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Closed-loop neuromodulation in an individual with treatment-resistant depression

Abstract

Deep brain stimulation is a promising treatment for neuropsychiatric conditions such as major depression. It could be optimized by identifying neural biomarkers that trigger therapy selectively when symptom severity is elevated. We developed an approach that first used multi-day intracranial electrophysiology and focal electrical stimulation to identify a personalized symptom-specific biomarker and a treatment location where stimulation improved symptoms. We then implanted a chronic deep brain sensing and stimulation device and implemented a biomarker-driven closed-loop therapy in an individual with depression. Closed-loop therapy resulted in a rapid and sustained improvement in depression. Future work is required to determine if the results and approach of this n-of-1 study generalize to a broader population.

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Fig. 1: Neural biomarker and limbic subnetwork of depression.
Fig. 2: Implementation of closed-loop neuromodulation.

Data availability

The data that support the findings in this article are available within the article itself, the source data and within our publicly available GitHub repository. Raw neural signals are freely available from the corresponding author upon request. The data used to produce the results and figures in this paper are available at https://github.com/ScangosLab/closed_loop_mdd. Source data are provided with this paper.

Code availability

The code used to produce the results and figures in this paper is available at https://github.com/ScangosLab/closed_loop_mdd.

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Acknowledgements

This work was supported by a National Institutes of Health award no. K23NS110962 (K.W.S.), NARSAD Young Investigator grant from the Brain & Behavior Research Foundation (K.W.S.), 1907 Trailblazer Award (K.W.S.) and a Ray and Dagmar Dolby Family Fund through the Department of Psychiatry at UCSF (K.W.S., A.D.K., E.F.C., L.P.S., A.N.K., P.M.S., G.S.M., H.Z., T.X.L., V.R.R., K.K.S. and H.E.D.).

Author information

Affiliations

Authors

Contributions

K.W.S., A.N.K., A.D.K. and E.F.C. initiated the work and supervised the study with K.W.S. and A.N.K. contributing equally to the work. H.E.D. and P.A.S. contributed to the conceptualization and evaluation of the study protocol. K.W.S. drafted the manuscript. P.M.D., G.S.M., K.W.S., A.N.K., H.Z., T.X.L., V.R.R., K.K.S. and L.P.S. collected and analyzed the data. A.D.K. and E.F.C. finalized the manuscript. All authors approved the work and take responsibility for its integrity.

Corresponding author

Correspondence to Katherine W. Scangos.

Ethics declarations

Competing interests

A.D.K. consults for Eisai, Evecxia Therapeutics, Ferring Pharmaceuticals, Galderma, Harmony Biosciences, Idorsia, Jazz Pharmaceuticals, Janssen Pharmaceuticals, Merck, Neurocrine Biosciences, Pernix Pharma, Sage Therapeutics, Takeda Pharmaceutical Company, Big Health, Millennium Pharmaceuticals, Otsuka Pharmaceutical and Neurawell Therapeutics. A.D.K. acknowledges support from Janssen Pharmaceuticals, Jazz Pharmaceuticals, Axsome Therapeutics (no. AXS-05-301) and Reveal Biosensors. K.W.S. serves on the advisory board of Nesos. UCSF and E.F.C. have patents related to brain stimulation for the treatment of neuropsychiatric disorders. V.R.R. has served as a paid consultant for NeuroPace but declares no targeted funding from NeuroPace for this study. P.A.S. receives research support from Medtronic. The other authors declare no competing interests.

Additional information

Peer review information Nature Medicine thanks Ziv 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 Network Activity and Connectivity.

a. Biomarker identification was performed at two levels of spatial resolution (see also Fig. 1c). In the brain region level model, spectral power was averaged across all contacts within brain regions (60 features). Top neural features (defined by F-score, ANOVA) that discriminated high vs. low symptom severity states are shown. Gamma power in the bilateral AMY, right OFC, left SGC, and right HPC had high state discriminative potential (Accuracy: mean 0.73, std 0.08; AUC: mean 0.76, std 0.10). ROC curves reflect mean ± SEM over n=1000 randomly sampled features for the true model (blue) and the shuffled model (gray). b. Evoked potentials (z-scored relative to baseline) across the corticolimbic network due to single pulse stimulation in the right VC/VS, OFC and SGC is shown overlayed on brain as heatmap. Warmer colors indicate a larger N1 amplitude. c. Location of right sided sEEG leads targeting AMY (pink), VC/VS (orange), SGC (green), and OFC (blue). Fiber tracts (color coded by orientation) show putative structural connections between candidate pairs of stimulation and sensing contacts (VC/VS-AMY, VC/VS-OFC, SGC-AMY, OFC-AMY) from deterministic tractography using 3 mm ROIs centered on each contact. Tractography parameters were the same for all pairs: minimum FA = 0.1; minimum fiber length = 80 mm; maximum angulation = 20 degrees.

Source data

Extended Data Fig. 2 Overall Approach to Stimulation and Sensing Target Selection.

Multimodal method for personalized responsive stimulation multi-lead targeting began with clinical mapping to identify candidate sites where stimulation reliably led to symptom improvement across a range of doses and symptom severity states. Candidate sensing locations were identified by pairing resting state neural activity with symptom severity ratings to identify spectral power biomarkers that correlated with depression. The relationship between candidate stimulation and sensing targets were then tested using three approaches. First, effective network connectivity was assessed by examining the evoked response at nodes across the network due to single pulse stimulation at candidate targets. Second, structural connectivity between candidate contact pairs was assessed using tractography. Influential tracts were identified to help with retargeting during implantation of chronic stimulation device. Finally, the feasibility of closed-loop control was assessed by examining the effect of stimulation in candidate stimulation sites on putative biomarkers identified in candidate sensing locations. This personalized approach enabled us to identify one best stimulation and sensing target pair which were then utilized for the implantation of the RNS System and delivery of closed-loop neurostimulation therapy.

Supplementary information

Source data

Source Data Fig. 1

Raw data. Statistical source data for Source Data Fig. 1.

Source Data Fig. 2

Raw data. Statistical source data for Source Data Fig. 2.

Source Data Extended Data Fig. 1

Raw data. Statistical source data for Extended Data Fig. 1.

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Scangos, K.W., Khambhati, A.N., Daly, P.M. et al. Closed-loop neuromodulation in an individual with treatment-resistant depression. Nat Med 27, 1696–1700 (2021). https://doi.org/10.1038/s41591-021-01480-w

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