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

Nature Medicine volume 27, pages 2154–2164 (2021)Cite this article

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Abstract

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.

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Fig. 1: Streaming of intracranial electrophysiological data in the clinic and at home.
Fig. 2: Anatomical localization of DBS lead placement.
Fig. 3: Intracranial ventral striatum local field potentials synchronized with continuous affect estimation during DBS programming for OCD.
Fig. 4: At-home symptom monitoring synchronized with ecologically valid intracranial electrophysiology.
Fig. 5: Intracranial electrophysiology during exposure and response prevention teletherapy at home with participant P4.
Fig. 6: Ventral capsule/ventral striatum spectral power shows correlations with obsessive-compulsive disorder symptom intensity during P4 natural exposures at home.

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Data availability

The complete datasets generated (excluding video/audio) and analyzed during the current study will be made publicly available at study completion and will be deposited in the NIH Data Archive for the Brain Initiative. The minimum dataset required to reproduce all results of the paper (excluding video and audio) is publicly available through the associated Open Science Framework project at https://doi.org/10.17605/OSF.IO/YQA2K.

Code availability

Custom code used to produce the results in this paper is available at https://github.com/neuromotion/ecological-ephys-behav-ocd.

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Acknowledgements

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.).

Author information

Author notes

  1. These authors jointly supervised this work: Wayne K. Goodman, David A. Borton.

Authors and Affiliations

  1. Brown University School of Engineering, Providence, RI, USA

    Nicole R. Provenza, Evan M. Dastin-van Rijn, David Xing, Evan Matteson & David A. Borton

  2. Charles Stark Draper Laboratory, Cambridge, MA, USA

    Nicole R. Provenza

  3. Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA

    Sameer A. Sheth, Raissa K. Mathura, Ashwin Viswanathan & Kelly R. Bijanki

  4. Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA

    Yaohan Ding

  5. Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA

    Gregory S. Vogt, Michelle Avendano-Ortega, Nithya Ramakrishnan, Andrew D. Wiese, Junqian Xu, Kelly R. Bijanki, Eric A. Storch & Wayne K. Goodman

  6. MGH/HST Martinos Center for Biomedical Imaging, Charlestown, MA, USA

    Noam Peled

  7. Harvard Medical School, Cambridge, MA, USA

    Noam Peled

  8. Center for Computation and Visualization, Brown University, Providence, RI, USA

    Luiz Fernando Fracassi Gelin

  9. Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA

    Laszlo A. Jeni

  10. Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, the Netherlands

    Itir Onal Ertugrul

  11. The Warren Alpert Medical School of Brown University, Providence, RI, USA

    Adriel Barrios-Anderson

  12. Department of Psychology, University of Missouri-Kansas City, Kansas City, MO, USA

    Andrew D. Wiese

  13. Department of Radiology, Baylor College of Medicine, Houston, TX, USA

    Junqian Xu

  14. Division of Applied Mathematics, Brown University, Providence, RI, USA

    Matthew T. Harrison

  15. Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA

    Jeffrey F. Cohn

  16. Carney Institute for Brain Science, Brown University, Providence, RI, USA

    David A. Borton

  17. Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs, Providence, RI, USA

    David A. Borton

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  1. Nicole R. Provenza
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Contributions

W.K.G., J.F.C., S.A.S. and D.A.B. conceived of the study. N.R.P. conceptualized data analysis procedures, performed data analysis, interpreted data and prepared figures and results with support from E.M.D.-v.R., M.T.H., R.K.M., N.P., Y.D., A.B.-A., S.A.S. and D.A.B. E.M.D.-v.R. carried out packet loss correction and artifact removal procedures with support from N.R.P. and M.T.H. J.X. optimized the MRI protocol. R.K.M., N.P., K.B. and N.R.P. performed MRI analysis, and N.P. developed the Multi-Modal Visualization Tool software. L.A.J. and I.O.E. developed AFAR analysis methodology. Y.D. and L.A.J. performed AFAR analysis, and N.R.P., L.A.J. and Y.D. created the supplementary videos. G.S.V., M.A.-O., N.R. and N.R.P. performed data collection in the clinic. E.R.M. supported data collection. N.R.P., G.S.V. and M.A.-O. guided participant data collection at home. A.D.W. provided clinical ERP sessions, and A.D.W. and N.R.P. collected data during ERP, supervised by E.A.S. A.B.-A. documented SUD ratings using ERP videos. L.F.F.G. and D.X. created new software to enable the collection of intracranial electrophysiological data at home. N.R.P. and S.A.S. wrote the first draft of the manuscript and all authors contributed to the writing and revision of the manuscript. W.K.G., S.A.S., A.V. and E.A.S. performed the clinical care aspects of the study. S.A.S. and A.V. performed the study surgical procedures. W.K.G., S.A.S., J.F.C., E.A.S. and D.A.B. oversaw the collection of data, analysis and manuscript completion.

Corresponding author

Correspondence to David A. Borton.

