Long-term ecological assessment of intracranial electrophysiology synchronized to behavioral markers in obsessive-compulsive disorder.

Detection of neural signatures related to pathological behavioral states could enable adaptive deep brain stimulation (DBS), a potential strategy for improving efficacy of DBS for neurological and psychiatric disorders. This approach requires identifying neural biomarkers of relevant behavioral states, a task best performed in ecologically valid environments. Here, in human participants with obsessive-compulsive disorder (OCD) implanted with recording-capable DBS devices, we synchronized chronic ventral striatum local field potentials with relevant, disease-specific behaviors. We captured over 1,000 h of local field potentials in the clinic and at home during unstructured activity, as well as during DBS and exposure therapy. The wide range of symptom severity over which the data were captured allowed us to identify candidate neural biomarkers of OCD symptom intensity. This work demonstrates the feasibility and utility of capturing chronic intracranial electrophysiology during daily symptom fluctuations to enable neural biomarker identification, a prerequisite for future development of adaptive DBS for OCD and other psychiatric disorders.

Uncovering biomarkers during therapeutic neuromodulation with PARRM: Period-based Artifact Reconstruction and Removal Method.

Advances in therapeutic neuromodulation devices have enabled concurrent stimulation and electrophysiology in the central nervous system. However, stimulation artifacts often obscure the sensed underlying neural activity. Here, we develop a method, termed Period-based Artifact Reconstruction and Removal Method (PARRM), to remove stimulation artifacts from neural recordings by leveraging the exact period of stimulation to construct and subtract a high-fidelity template of the artifact. Benchtop saline experiments, computational simulations, five unique in vivo paradigms across animal and human studies, and an obscured movement biomarker are used for validation. Performance is found to exceed that of state-of-the-art filters in recovering complex signals without introducing contamination. PARRM has several advantages: (1) it is superior in signal recovery; (2) it is easily adaptable to several neurostimulation paradigms; and (3) it has low complexity for future on-device implementation. Real-time artifact removal via PARRM will enable unbiased exploration and detection of neural biomarkers to enhance efficacy of closed-loop therapies.

The Case for Adaptive Neuromodulation to Treat Severe Intractable Mental Disorders.

Mental disorders are a leading cause of disability worldwide, and available treatments have limited efficacy for severe cases unresponsive to conventional therapies. Neurosurgical interventions, such as lesioning procedures, have shown success in treating refractory cases of mental illness, but may have irreversible side effects. Neuromodulation therapies, specifically Deep Brain Stimulation (DBS), may offer similar therapeutic benefits using a reversible (explantable) and adjustable platform. Early DBS trials have been promising, however, pivotal clinical trials have failed to date. These failures may be attributed to targeting, patient selection, or the "open-loop" nature of DBS, where stimulation parameters are chosen ad hoc during infrequent visits to the clinician's office that take place weeks to months apart. Further, the tonic continuous stimulation fails to address the dynamic nature of mental illness; symptoms often fluctuate over minutes to days. Additionally, stimulation-based interventions can cause undesirable effects if applied when not needed. A responsive, adaptive DBS (aDBS) system may improve efficacy by titrating stimulation parameters in response to neural signatures (i.e., biomarkers) related to symptoms and side effects. Here, we present rationale for the development of a responsive DBS system for treatment of refractory mental illness, detail a strategic approach for identification of electrophysiological and behavioral biomarkers of mental illness, and discuss opportunities for future technological developments that may harness aDBS to deliver improved therapy.