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

Nicole R. Provenza, Sameer A. Sheth, Evan M. Dastin-van Rijn, Raissa K. Mathura, Yaohan Ding, Gregory S. Vogt, Michelle Avendano-Ortega, Nithya Ramakrishnan, Noam Peled, Luiz Fernando Fracassi Gelin, David Xing, Laszlo A. Jeni, Itir Onal Ertugrul, Adriel Barrios-Anderson, Evan Matteson, Andrew D. Wiese, Junqian Xu, Ashwin Viswanathan, Matthew T. Harrison, Kelly R. BijankiEric A. Storch, Jeffrey F. Cohn, Wayne K. Goodman, David A. Borton

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)2154-2164
Number of pages11
JournalNature Medicine
Issue number12
StatePublished - Dec 2021

Bibliographical note

Funding Information:
The authors thank the participants and their families for their involvement in the research program. The authors also thank K. Lane for artistic contribution in the creation of Fig. 1. This work relied heavily on the community expertise and resources made available by the Open Mind Consortium (https://openmind-consortium. github.io/). Summit RC+S devices were donated by Medtronic as part of the BRAIN Initiative Public-Private Partnership Program. We thank J. Murphy for expertise and contributions in designing and machining equipment used in this study. Part of this research was conducted with the help of research staff at the Center for Computation and Visualization, Brown University (senior research software engineers B. Roarr and M. McGrath). The research was supported by the National Institutes of Health (NIH) NINDS BRAIN Initiative via contracts UH3NS100549 (to S.A.S., J.F.C., D.A.B., E.A.S. and W.K.G.) and UH3NS103549 (to S.A.S.), the Charles Stark Draper Laboratory Fellowship (to N.R.P.), the McNair Foundation (to S.A.S.), the Texas Higher Education Coordinating Board NIH 1RF1MH121371 and U54HD083092 (to E.A.S.), NIH MH096951 (to J.F.C.), K01MH116364 and R21NS104953 (to K.B.), 3R25MH101076-05S2 (to A.B.-A.), award 1S10OD025181 (to J. Sanes at Brown University for computational resources) and the Karen T. Romer Undergraduate Teaching and Research Award at Brown University (E.M.D.-v.R. under the guidance of D.A.B.).

Publisher Copyright:
© 2021, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't


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