Ethics declarations

Competing interests

D.A.B. and W.K.G. received device donations from Medtronic as part of the NIH BRAIN Public-Private Partnership Program. W.K.G. received honoraria from Biohaven Pharmaceuticals. S.A.S. has consulting agreements with Boston Scientific, NeuroPace, Koh Young, Zimmer Biomet and Abbott. N.P. is a co-founder and stocks holder at FIND Surgical Sciences. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Medicine thanks Nolan Williams and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Jerome Staal was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Anatomical localization of DBS lead placement (P1, P2, P3, P5).

(a, b) Coronal (a) and axial (b) T1-weighted (T1w) MRI in radiographic convention from participants P1, P2, P3, and P5 overlaid with reconstructed DBS lead trajectories. Colored regions indicate anterior commissure (AC), caudate, putamen, and ventral striatum (VS). The MRI slice shown is immediately posterior (a; coronal) or inferior (b; axial) to the most ventral contact. Enlarged coronal slices (corresponding to white box outlines in panel a) showing DBS contact locations in each hemisphere are shown on either side of the full coronal slice. Green spheres indicate sensing contacts, red spheres indicate stimulating contacts, black spheres indicate contacts that were used for neither stimulation nor sensing. In each participant, the tips of the leads were targeted to either the VS or the bed nucleus of the stria terminalis (BNST) (target regions for each participant are included in Extended Data Table 1). Enlarged slices shown are immediately posterior to the most ventral contact in each hemisphere. Anterior-posterior slice location (y) is referenced to the posterior border of the AC, which is defined as y = 0. (d, e) Front (d) and top-down (e) view of the reconstructed cortical surface, subcortical structures, DBS leads, and AC, shown in radiographic convention.

Extended Data Fig. 2 Impedance of sensing and stimulation electrode contacts reflect long term stability at device-tissue interface.

(a) Impedance in kOhms of sensing and stimulation electrode contacts in the left (left panel) and right (right panel) VC/VS of P3. Blue points indicate impedance between the two sensing contacts. Green points indicate the impedance between the deepest (light green) and shallowest (dark green) sensing contact and the INS case. Light red points indicate the impedance between the stimulation contact and the INS case. Sense and stimulation electrode contacts on the Medtronic 3387 leads are visualized to the right of each panel, with contact 0 as the deepest contact on the left, and contact 8 as the deepest contact on the right. Light green indicates the deepest sensing contact, Green indicates the shallowest sensing contact, and light red indicates the stimulation contact. Black contacts are unused. (b) Impedance in kOhms of sensing and stimulation electrode contacts in the left (left panel) and right (right panel) VC/VS of P4. Gray shaded region indicates the timespan when sensing and stimulation contacts correspond to the Medtronic 3387 lead diagram labelled with “A”, whereas the following timespan with no shading corresponds to the Medtronic 3387 lead diagram labelled with “B”. Elsewise, the format is identical to panel a. (c) Impedance in kOhms of sensing and stimulation electrode contacts in the left (left panel) and right (right panel) VC/VS of P5. Format is identical to panel A.

Extended Data Fig. 3 Distribution of self-reported, Subjective Units of Distress (SUDs) ratings collected by participants during Exposure Response Prevention (ERP) teletherapy.

(a) The distribution of SUDs ratings by participant P3 for all recorded sessions. The Y-axis shows SUDs ratings provided by the participant after being prompted to indicate their level of OCD related distress at irregular time intervals during each ERP session on a scale of 0–10 with 0 representing ‘no distress’ and 10 representing ‘the worst distress.’ The X-Axis shows each consecutive, hour-long, recorded ERP session (n = 13 to n = 14 for each participant) completed. Gray shading indicates the session analyzed in Extended Data Fig. 6. (b) The distribution of SUDs ratings by participant P4 for all recorded sessions. Format is identical to panel A. Gray shading indicates the session analyzed in Extended Data Fig. 7. (c) The distribution of SUDs ratings by participant P5 for all recorded sessions. Format is identical to panel A. Gray shading indicates the session analyzed in Extended Data Fig. 8.

Extended Data Fig. 4 Intracranial electrophysiology during Exposure and Response Prevention (ERP) teletherapy at home with Participant P3.

(a) Calendar availability plot of ERP sessions for participant P4, over days since the first ERP session. Shaded portions indicate data availability for ERP video, Apple watch heart rate, Apple watch acceleration, RC + S acceleration, and RC + S LFP. Rectangular dotted line corresponds to the ERP session example data shown in panels B-D. (b) Video of participant P4 (left), clinician (right). (c) Time-course in minutes of self-reported Subjective Units of Distress (SUDs) ratings. Vertical black line corresponds to the video frame shown in panel b. (d) Ten seconds of example data synchronized to video, including RC + S acceleration, and two bipolar LFP channels. Vertical black line corresponds to the video frame shown in panel B.

