Objective: To compare the efficacy of a novel automated connectomic programming (ACP) algorithm to standard clinical programming (SCP) in patients undergoing deep brain stimulation (DBS) for Parkinson’s Disease (PD).

Background: We recently demonstrated the safety and preliminary efficacy of ACP in 13 patients [1].To clarify ACP’s potential role in clinical practice, we designed a two-cohort crossover study to directly compare ACP with SCP.

Method: Patients underwent preoperative MRI, DBS implantation per routine practice, and postoperative CT. Using a driving-force model of axonal pathway recruitment [2] and an optimization algorithm [1], we derived ACP parameters that maximize therapeutic pathway stimulation while minimizing side-effect pathway recruitment. During the initial programming visit, both ACP and SCP were implemented, and patients were randomized in a blinded fashion to start on one approach for 2–3 weeks, then crossover for another 2–3 weeks before final assessment. A blinded evaluator performed motor assessments at each visit, and patients stated their program preference at the final visit.

Results: ACP took 20-40 minutes to implement, while SCP took 1–2 hours. Of the 7 patients enrolled, 4 were randomized to start on SCP and 3 to ACP. 4 patients withdrew from the study after their crossover, opting to revert to their initial program and forgo final assessment. The high dropout rate limits our ability to quantitatively compare ACP to SCP. All 7 patients preferred their initial program regardless of SCP or ACP assignment. According to our model, ACP recruited more therapeutic pathways for all patients; and recruited more side effect pathways in 2 patients.

Conclusion: ACP was faster to implement. Patients consistently preferred their initial program, whether SCP or ACP. More than half of patients dropped out, making this crossover design ineffective for evaluating DBS program efficacy. For ACP programs, side effect pathway recruitment was comparable or lower for most patients, while therapeutic pathway recruitment was universally higher. During DBS programming, patients with PD may experience heightened anxiety about programming changes due to fears of symptom regression [3-6]. With this context and with a high dropout rate, future studies must adopt a two-arm randomized controlled trial design.

References: [1] Hines, K., Noecker, A. M., Frankemolle‐Gilbert, A. M., Liang, T., Ratliff, J., Heiry, M., McIntyre, C. C., & Wu, C. (2024). Prospective connectomic‐based deep brain stimulation programming for parkinson’s disease. Movement Disorders, 39(12), 2249–2258. https://doi.org/10.1002/mds.30026

[2] Noecker, A. M., Frankemolle-Gilbert, A. M., Howell, B., Petersen, M. V., Beylergil, S. B., Shaikh, A. G., & McIntyre, C. C. (2021a). StimVision V2: Examples and applications in subthalamic deep brain stimulation for parkinson’s disease. Neuromodulation: Technology at the Neural Interface, 24(2), 248–258. https://doi.org/10.1111/ner.13350

[3] Cole, E. R., & Miocinovic, S. (2025). Are we ready for Automated Deep Brain Stimulation Programming? Parkinsonism & Related Disorders, 107347.
https://doi.org/10.1016/j.parkreldis.2025.107347

[4] Colloca, L. (2017). Nocebo effects can make you feel pain. Science, 358(6359), 44–44. https://doi.org/10.1126/science.aap8488

[5] Colloca, L., Lopiano, L., Lanotte, M., & Benedetti, F. (2004). Overt versus covert treatment for pain, anxiety, and parkinson’s disease. The Lancet Neurology, 3(11), 679–684. https://doi.org/10.1016/s1474-4422(04)00908-1

[6] Mestre, T. A., Lang, A. E., & Okun, M. S. (2016). Factors influencing the outcome of deep brain stimulation: Placebo, nocebo, Lessebo, and lesion effects. Movement
Disorders, 31(3), 290–298. https://doi.org/10.1002/mds.26500

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MDS Abstracts - https://www.mdsabstracts.org/abstract/initial-experience-with-a-crossover-study-comparing-automated-connectomic-programming-to-standard-clinical-programming-for-dbs/