Evidence map›Paper›PMID 29066947›Full record

ReviewFrontiers in neuroscience2017

Biomarkers and Stimulation Algorithms for Adaptive Brain Stimulation.

Kimberly B Hoang, Isaac R Cassar, Warren M Grill, Dennis A Turner

Abstract readReview
In one paragraph

Review in Frontiers in neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 1 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

39 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. 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
  3. Article
  4. Review
  5. Article
  6. Article
  7. Development and Evaluation of a Real-Time Phase-Triggered Stimulation Algorithm for the CorTec Brain Interchange.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2024
    Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Cross-Frequency Coupling and Intelligent Neuromodulation.Cyborg and bionic systems (Washington, D.C.) · 2023
    Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  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

4 authors.

Kimberly B HoangDepartment of Neurosurgery, Duke University, Durham, NC, United States.
Isaac R CassarDepartment of Biomedical Engineering, Duke University, Durham, NC, United States.
Warren M GrillDepartment of Neurosurgery, Duke University, Durham, NC, United States.
Dennis A TurnerDepartment of Neurosurgery, Duke University, Durham, NC, United States.

Funding

Temporal Patterns of Deep Brain StimulationR37NS040894 · NINDS · DUKE UNIVERSITY · PI GRILL, WARREN M. · 2015 to 2021
$4.2M
Scalar Closed-Loop STN/GPi DBS Based on Evoked and Spontaneous PotentialsUH3NS103468 · NINDS · DUKE UNIVERSITY · PI TURNER, DENNIS ALAN · 2017 to 2021
$2.0M
Recording Evoked Potentials for Closed-Loop DBSR01NS079312 · NINDS · DUKE UNIVERSITY · PI GRILL, WARREN M. · 2012 to 2014
$1.1M
NINDS NIH HHS R01 NS079312NINDS NIH HHS R37 NS040894NINDS NIH HHS UH3 NS103468
6 · The paper itself

Abstract

The goal of this review is to describe in what ways feedback or adaptive stimulation may be delivered and adjusted based on relevant biomarkers. Specific treatment mechanisms underlying therapeutic brain stimulation remain unclear, in spite of the demonstrated efficacy in a number of nervous system diseases. Brain stimulation appears to exert widespread influence over specific neural networks that are relevant to specific disease entities. In awake patients, activation or suppression of these neural networks can be assessed by either symptom alleviation (i.e., tremor, rigidity, seizures) or physiological criteria, which may be predictive of expected symptomatic treatment. Secondary verification of network activation through specific biomarkers that are linked to symptomatic disease improvement may be useful for several reasons. For example, these biomarkers could aid optimal intraoperative localization, possibly improve efficacy or efficiency (i.e., reduced power needs), and provide long-term adaptive automatic adjustment of stimulation parameters. Possible biomarkers for use in portable or implanted devices span from ongoing physiological brain activity, evoked local field potentials (LFPs), and intermittent pathological activity, to wearable devices, biochemical, blood flow, optical, or magnetic resonance imaging (MRI) changes, temperature changes, or optogenetic signals. First, however, potential biomarkers must be correlated directly with symptom or disease treatment and network activation. Although numerous biomarkers are under consideration for a variety of stimulation indications the feasibility of these approaches has yet to be fully determined. Particularly, there are critical questions whether the use of adaptive systems can improve efficacy over continuous stimulation, facilitate adjustment of stimulation interventions and improve our understanding of the role of abnormal network function in disease mechanisms.

Indexed as

beta hypersynchronyclosed loopdeep brain stimulationepilepsyevoked field potentialsParkinson's diseasephase amplitude couplingresponsive brain stimulation

Identifiers

PMID29066947
PMCPMC5641319

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.