ReviewParkinsonism & related disorders2025
Closing the loop in DBS: A data-driven approach.
Review in Parkinsonism & related disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- N2G calibrator: a cross-subject adversarial learning framework for neural signal-driven gait tracking in Parkinson's disease.Communications engineering · 2026Article
- Cognitive trajectories in Parkinson's disease patients, a review on the impact of subthalamic deep brain stimulation (STN-DBS) and emerging adaptive strategies.Translational psychiatry · 2026Review
- Heart-Brain Axis Dysregulation in Depression: Mechanisms and Implications for Neuromodulation.Neuropsychiatric disease and treatment · 2026Review
- Reduced pre-movement subthalamic beta desynchronization marks motor deficit in Parkinson's disease.Brain communications · 2026Article
- The Modern Utility of Awake Deep Brain Stimulation Surgery.medRxiv : the preprint server for health sciences · 2025Article
- Locally adapting digital health technologies to address the global challenge of Parkinson's disease.Equity neuroscience · 2025Article
- Artificial intelligence-enhanced pain management: the NXTSTIM EcoAI platform.Pain management · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Deep brain stimulation (DBS) has transformed the treatment of movement disorders like Parkinson's Disease (PD). Innovations in DBS technology and experimentation have fostered adaptive DBS (aDBS), which employs a closed-loop system that senses physiological biomarkers to inform precise neuromodulation and personalized therapy. This review analyzes several promising advances in aDBS, including biomarker detection, control policies, mechanisms of efficacy, and a data-driven approach using artificial intelligence to decode motor states from neural signals. Investigations into data-driven approaches have expanded biomarker detection beyond subcortical beta oscillations, leveraging other neural and kinematic signals. Future aDBS systems that accommodate multi-modal inputs have the potential to bolster therapeutic efficacy and address symptoms not addressed by beta-driven aDBS. Continuing investigation is necessary to address existing technical and computational challenges for further clinical translation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.