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Towards a personalized multi-domain digital neurophenotyping model for the detection and treatment of mood trajectories

Sela, Yaron, Santamaria, Lorena, Amichai-Hamburge, Yair and Leong, Victoria 2020. Towards a personalized multi-domain digital neurophenotyping model for the detection and treatment of mood trajectories. Sensors 20 (20) , 5781. 10.3390/s20205781

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Psychology
Cardiff University Brain Research Imaging Centre (CUBRIC)
Additional Information: Full text licensed under CC BY 4.0
Publisher: MDPI
ISSN: 1424-8220
Date of First Compliant Deposit: 29 October 2020
Date of Acceptance: 8 October 2020
Last Modified: 29 Oct 2020 17:00
URI: http://orca.cf.ac.uk/id/eprint/136049

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