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Towards a Personalized Multi-Domain Digital Neurophenotyping Model for the Detection and Treatment of Mood Trajectories.

Accepted version
Peer-reviewed

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Type

Article

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Authors

Sela, Yaron 
Amichai-Hamburge, Yair  ORCID logo  https://orcid.org/0000-0003-3826-9737

Abstract

The commercial availability of many real-life smart sensors, wearables, and mobile apps provides a valuable source of information about a wide range of human behavioral, physiological, and social markers that can be used to infer the user's mental state and mood. However, there are currently no commercial digital products that integrate these psychosocial metrics with the real-time measurement of neural activity. In particular, electroencephalography (EEG) is a well-validated and highly sensitive neuroimaging method that yields robust markers of mood and affective processing, and has been widely used in mental health research for decades. The integration of wearable neuro-sensors into existing multimodal sensor arrays could hold great promise for deep digital neurophenotyping in the detection and personalized treatment of mood disorders. In this paper, we propose a multi-domain digital neurophenotyping model based on the socioecological model of health. The proposed model presents a holistic approach to digital mental health, leveraging recent neuroscientific advances, and could deliver highly personalized diagnoses and treatments. The technological and ethical challenges of this model are discussed.

Description

Keywords

digital phenotyping, dual-EEG, mood disorders, neurosensors, Affect, Electroencephalography, Humans, Mental Health, Mobile Applications, Technology

Journal Title

Sensors (Basel)

Conference Name

Journal ISSN

1424-8220
1424-8220

Volume Title

20

Publisher

MDPI AG

Rights

All rights reserved
Sponsorship
Economic and Social Research Council (ES/N006461/1)
This work was funded by a UK Economic and Social Research Council (ESRC) Transforming Social Sciences Grant ES/N006461/1 (to V.L.), a Nanyang Technological University start-up Grant M4081585.SS0 (to V.L.), and Ministry of Education (Singapore) Tier 1 grants M4012105.SS0 and M4011750.SS0 (V.L.).