Evidence map›Paper›PMID 35478842›Full record

ReviewFrontiers in neuroscience2022

Deep Brain Stimulation: Emerging Tools for Simulation, Data Analysis, and Visualization.

Karin Wårdell, Teresa Nordin, Dorian Vogel, Peter Zsigmond, Carl-Fredrik Westin, Marwan Hariz, Simone Hemm

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.9field-weighted citation impact, top 8% of its field
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

8 citing papers in PubMed, 25 citations in OpenAlex.

  1. Review
  2. Article
  3. Clinical neurophysiology in the treatment of movement disorders: IFCN handbook chapter.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2024
    Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. Review
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

7 authors at 3 institutions in 4 countries.

Karin WårdellNeuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
Teresa NordinNeuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
Dorian VogelNeuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
Peter ZsigmondDepartment of Neurosurgery and Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden.
Carl-Fredrik WestinNeuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
Marwan HarizUnit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, London, United Kingdom.
Simone HemmNeuroengineering Lab, Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
Linköping University · SEFHNW University of Applied Sciences and Arts · CHNational Hospital for Neurology and Neurosurgery · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deep brain stimulation (DBS) is a well-established neurosurgical procedure for movement disorders that is also being explored for treatment-resistant psychiatric conditions. This review highlights important consideration for DBS simulation and data analysis. The literature on DBS has expanded considerably in recent years, and this article aims to identify important trends in the field. During DBS planning, surgery, and follow up sessions, several large data sets are created for each patient, and it becomes clear that any group analysis of such data is a big data analysis problem and has to be handled with care. The aim of this review is to provide an update and overview from a neuroengineering perspective of the current DBS techniques, technical aids, and emerging tools with the focus on patient-specific electric field (EF) simulations, group analysis, and visualization in the DBS domain. Examples are given from the state-of-the-art literature including our own research. This work reviews different analysis methods for EF simulations, tractography, deep brain anatomical templates, and group analysis. Our analysis highlights that group analysis in DBS is a complex multi-level problem and selected parameters will highly influence the result. DBS analysis can only provide clinically relevant information if the EF simulations, tractography results, and derived brain atlases are based on as much patient-specific data as possible. A trend in DBS research is creation of more advanced and intuitive visualization of the complex analysis results suitable for the clinical environment.

Indexed as

connectivitydeep brain stimulation (DBS)intraoperative measurementsmodeling and simulationneuroimagingprobabilistic mappingvisualization

Identifiers

PMID35478842
PMCPMC9036439
OpenAlexW4223555048

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.