Evidence map›Paper›PMID 31001196›Full record

ReviewFrontiers in neurology2019

Deep Brain Stimulation Programming 2.0: Future Perspectives for Target Identification and Adaptive Closed Loop Stimulation.

Franz Hell, Carla Palleis, Jan H Mehrkens, Thomas Koeglsperger, Kai Bötzel

Abstract readReview
In one paragraph

Review in Frontiers in neurology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

33 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Adaptive Deep Brain Stimulation for Parkinson's Disease: Navigating the Roadblocks to Clinical Implementation.Movement disorders : official journal of the Movement Disorder Society · 2026
    Review
  4. Article
  5. Effect of frequency on dual target deep brain stimulation.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2026
    Article
  6. Article
  7. A Wearable Dual-Mode Probe for Image-Guided Closed-Loop Ultrasound Neuromodulation.IEEE transactions on biomedical circuits and systems · 2025
    Article
  8. Review
  9. Review
  10. Article
  11. Computational Neurosurgery in Deep Brain Stimulation.Advances in experimental medicine and biology · 2024
    Review
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Review
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Franz HellDepartment of Neurology, Ludwig Maximilians University, Munich, Germany.
Carla PalleisDepartment of Neurology, Ludwig Maximilians University, Munich, Germany.
Jan H MehrkensDepartment of Neurosurgery, Ludwig Maximilians University, Munich, Germany.
Thomas KoeglspergerDepartment of Neurology, Ludwig Maximilians University, Munich, Germany.
Kai BötzelDepartment of Neurology, Ludwig Maximilians University, Munich, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deep brain stimulation has developed into an established treatment for movement disorders and is being actively investigated for numerous other neurological as well as psychiatric disorders. An accurate electrode placement in the target area and the effective programming of DBS devices are considered the most important factors for the individual outcome. Recent research in humans highlights the relevance of widespread networks connected to specific DBS targets. Improving the targeting of anatomical and functional networks involved in the generation of pathological neural activity will improve the clinical DBS effect and limit side-effects. Here, we offer a comprehensive overview over the latest research on target structures and targeting strategies in DBS. In addition, we provide a detailed synopsis of novel technologies that will support DBS programming and parameter selection in the future, with a particular focus on closed-loop stimulation and associated biofeedback signals.

Indexed as

adaptiveDBS targetdeep brain stimulationfeedbackmachine learningreinforcement learning

Identifiers

PMID31001196
PMCPMC6456744

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.