Evidence map›Paper›PMID 35104499›Full record

ReviewExperimental neurology2022

Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation.

Timon Merk, Victoria Peterson, Richard Köhler, Stefan Haufe, R Mark Richardson, Wolf-Julian Neumann

Open access · hybridAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Experimental neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 1 pooled it
9.8field-weighted citation impact, top 1% 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

41 citing papers in PubMed, 1 synthesis or guideline pooled it, 84 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Review
  5. Article
  6. Article
  7. Review
  8. Review
  9. Review
  10. Review
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  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

6 authors at 3 institutions in 2 countries.

Timon MerkMovement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Chariteplatz 1, 10117 Berlin, Germany.
Victoria PetersonDepartment of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, United States.
Richard KöhlerMovement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Chariteplatz 1, 10117 Berlin, Germany.
Stefan HaufeBerlin Center for Advanced Neuroimaging (BCAN), Charité - Universitätsmedizin Berlin, Chariteplatz 1, 10117 Berlin, Germany.
R Mark RichardsonDepartment of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, United States.
Wolf-Julian NeumannMovement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Chariteplatz 1, 10117 Berlin, Germany. Electronic address: julian.neumann@charite.de.
Charité - Universitätsmedizin Berlin · DEHarvard University · USMassachusetts General Hospital · US

Funding

DBS Protocols for Long-Lasting Therapeutic Benefit in Mouse and Primate Models of Parkinson's DiseaseR01NS117058 · NINDS · CARNEGIE-MELLON UNIVERSITY · PI GITTIS, ARYN HILARY, TURNER, ROBERT STERLING · 2020 to 2024
$2.8M
CRCNS: Deep Neural Network Approaches for Closed-Loop Deep Brain StimulationR01NS110424 · NINDS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI RICHARDSON, ROBERT MARK, TURNER, ROBERT STERLING · 2018 to 2020
$673k
NINDS NIH HHS R01 NS110424NINDS NIH HHS R01 NS117058
6 · The paper itself

Abstract

Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.

Indexed as

Brain-Computer InterfacesDeep Brain StimulationAlgorithmsBrainMachine LearningAdaptive deep brain stimulationBrain-computer interfaceClosed-loop DBSMovement disordersNeural decodingReal-time classification

Identifiers

PMID35104499
PMCPMC10521329
OpenAlexW4210458691

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.