Control Systems and Adaptive Neurostimulation in Deep Brain Stimulation (DBS) for Treatment-Resistant Obsessive-Compulsive Disorder (TR-OCD): Architecture, Brain-Sensing, and Closed-Loop Strategies

Hedya Nadhrati Surura, Rina Hastuti Lubis, Nashrul Fazli Mohd Nasir, Joandre Fauza


Abstract


Deep Brain Stimulation (DBS) is an implantable neuromodulation system that integrates electronic components, intracranial electrodes, and biological neural networks into a bioelectronic control system designed to therapeutically modulate brain activity. The development of DBS technology for treatment-resistant obsessive-compulsive disorder (TR-OCD) has evolved from a purely anatomical target-based stimulation approach toward the integration of system architecture, control strategies, and biomarker-driven adaptive neurostimulation. This narrative review examines the architecture of DBS systems, open-loop and closed-loop control strategies, brain-sensing technologies, neural biomarkers, and recent advances in adaptive neurostimulation for TR-OCD. The review was conducted through an appraisal of contemporary literature addressing the intersection of neuroscience, electrical engineering, and biomedical engineering in the implementation of DBS. The findings indicate that a DBS system comprises an implantable pulse generator (IPG), stimulation electrodes, signal transmission components, and target neural networks that collectively form a neuromodulation control system. Conventional DBS remains predominantly based on an open-loop paradigm, in which continuous stimulation is delivered according to predefined parameters without real-time neurophysiological feedback. In contrast, advances in brain-sensing technologies have enabled the recording of neural biomarkers, particularly local field potentials (LFPs), which serve as the foundation for the development of closed-loop DBS systems. These systems allow automatic adjustment of stimulation parameters through feedback-driven mechanisms, thereby offering the potential to enhance therapeutic efficacy, improve device energy efficiency, and facilitate personalized treatment. The integration of neural biomarkers, adaptive control algorithms, and connectomic DBS approaches is expected to establish the foundation for next-generation intelligent neuromodulation systems and support the implementation of precision psychiatry in the management of TR-OCD.

Keywords


Deep Brain Stimulation; Control Systems; Adaptive Neurostimulation; Neural Biomarkers; Closed-Loop Control

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References


Meyer, G.M., Hollunder, B., Li, N., Butenko, K., Dembek, T.A., Hart, L., Nombela, C., Mosley, P., Akram, H., Acevedo, N., et al. (2024) 'Deep Brain Stimulation for Obsessive-Compulsive Disorder: Optimal Stimulation Sites', Biological Psychiatry, 96(2), pp. 101–113. Available at: http://doi.org/10.1016/j.biopsych.2023.12.010

Arbab, T., Denys, D. et al. (2025) 'Intracranial electrophysiological biomarkers of compulsivity in obsessive–compulsive disorder', Nature Mental Health, 3(8), pp. 889–898. Available at: http://doi.org/10.1038/s44220-025-00457-9

Acevedo, N., Rossell, S., Castle, D., Groves, C., Cook, M., McNeill, P., Olver, J., Meyer, D., Perera, T. and Bosanac, P. (2024) 'Clinical outcomes of deep brain stimulation for obsessive-compulsive disorder: Insight as a predictor of symptom changes', Psychiatry and Clinical Neurosciences, 78(3), pp. 131–141. Available at: http://doi.org/10.1111/pcn.13619

Gadot, R., Najera, R., Hirani, S., Anand, A., Storch, E., Goodman, W.K., Shofty, B. and Sheth, S.A. (2022) 'Efficacy of deep brain stimulation for treatment-resistant obsessive-compulsive disorder: systematic review and meta-analysis', Journal of Neurology, Neurosurgery & Psychiatry, 93(11), pp. 1166–1173. Available at: http://doi.org/10.1136/jnnp-2021-328738.

Provenza, N.R., Reddy, S., Allam, A.K., Rajesh, S.V., Diab, N., Reyes, G., Caston, R.M., Katlowitz, K.A., Gandhi, A.D., Dang, H.Q., et al. (2024) 'Disruption of neural periodicity predicts clinical response after deep brain stimulation for obsessive-compulsive disorder', Nature Medicine, 30(10), pp. 3004–3014. Available at: http://doi.org/10.1038/s41591-024-03125-0.

