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Brain–computer interfaces for neuropsychiatric disorders - Nature.com

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Abstract

Neuropsychiatric disorders such as major depression are a leading cause of disability worldwide with standard treatments, including psychotherapy or medication, failing many patients. Deep brain stimulation holds great potential as an alternative therapy for treatment-resistant cases; however, improving the efficacy of stimulation therapy for neuropsychiatric disorders is hindered by the complexity as well as inter-individual and intra-individual variability in symptom manifestations, neural representations and response to therapy. These challenges motivate the development of brain–computer interfaces (BCIs) that can decode the symptom state of a patient from brain activity as feedback to personalize the stimulation therapy in closed loop. Here we review progress on developing BCIs for neuropsychiatric care, focusing on neural biomarkers for decoding symptom states, stimulation site selection and closed-loop stimulation strategies. Moreover, we highlight promising data-driven machine learning and system design approaches and provide a roadmap for realizing these BCIs. Finally, we review current limitations, discuss extensions to other treatment modalities and outline the required scientific and technological advances. These advances can enable next-generation BCIs that provide an alternative therapy for treatment-resistant neuropsychiatric disorders.

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Fig. 1: Closed-loop, personalized brain–computer interfaces for neuropsychiatric care.
Fig. 2: Machine learning methods for data-driven biomarker discovery and brain–computer interface decoder design.
Fig. 3: Design of the stimulation strategy.

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Acknowledgements

The authors thank H. C. Jo, O. G. Sani, T. Jani and N. Sadras in the Shanechi laboratory for helpful feedback. This work was partly supported by the US National Institutes of Health grants R01MH123770 and R61MH135407, the One Mind Rising Star Award, and the Foundation for OCD Research.

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Authors and Affiliations

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

    Lucine L. Oganesian & Maryam M. Shanechi

  2. Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA

    Maryam M. Shanechi

  3. Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA

    Maryam M. Shanechi

  4. Thomas Lord Department of Computer Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA

    Maryam M. Shanechi

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L.L.O. and M.M.S. developed the content and wrote the manuscript.

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Correspondence to Maryam M. Shanechi.

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M.M.S. is an inventor on University of Southern California’s patents or patent applications related to decoding and closed-loop control approaches, and is a consultant for Paradromics Inc. L.L.O. declares no competing interests.

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Oganesian, L.L., Shanechi, M.M. Brain–computer interfaces for neuropsychiatric disorders. Nat Rev Bioeng (2024). https://ift.tt/HhcsSuT

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