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Decoding mood

Nature Biotechnology volume 36, pages 932–933 (2018)Cite this article

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Invasive recordings of human brain activity predict mood fluctuations.

The subjective, shifting nature of mood has long proved recalcitrant to brain-imaging technologies. In this issue, Sani et al.1 overcome this roadblock for the first time with a report on a machine-learning model capable of predicting mood using only electrical signals from the brain. Their findings open an exciting new door into the possibilities of decoding mood-related neural signals in humans.

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Figure 1: Experimental design and analysis approach.

References

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Authors and Affiliations

  1. Amit Etkin is at the Department of Psychiatry and Behavioral Sciences and Stanford Neurosciences Institute, Stanford University, Stanford, California, USA, and at the Veterans Affairs Palo Alto Healthcare System and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, California, USA.,

    Amit Etkin

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  1. Amit Etkin
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Correspondence to Amit Etkin.

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Competing interests

A.E. has received stock options in Mindstrong Health and Akili Interactive for unrelated consulting.

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Etkin, A. Decoding mood. Nat Biotechnol 36, 932–933 (2018). https://doi.org/10.1038/nbt.4258

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