Evidence map›Paper›PMID 34365114›Full record

ReviewCurrent opinion in biotechnology2021

Learning from the brain's architecture: bioinspired strategies towards implantable neural interfaces.

Nicholas J Rommelfanger, Carl Hc Keck, Yihang Chen, Guosong Hong

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Current opinion in biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Nicholas J RommelfangerDepartment of Applied Physics, Stanford University, Stanford, CA 94305, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305, USA.
Carl Hc KeckDepartment of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305, USA.
Yihang ChenDepartment of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305, USA.
Guosong HongDepartment of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA 94305, USA. Electronic address: guosongh@stanford.edu.

Funding

Brain Aging Studies with Single-Neuron Resolution Using Syringe-Injectable ElectronicsR00AG056636 · NIA · STANFORD UNIVERSITY · PI HONG, GUOSONG · 2018 to 2020
$745k
NIA NIH HHS R00 AG056636
6 · The paper itself

Abstract

While early neural interfaces consisted of rigid, monolithic probes, recent implantable technologies include meshes, gels, and threads that imitate various properties of the neural tissue itself. Such mimicry brings new capabilities to the traditional electrophysiology toolbox, with benefits for both neuroscience studies and clinical treatments. Specifically, by matching the multi-dimensional mechanical properties of the brain, neural implants can preserve the endogenous environment while functioning over chronic timescales. Further, topological mimicry of neural structures enables seamless integration into the tissue and provides proximal access to neurons for high-quality recordings. Ultimately, we envision that neuromorphic devices incorporating functional, mechanical, and topological mimicry of the brain may facilitate stable operation of advanced brain machine interfaces with minimal disruption of the native tissue.

Indexed as

BrainNeuronsBiomimeticsElectrophysiological PhenomenaProstheses and Implants

Identifiers

PMID34365114
PMCPMC8671194

What OpenQuestion holds

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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.