Evidence map›Paper›PMID 32759488›Full record

ArticleJournal of neural engineering2020

A comprehensive model-based framework for optimal design of biomimetic patterns of electrical stimulation for prosthetic sensation.

Karthik Kumaravelu, Tucker Tomlinson, Thierri Callier, Joseph Sombeck, Sliman J Bensmaia, Lee E Miller, Warren M Grill

Open access · greenAbstract read
In one paragraph

Article in Journal of neural engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.4field-weighted citation impact, top 11% 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

15 citing papers in PubMed, 32 citations in OpenAlex.

  1. Review
  2. Integration of learned artificial sensation with vision during freely moving navigation.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  3. Article
  4. NRV: An open framework forbioRxiv : the preprint server for biology · 2024
    Article
  5. Neural Plasticity in Sensorimotor Brain-Machine Interfaces.Annual review of biomedical engineering · 2023
    Review
  6. Article
  7. Review
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. 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

7 authors at 3 institutions in 1 country.

Karthik KumaraveluDepartment of Biomedical Engineering, Duke University, Durham, NC, United States of America.ORCID 0000-0002-7619-322X
Tucker TomlinsonDeptartment of Physiology, Northwestern University, Chicago, IL, United States of America.
Thierri CallierDepartment of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, United States of America.
Joseph SombeckDepartment of Biomedical Engineering, Northwestern University, Chicago, IL, United States of America.
Sliman J BensmaiaDepartment of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, United States of America.ORCID 0000-0003-4039-9135
Lee E MillerDeptartment of Physiology, Northwestern University, Chicago, IL, United States of America.ORCID 0000-0001-8675-7140
Warren M GrillDepartment of Biomedical Engineering, Duke University, Durham, NC, United States of America.ORCID 0000-0001-5240-6588
Northwestern University · USDuke University · USUniversity of Chicago · US

Funding

Biomimetic Somatosensory Feedback through IntracorticalmicrostimulationR01NS095251 · NINDS · NORTHWESTERN UNIVERSITY AT CHICAGO · PI BENSMAIA, SLIMAN, GRILL, WARREN M. · 2016 to 2020
$3.7M
NINDS NIH HHS R01 NS095251
6 · The paper itself

Abstract

objectiveTouch and proprioception are essential to motor function as shown by the movement deficits that result from the loss of these senses, e.g. due to neuropathy of sensory nerves. To achieve a high-performance brain-controlled prosthetic arm/hand thus requires the restoration of somatosensation, perhaps through intracortical microstimulation (ICMS) of somatosensory cortex (S1). The challenge is to generate patterns of neuronal activation that evoke interpretable percepts. We present a framework to design optimal spatiotemporal patterns of ICMS (STIM) that evoke naturalistic patterns of neuronal activity and demonstrate performance superior to four previous approaches. APPROACH: We recorded multiunit activity from S1 during a center-out reach task (from proprioceptive neurons in Brodmann's area 2) and during application of skin indentations (from cutaneous neurons in Brodmann's area 1). We implemented a computational model of a cortical hypercolumn and used a genetic algorithm to design STIM that evoked patterns of model neuron activity that mimicked their experimentally-measured counterparts. Finally, from the ICMS patterns, the evoked neuronal activity, and the stimulus parameters that gave rise to it, we trained a recurrent neural network (RNN) to learn the mapping function between the physical stimulus and the biomimetic stimulation pattern, i.e. the sensory encoder to be integrated into a neuroprosthetic device. MAIN

resultsWe identified ICMS patterns that evoked simulated responses that closely approximated the measured responses for neurons within 50 µm of the electrode tip. The RNN-based sensory encoder generalized well to untrained limb movements or skin indentations. STIM designed using the model-based optimization approach outperformed STIM designed using existing linear and nonlinear mappings. SIGNIFICANCE: The proposed framework produces an encoder that converts limb state or patterns of pressure exerted onto the prosthetic hand into STIM that evoke naturalistic patterns of neuronal activation.

Indexed as

BiomimeticsTouch PerceptionElectric StimulationSomatosensory CortexTouch

Identifiers

PMID32759488
PMCPMC8559728
OpenAlexW3048026093

What OpenQuestion holds

Textmetadata
LicenceTDM
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.