Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145867
Title: Towards a personalized multi-domain digital neurophenotyping model for the detection and treatment of mood trajectories
Authors: Sela, Yaron
Santamaria, Lorena
Amichai-Hamburge, Yair
Leong, Victoria
Keywords: Social sciences::Psychology
Issue Date: 2020
Source: Sela, Y., Santamaria, L., Amichai-Hamburge, Y., & Leong, V. (2020). Towards a personalized multi-domain digital neurophenotyping model for the detection and treatment of mood trajectories. Sensors, 20(20), 5781-. doi:10.3390/s20205781
Project: M4012105.SS0
M4011750.SS0
M4081585.SS0
Journal: Sensors
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.
URI: https://hdl.handle.net/10356/145867
ISSN: 1424-8220
DOI: 10.3390/s20205781
Rights: © 2020 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SSS Journal Articles

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