Volume12
Pages640741-640741
Publication date2021-05-06
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Psychiatry (JOURNAL)
Journal identifiersISSN: 1664-0640 • E-ISSN: 1664-0640
PublisherFrontiers Media (PUBLISHER • CH)
DOI10.3389/fpsyt.2021.640741
PMID34025472
OpenAlexW3158697864
LanguageEN
Citations received2
References cited24

Background: Digital technologies have the potential to provide objective and precise tools to detect depression-related symptoms. Deployment of digital technologies in clinical research can enable collection of large volumes of clinically relevant data that may not be captured using conventional psychometric questionnaires and patient-reported outcomes. Rigorous methodology studies to develop novel digital endpoints in depression are warranted. Objective: We conducted an exploratory, cross-sectional study to evaluate several digital technologies in subjects with major depressive disorder (MDD) and persistent depressive disorder (PDD), and healthy controls. The study aimed at assessing utility and accuracy of the digital technologies as potential diagnostic tools for unipolar depression, as well as correlating digital biomarkers to clinically validated psychometric questionnaires in depression. Methods: A cross-sectional, non-interventional study of 20 participants with unipolar depression (MDD and PDD/dysthymia) and 20 healthy controls was conducted at the Centre for Human Drug Research (CHDR), the Netherlands. Eligible participants attended three in-clinic visits (days 1, 7, and 14), at which they underwent a series of assessments, including conventional clinical psychometric questionnaires and digital technologies. Between the visits, there was at-home collection of data through mobile applications. In all, seven digital technologies were evaluated in this study. Three technologies were administered via mobile applications: an interactive tool for the self-assessment of mood, and a cognitive test; a passive behavioral monitor to assess social interactions and global mobility; and a platform to perform voice recordings and obtain vocal biomarkers. Four technologies were evaluated in the clinic: a neuropsychological test battery; an eye motor tracking system; a standard high-density electroencephalogram (EEG)-based technology to analyze the brain network activity during cognitive testing; and a task quantifying bias in emotion perception. Results: Our data analysis was organized by technology - to better understand individual features of various technologies. In many cases, we obtained simple, parsimonious models that have reasonably high diagnostic accuracy and potential to predict standard clinical outcome in depression. Conclusion: This study generated many useful insights for future methodology studies of digital technologies and proof-of-concept clinical trials in depression and possibly other indications

Cognition · Depression (economics · Digital health · Health care · Major depressive disorder · Mood · Neuropsychology · Psychiatry · Clinical Psychology · Digital Mental Health Interventions · Functional Brain Connectivity Studies · Medicine · Mental Health Research Topics

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