Closed-loop neuromodulation in an individual with treatment-resistant depression (original) (raw)

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.

References

  1. Howland, R. H. Sequenced Treatment Alternatives to Relieve Depression (STAR*D). Part 2: study outcomes. J. Psychosoc. Nurs. Ment. Health Serv. 46, 21–24 (2008).
    PubMed Google Scholar
  2. Holtzheimer, P. E. et al. Subcallosal cingulate deep brain stimulation for treatment-resistant depression: a multisite, randomised, sham-controlled trial. Lancet Psychiatry 4, 839–849 (2017).
    Article Google Scholar
  3. Dougherty, D. D. et al. A randomized sham-controlled trial of deep brain stimulation of the ventral capsule/ventral striatum for chronic treatment-resistant depression. Biol. Psychiatry 78, 240–248 (2015).
    Article Google Scholar
  4. Bergfeld, I. O. et al. Deep brain stimulation of the ventral anterior limb of the internal capsule for treatment-resistant depression: a randomized clinical trial. JAMA Psychiatry 73, 456–464 (2016).
    Article Google Scholar
  5. Williams, L. M. Precision psychiatry: a neural circuit taxonomy for depression and anxiety. Lancet Psychiatry 3, 472–480 (2016).
    Article Google Scholar
  6. Bouthour, W. et al. Biomarkers for closed-loop deep brain stimulation in Parkinson disease and beyond. Nat. Rev. Neurol. 15, 343–352 (2019).
    Article Google Scholar
  7. Scangos, K. W., Makhoul, G. S., Sugrue, L. P., Chang, E. F. & Krystal, A. D. State-dependent responses to intracranial brain stimulation in a patient with depression. Nat. Med. 27, 229–231 (2021).
    Article CAS Google Scholar
  8. Keller, C. J. et al. Mapping human brain networks with cortico-cortical evoked potentials. Philos. Trans. R. Soc. Lond. B Biol. Sci. 369, 20130528 (2014).
    Article Google Scholar
  9. Sun, F. T. & Morrell, M. J. The RNS System: responsive cortical stimulation for the treatment of refractory partial epilepsy. Expert Rev. Med. Devices 11, 563–572 (2014).
    Article CAS Google Scholar
  10. Giorgino, T. Computing and visualizing dynamic time warping alignments in R: the dtw package. J. Stat. Softw. 31 (2009).
  11. Price, J. L. & Drevets, W. C. Neurocircuitry of mood disorders. Neuropsychopharmacology 35, 192–216 (2010).
    Article Google Scholar
  12. Kirkby, L. A. et al. An amygdala-hippocampus subnetwork that encodes variation in human mood. Cell 175, 1688–1700.e14 (2018).
    Article CAS Google Scholar
  13. Malone, D. A. Jr. et al. Deep brain stimulation of the ventral capsule/ventral striatum for treatment-resistant depression. Biol. Psychiatry 65, 267–275 (2009).
    Article Google Scholar
  14. Mayberg, H. S. et al. Deep brain stimulation for treatment-resistant depression. Neuron 45, 651–660 (2005).
    Article CAS Google Scholar
  15. Haq, I. U. et al. Smile and laughter induction and intraoperative predictors of response to deep brain stimulation for obsessive-compulsive disorder. Neuroimage 54, S247–S255 (2011).
    Article Google Scholar
  16. Dunner, D. L. et al. Prospective, long-term, multicenter study of the naturalistic outcomes of patients with treatment-resistant depression. J. Clin. Psychiatry 67, 688–695 (2006).
    Article Google Scholar
  17. Waltz, E. Green light for deep brain stimulator incorporating neurofeedback. Nat. Biotechnol. 38, 1014–1015 (2020).
    Article CAS Google Scholar
  18. Riva-Posse, P. et al. Defining critical white matter pathways mediating successful subcallosal cingulate deep brain stimulation for treatment-resistant depression. Biol. Psychiatry 76, 963–969 (2014).
    Article Google Scholar
  19. Campbell, M. C. et al. Mood response to deep brain stimulation of the subthalamic nucleus in Parkinson’s disease. J. Neuropsychiatry Clin. Neurosci. 24, 28–36 (2012).
    Article Google Scholar
  20. Timmerby, N., Andersen, J. H., Søndergaard, S., Østergaard, S. D. & Bech, P. A systematic review of the clinimetric properties of the 6-item version of the Hamilton Depression Rating Scale (HAM-D6). Psychother. Psychosom. 86, 141–149 (2017).
    Article CAS Google Scholar
  21. Salloum, N. C. et al. Efficacy of intravenous ketamine treatment in anxious versus nonanxious unipolar treatment-resistant depression. Depress. Anxiety 36, 235–243 (2019).
    Article CAS Google Scholar
  22. Yeung, A. W. K. & Wong, N. S. M. The historical roots of visual analog scale in psychology as revealed by reference publication year spectroscopy. Front. Hum. Neurosci. 13, 86 (2019).
    Article Google Scholar
  23. Harris, P. A. et al. The REDCap consortium: building an international community of software platform partners. J. Biomed. Inform. 95, 103208 (2019).
    Article Google Scholar
  24. Harris, P. A. et al. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J. Biomed. Inform. 42, 377–381 (2009).
    Article Google Scholar
  25. Chang, E. F. Towards large-scale, human-based, mesoscopic neurotechnologies. Neuron 86, 68–78 (2015).
    Article CAS Google Scholar
  26. Matsumoto, R. et al. Functional connectivity in the human language system: a cortico-cortical evoked potential study. Brain 127, 2316–2330 (2004).
    Article Google Scholar
  27. Rao, V. R. et al. Direct electrical stimulation of lateral orbitofrontal cortex acutely improves mood in individuals with symptoms of depression. Curr. Biol. 28, 3893–3902.e4 (2018).
    Article CAS Google Scholar
  28. Schiff, S. J., Aldroubi, A., Unser, M. & Sato, S. Fast wavelet transformation of EEG. Electroencephalogr. Clin. Neurophysiol. 91, 442–455 (1994).
    Article CAS Google Scholar
  29. Friston, K. J. Functional and effective connectivity: a review. Brain Connect. 1, 13–36 (2011).
    Article Google Scholar
  30. Bassett, D. S. & Sporns, O. Network neuroscience. Nat. Neurosci. 20, 353–364 (2017).
    Article CAS Google Scholar
  31. Khambhati, A. N. et al. Functional control of electrophysiological network architecture using direct neurostimulation in humans. Netw. Neurosci. 3, 848–877 (2019).
    Article Google Scholar
  32. Riva-Posse, P. et al. A connectomic approach for subcallosal cingulate deep brain stimulation surgery: prospective targeting in treatment-resistant depression. Mol. Psychiatry 23, 843–849 (2018).
    Article CAS Google Scholar
  33. Choi, K. S., Riva-Posse, P., Gross, R. E. & Mayberg, H. S. Mapping the “depression switch” during intraoperative testing of subcallosal cingulate deep brain stimulation. JAMA Neurol. 72, 1252–1260 (2015).
    Article Google Scholar
  34. Sokolov, A. A. et al. Linking structural and effective brain connectivity: structurally informed Parametric Empirical Bayes (si-PEB). Brain Struct. Funct. 224, 205–217 (2019).
    Article Google Scholar
  35. Mori, S. & van Zijl, P. C. Fiber tracking: principles and strategies—a technical review. NMR Biomed. 15, 468–480 (2002).
    Article Google Scholar
  36. Rabiner, L. R & Juang, B. H. Fundamentals of Speech Recognition (PTR Prentice Hall, 1993).
    Google Scholar
  37. Sakoe, H. & Chiba, S. Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. 26, 43–49 (1978).
    Article Google Scholar

