Abstract

The ability to decode mood state over time from neural activity could enable closed-loop systems to treat neuropsychiatric disorders. However, this decoding has not been demonstrated, partly owing to the difficulty of modeling distributed mood-relevant neural dynamics while dealing with the sparsity of mood state measurements. Here we develop a modeling framework to decode mood state variations from multi-site intracranial recordings in seven human subjects with epilepsy who self-reported their mood state intermittently over multiple days. We built dynamic neural encoding models of mood state and corresponding decoders for each individual and demonstrated that mood state variations over time can be decoded from neural activity. Across subjects, the decoders largely recruited neural signals from limbic regions, whose spectro-spatial features were tuned to mood variations. The dynamic models also provided an analytical tool to compute the timescales of the decoded mood state. These results provide an initial line of evidence indicating the feasibility of mood state decoding.

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Acknowledgements

This research was partially funded by the Defense Advanced Research Projects Agency (DARPA) under Cooperative Agreement Number W911NF-14-2-0043 (to M.M.S. and E.F.C.), issued by the Army Research Office contracting office in support of DARPA's SUBNETS program. The views, opinions and/or findings expressed are those of the author(s) and should not be interpreted as representing the official views or policies of the Department of Defense or the US Government.

Author information

Author notes

    • Omid G Sani
    •  & Yuxiao Yang

    These authors contributed equally to this work.

    • Edward F Chang
    •  & Maryam M Shanechi

    These authors served as co-senior authors, with M.M.S. leading the decoding work and E.F.C. leading the data collection.

Affiliations

  1. Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA.

    • Omid G Sani
    • , Yuxiao Yang
    •  & Maryam M Shanechi
  2. Department of Neurological Surgery, University of California, San Francisco, California, USA.

    • Morgan B Lee
    • , Heather E Dawes
    •  & Edward F Chang
  3. Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA.

    • Morgan B Lee
    • , Heather E Dawes
    •  & Edward F Chang
  4. Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, California, USA.

    • Morgan B Lee
    • , Heather E Dawes
    •  & Edward F Chang
  5. Neuroscience Graduate Program, University of Southern California, Los Angeles, California, USA.

    • Maryam M Shanechi

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Contributions

M.M.S. and E.F.C. supervised the project. M.M.S. conceived the dynamic modeling and decoding framework, and M.M.S., O.G.S. and Y.Y. developed it. E.F.C. and M.B.L. developed and implemented the continuous mood testing protocol and coordinated data collection for all the intracranial recording and imaging data. O.G.S. and Y.Y. implemented and performed the modeling and analyses. M.M.S. supervised all the modeling and analyses work. O.G.S., Y.Y. and M.M.S. wrote the manuscript with input from E.F.C. and H.E.D.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Edward F Chang or Maryam M Shanechi.

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https://doi.org/10.1038/nbt.4200