Category: Parkinson's Disease: Surgical Therapy
Objective: We conducted a systematic review and meta-analysis (SRMA) to evaluate the efficacy of adaptive Deep Brain Stimulation (aDBS) compared to conventional Deep Brain Stimulation (cDBS), to improve motor fluctuation in parkinson’s
Background: Parkinson’s disease (PD), has a prevalence of nearly 1% of population over 65. While cDBS sends unmodulated stimulation that fails to account for dynamic physiological brain states, aDBS have an advancement by utilising AI-driven sensors to detect neural biomarkers. aDBS adapts to provide personalized therapy, demonstrating better acute management of motor fluctuations signified by improved Unified Parkinson’s disease rating scale (UPDRS-III) scores.
Method: This SRMA is conducted in respect with PRISMA guidelines and is registered in PROSPERO under registration number CRD420261333851. We systematically searched databases including PubMed and Cochrane as in Figure 1 to identify trials comparing the efficacy of aDBS with cDBS in improving motor fluctuations in PD. Meta-analyses were performed using a random-effects model to account for inter-study heterogeneity. The primary outcome to analyse the efficacy of cDBS and aDBS for improving motor fluctuations was assessed by using Mean difference (MD).
Results: 11 studies were identified of which 4 provided data for meta-analysis. A pooled analysis of 4 studies comparing aDBS with cDBS in PD. The primary pooled outcome of assessing the efficacy of both modalities came as an overall MD of −2.15 (95% CI: −5.21 – 0.92) shown in Figure 2, indicating a trend toward improved motor outcomes with aDBS compared with cDBS. A secondary analysis restricted to acute stimulation showed a pooled MD of −3.91 (95% CI: −10.41 – 2.59) shown in Figure 3 again favoring aDBS in case of acute stimulation but without statistical significance.
Conclusion: This study indicates that aDBS may offer better outcomes compared with cDBS in case of PD, as indicated by consistently negative pooled MD across studies. Although the pooled analysis was not of statistical significance, the direction of effect favored aDBS. Our findings underscores the potential of aDBS as a more responsive neuromodulation strategy and emphasis the need of larger trials in future in this field to establish and confirm the clinical benefits of aDBS over cDBS.
Figure 1
Figure 2
Figure 3
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To cite this abstract in AMA style:
A. Gunasekar, M. Ahmad, M. Thavasi, P. Subburaj, M. Venkastesan, S. Sv, A. Soroush, K. Prakash Meena, R. Ramananthan, R. Vignesh, J. Srirangaramasamy, B. Kamaraj. Automated Closed-Loop DBS Using AI Decoding in Parkinson’s Disease: A Systematic Review and Meta-Analysis of UPDRS-III Improvements in tremor, bradykinesia and Motor Fluctuations. [abstract]. Mov Disord. 2026; 41 (suppl 1). https://www.mdsabstracts.org/abstract/automated-closed-loop-dbs-using-ai-decoding-in-parkinsons-disease-a-systematic-review-and-meta-analysis-of-updrs-iii-improvements-in-tremor-bradykinesia-and-motor-fluctuations/. Accessed October 1, 2026.« Back to 2026 International Congress
MDS Abstracts - https://www.mdsabstracts.org/abstract/automated-closed-loop-dbs-using-ai-decoding-in-parkinsons-disease-a-systematic-review-and-meta-analysis-of-updrs-iii-improvements-in-tremor-bradykinesia-and-motor-fluctuations/