Sellers, K.K., Cohen, J.L., Khambhati, A.N., Fan, J.M., Lee, A.M., Chang, E.F. and Krystal, A.D. (2024) 'Closed-loop neurostimulation for the treatment of psychiatric disorders', Neuropsychopharmacology, 49(1), pp. 163–178. Available at: http://doi.org/10.1038/s41386-023-01631-2.

Horn, A., Li, N., Meyer, G.M., Gadot, R., Provenza, N.R. and Sheth, S.A. (2026) 'Deep Brain Stimulation Response Circuits in Obsessive-Compulsive Disorder', Biological Psychiatry, 99(3), pp. 189–199. Available at: http://doi.org/10.1016/j.biopsych.2025.03.008.

Provenza, N.R., Herron, J.A., Goodman, W.K. and Sheth, S.A. (2022) 'Toward Closed-Loop Intracranial Neurostimulation in Obsessive-Compulsive Disorder', Biological Psychiatry, 91(4), e29–e31. Available at: http://doi.org/10.1016/j.biopsych.2021.09.020.

Fanty, L., Yu, J., Chen, N., Fletcher, D., Hey, G., Okun, M. and Wong, J. (2023) 'The current state, challenges, and future directions of deep brain stimulation for obsessive compulsive disorder', Expert Review of Medical Devices, 20(10), pp. 829–842. Available at: http://doi.org/10.1080/17434440.2023.2252732.

Groppa, S., Gonzalez-Escamilla, G., Tinkhauser, G., Baqapuri, H.I., Sajonz, B., Wiest, C., Pereira, J., Herz, D.M., Dold, M.R., Bange, M., et al. (2024) 'Perspectives of Implementation of Closed-Loop Deep Brain Stimulation: From Neurological to Psychiatric Disorders', Stereotactic and Functional Neurosurgery, 102(1), pp. 40–54. Available at: http://doi.org/10.1159/000535114.

Sarica, C., Iorio-Morin, C., Aguirre-Padilla, D.H., Najjar, A., Paff, M., Fomenko, A., Yamamoto, K., Zemmar, A., Lipsman, N., Ibrahim, G.M., Hamani, C., Hodaie, M., Lozano, A.M., Munhoz, R.P., Fasano, A. and Kalia, S.K. (2021) 'Implantable Pulse Generators for Deep Brain Stimulation: Challenges, Complications, and Strategies for Practicality and Longevity', Frontiers in Human Neuroscience, 15, Article 708481. Available at: http://doi.org/10.3389/fnhum.2021.708481

Fariba, K.A. and Gupta, V. (2023) Deep Brain Stimulation. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing. Available at: https://www.ncbi.nlm.nih.gov/books/NBK557847/ (Accessed: 1 June 2026).

Abdelnaim, M.A., Lang-Hambauer, V., Hebel, T., Schoisswohl, S., Schecklmann, M., Deuter, D., Schlaier, J. and Langguth, B. (2023) 'Deep brain stimulation for treatment resistant obsessive compulsive disorder; an observational study with ten patients under real-life conditions', Frontiers in Psychiatry, 14, Article 1242566. Available at: http://doi.org/10.3389/fpsyt.2023.1242566.

Whitestone, J., Salih, A. and Goswami, T. (2023) 'Investigation of a Deep Brain Stimulator (DBS) System', Bioengineering, 10(10), Article 1160. Available at: http://doi.org/10.3390/bioengineering10101160.

Chen, J., Volkmann, J. and Ip, C.W. (2024) 'A framework for translational therapy development in deep brain stimulation', npj Parkinson’s Disease, 10, Article 216. Available at: http://doi.org/10.1038/s41531-024-00829-5

Ng, P.R., Bush, A., Vissani, M., McIntyre, C.C. and Richardson, R.M. (2024) 'Biophysical Principles and Computational Modeling of Deep Brain Stimulation', Neuromodulation: Technology at the Neural Interface, 27(3), pp. 422–439. Available at: http://doi.org/10.1016/j.neurom.2023.04.471.

Attilio, D.T., Domenico, L.T., Giusy, G., Giorgio, V. and Angelo, L. (2025) 'Innovative developments in deep brain stimulation devices', Interdisciplinary Neurosurgery: Advanced Techniques and Case Management, 40, Article 102035. Available at: http://doi.org/10.1016/j.inat.2025.102035

Degirmenci, Y. (2024) 'Current DBS programming', Deep Brain Stimulation, 4, pp. 29–31. Available at: http://doi.org/10.1016/j.jdbs.2023.12.002.