Extended Data Fig. 5 Intracranial electrophysiology during Exposure and Response Prevention (ERP) teletherapy at home with Participant P5.

(a) Calendar availability plot of ERP sessions for participant P4, over days since the first ERP session. Shaded portions indicate data availability for ERP video, Apple watch heart rate, Apple watch acceleration, RC + S acceleration, and RC + S LFP. Rectangular dotted line corresponds to the ERP session example data shown in panels b–d. (b) Video of participant P4 (left), clinician (right). (c) Time-course in minutes of self-reported Subjective Units of Distress (SUDs) ratings. Vertical black line corresponds to the video frame shown in panel b. (d) Ten seconds of example data synchronized to video, including RC + S acceleration, and two bipolar LFP channels. Vertical black line corresponds to the video frame shown in panel b.

Extended Data Fig. 6 Ventral Capsule/Ventral Striatum spectral activity vs. SUDs ratings during P3 Exposure and Response Prevention (ERP) teletherapy recording.

(a) Self-reported intensity of OCD symptoms (scatter points) shown over time in hours with LFP data availability (orange shading). Vertical black lines indicate timepoints of OCD exposures. b) Normalized left VC/VS spectral power in Delta (0–4 Hz), Theta (4–8 Hz), Alpha (8–15 Hz), Beta (15–30 Hz), and Gamma (30–55 Hz) (from left to right) vs. self-reported OCD symptom intensity from zero to 10. Black lines represent the line of least squares. R values correspond to the coefficient of correlation. (c) Normalized right VC/VS spectral power in frequency bands of interest vs. self-reported OCD symptom intensity from zero to 10. Format is identical to panel b.

Extended Data Fig. 7 Ventral Capsule/Ventral Striatum spectral activity vs. SUDs ratings during P4 Exposure and Response Prevention teletherapy recording.

(a) Self-reported intensity of OCD symptoms (scatter points) shown over time in hours with LFP data availability (orange shading). Vertical black lines indicate timepoints of OCD exposures. b) Normalized left VC/VS spectral power in Delta (0–4 Hz), Theta (4–8 Hz), Alpha (8–15 Hz), Beta (15–30 Hz), and Gamma (30–55 Hz) (from left to right) vs. self-reported OCD symptom intensity from zero to 10. Black lines represent the line of least squares. R values correspond to the coefficient of correlation. (c) Normalized right VC/VS spectral power in frequency bands of interest vs. self-reported OCD symptom intensity from zero to 10. Format is identical to panel B.

Extended Data Fig. 8 Ventral Capsule/Ventral Striatum spectral activity vs. SUDs ratings during P5 Exposure and Response Prevention teletherapy recording.

(a) Self-reported intensity of OCD symptoms (scatter points) shown over time in hours with LFP data availability (orange shading). Vertical black lines indicate timepoints of OCD exposures. b) Normalized left VC/VS spectral power in Delta (0–4 Hz), Theta (4–8 Hz), Alpha (8–15 Hz), Beta (15–30 Hz), and Gamma (30–55 Hz) (from left to right) vs. self-reported OCD symptom intensity from zero to 10. Black lines represent the line of least squares. R values correspond to the coefficient of correlation. (c) Normalized right VC/VS spectral power in frequency bands of interest vs. self-reported OCD symptom intensity from zero to 10. Format is identical to panel b.

Extended Data Table 1 Participant demographics, DBS surgery device and targets, and stimulation and sensing contact information
Full size table

Supplementary information

Reporting Summary

Supplementary Video 1

The video shows a positive emotional response to the initial DBS programming session for participant P5 (companion video for Fig. 3). A video of the face (used with permission) was used to do automatic 3D face tracking. Arrows indicate the tracked three degrees of freedom of head pose. The contours of tracked key facial parts are highlighted in green. Positive affect was on a scale of zero to five based on facial action units 6 and 12. Head velocity was estimated in terms of yaw and pitch in units of degrees of displacement per second. BVP and ECG were used to estimate heart rate in beats per minute. Additional electrophysiological recordings included 64-channel EEG, and bilateral (left and right hemisphere) VS LFP recordings. Electrophysiological data are shown alongside DBS amplitude in mA and RC + S acceleration (xyz) in g.

Supplementary Video 2

The video shows intracranial electrophysiology recorded during ERP teletherapy at home with P4. The participant was exposed to a fear trigger (a wart) during the ERP teletherapy session and was asked to resist engaging in compulsions or diverting thoughts away from the trigger. The video of the participant and clinician was synchronized to Apple Watch acceleration, RC + S acceleration and bilateral (left and right hemisphere) VS LFP recordings. The vertical black line corresponds to the video frame shown.

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Provenza, N.R., Sheth, S.A., Dastin-van Rijn, E.M. et al. Long-term ecological assessment of intracranial electrophysiology synchronized to behavioral markers in obsessive-compulsive disorder. Nat Med 27, 2154–2164 (2021). https://doi.org/10.1038/s41591-021-01550-z

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