Download references

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

Author notes

  1. These authors contributed equally: Andrew D. Krystal, Edward F. Chang.

Authors and Affiliations

  1. Weill Institute for Neurosciences, University of California, San Francsico, CA, USA
    Katherine W. Scangos, Ankit N. Khambhati, Patrick M. Daly, Ghassan S. Makhoul, Leo P. Sugrue, Hashem Zamanian, Tony X. Liu, Vikram R. Rao, Kristin K. Sellers, Heather E. Dawes, Philip A. Starr, Andrew D. Krystal & Edward F. Chang
  2. Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA
    Katherine W. Scangos, Patrick M. Daly, Ghassan S. Makhoul, Hashem Zamanian, Tony X. Liu & Andrew D. Krystal
  3. Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, USA
    Ankit N. Khambhati, Kristin K. Sellers, Heather E. Dawes, Philip A. Starr & Edward F. Chang
  4. Department of Radiology, University of California, San Francisco, San Francisco, CA, USA
    Leo P. Sugrue
  5. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
    Vikram R. Rao

Authors

  1. Katherine W. Scangos
  2. Ankit N. Khambhati
  3. Patrick M. Daly
  4. Ghassan S. Makhoul
  5. Leo P. Sugrue
  6. Hashem Zamanian
  7. Tony X. Liu
  8. Vikram R. Rao
  9. Kristin K. Sellers
  10. Heather E. Dawes
  11. Philip A. Starr
  12. Andrew D. Krystal
  13. Edward F. Chang

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 toKatherine 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

Rights and permissions

About this article

Cite this article

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

Download citation