Evidence map›Paper›PMID 34955714›Full record

ReviewFrontiers in neuroscience2021

Closing the Loop With Cortical Sensing: The Development of Adaptive Deep Brain Stimulation for Essential Tremor Using the Activa PC+S.

Tomasz M Fra Czek, Benjamin I Ferleger, Timothy E Brown, Margaret C Thompson, Andrew J Haddock, Brady C Houston, Jeffrey G Ojemann, Andrew L Ko, Jeffrey A Herron, Howard J Chizeck

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 25% 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, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Review
  6. Review
  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. 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

10 authors at 2 institutions in 1 country.

Tomasz M Fra CzekNeuroscience Program, University of Washington, Seattle, WA, United States.
Benjamin I FerlegerDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States.
Timothy E BrownDepartment of Philosophy, University of Washington, Seattle, WA, United States.
Margaret C ThompsonDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States.
Andrew J HaddockDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States.
Brady C HoustonNeuroscience Program, University of Washington, Seattle, WA, United States.
Jeffrey G OjemannDepartment of Neurological Surgery, University of Washington, Seattle, WA, United States.
Andrew L KoDepartment of Neurological Surgery, University of Washington, Seattle, WA, United States.
Jeffrey A HerronDepartment of Neurological Surgery, University of Washington, Seattle, WA, United States.
Howard J ChizeckDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States.
University of Washington · USNeurological Surgery · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deep Brain Stimulation (DBS) is an important tool in the treatment of pharmacologically resistant neurological movement disorders such as essential tremor (ET) and Parkinson's disease (PD). However, the open-loop design of current systems may be holding back the true potential of invasive neuromodulation. In the last decade we have seen an explosion of activity in the use of feedback to "close the loop" on neuromodulation in the form of adaptive DBS (aDBS) systems that can respond to the patient's therapeutic needs. In this paper we summarize the accomplishments of a 5-year study at the University of Washington in the use of neural feedback from an electrocorticography strip placed over the sensorimotor cortex. We document our progress from an initial proof of hardware all the way to a fully implanted adaptive stimulation system that leverages machine-learning approaches to simplify the programming process. In certain cases, our systems out-performed current open-loop approaches in both power consumption and symptom suppression. Throughout this effort, we collaborated with neuroethicists to capture patient experiences and take them into account whilst developing ethical aDBS approaches. Based on our results we identify several key areas for future work. "Graded" aDBS will allow the system to smoothly tune the stimulation level to symptom severity, and frequent automatic calibration of the algorithm will allow aDBS to adapt to the time-varying dynamics of the disease without additional input from a clinician. Additionally, robust computational models of the pathophysiology of ET will allow stimulation to be optimized to the nuances of an individual patient's symptoms. We also outline the unique advantages of using cortical electrodes for control and the remaining hardware limitations that need to be overcome to facilitate further development in this field. Over the course of this study we have verified the potential of fully-implanted, cortically driven aDBS as a feasibly translatable treatment for pharmacologically resistant ET.

Indexed as

adaptive deep brain stimulationdeep brain stimulationessential tremorfully implantablemachine learningmotor cortex

Identifiers

PMID34955714
PMCPMC8695120
OpenAlexW4200089007

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.