Sandoval-Pistorius, S.S., Hacker, M.L., Waters, A.C., Wang, J., Provenza, N.R., de Hemptinne, C., Johnson, K.A., Morrison, M.A. and Cernera, S. (2023) 'Advances in Deep Brain Stimulation: From Mechanisms to Applications', The Journal of Neuroscience, 43(45), pp. 7575–7586. Available at: http://doi.org/10.1523/JNEUROSCI.1427-23.2023

Stanslaski, S., Summers, R.L.S., Tonder, L., Tan, Y., Case, M., Raike, R.S., Morelli, N., Herrington, T.M., Beudel, M., Ostrem, J.L., et al. (2024) 'Sensing data and methodology from the Adaptive DBS Algorithm for Personalized Therapy in Parkinson’s Disease (ADAPT-PD) clinical trial', npj Parkinson’s Disease, 10, Article 174. Available at: http://doi.org/10.1038/s41531-024-00772-5.

Kachhadia, M.P., Sibhai, I., Vaghela, R., Topiwala, U., Shaikh, J.D., Tsai, M., Borad, N., Simon, M., Ilyas, H. and Zahra, T. (2025) 'Open-Loop and Closed-Loop Neuromodulation Across Neurological Disorders Toward Personalized Brain Stimulation: A Narrative Review', Cureus, 17(12), e98624. Available at: http://doi.org/10.7759/cureus.98624.

Widge, A.S. (2023) 'Closed Loop Deep Brain Stimulation for Psychiatric Disorders', Harvard Review of Psychiatry, 31(3), pp. 162–171. Available at: http://doi.org/10.1097/HRP.0000000000000367.

Widge, A.S. (2024) 'Closing the loop in psychiatric deep brain stimulation: physiology, psychometrics, and plasticity', Neuropsychopharmacology, 49(1), pp. 138–149. Available at: http://doi.org/10.1038/s41386-023-01643-y.

Khosravi, M. (2025) 'Deep Brain Stimulation in Treatment-Resistant Psychiatric Disorders: Efficacy, Safety, and Future Directions', Brain Sciences, 15(11), Article 1244. Available at: http://doi.org/10.3390/brainsci15111244.

Groppa, S., Gonzalez-Escamilla, G., Tinkhauser, G., Baqapuri, H.I., Sajonz, B., Wiest, C., Pereira, J., Herz, D.M., Dold, M.R., Bange, M., et al. (2024) 'Perspectives of Implementation of Closed-Loop Deep Brain Stimulation: From Neurological to Psychiatric Disorders', Stereotactic and Functional Neurosurgery, 102(1), pp. 40–54. Available at: http://doi.org/10.1159/000535114.

Nho, Y.H., Rolle, C.E., Topalovic, U., Shivacharan, R.S., Cunningham, T.N., Hiller, S., Batista, D., Feng, A., Espil, F.M., Kratter, I.H., et al. (2024) 'Responsive deep brain stimulation guided by ventral striatal electrophysiology of obsession durably ameliorates compulsion', Neuron, 112(1), pp. 73–83. Available at: http://doi.org/10.1016/j.neuron.2023.09.034.

Baylor College of Medicine (2025) Phase II Adaptive Deep Brain Stimulation for Obsessive-Compulsive Disorder (NCT04806516). ClinicalTrials.gov. Available at: https://clinicaltrials.gov/study/NCT04806516 (Accessed: 1 June 2026).

Bazarra Castro, G.J., Casitas, V., Martínez Macho, C., Madero Pohlen, A., Álvarez-Salas, A., Barbero Pablos, E., Fernández-Alén, J.A. and Torres Díaz, C.V. (2024) 'Biomarkers: The Key to Enhancing Deep Brain Stimulation Treatment for Psychiatric Conditions', Brain Sciences, 14(11), 1065. Available at: http://doi.org/10.3390/brainsci14111065.

Bervoets, C., Heylen, H., Nuttin, B. and Mc Laughlin, M. (2022) 'Local field potentials in the BNST in patients with OCD: acute effects of DBS after symptom provocation', European Psychiatry, 65(S1), Abstract EPV1232. Available at: http://doi.org/10.1192/j.eurpsy.2022.1907.

Wang, S., Zhu, G., Shi, L., Zhang, C., Wu, B., Yang, A., Meng, F., Jiang, Y. and Zhang, J. (2023) 'Closed-Loop Adaptive Deep Brain Stimulation in Parkinson’s Disease: Procedures to Achieve It and Future Perspectives', Journal of Parkinson’s Disease, 13(4), pp. 453–471. Available at: http://doi.org/10.3233/JPD-225053.

