Closing the Loop in Deep Brain Stimulation for Psychiatric Disorders: Lessons from Motor Neural Prosthetics

摘要

Deep brain stimulation (DBS) is a promising technique for modulating circuits underlying mental illnesses, but has not done well in clinical trials (Dougherty et al , 2015). Advocates have argued that the trial failures arise from a need to better define the anatomic target for stimulation (Riva-Posse et al , 2014). This ignores a larger issue: DBS is an open-loop, static therapy. Patients' disorders, on the other hand, are not static. Symptoms change over hours to days, but DBS programming visits occur every 412 weeks. To resolve that mismatch, investigators are now pursuing closed-loop' DBS, where the device itself monitors patients' brain activity and self-titrates therapy to a desired endpoint (Figure 1). The challenge, however, is determining what to monitor. Verified neural biomarkers for psychiatric disorders remain elusive. Preliminary data suggest candidate markers (Widge et al , 2015), but they are far from the real-time algorithms needed for effective feedback-controlled DBS.A different neuroscience community has had greater success in reading out' the brain: braincomputer interface (BCI) researchers. Their technologies decode' movement signals from the cortex, then convey movement goals to assistive devices. Closed-loop DBS researchers seek to do something similar, decoding a patient's emotional state. BCI investigators have uncovered two insights that could assist psychiatry's quest. First, encoding mattersdecoding is better with a robust model of how cortical regions encode mental states. This matters for psychiatry, because disorders like depression and post-traumatic stress disorder are heterogeneous. Effective decoding may require identification of discrete circuit-based endophenotypes, analogous to research domain criteria constructs. For instance, preliminary data suggest that DBS response at the ventral striatum target may depend on changes in fronto-cingulate activity evoked by Stroop-like tasks (Widge et al , 2015). This cross-diagnostic approach may be broadly useful in dissecting DBS' mechanisms of action.