Towards a Personalized Multi-Domain Digital Neurophenotyping Model for the Detection and Treatment of Mood Trajectories.
Sensors (Basel, Switzerland)
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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 (Basel, Switzerland), 20 (20)https://doi.org/10.3390/s20205781
The commercial availability of many real-life smart sensors, wearables and mobile apps provides a valuable information resource about a wide range of human behavioral, physiological and social markers which 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 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 which is based on the socio-ecological model of health. The proposed model presents a holistic approach of digital mental health, leveraging on recent neuroscientific advances, and therefore could deliver highly personalized diagnoses and treatments. The technological and ethical challenges of this model are discussed
Humans, Electroencephalography, Affect, Mental Health, Technology, Mobile Applications
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.).
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External DOI: https://doi.org/10.3390/s20205781
This record's URL: https://www.repository.cam.ac.uk/handle/1810/311332
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