Oehrn, C.R., Cernera, S., Hammer, L.H., Shcherbakova, M., Yao, J., Hahn, A., Wang, S., Ostrem, J.L., Little, S. and Starr, P.A. (2024) 'Chronic adaptive deep brain stimulation versus conventional stimulation in Parkinson's disease: a blinded randomized feasibility trial', Nature Medicine, 30(11), pp. 3345–3356. Available at: http://doi.org/10.1038/s41591-024-03196-z.

Busch, J.L., Kaplan, J., Habets, J.G.V., Feldmann, L.K., Roediger, J., Kohler, R.M., Merk, T., Faust, K., Schneider, G.H., Bergman, H., Neumann, W.J. and Kühn, A.A. (2024) 'Single threshold adaptive deep brain stimulation in Parkinson’s disease depends on parameter selection, movement state and controllability of subthalamic beta activity', Brain Stimulation, 17(1), pp. 125–133. Available at: http://doi.org/10.1016/j.brs.2024.01.007.

Stanslaski, S., Summers, R.L.S., Tonder, L., Tan, Y., Case, M., Raike, R.S., Morelli, N., Herrington, T.M., Beudel, M., Ostrem, J.L., Little, S., Almeida, L., Ramirez-Zamora, A., Fasano, A., Hassell, T., Mitchell, K.T., Moro, E., Gostkowski, M., Sarangmat, N. and Bronte-Stewart, H. (2024) 'Sensing data and methodology from the Adaptive DBS Algorithm for Personalized Therapy in Parkinson’s Disease (ADAPT-PD) clinical trial', npj Parkinson’s Disease, 10, Article 174. Available at: http://doi.org/10.1038/s41531-024-00772-5.

Acharyya, P., Daley, K.W., Choi, J.W., Wilkins, K.B., Karjagi, S., Cui, C., Seo, G., Abay, A.K. and Bronte-Stewart, H.M. (2025) 'Closing the loop in DBS: A data-driven approach', Parkinsonism and Related Disorders, 134, 107348. Available at: http://doi.org/10.1016/j.parkreldis.2025.107348.

Tian, Y., Saradhi, S., Bello, E., Johnson, M.D., D’Eleuterio, G., Popovic, M.R. and Lankarany, M. (2024) 'Model-based closed-loop control of thalamic deep brain stimulation', Frontiers in Network Physiology, 4, 1356653. Available at: http://doi.org/10.3389/fnetp.2024.1356653.

Steffen, S. and Cannon, M. (2025) 'Deep Learning Model Predictive Control for Deep Brain Stimulation in Parkinson’s Disease', arXiv Preprint, arXiv:2504.00618. Available at: http://doi.org/10.48550/arXiv.2504.00618

Schopp, L., Starke, G. and Ienca, M. (2025) 'Clinician perspectives on explainability in AI-driven closed-loop neurotechnology', Scientific Reports, 15, 34638. Available at: http://doi.org/10.1038/s41598-025-19510-9.

Ravivarapu, H., Bagwe, G., Yuan, X., Yu, C. and Zhang, L. (2025) 'Sample-Efficient Reinforcement Learning Controller for Deep Brain Stimulation in Parkinson’s Disease', arXiv Preprint, arXiv:2507.06326. Available at: http://doi.org/10.48550/arXiv.2507.06326.

Zhao, T., Faustino, B.L., Jagatheesaperumal, S.K., Rolim, F.P.S. and de Albuquerque, V.H.C. (2025) 'Reinforcement learning-based adaptive deep brain stimulation computational model for the treatment of tremor in Parkinson’s disease', Expert Systems with Applications, 267, 126154. Available at: http://doi.org/10.1016/j.eswa.2024.126154.

Li, Y., Nie, Y., Li, X., Cheng, X., Zhu, G., Zhang, J., Quan, Z. and Wang, S. (2025) 'Closed-Loop Deep Brain Stimulation Platform for Translational Research', Neuromodulation: Technology at the Neural Interface, 28(3), pp. 464–471. Available at: http://doi.org/10.1016/j.neurom.2024.10.012.

Cole, E.R. and Miocinovic, S. (2025) 'Are we ready for automated deep brain stimulation programming?', Parkinsonism and Related Disorders, 134, 107347. Available at: http://doi.org/10.1016/j.parkreldis.2025.107347.




DOI: https://doi.org/10.30743/jet.v11i2.13736

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