Evidence map›Paper›PMID 36644311›Full record

ReviewNational science review2022

Deep brain-machine interfaces: sensing and modulating the human deep brain.

Yanan Sui, Huiling Yu, Chen Zhang, Yue Chen, Changqing Jiang, Luming Li

Abstract readReview
In one paragraph

Review in National science review, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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

6 authors.

Yanan SuiNational Engineering Research Center of Neuromodulation, Tsinghua University, Beijing  100084, China.ORCID https://orcid.org/0000-0002-9480-627X
Huiling YuNational Engineering Research Center of Neuromodulation, Tsinghua University, Beijing  100084, China.
Chen ZhangNational Engineering Research Center of Neuromodulation, Tsinghua University, Beijing  100084, China.
Yue ChenNational Engineering Research Center of Neuromodulation, Tsinghua University, Beijing  100084, China.
Changqing JiangNational Engineering Research Center of Neuromodulation, Tsinghua University, Beijing  100084, China.
Luming LiNational Engineering Research Center of Neuromodulation, Tsinghua University, Beijing  100084, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Different from conventional brain-machine interfaces that focus more on decoding the cerebral cortex, deep brain-machine interfaces enable interactions between external machines and deep brain structures. They sense and modulate deep brain neural activities, aiming at function restoration, device control and therapeutic improvements. In this article, we provide an overview of multiple deep brain recording and stimulation techniques that can serve as deep brain-machine interfaces. We highlight two widely used interface technologies, namely deep brain stimulation and stereotactic electroencephalography, for technical trends, clinical applications and brain connectivity research. We discuss the potential to develop closed-loop deep brain-machine interfaces and achieve more effective and applicable systems for the treatment of neurological and psychiatric disorders.

Indexed as

deep brain–machine interfacedeep brain stimulationsensing and modulationstereotactic electroencephalography

Identifiers

PMID36644311
PMCPMC9834907

What OpenQuestion holds

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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.