Evidence map›Paper›PMID 38760597›Full record

ReviewMedical & biological engineering & computing2024

Sensory integration for neuroprostheses: from functional benefits to neural correlates.

Keqin Ding, Mohsen Rakhshan, Natalia Paredes-Acuña, Gordon Cheng, Nitish V Thakor

Abstract readReview
PubMed Publisher
In one paragraph

Review in Medical & biological engineering & computing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Neural Mechanisms Underlying Robust Target Selection in Response to Microstimulation of the Oculomotor System.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    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

5 authors.

Keqin DingDepartment of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA. kding3@jhu.edu.ORCID http://orcid.org/0000-0002-0680-673X
Mohsen RakhshanDepartment of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, 32816, USA.
Natalia Paredes-AcuñaInstitute for Cognitive Systems, School of Computation, Information and Technology, Technical University of Munich, 80333, Munich, Germany.
Gordon ChengInstitute for Cognitive Systems, School of Computation, Information and Technology, Technical University of Munich, 80333, Munich, Germany.
Nitish V ThakorDepartment of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA.

Funding

Advanced Scientific Computing Research DE-SC0022150Congressionally Directed Medical Research Programs W81XWH2010842
6 · The paper itself

Abstract

In the field of sensory neuroprostheses, one ultimate goal is for individuals to perceive artificial somatosensory information and use the prosthesis with high complexity that resembles an intact system. To this end, research has shown that stimulation-elicited somatosensory information improves prosthesis perception and task performance. While studies strive to achieve sensory integration, a crucial phenomenon that entails naturalistic interaction with the environment, this topic has not been commensurately reviewed. Therefore, here we present a perspective for understanding sensory integration in neuroprostheses. First, we review the engineering aspects and functional outcomes in sensory neuroprosthesis studies. In this context, we summarize studies that have suggested sensory integration. We focus on how they have used stimulation-elicited percepts to maximize and improve the reliability of somatosensory information. Next, we review studies that have suggested multisensory integration. These works have demonstrated that congruent and simultaneous multisensory inputs provided cognitive benefits such that an individual experiences a greater sense of authority over prosthesis movements (i.e., agency) and perceives the prosthesis as part of their own (i.e., ownership). Thereafter, we present the theoretical and neuroscience framework of sensory integration. We investigate how behavioral models and neural recordings have been applied in the context of sensory integration. Sensory integration models developed from intact-limb individuals have led the way to sensory neuroprosthesis studies to demonstrate multisensory integration. Neural recordings have been used to show how multisensory inputs are processed across cortical areas. Lastly, we discuss some ongoing research and challenges in achieving and understanding sensory integration in sensory neuroprostheses. Resolving these challenges would help to develop future strategies to improve the sensory feedback of a neuroprosthetic system.

Indexed as

Neural ProsthesesHumansMultisensory integrationNeuroprosthesesSensory integrationSensory stimulation

Identifiers

What OpenQuestion holds

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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.